Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities

Remote Sensing of Environment 233 (2019) 111401

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Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities

T

William K. Smitha,1, , Matthew P. Dannenberga,b,1, , Dong Yana, Stefanie Herrmannc, Mallory L. Barnesd, Greg A. Barron-Gafforde, Joel A. Biedermanf, Scott Ferrenbergg, Andrew M. Foxa,h, Amy Hudsona, John F. Knowlese,f, Natasha MacBeani, David J.P. Moorea, Pamela L. Naglerj, Sasha C. Reedk, William A. Rutherforda, Russell L. Scottf, Xian Wanga, Julia Yange ⁎

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a

School of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85719, United States of America Dept. of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA 52242, United States of America c Biosystems Engineering, University of Arizona, Tucson, AZ 85719, United States of America d O’Neill School of Public and Environmental Affairs, Indiana University, Bloomington, IN 47405, United States of America e School of Geography and Development, University of Arizona, Tucson, AZ 85719, United States of America f Agricultural Research Service, U.S. Department of Agriculture, Tucson, AZ 85719, United States of America g Dept. of Biology, New Mexico State University, Las Cruces, NM 88003, United States of America h Joint Center for Satellite Data Assimilation, Boulder, CO 80301, United States of America i Dept. of Geography, Indiana University, Bloomington, IN 47405, United States of America j U.S. Geological Survey, Southwest Biological Science Center, Tucson, AZ 85719, United States of America k U.S. Geological Survey, Southwest Biological Science Center, Moab, UT 84532, United States of America b

ARTICLE INFO

ABSTRACT

Keywords: Drylands Vegetation indices Leaf area index Biocrusts Phenology Primary production Evapotranspiration LiDAR Hyperspectral Fluorescence Thermal infrared

Drylands make up roughly 40% of the Earth's land surface, and billions of people depend on services provided by these critically important ecosystems. Despite their relatively sparse vegetation, dryland ecosystems are structurally and functionally diverse, and emerging evidence suggests that these ecosystems play a dominant role in the trend and variability of the terrestrial carbon sink. More, drylands are highly sensitive to climate and are likely to have large, non-linear responses to hydroclimatic change. Monitoring the spatiotemporal dynamics of dryland ecosystem structure (e.g., leaf area index) and function (e.g., primary production and evapotranspiration) is therefore a high research priority. Yet, dryland remote sensing is defined by unique challenges not typically encountered in mesic or humid regions. Major challenges include low vegetation signal-to-noise ratios, high soil background reflectance, presence of photosynthetic soils (i.e., biological soil crusts), high spatial heterogeneity from plot to regional scales, and irregular growing seasons due to unpredictable seasonal rainfall and frequent periods of drought. Additionally, there is a relative paucity of continuous, long-term measurements in drylands, which impedes robust calibration and evaluation of remotely-sensed dryland data products. Due to these issues, remote sensing techniques developed in other ecosystems or for global application often result in inaccurate, poorly constrained estimates of dryland ecosystem structural and functional dynamics. Here, we review past achievements and current progress in remote sensing of dryland ecosystems, including a detailed discussion of the major challenges associated with remote sensing of key dryland structural and functional dynamics. We then identify strategies aimed at leveraging new and emerging opportunities in remote sensing to overcome previous challenges and more accurately contextualize drylands within the broader Earth system. Specifically, we recommend: 1) Exploring novel combinations of sensors and techniques (e.g., solar-induced fluorescence, thermal, microwave, hyperspectral, and LiDAR) across a range of spatiotemporal scales to gain new insights into dryland structural and functional dynamics; 2) utilizing near-continuous observations from new-and-improved geostationary satellites to capture the rapid responses of dryland ecosystems to diurnal variation in water stress; 3) expanding ground observational networks to better represent the heterogeneity of dryland systems and enable robust calibration and evaluation; 4) developing algorithms that are specifically

Correspondence to: W.K. Smith, 1064 E. Lowell St., University of Arizona, Tucson, AZ 85719, United States of America. Correspondence to: M.P. Dannenberg, 308 Jessup Hall, University of Iowa, Iowa City, IA 52242, United States of America. E-mail addresses: [email protected] (W.K. Smith), [email protected] (M.P. Dannenberg). 1 Co-lead authors. ⁎

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https://doi.org/10.1016/j.rse.2019.111401 Received 13 November 2018; Received in revised form 26 July 2019; Accepted 25 August 2019 0034-4257/ © 2019 Elsevier Inc. All rights reserved.

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tuned to dryland ecosystems by utilizing expanded ground observational network data; and 5) coupling remote sensing observations with process-based models using data assimilation to improve mechanistic understanding of dryland ecosystem dynamics and to better constrain ecological forecasts and long-term projections.

1. Introduction

irregular growing seasons due to unpredictable seasonal rainfall and frequent periods of drought (Bestelmeyer et al., 2006; Haughton et al., 2018; Wu and Archer, 2005; Zhou et al., 2017). Due to these issues, remote sensing techniques developed in other ecosystems or for global application often result in inaccurate, poorly-constrained estimates of dryland ecosystem structural and functional dynamics. For example, across dryland sites of the United States (U.S.) Southwest and Mexico, widely used remote sensing-based estimates of gross primary productivity (GPP) and evapotranspiration (ET) captured only 31% and 29% of the interannual variability (IAV) in site-measured GPP and ET, respectively (Biederman et al., 2017). Overcoming the limitations of current remote sensing techniques is thus of critical importance to constrain projections of dryland carbon and water fluxes. Dryland ecosystems remain an area of relatively limited knowledge because they are: 1) structurally and functionally dynamic at relatively fine spatiotemporal scales due to their tight coupling with intermittent and unpredictable water availability; and 2) underrepresented by longterm, continuous field measurements that are synthesized into larger, easily-accessible networks (Biederman et al., 2017; Smith et al., 2018). Fortunately, new and emerging advances in remote sensing hold significant promise for overcoming past challenges of dryland remote sensing, and thus could revolutionize our ability to monitor dryland ecosystems across spatiotemporal scales (Stavros et al., 2017). New sensors and techniques, including space-borne solar-induced chlorophyll fluorescence (SIF) and thermal infrared (TIR) sensors capable of unprecedented spatiotemporal observation frequency, offer exciting new insights into vegetation physiological function, with opportunities to significantly improve remote sensing-based estimates of GPP and ET in drylands and beyond. Moreover, space-borne light detection and ranging (LiDAR) and hyperspectral sensors offer new information on structural and spectral vegetation traits critical to mechanistic understanding of plant survival strategies. Complementary instruments that

Drylands – defined as regions where annual potential evapotranspiration substantially exceeds precipitation (Fig. 1) – are critically important to society, yet exceptionally vulnerable to climate change. Drylands make up > 40% of the Earth's land surface and provide ecosystem services to more than two billion people, including providing habitat for numerous rare and endemic organisms, and supporting significant crop production and forage for wildlife and domestic livestock (Bestelmeyer et al., 2015). While drylands are sparsely vegetated with low annual productivity, they have been recently identified as an important regulator of global trends and variability in atmospheric carbon dioxide (CO2) concentrations (Ahlström et al., 2015; Biederman et al., 2017; Humphrey et al., 2018; Poulter et al., 2014). Dryland CO2 uptake is strongly associated with variation of both precipitation and air temperature, such that drought events have been linked to declines in ecosystem function and widespread increases in plant mortality, highlighting the acute vulnerability of these ecosystems to changes in aridity (Bestelmeyer et al., 2015; Breshears et al., 2005; Scott et al., 2015; Williams et al., 2013). Climate projections indicate that hotter, drier conditions will continue to intensify across global drylands (Cayan et al., 2010; Cook et al., 2015), likely resulting in the global expansion and further desiccation and degradation of dryland areas (Huang et al., 2017, 2016). As a result, there is a pressing need to improve our understanding of these important and vulnerable ecosystems. Satellite remote sensing has been instrumental in elucidating the role of drylands within the context of the broader Earth system (Humphrey et al., 2018; Poulter et al., 2014). Yet, dryland remote sensing has been impeded by unique challenges not typically encountered in mesic regions, including low vegetation signal-to-noise ratios, high soil background reflectance, presence of photosynthetic soils, high spatial heterogeneity from plot to regional scales, and

Fig. 1. Distribution of global drylands, and representation by the FLUXNET 2015 network of eddy covariance flux towers. (A) Global map of drylands defined by the ratio of precipitation to potential evapotranspiration (UNESCO, 1977) based on 1981–2010 mean CRU TS4.01 gridded surface climate (Harris et al., 2014). Point locations for eddy covariance flux towers from the FLUXNET 2015 database are shown as black dots. (B) Proportion of FLUXNET 2015 site-years that are located within drylands (left) compared to their proportion of global land area (right). Note that there are no flux sites located in hyperarid regions.

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In terms of carbon dynamics, surface-atmosphere CO2 exchange following a precipitation pulse is at first dominated by respiration of accumulated substrate, resulting in a large CO2 efflux to the atmosphere (Cable et al., 2008; Jarvis et al., 2007). After plant physiological upregulation, photosynthesis usually becomes the dominant flux, leading to net CO2 uptake by the ecosystem (Jenerette et al., 2008). Vegetative species differ greatly in their rates of up-regulation (Potts et al., 2014); thus, the lag between the timing of a precipitation event and the pulse of biological activity can vary across a landscape (Barron-Gafford et al., 2014; Hibbard et al., 2003) and certainly within a given pixel of moderate- to coarse-resolution sensors. Biological soil crusts or biocrusts – communities of photosynthetic mosses, lichens, and/or cyanobacteria found at the soil surface in the interspace between plants in drylands worldwide (Fig. 2) – are among the first responders and can contribute to CO2 uptake solely by surficial wetting (Tucker et al., 2017). Depending on the size of the precipitation event, soils will then dry at a rate mediated by soil type and texture, and the depletion of biologically-available water quickly constrains biological T and CO2 uptake. Simultaneously, increasing vapor pressure deficits (VPD) driven by both reduced ET and higher temperature - can also force stomatal closure due to plant hydraulic constraints (Novick et al., 2016). During this period of drydown, vegetation and soil function decrease even though their ‘greenness’ may remain relatively constant (Yan et al., 2019). This decoupling between ecosystem function and its optical reflectance remains one of the greatest obstacles for remote sensing of ecosystem structure and function in dryland systems. Further, over seasonal to annual time scales, changing water availability can result in changes in vegetation cover and composition (Gremer et al., 2015), and frequent or intense droughts can radically alter ecosystem structure (van der Molen et al., 2011). Capturing all of these rapidly varying responses in time presents a fundamental challenge for temporally-intermittent remote sensing measurements. In addition to their temporal variability, drylands present unique spatial variation across plot, ecosystem, and regional scales (Fig. 3). Within a given area, dryland ecosystems consist of multiple vegetation types, multilayer canopies, and high proportions of biological soil crusts or bare soil, with opportunistic annual plants during wet periods (Figs. 2, 3). Dryland regions often have large differences in community composition, ecosystem structure, and plant functional characteristics [e.g., C3, C4, or crassulacean acid metabolism (CAM) photosynthetic pathways– each sensitive to different climate drivers] over short lateral distances, reflecting changes in water availability with elevation, soil type, and water source (Biederman et al., 2017; Cable et al., 2008; Swetnam et al., 2017). The patchy, heterogeneous nature of dryland ecosystems presents challenges for upscaling ground measurements and

can be deployed in the field, installed at tower sites, equipped on unmanned aerial systems (UASs), and/or flown by aircraft are also becoming less expensive and more prevalent, thus increasing opportunities for new insights into scaling from leaf to globe (Aubrecht et al., 2016; Sankey et al., 2017; Yang et al., 2018). Here, we review past achievements and current progress in remote sensing of dryland ecosystem structure (amount, composition, and three-dimensional arrangement of vegetation) and function (physiological, biophysical, and biogeochemical dynamics), with a specific focus on highlighting and describing major historical milestones and challenges. We then outline a plan to overcome these past barriers based on key recommendations that incorporate existing and future remote sensing assets and techniques better equipped to capture various aspects of dryland dynamics. We focus on four major aspects of dryland remote sensing: 1) the ecohydrological, structural, and functional characteristics of drylands that make them uniquely challenging for remote sensing; 2) key milestones in the historical development of dryland remote sensing; 3) the challenges that have traditionally hampered remote monitoring of dryland vegetation structure and function; and 4) strategies to improve monitoring and modeling of dryland ecosystems from ground-, aerial-, and space-based remote sensing platforms. 2. Overview of dryland ecosystem structural and functional dynamics Spatial and temporal variation of water availability strongly governs dryland vegetation, making the links between ecosystem ecology and hydrology readily apparent in these regions (D'Odorico and Porporato, 2006; Scott et al., 2014). Plant structure affects the proportion of plant available precipitation (Villegas et al., 2015; Wang et al., 2010), and plant function can drive the soil moisture state of a system through transpiration of soil water regulated by stomatal aperture and redistribution of soil water regulated by roots (Barron-Gafford et al., 2017; Scott et al., 2014). Temporal variation of precipitation is greater in drylands than in other climate regions across diurnal, seasonal, and interannual scales (Biederman et al., 2017; Noy-Meir, 1973; Reynolds et al., 2007). Dry periods are punctuated by precipitation pulses that lead to a cascade of pulsed ecosystem responses, ultimately expressed in rapid changes in the land-atmosphere exchanges of energy, water, and carbon (Huxman et al., 2004; Scott et al., 2009). Hydrologically, each precipitation pulse leads first to a rapid increase in ET, consisting mainly of abiotic evaporation (E) from soil and plant surfaces. Subsequently, if the water input is sufficient to wet the root zone, plant transpiration (T) can contribute significantly to total ET.

Fig. 2. Biological soil crusts (or biocrusts) common in drylands worldwide. (A) Biocrusts are clearly visible in the foreground as black, bumpy features, shown here in a Colorado Plateau desert. Photograph by Bill Bowman. (B) Biocrusts can alter the color and spectral characteristics of the soil, as shown when the dark biocrust layer is removed to expose the sand underneath. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Photograph by Kristina Young. 3

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Fig. 3. Multi-scale heterogeneity of dryland ecosystems in the U.S. Southwest. Locations of eddy covariance flux towers (dots) across southwestern United States overlaid on the NLCD 2011 land cover classification. Insets show fine-resolution imagery of several flux sites (labeled and highlighted in yellow in the map) from Google Earth, demonstrating the high spatial heterogeneity of dryland ecosystems. Each inset represents the boundary of a 500-m pixel (approximating MODIS resolution) around each tower. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

for interpreting signals collected over larger scales, including moderateto coarse-resolution remotely-sensed imagery (Barnes et al., 2016; Biederman et al., 2017; Knowles et al., 2018; Smith et al., 2018). Linking leaf-to-ecosystem scale understanding of dryland structural and functional dynamics has therefore required an evolution of not only the tools that are used, but also a merging of multiple disciplines (ecology, plant physiology, soil science, hydrology, and geospatial science, to name only a few) and of otherwise disparate datasets (Jenerette et al., 2012; Peters et al., 2018). Remote sensing in drylands is poised to build from this collaborative approach and utilize the integration of measures that reach from outside Earth's atmosphere to experimental units down on the ground.

important for increasing data availability in global drylands, where vast expanses of sparsely populated land with limited infrastructure meant a lower density of ground observations than were available in more humid, and typically more developed, areas. The great need for more data was complemented by favorable atmospheric conditions in drylands; that is, the increased chance of high-quality observations due to lower cloud cover offered advantages for optical remote sensing. As a result, many remote sensing techniques were pioneered in drylands, including the retrieval of surface reflectance from Landsat imagery (e.g., Kowalik et al., 1982; Marsh and Lyon, 1980) and quantitative estimates of vegetation conditions in the rangelands of the Great Plains using the red and near-infrared bands of Landsat MultiSpectral Scanner (MSS) (Rouse et al., 1974), which would later become the Normalized Difference Vegetation Index (NDVI) (Tucker, 1979) (Fig. 4). The significance of the NDVI, which is still one of the most widely used vegetation indices, lies in its high correlation with green biomass and its potential for comparing different regions through time, as the normalization minimizes effects from sun-sensor geometry, shadows, and

3. Historical milestones in remote sensing of dryland ecosystems 3.1. The role of drylands as early test grounds for remote sensing techniques The advent of Earth Observation in the 1970s was particularly 4

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Fig. 4. Historical milestones in multi-spectral remote sensing of dryland ecosystems and development in satellite optical, thermal infrared (TIR), microwave, chlorophyll fluorescence (ChlF), LiDAR, and hyperspectral (Hyperspec) capabilities. Sensor timelines are provided for context and are not meant to provide a comprehensive overview of available sensors. Also, developments of commercial satellite operations (e.g., Digital Globe, Planet, etc.) are not shown. The timeline showing the development of satellite capabilities from optical to hyperspectral demonstrates both the increasing spatiotemporal resolution of satellite data as well as the expanding diversity of remote sensing techniques. References associated with each historical milestone: 1) Tucker (1979); 2) Tucker et al. (1983); Tucker et al. (1985); 3) Tucker and Choudhury (1987); 4) Huete (1988); 5) Tucker et al. (1991); 6) Karnieli (1997); 7) Leuning et al. (2005), Scott et al. (2008), and Scott et al. (2009); 8) Briske et al. (2003); 9) Anyamba and Tucker (2005), Donohue et al. (2009), and Fensholt and Rasmussen (2011); 10) Helldén and Tottrup (2008), Fensholt et al. (2012), and Donohue et al. (2013); 11) Bastin et al. (2017); 12) Poulter et al. (2014), Ahlström et al. (2015), Biederman et al. (2017), and Humphrey et al. (2018).

topographic variation (Anyamba and Tucker, 2005). However, in drylands with generally low vegetation canopy cover, the soil background tends to significantly influence NDVI, leading to further development of vegetation indices, particularly the widely-used Soil Adjusted Vegetation Index (SAVI), which adjusts the NDVI using a soil-dependent constant (Huete, 1988), as well as related vegetation indices like the Soil Adjusted Total Vegetation Index (SATVI) (Marsett et al., 2006) and Enhanced Vegetation Index (EVI) (Huete et al., 2002, 1994).

Knauer et al., 2014). These insights were key in refuting the hypothesis of irreversible desertification in the Sahel (Herrmann and Hutchinson, 2005), spurring a range of remote sensing-based studies investigating long-term trends in and driving forces behind regional vegetation variability using AVHRR NDVI time series (Anyamba and Tucker, 2005; Donohue et al., 2009; Fensholt and Rasmussen, 2011). Subsequent global-scale analyses have ultimately resulted in a recent paradigm shift: that global drylands are greening due largely to CO2 fertilization effects (Donohue et al., 2013; Fensholt et al., 2012; Helldén and Tottrup, 2008; Smith et al., 2016; Zhu et al., 2016). Remote monitoring of semiarid Australian rangelands, where land degradation, soil erosion, and salinity have been major concerns (Graetz, 1987), also led to a new non-equilibrium paradigm in rangeland ecology that challenged the generally accepted idea that ecosystems are stable and rapidly recover back to a state of stability following natural or human disturbances (Briske et al., 2017, 2003). These developments drew on experiences in the Australian rangelands among others (Schlesinger et al., 1990), where limitations of the successional model had become evident (Westoby et al., 1989). Non-equilibrium dynamics made it difficult to collect and interpret data meaningfully, and remote sensing-based assessments were ideally suited for uncovering the large-scale patterns of spatial and temporal change indicative of shifts in rangeland conditions in response to precipitation and grazing pressure (Pickup, 1998; Pickup et al., 1994). Abrupt changes in NDVI trends are a good indicator of such shifts or transitions between different states, and these changes have since been globally detected and captured with automated algorithms such as the Breaks for Additive Season and Trend (BFAST) approach (de Jong et al., 2011; Verbesselt et al., 2011).

3.2. Insights into dryland vegetation dynamics from time series analyses With the launch of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) sensors, new possibilities for remote monitoring of temporal dynamics of vegetation emerged. Although the AVHRR has a relatively coarse spatial resolution (1.1 km2 nominal resolution at nadir view angles), its daily overpasses greatly enhanced the utility of remote sensing for large-scale analysis of seasonal cycles of vegetation activity (i.e., phenology) (Goward et al., 1985; Justice et al., 1985) and sparked early efforts at large-scale quantification of primary production (Prince, 1991; Tucker et al., 1985, 1983; Tucker and Choudhury, 1987) and evapotranspiration (Goward and Hope, 1989; Kerr et al., 1989; Nemani and Running, 1989). These high-frequency, global-scale NDVI retrievals revolutionized our understanding of dryland dynamics, beginning in the semiarid grasslands of the Senegalese Ferlo region (Tucker et al., 1985; Tucker et al., 1983; Tucker and Choudhury, 1987). Regionalscale applications in the entire West African Sahel showed, for the first time, that interannual variation of vegetation greenness in this semiarid environment closely tracks rainfall variability (Tucker et al., 1991; Tucker and Nicholson, 1999), with an increase in precipitation following the strong droughts of the 1970s and 1980s leading in part to a decades-long greening trend in the Sahel (Heumann et al., 2007; 5

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3.3. Remote sensing of biological soil crusts and their distinct role in dryland ecosystems

(Anderson-Teixeira et al., 2011; Biederman et al., 2016; Scott et al., 2015, 2010), highlighting in particular how remotely sensed GPP and ET products tend to underestimate variability of these fluxes (Biederman et al., 2017). While historically underrepresented in many of the global flux networks, flux tower networks have been expanding throughout Asia as well, including the AsiaFlux (http://asiaflux.net/), ChinaFlux (http://www.chinaflux.org/), and Heihe Watershed Allied Telemetry Experimental Research (HiWATER) (Li et al., 2013) networks. The development of these key dryland flux networks have contributed, and continue to contribute, both to understanding of dryland ecosystem function and to remotely sensed vegetation modeling in dryland systems.

In the deserts of the Middle East, notably the Negev Desert, a striking contrast between the protected northwestern Negev and the heavily grazed Sinai side of the Israeli-Egyptian border (observed on Landsat imagery) inspired investigations into the effects of grazing on surface albedo, and subsequently on climate (Otterman, 1974). Using a global circulation model, Charney (1975) showed a positive biophysical feedback mechanism between vegetation cover and rainfall such that overgrazing could initiate or perpetuate drought conditions and explain desertification. While theoretically possible, follow-up work based on actual satellite measurements showed that albedo shifts were not large or persistent enough to drive measurable changes in precipitation (Folland et al., 1991). An alternative hypothesis postulated that the contrast was not a result of severe overgrazing of vascular vegetation but rather due to intact biological soil crust cover on the Negev side, while animal and human activities prevented such a biocrust from forming on the Sinai side (Karnieli and Tsoar, 1995). Using remote sensing to characterize cover, development, health, and community composition of biocrusts is still a relatively new discipline, developing largely since the mid- to late 1990s (O'Neill, 1994) following first detection of lichen-dominated crusts versus bare soil in the early 1980s (Graetz and Gentle, 1982). Karnieli (1997) developed a spectral crust index using a transformation of the blue and red spectral bands, which is sensitive to soil crusts and can be used to map active sands, crusted interdune areas, and playas (Fig. 4). Multispectral indices developed specifically for the detection of biocrusts include the Crust Index (CI) (Karnieli, 1997) and the Biological Soil Crust Index (BSCI) (Chen et al., 2005), but other indices (e.g., NDVI, EVI, Brightness Index, Moisture Stress Index, and Normalized Difference Water Index) have also been applied for the detection of biocrusts in dryland regions (Neta et al., 2011; Rodríguez-Caballero et al., 2015; Rozenstein et al., 2014). Improvements in biocrust detection have since been made with the use of hyperspectral indices (e.g., Continuum Removal Crust Identification Algorithm and Crust Development Index), especially when combined with machine learning approaches (Rodríguez-Caballero et al., 2014; Weber et al., 2008).

3.5. Remote sensing highlights drylands as critical to Earth system functioning While the issues of global climate and environmental change have driven the urgency for global assessment and monitoring, the revolutions in big data and high-performance cloud computing (see §3.6 below) over the past decade have enhanced our capability for global observations in multi-scale and real-time perspectives (Guo et al., 2015). This means that drylands can now be monitored at global scales and in a global context, leading to recognition of the importance of drylands to Earth's carbon, water, and nutrient cycles (Ahlström et al., 2015; Humphrey et al., 2018; Poulter et al., 2014) (Fig. 4). Using the MODIS Net Primary Production (NPP) dataset, long-term AVHRR fraction of photosynthetically active radiation (FPAR) estimates, and vegetation optical depth (VOD) from microwave sensors, Poulter et al. (2014) identified how the global carbon sink anomaly of 2011 was driven by widespread greening in drylands of the southern hemisphere, especially Australia. Ahlström et al. (2015) expanded upon this initial finding by showing that dryland ecosystems play a dominant role in the trend and interannual variability of the global carbon sink. Finally, Humphrey et al. (2018) verified the importance of drylands by showing that interannual changes in terrestrial water storage estimated from the Gravity Recovery and Climate Experiment (GRACE) data are 1) dominated by changes in water storage on semi-arid lands, and 2) tightly linked to changes in atmospheric CO2 concentrations.

3.4. The development of key ground-based dryland eddy covariance networks

3.6. Dryland remote sensing in the era of big data The 2000s have seen an exponential increase in the volume of remote sensing data, resulting from improvements in all resolution domains (spatial, temporal, spectral, and radiometric) as well as the rapid expansion of sensors and products (Fig. 4). The MODIS sensor (first launched in December 1999) and its derived data products (e.g., Friedl et al., 2010; Ganguly et al., 2010; Myneni et al., 2002; Running et al., 2004) represented a clear advancement over the AVHRR time series, which as the legacy sensor continues to add to its long data record (Pinzon and Tucker, 2014) (Fig. 4). While Landsat 8 extended the 40year Landsat record (Roy et al., 2014), the European Commission developed their own high spatial resolution, multispectral instrument with the Copernicus Sentinel-2 mission (Drusch et al., 2012; Gascon et al., 2017). The land science community also embraced new interagency partnerships between the National Aeronautics and Space Administration (NASA) and U.S. Geological Survey (USGS), as well as with the European Space Agency (ESA), leading to the development of a harmonized Landsat/Sentinel-2 surface reflectance product (Masek et al., 2013, 2006). This data fusion approach integrating observations from multispectral sensors has been shown to accurately characterize land surface phenology in drylands, which would have been precluded by data limitations of individual sensors (Pastick et al., 2018). Furthermore, very high-resolution imagery provided by commercial satellite remote sensing operations (e.g., from DigitalGlobe and Planet) has been changing the landscape of remote sensing since the early 2000s (Butler, 2014), with current constellations of satellites in a low

Remote sensing-based estimation of energy, water, and carbon fluxes requires extensive ground-based data for calibration and validation, chiefly from eddy covariance flux towers, but drylands were long underrepresented in these networks (Fig. 1). Beginning in the early 2000s, new networks were established in dryland regions, particularly in Australia and the southwestern U.S. The OzFlux regional network of flux tower sites throughout Australia and New Zealand began in 2001 and has been used extensively to validate satellite remote sensing products including MODIS gross primary productivity (Beringer et al., 2016; Kanniah et al., 2009; Restrepo-Coupe et al., 2016). OzFlux covers a range of dryland ecosystems, where upscaling using validated remote sensing products is essential for complementing limited ground-based observations (Beringer et al., 2016), including the only flux sites for eucalypts and acacias that occur primarily in arid and semiarid regions and are crucial to understanding the role of these vegetation types in the global carbon cycle (e.g., Leuning et al., 2005). Further, measurements from OzFlux, along with the Semiarid ECohydrology Array (SECA; also founded in 2001) and the New Mexico Elevation Gradient (NMEG, started in 2007) flux tower networks in the southwestern U.S., have been used to parameterize models that ingest remotely sensed data (Barraza et al., 2015, 2014; Nagler et al., 2005b; Scott et al., 2008) and to evaluate sensor continuity (Liu et al., 2017; Obata et al., 2013). The two southwestern U.S. networks have also contributed to understanding coupled carbon-water dynamics in dryland ecosystems 6

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Earth orbit (LEO) providing sub-meter resolution imagery with relatively frequent revisit rates. The accessibility of data has increased exponentially along with its availability. Beginning with the opening of the Landsat archive in 2008 (Woodcock et al., 2008), petabytes of remote sensing data have become freely available from US government agencies such as NASA, NOAA, and USGS, as well as ESA. A substantial shortening of the time lag between image acquisition and delivery/data dissemination also makes near real-time remote sensing applications possible (Butler, 2014), a particularly important development for drought early warning and food security monitoring in drylands (Fritz et al., 2018). Part of the big data revolution has been the increasing availability of cloud-based computing and tools to facilitate large-scale processing of data. The Google Earth Engine platform has assumed a leadership role in this trend for the geospatial and remote sensing community, offering access to high performance computing resources as well as a large and growing curated repository of publicly available geospatial datasets (Gorelick et al., 2017). Using these new technologies within Google Earth Engine led to a recent landmark discovery that dryland tree cover exceeds previous estimates by over 40% (Bastin et al., 2017). These new insights into dryland dynamics would not be possible without simultaneous advances in data management and cloud computing capabilities.

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sub-pixel scales that challenges parameterization of models based on moderate- to coarse-resolution imagery. Decoupling of vegetation function (e.g., stomatal conductance and photosynthetic capacity) from optical reflectance, especially for drought-tolerant evergreen shrubs which can remain green even during periods of extreme moisture shortage. Relatively sparse ground-based data networks needed for calibration and validation of remote sensing algorithms. Tight coupling of vegetation activity to soil moisture, which is difficult to estimate at appropriate spatiotemporal scales and results in temporal mismatches between process and observation scales (i.e., vegetation activity following rain events may not be captured by spectral vegetation indices with weekly or lower temporal frequency).

Below, we review specific challenges encountered in remote sensing of key dryland surface characteristics, starting with biophysical and structural attributes (surface reflectance, leaf area index, and canopy radiation) followed by functional and biogeochemical processes (phenology, primary production, and evapotranspiration). We also discuss recent progress towards addressing these limitations. 4.1. Surface reflectance: aerosols, adjacency, anisotropy, and dust accumulation

3.7. Historical summary In summary, drylands started as a testbed for new remote sensing techniques, in large part because of their favorable atmospheric conditions. As these same remote sensing techniques matured, they shed new light on dryland ecosystem dynamics and revealed drylands to be important regions in global carbon, water, and nutrient cycling. Emerging evidence of the prominent role of drylands in global Earth system functioning highlights the need for a renewed focus on the application of remote sensing to these ecosystems, particularly in the context of global environmental change.

Atmospheric correction is necessary to retrieve surface reflectance, vegetation indices, and many ecosystem structural and functional properties, such as leaf area index (LAI) (Vermote et al., 1997a), that are essential to land change studies (Masek et al., 2006; Vermote et al., 2016). Significant developments in the methodologies for atmospheric correction over the past 30 years have been largely driven and motivated by improved sensor design and increased computing power (Vermote et al., 1997b, 2016), but high-quality surface reflectance retrievals over drylands remains challenging due to high aerosol concentrations, pronounced adjacency effects, dust accumulation on leaves (Houborg and McCabe, 2016), and high surface anisotropy in sparse and heterogeneous canopies (Middleton et al., 1987; Van Leeuwen et al., 1994). While adjacency effects can be corrected, the solution is computationally expensive when applied to fine-resolution images (e.g., 30 m or less) and thus has not yet been implemented in the Landsat surface reflectance algorithms (Vermote et al., 2016). Although there have been promising developments to address these challenging aspects for retrieving surface reflectance over dryland agricultural regions using ultra fine-resolution satellite imagery (Houborg and McCabe, 2016), a computationally inexpensive solution to operationally retrieve high quality surface reflectance from the Landsat image archive for global dryland ecosystems still merits high research priority.

4. Challenges for remote sensing of dryland ecosystems While the past 50 years has seen many productive synergies between remote sensing science and dryland research, the ecological and hydrological characteristics of dryland ecosystems make them uniquely challenging for remote sensing of structural, functional, biophysical, and biogeochemical properties. Some general challenges encountered in dryland ecosystems include:

• Sparse vegetation canopies resulting in a large influence of soils and senesced or inactive vegetation on reflectance spectra. • High heterogeneity of vegetation form (e.g., woody vs. herbaceous) and function (e.g., C3, C4, and CAM photosynthetic pathways) at

Fig. 5. The influence of soil brightness on relative surface reflectance for a sparse canopy (A) and a dense canopy (B). Simulations were conducted using PROSAIL version 5B (Jacquemoud et al., 2009), based on a coupling of the PROSPECT leaf optical model (Feret et al., 2008) and the SAIL canopy bidirectional reflectance model (Verhoef, 1984). Only leaf area index and soil reflectance spectra were varied, while all simulations were conducted with a spherical leaf angle distribution, 30° solar zenith angle, nadir view angle, 40 μg cm−2 chlorophyll a + b content, 8 μg cm−2 carotenoid content, 0.01 cm equivalent water thickness, and 0.009 g cm−2 dry matter content.

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4.2. Vegetation indices: effects of soil and leaf litter reflectance

(Carlson and Ripley, 1997), land surface phenology (Zhang et al., 2003), primary productivity (Running et al., 2004; Smith et al., 2016) and evapotranspiration (Nagler et al., 2005a). However, the effectiveness of using VIs to determine vegetation properties are substantially reduced in dryland ecosystems, which are discussed in further detail in Sections 4.3–4.7 below.

Spectral vegetation indices (VIs) have played an important role in inferring changes in drylands across a wide range of spatiotemporal scales (e.g., Brown et al., 2010; Donohue et al., 2013; Tucker et al., 1991, 1985). VIs are generally based on the high absorption by plants in the red (R) wavelength range for photosynthesis and the high reflectivity of plants in the near-infrared (NIR) wavelength range, thus together providing a proxy for vegetation cover and photosynthetic capacity, with the most common and widely used index being NDVI (Tucker, 1979), calculated as NDVI = (NIR − R)/(NIR + R). However, in dryland ecosystems, the presence of senesced vegetation and standing litter (Huete and Jackson, 1987; Van Leeuwen and Huete, 1996), along with variable soil background reflectance, can have a very large influence on NDVI and other VIs (Baret and Guyot, 1991; Huete and Jackson, 1987; Huete and Tucker, 1991). This increases the difficulty of differentiating actual vegetation change from spurious changes induced by variation of soil background reflectance (Fig. 5) (Elvidge and Lyon, 1985; Huete, 1988; Huete and Tucker, 1991). The Soil-Adjusted Vegetation Index (SAVI) introduced a soil adjustment factor (L) to account for differences in red and near-infrared reflectance from the soil background, but the ability of SAVI to differentiate vegetation from soil under sparse vegetation cover conditions is still limited (Huete, 1988); beyond a threshold of vegetation sparseness reliable retrievals of VIs become impractical (Okin et al., 2001). SAVI also requires prior knowledge to determine the optimal soil adjustment factor, which varies with vegetation density but is typically set as L = 0.5 (Huete and Jackson, 1987), leading to the development of many variants on the SAVI formula: the transformed SAVI (TSAVI) (Baret et al., 1989), modified SAVI (MSAVI) (Qi et al., 1994), optimized SAVI (OSAVI) (Rondeaux et al., 1996), generalized SAVI (GESAVI) (Gilabert et al., 2002), and the Soil Adjusted Total Vegetation Index (SATVI) (Marsett et al., 2006). VIs have been used to infer a wide range of vegetation properties including leaf chlorophyll content (Gitelson and Merzlyak, 1997), leaf area index (Chen and Cihlar, 1996), fractional vegetation cover

4.3. Leaf area index and FPAR: issues in sparse and heterogeneous canopies Leaf area index (LAI) – typically defined as half the total leaf area per unit ground area (Chen and Black, 1992) – provides the key link between soils, plants, and the atmosphere in physically-based biogeochemical and land surface models. Its retrieval via remote sensing typically relies either on empirical relationships between VIs and LAI or inversion of canopy radiative transfer models to retrieve LAI (Song, 2013). While sparsely-vegetated dryland ecosystems are unlikely to suffer from the saturation of VIs observed in densely-vegetated ecosystems, many of the optical methods for retrieving LAI (both in situ and from air- or space-borne sensors) perform best in uniform, homogenous, and closed canopies, whereas non-random foliage distributions and the presence of woody tissue within the field of view make indirect, optical retrieval of LAI more complicated (Gower et al., 1999; Ryu et al., 2010). Further, the soil background adds considerable noise to the relationship between VIs and LAI (Fig. 6A–B). Baret and Guyot (1991), for example, found that the relative noise in the VI-LAI relationship increases with decreasing LAI for sparse canopies (LAI < 3). Many of the same issues plague remotely-sensed estimates of the fraction of absorbed photosynthetically active radiation (FPAR) by plant canopies in drylands, with highest relative errors for sparse canopies with low FPAR, particularly for NDVI-based estimates (Baret and Guyot, 1991). Aside from soil background reflectance in sparse canopies (Figs. 5 and 6C–D), variation in sun-sensor geometry leads to variation in the relative proportions of soil and vegetation that are illuminated and viewed by the sensor, with higher fractions of illuminated soil when solar elevation angles are high (Sims et al., 2006). Likewise, the open canopies characteristic of dryland systems generally Fig. 6. Relationships between vegetation indices (NDVI and EVI) and vegetation biophysical variables (LAI and FPAR) for soil backgrounds with differing reflectance spectra. Simulations were conducted using the same methods as Fig. 5. FPAR was estimated from LAI as: FPAR = FPAR∞[1 − exp (−K × LAI)], where FPAR∞ is the FPAR of an infinitely thick canopy and K is the extinction coefficient (set as 0.94 and 1.0, respectively, following Baret and Guyot (1991)). NDVI and EVI were calculated from PROSAIL-simulated directional reflectance and the Landsat 5 TM relative spectral response functions in the blue, red, and NIR bands.

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have greater proportions of woody and senesced vegetation in the fieldof-view than in dense, closed-canopy ecosystems. Absorption of light by senesced, non-photosynthetic vegetation significantly influences reflectance and VIs (Huete and Jackson, 1987; Van Leeuwen and Huete, 1996), thus contributing to the FPAR signal retrieved from VIs despite being unused for photosynthesis (Asner et al., 1998a, 1998b). Failing to account for these background conditions can bias FPAR estimates by as much as ± 0.2, thus contributing to substantial errors in downstream estimates of primary production and ET that rely on FPAR as a key input (Nagler et al., 2000). In order to retrieve LAI and FPAR over large areas with remote sensing, models must make assumptions about vegetation type, canopy architecture, and model parameters (Baret and Guyot, 1991). The global MODIS LAI and FPAR products, for example, assume that each pixel consists of one biome type, with constant canopy and soil properties within that biome (e.g., leaf angle distribution and the optical properties of different plant tissues and soils) (Myneni et al., 2002). While these simplifying assumptions allow for BRDF-based inversion of radiative transfer models to retrieve LAI and FPAR, the assumptions of a single biome type per pixel and of constant within-biome soil and vegetation properties are unlikely to hold in drylands, which often represent heterogeneous mosaics of C3 and C4 grasses and shrubs, senesced plant tissue, and various soil types/colors. However, despite these limitations, the MODIS algorithm effectively captures the dominant seasonal patterns of LAI and FPAR in many dryland ecosystems (Fensholt et al., 2004), though persistent issues with overestimation of LAI and FPAR frequently occur in arid and semiarid regions (Fensholt et al., 2004; Turner et al., 2006a, 2006b, 2005), particularly outside of the peak growing season (Kappas and Propastin, 2012).

carbon and nitrogen, biocrusts are collectively estimated to contribute significantly to global NPP (~0.58 Pg yr−1 of C) and N2 fixation (~24.39 Tg yr−1 of N) (Rodriguez-Caballero et al., 2018). However, detailed data on biocrust cover and composition are extremely limited in spatial and temporal extent, leading to large uncertainty in estimates of biocrust cover and function (Ferrenberg et al., 2017). Further, many of the methods used to assess carbon cycling – such as eddy covariance and inverse modeling – may implicitly include biocrusts' CO2 exchange with the atmosphere, yet this component of the flux is unquantified. A relatively poor understanding of biocrust cover and a low scalability of biocrust function significantly limits our fundamental understanding of biocrusts' contribution to global cycles, as well as a predictive understanding of how the effects of climate change on biocrusts will influence future climate via alteration to dryland CO2 efflux, soil nutrient cycling (Reed et al., 2012; Darrouzet-Nardi et al., 2015, 2018), and albedo (Rutherford et al., 2017). Accordingly, remote sensing offers an opportunity to vastly improve our understanding of biocrust distribution and the roles they play in global biogeochemical cycling (Ferrenberg et al., 2017). Variation in the dominant community constituents (e.g., dominance by cyanobacteria vs. lichens and/or mosses) leads to markedly different rates and magnitudes of ecological functions (Ferrenberg et al., 2017; Tucker et al., 2018), but also to distinct biocrust spectral signatures (Rutherford et al., 2017) that could allow for categorization of biocrusts into community and functional types (see §5.1 below). However, several factors inhibit remote monitoring of biocrusts, including: i) high spatial resolution requirements to differentiate biocrusts from vascular plant and mineral soil signals; ii) the combined influences of natural and successional state variation in biocrust communities at fine spatial scales, iii) the ability of some widespread cyanobacterial species to move vertically and be photosynthetically active within the soil surface where they shelter from UV radiation, iv) notable changes in biocrust pigmentation and spectral signatures between their active-moist and inactive-desiccated states, and v) strong spectral differences between bare soil and late-successional biocrust-colonized surfaces which may skew remote sensing-derived estimates of albedo, evapotranspiration, and vegetation (e.g., NDVI, EVI, etc.) in dryland systems. Collectively, these issues add uncertainties to remotely-sensed estimates of biocrust cover and composition that are not encountered in studies of vascular

4.4. Biological soil crusts: a distinct and understudied feature of dryland ecosystems Biological soil crusts (also known as biocrusts, cryptogamic crusts, and biogenic crusts) are photoautotrophic soil surface communities of cyanobacteria, lichens, and mosses that can dominate the vegetation interspaces in dryland systems worldwide (Belnap, 2003) (Fig. 2). Model estimates suggest that biocrusts cover 12% of the terrestrial Earth surface, and, due to the ability of many biocrust species to fix

Fig. 7. Failure rate of annual start of growing season dates from AVHRR NDVI. Based on data and methods from Dannenberg and Wise (2017) and Dannenberg et al. (2018). Inset aridity classification is based on the CRU grid cell in which each pixel falls (Fig. 1). 9

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plants (Ferrenberg et al., 2017). Regardless, one of the largest hindrances to remote sensing of biocrusts may be a widespread lack of awareness of their existence and importance.

include: i) poor performance of optical VIs over sparse canopies, in which the vegetation signal is low relative to the background noise (White et al., 1997); ii) VI smoothing functions that assume one or two well-defined seasonal cycles per year, which are well adapted to temperate deciduous forests and grasslands (e.g., Jönsson and Eklundh, 2004; Zhang et al., 2003) but not for the intermittent, pulse-driven VI “spikes” observed in many dryland ecosystems; and iii) tradeoffs between spatial and temporal resolution, which often necessitate a coarse spatial resolution to achieve sufficiently frequent observations but that masks the high spatial heterogeneity of dryland vegetation and precipitation patterns (White et al., 1997). Improving remote sensing methods for land surface phenology retrieval in dry regions remains one of the most pressing needs in phenology research (Ganguly et al., 2010).

4.5. Land surface phenology: coping with short and sporadic growing seasons Vegetation phenology – the seasonal cycles of vegetation growth driven by environmental cues such as temperature, day length, and soil moisture availability – has a large impact on ecological systems (e.g., determining availability of forage for animals), biogeochemical cycles (e.g., through photosynthesis), and biophysical processes (e.g., regulation of energy and water cycling). In drylands, the timing of vegetation growth, senescence, and dormancy are spatially heterogeneous and temporally dynamic, due both to the heterogeneous mixtures of vegetation within dryland ecosystems and the reliance of plant activity on scarce and unpredictable precipitation (Jenerette et al., 2012). In contrast to more temperate and mesic ecosystems, in which vegetation growth coincides with relatively consistent seasonal cycles of temperature and photoperiod, dryland vegetation activity is largely cued by pulses of rainfall (Huxman et al., 2004) and therefore tends to be relatively short and “flashy,” often with multiple growth cycles per year and large variability from year to year (Broich et al., 2014). These phenological characteristics make dryland ecosystems one of the most challenging regions for retrieval of “land surface phenology” (the seasonal cycles of vegetation “greenness” at the pixel scale). Retrieving estimates of phenological timing from time series of remotely sensed vegetation indices typically proceeds through some combination of: i) quality screening, ii) function fitting or smoothing of the seasonal vegetation index observations, and iii) thresholding or change-point detection of the fitted time series to estimate key transition dates in the phenological cycle. However, even when using the same underlying VI data, different methods of estimating phenological transition dates are largely inconsistent with each other in drylands (White et al., 2009), and many phenology algorithms often fail to retrieve dryland phenology altogether (Dannenberg et al., 2018, 2015; Ganguly et al., 2010; Zhang et al., 2006) (Fig. 7). These issues can be traced both to the inherently high spatial and temporal heterogeneity of dryland phenology, as well as to limitations in the remote sensing data and methods used to retrieve phenology estimates. These limitations

4.6. Primary productivity: capturing structural and physiological responses to moisture stress Terrestrial primary production – the carbohydrates fixed by plants through photosynthesis – represents the largest annual flux in the global carbon cycle. The dominant paradigm for operational modeling of primary production with remote sensing is light-use efficiency (LUE) theory, which estimates plant production as the product of absorbed photosynthetically active radiation (APAR) by plant canopies and their efficiency at converting APAR to carbohydrates (ε) (Monteith, 1977, 1972; Song et al., 2013): (1)

GPP = APAR × .

In practice, APAR is estimated as the product of incident PAR at the top of the canopy and the fraction that is absorbed by the canopy (FPAR). Since ε is not directly observable, most models estimate it as the product of a global or biome-specific optimal light-use efficiency (ε0) constrained by a set of environmental stress functions (f(E)) that reduce the optimal light-use efficiency under nonoptimal climatic conditions:

GPP = PAR × FPAR ×

0

× f (E ).

(2)

While these models have proven highly successful for monitoring global primary production (e.g., Potter et al., 1993; Running et al., 2004; Yuan et al., 2010, 2007; Zhang et al., 2016), they often fail to capture the dominant temporal dynamics of primary production in

Fig. 8. Comparisons of satellite-based and eddy covariance tower-based (A) annual gross primary productivity (GPP; gC m−2 y−1) and (B) annual evapotranspiration (ET; mm y−1). Satellite-based GPP and ET are derived from the widely used MODIS GPP (Zhao et al., 2005) and ET (Mu et al., 2011) data products. Red lines and text show spatial relationships across mean annual values, while smaller black lines show site-level temporal relationships. Dashed gray lines show the ideal 1:1 relationship. Inset panels show temporal variations expressed as annual deviations from each site's mean annual values. All linear fits shown are significant (p < .05). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Adapted from Biederman et al. (2017). 10

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drylands. The MODIS primary production algorithm, for example, only captures ~30% of interannual variability of GPP across dryland sites in southwestern North America (Fig. 8A) (Biederman et al., 2017). This poor performance of primary production models in dryland systems can be partly traced to limitations in the data used to calibrate and drive LUE models. The most common GPP datasets used to calibrate LUE models are eddy covariance flux tower networks (e.g., Jung et al., 2017; Smith et al., 2016; Xiao et al., 2010; Yuan et al., 2010; Zhang et al., 2016). However, as shown in Fig. 1, the distribution of flux towers within dryland systems is relatively limited, and long-term flux sites are particularly rare, with some notable exceptions in North America and Australia. Fluxes of carbon, water and energy in dryland ecosystems tend to be more “unique” and less predictable than in many other biomes (Haughton et al., 2018), so LUE models that are trained on a limited number of dryland flux sites may not capture dynamics elsewhere. Further, in arid and semiarid systems, the meteorological data needed to estimate both incoming PAR and f(E) are often plagued by low station density (White et al., 1997) and by comparatively poor accuracy, particularly for hydrometeorological variables like humidity/ VPD (Bohn et al., 2013; Heinsch et al., 2006; Yi et al., 2011) and precipitation (Fekete et al., 2004; Maggioni et al., 2016). Aside from limitations in calibration and driver datasets, our ability to constrain nearly every variable in Eq. (2) remains limited in dryland systems. The primary remotely sensed input into LUE models is FPAR, but, as discussed in §4.3, FPAR by chlorophyll (as opposed to nonphotosynthetic tissues) can be difficult to estimate for sparse canopies, where the presence of senesced grasses, standing litter, woody tissue, and exposed soil can distort the vegetation signal (Asner et al., 1998a, 1998b; Huete and Jackson, 1987; Van Leeuwen and Huete, 1996). Indeed, errors in MODIS-derived dryland primary production have been largely traced to errors and biases in FPAR (Turner et al., 2006a, 2006b, 2005). Estimating the optimal LUE (ε0) poses additional difficulties since dryland ecosystems represent heterogeneous mixtures of soils, succulents utilizing the CAM photosynthetic pathway, C3 and C4 grasses, shrubs, and trees – with differences among species in photosynthetic capacity and drought strategies – and ε0 can therefore change over very short distances in these heterogeneous environments (Turner et al., 2006a). The typical assumption of a single biome type per pixel, as opposed to a mixture of functional and structural types, can introduce substantial uncertainty into estimates of ε0 (Heinsch et al., 2006). Finally, estimating water stress effects on LUE is particularly challenging (Zhang et al., 2015), especially in arid and semiarid regions and during periods of extreme drought (Kanniah et al., 2009; Leuning et al., 2005; Mu et al., 2007b; Zhang et al., 2007). While VPD is a reasonable proxy of water stress in many mesic ecosystems, it does not completely capture water stress in dryland ecosystems, which are highly responsive to soil moisture deficits that are not captured by VPD or reflected in VIs (Novick et al., 2016; Stocker et al., 2018). However, approaches for capturing soil moisture stress at spatiotemporal resolutions appropriate for LUE modeling remain problematic (Song et al., 2015, 2013). For instance, radar and microwave sensors provide surface soil moisture estimates but at relatively coarse spatial resolution (Petropoulos et al., 2015) and water balance estimates from hydrological models are often too computationally intensive for large-scale and/or operational applications (Gremer et al., 2015). Given these current limitations, remote sensing of dryland primary production would benefit from two key improvements in LUE models: 1) direct estimation of actual LUE via new remote sensing proxies like the photochemical reflectance index (PRI) or SIF (see §5.1 below), or 2) improved modeling of soil moisture stress that effectively captures water limitation in dryland environments.

4.7. Evapotranspiration: a small but dynamic portion of the dryland energy balance Evapotranspiration (ET) – consisting of both evaporation from plant and soil surfaces and transpiration through stomata – is a key process in the hydrological cycle which links water, carbon, and energy cycles (Fisher et al., 2017). In drylands, water is the dominant constraint on plant productivity such that ET can be used as an indicator of vegetation resilience and resistance to drought (Nagler et al., 2018a, 2018b), and remote sensing currently provides widely-used estimates of ET at spatiotemporal resolutions relevant for on-the-ground management (Anderson et al., 2012; Fisher et al., 2017). There are three primary methods for estimating ET from remote sensing: (i) empirical/statistical methods which upscale field measurements of ET using remotelysensed VIs (e.g., Nagler et al., 2005a, 2005b, 2009; Yang et al., 2006); (ii) semi-empirical methods that use remotely sensed land surface temperature (LST) and/or VIs to derive key parameters in physical models of latent heat flux (e.g., Cleugh et al., 2007; Mu et al., 2011, 2007a; Yuan et al., 2010); and (iii) surface energy balance (SEB) methods which estimate latent heat flux as the residual in the SEB equation using remotely-sensed surface reflectance and LST data (e.g., Anderson et al., 2008, 1997; Colaizzi et al., 2012). Purely empirical approaches are generally only applicable to the specific regions and conditions in which they were calibrated (Glenn et al., 2010), with limited ability to extrapolate or operationalize the algorithms, so here we focus on current status and challenges associated with the application of the semi-empirical/physical modeling and SEB methods of ET estimation across drylands. For the semi-empirical approaches, potential (or reference) ET is estimated using physical models like the Penman-Monteith (Monteith, 1965; Penman, 1948) or Priestley-Taylor (Priestley and Taylor, 1972) methods based in part on remotely-sensed inputs (e.g., LST and/or VIs), and adjusted downward during periods when moisture is insufficient to meet atmospheric demand or when plants are inactive. Prominent, operational models that fall into this category are the PT-JPL method (Fisher et al., 2008), the MODIS evapotranspiration model (MOD16) (Mu et al., 2011, 2007a), and the LSA-SAF MSG algorithm (Ghilain et al., 2011). These models face some of the same challenges in drylands as semi-empirical LUE primary production models (see §4.6 above). For instance, they apply a coarse, fixed parameterization to characterize stomatal responses to water constraints – a process that is highly heterogeneous at the sub-pixel level and dynamic over time, especially in dryland ecosystems. While operational semi-empirical models are quite successful at simulating ET in many forested regions (Chen et al., 2014; Michel et al., 2016), multiple studies have noted issues with poor performance of these algorithms in drylands (Fig. 8B) (Biederman et al., 2017; Hu et al., 2015; Michel et al., 2016). The SEB methods estimate ET as the residual of the surface energy balance:

ET = Rn

G

H

(3)

where λET represents the energy needed to evaporate or transpire water; Rn represents net radiation; G represents soil heat flux; and H represents sensible heat flux. For satellite sensors measuring radiation in the thermal infrared bands (Fig. 4), H can be estimated based on the difference between aerodynamic temperature (approximated with LST) and near-surface air temperature (Ta): H = (Ta − LST) × Heat Transfer Coefficient. Rn is relatively easy to estimate and G is usually small compared to Rn (and negligible on a daily time step), so λET can be calculated as the “residual” of Eq. (3). ET is typically estimated for each image date throughout the growing season and then summed over the season by temporally interpolating evaporative fraction or fraction of reference ET between image dates (Kalma et al., 2008). SEB methods have been used to successfully map crop water use for managers, providing ET over multiple decades in some semiarid environments (Senay et al., 2017). However, in arid lands with sparsely vegetated land area, 11

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the radiative surface temperature differs from the aerodynamic surface temperature (Chehbouni et al., 1997; Kustas and Norman, 1999). Further, the application of SEB approaches for native dryland vegetation can prove difficult since λET is typically small relative to Rn and H (Anderson et al., 2012; Glenn et al., 2011, 2010, 2007), so the uncertainty in ET can be larger than total ET. Regardless of which method is chosen, several key issues occur in drylands that make ET difficult to estimate. In water-limited ecosystems, partitioning ecosystem scale ET fluxes between plant transpiration and soil/canopy evaporation remains a theoretical and technical challenge (Wang et al., 2010). Further, because of the relatively infrequent return interval of moderate-resolution sensors like Landsat, ET estimates represent “snapshots” in time. ET from precipitation events that occur between image acquisitions can therefore be missed when integrated over the growing season. Even sensors with daily overpasses (e.g., MODIS) cannot completely capture the temporal dynamics of dryland ET (Tang et al., 2013) since canopy conductance varies diurnally, often with highest conductance in the morning when VPD is low followed by mid-day suppression (Murray et al., 2009). Stomatal conductance is also highly species specific and varies according to photosynthetic pathway, rooting depth and access to deep water, and isohydricity. The once-daily overpass times of existing sensors capture instantaneous information on stomatal conductance and ET, but simple scaling methods to approximate daily mean values introduce significant bias in current ET estimates. Thermal infrared sensors that view Earth's surface multiple times per day, such as the ECOSTRESS mission onboard the International Space Station (see §5.1) or geostationary satellites equipped with thermal infrared bands (see §5.2), are therefore needed to fill gaps in the diurnal dynamics of ET (Fisher, 2018; Fisher et al., 2017; Stavros et al., 2017).

research, and dryland remote sensing in particular, should be a high research priority, especially given that > 2 billion people depend on services provided by dryland ecosystems. Below, we offer a framework by which current and near-term remote sensing activities could be optimally utilized to accelerate our knowledge of dryland ecosystem dynamics, which combines multi-scale remote sensing techniques and builds on the previously established and growing dryland ground observation networks. 5.1. Moving beyond “greenness” Ecosystem monitoring has been largely based on the multispectral “greenness” paradigm (i.e., VIs derived from some combination of near infrared and visible surface reflectance). While greenness indices have proven invaluable for monitoring trends and variability in land surface vegetation, and as the basis for biophysical and biogeochemical models, they suffer from several weaknesses in dryland ecosystems, as detailed in §4 above. Looking forward, research focused on integrating a diversity of new and emerging multi-scale remote sensing assets has the potential to significantly advance our current understanding of dryland ecosystems (Fig. 9). Below we briefly discuss key new remote sensing assets and techniques, and their application in drylands. Vegetation optical depth (VOD) from microwave remote sensing represents a measure of vegetation water content (Jones et al., 2011; Tian et al., 2016b) and relates closely with total aboveground biomass (Brandt et al., 2018b; Liu et al., 2015). Long-term VOD datasets based on the integration of multiple satellite instrument records are available and have provided key insights into dryland ecosystem structural and functional dynamics (Brandt et al., 2018a, 2018b, 2017, 2016; Tian et al., 2016a, 2016b). Looking forward, exciting opportunities exist to explore differences in high frequency (23 GHz; X-band) and low frequency (1.4 GHz; L-band) VOD derived from the AMSR-2 and SMOS satellite platforms, respectively (Fig. 4), which may enable insights into water dynamics at multiple levels in the vegetation profile (Brandt et al., 2018b). More, diurnal VOD dynamics can provide new insights into variability in daily integrated ecosystem water potential, enabling the characterization of key dryland plant survival strategies (Konings et al., 2017; Konings and Gentine, 2017; Li et al., 2017). To help facilitate these new satellite VOD research developments, we recommend the expansion of high temporal frequency, field-based vegetation water content measurement techniques (e.g., Small et al., 2010, 2014; Wan et al., 2015), which can be easily integrated across dryland tower

5. Recommendations and future directions Due to the challenges summarized above, we have yet to reach the full potential of dryland remote sensing, and as such our current understanding of dryland ecosystem structural and functional dynamics remains limited relative to other biomes. This gap in knowledge inhibits our ability to accurately map local- to regional-scale dryland processes at spatiotemporal resolutions relevant for land managers and reduces our ability to predict future variations in global C, water, and nutrient cycling resulting from dryland dynamics (Ahlström et al., 2015; Humphrey et al., 2018; Poulter et al., 2014). We argue that dryland

Fig. 9. Potential synergies across remote sensing techniques for improved understanding of dryland ecosystem structural and functional dynamics. Techniques (colored ovals) and synergistic structural and/or functional parameters (text) that could be derived when a given combination of techniques are co-located in space and time (Stavros et al., 2017). Structural and/or functional parameters of particular relevance to improved understanding of dryland ecosystems are underlined. Note, microwave remote sensing is not included in this figure yet could provide additional insights into biomass and water stress parameters when co-located with these other techniques (see §5.1). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 12

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observational networks (e.g., OzFlux, SECA, and NMEG). Chlorophyll fluorescence (ChlF) is the re-emittance of excess energy by the photosystems during the light reactions of photosynthesis, and active ChlF methods have been used in leaf level ecophysiology studies since the 1980s to improve our understanding of the dynamic response of a plant's photochemistry to its environment (Baker, 2008). Recent advances have enabled the passive remote sensing of solar-induced ChlF (SIF) (for in depth reviews, see: Meroni et al., 2009; Porcar-Castell et al., 2014), driving a new research frontier aimed at measuring SIF across multiple spatiotemporal scales (Porcar-Castell et al., 2014; Yang et al., 2017). Unlike VIs, which measure reflectance from pigments and therefore are often decoupled from photosynthetic activity on short time scales, ChlF is directly linked to electron transport (Genty et al., 1989; Van Der Tol et al., 2014) and thus is a more informative proxy of a plant's functional response to its dynamic environment (Porcar-Castell et al., 2014). SIF is a particularly promising technique for dryland vegetation productivity monitoring since it: 1) better captures productivity of evergreen and hardy deciduous vegetation (Smith et al., 2018; Zuromski et al., 2018); 2) is insensitive to changes in soil reflectance (Badgley et al., 2017; Smith et al., 2018); and 3) has the potential to capture the rapid functional responses of precipitation-pulse dynamics– key challenges for dryland remote sensing (see §4.2, §4.5, and §4.6). SIF measurement techniques are rapidly advancing from leaf to satellite scale (Alonso et al., 2007; Grossmann et al., 2018; Yang et al., 2018, 2015). A field observation network is being established and co-located with eddy covariance flux tower sites (Yang et al., 2018); aircraft and drone SIF instruments are becoming available

(Frankenberg et al., 2018; Zarco-Tejada et al., 2013); and satellite platforms able to monitor SIF are expanding (Joiner et al., 2016; Kohler et al., 2018; Sun et al., 2017) (Fig. 4). In coordination with these developments, expansion of high temporal frequency observations of SIF across drylands would: 1) improve understanding of diurnal SIF:GPP relationships in an ecosystem specific context; 2) enable informed scaling of instantaneous satellite SIF observations to daily integrated SIF estimates; and 3) assess the ability of SIF to capture the interannual variability of dryland carbon dynamics currently unaccounted for by LUE models. Thermal infrared (TIR) can be used to estimate LST, which can be integrated with measurements of air temperature to infer changes in stomatal conductance and rates of canopy transpiration (Anderson et al., 2012; Aubrecht et al., 2016). While TIR sensors have been included on satellite platforms for decades (Fig. 4), these observations are limited in that they are instantaneous and associated with fixed overpass times (see §4.7) and typically include a limited number of thermal bands, which inhibits accurate retrievals of LST and therefore ET (Fisher et al., 2017). Therefore, the expansion and use of high temporal frequency TIR measurements, including from ground-based and airborne sensors, across drylands could improve understanding of LST:ET relationships at diurnal timescales (Aubrecht et al., 2016). This is a particularly timely frontier given the recent launch of the NASA ECOSTRESS mission on the International Space Station (ISS) (Fig. 4), which will provide satellite-based estimates of LST and ET at variable overpass times, enabling first-time insight into sub-daily LST and ET dynamics from space (i.e., > 20 observations per hour of the day throughout the Fig. 10. Hyperspectral classification of common biocrust community constituents (i.e., three lichen species) and the detection of changes in water content. Red and blue lines in the upper panel represent field spectra of three lichen-dominated biocrust samples measured when dry and wet, respectively. Vertical gray bars in the upper panel represent the positions of the six terrestrial bands of the Landsat 8 Operational Land Imager. The pictures in the lower panel show round biocrust plugs that highlight the changes in tissue size and coloration of the lichen species samples from dry to wet. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Photographs by Robin Reibold.

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year) (Fisher et al., 2017; Stavros et al., 2017). Finally, co-location of high temporal frequency TIR and SIF observations will enable new insights into variability and trends in vegetation water use efficiency (GPP/ET) - a factor that is not well understood, yet predicted to change most rapidly across dryland ecosystems (Donohue et al., 2013; Smith et al., 2016). Hyperspectral imaging spectroscopy can provide orders of magnitude more information relative to traditional multispectral platforms (Fig. 4). A single full-range hyperspectral reflectance spectrum (400–2500 nm) can provide information on a variety of functional traits, including vegetation water, nitrogen, chlorophyll, carotenoid, and xanthophyll dynamics (Asner et al., 2016, 1998b; Barnes et al., 2017; Garbulsky et al., 2011; Rodríguez-Caballero et al., 2017; Sankey et al., 2017; Schneider et al., 2017) that can be used to map functional traits and life history strategies across the landscape (Sankey et al., 2017; Schneider et al., 2017). Accurate maps of plant functional types can be incorporated into satellite algorithms and models as critical constraints on carbon and water flux estimates (Jetz et al., 2016). Field imaging spectroscopy is becoming more widely available and cost effective (Sankey et al., 2017), and the National Ecological Observation Network (NEON) Airborne Observation Platform (AOP) will provide freely available full-range hyperspectral data collected during peak biomass for established NEON sites spanning all major biomes of the United States (Kampe et al., 2010). The upcoming HISUI instrument for deployment on the ISS will provide 30-m, full-range hyperspectral imaging from space (Stavros et al., 2017). In particular, multi-scale hyperspectral observations overcome many of the challenges associated with mapping biological soil crusts across drylands (§4.4) and could thus revolutionize our understanding of the functional role of biocrusts from the plot to the global scale (Fig. 10). Finally, LiDAR can be used to measure foliar vertical profiles, from which vegetation height and total biomass can be estimated (Jetz et al., 2016; Sankey et al., 2017; Schneider et al., 2017; Stavros et al., 2017). Further, foliar vertical profiles can be used to define structural or morphological traits, including leaf or plant area index, foliage height diversity, mean canopy height, and specific leaf area (Schneider et al., 2017). Maps of plant functional types derived from integrated LiDAR and hyperspectral data represent a powerful framework in trait mapping and alone could be used to predict carbon and water flux estimates using relationships between biodiversity and ecosystem functioning (Asner, 2007; Jetz et al., 2016; Schneider et al., 2017). Ground-based LiDAR techniques are becoming more widely available and cost effective (Sankey et al., 2017), and the NEON AOP, in addition to providing hyperspectral data, will also provide freely available LiDAR data (Kampe et al., 2010). The Global Ecosystem Dynamics Investigation (GEDI) instrument, recently deployed on the ISS, will provide 500-m LiDAR, enabling estimation of vegetation structural attributes on a global scale (Stavros et al., 2017).

along with the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) carried onboard ESA's Meteosat Second Generation geostationary satellites, all have comparable band designations (i.e., central wavelength and spatial resolution) to those of MODIS in the visible and near infrared spectrum (Schmit et al., 2005). Furthermore, this geostationary constellation provides almost complete coverage of global drylands in North/South America (ABI), Central Asia/Australia (AHI) and Africa/ the Middle East/Southern Europe (SEVIRI). Data from this geostationary constellation have already been used to study a wide range of dryland ecosystem processes including: land surface phenology (Yan et al., 2016), seasonal daily temperature range dynamics (Sun et al., 2006b, 2006a), and flash drought detection (Otkin et al., 2013). The upcoming Geostationary Carbon Cycle Observatory (GeoCarb) mission is capable of providing SIF measurements covering the CONUS region within less than 3 h, offering opportunities to study photosynthetic dynamics at diurnal scales across large regions (Moore et al., 2018). Fusion of LEO and geostationary sensors can also produce promising estimates of key ecosystem variables with high spatial and temporal resolutions, such as ET (Anderson et al., 2011) and land surface temperature (Wu et al., 2015). Continued and expanded use of high frequency observations from new-and-improved geostationary sensors – particularly for monitoring important vegetation functional proxies that respond rapidly to changes in biophysical drivers (e.g., SIF and LST) – could vastly improve our understanding of dryland vegetation sensitivity to sub-daily variation of water availability. 5.3. Better use and expansion of dryland observational networks Development of dryland-specific models and assessment of new remote sensing technologies in drylands cannot proceed without extensive observational networks. These networks currently exist in some regions (e.g., OzFlux, SECA, AsiaFlux, HiWATER, etc.), and a key first step in advancing dryland remote sensing is maintenance and better utilization of these existing observational networks. Simultaneously, dryland networks must continue to expand since observational sites generally remain clustered in and biased towards more temperate and mesic ecosystems (e.g., Fig. 1). Although drylands make up roughly 40% of Earth's land surface, eddy covariance flux tower networks vastly underrepresent arid regions and only recently came into rough proportionality with global semiarid regions (Fig. 1), while only 3% of International Long-Term Ecological Research (ILTER) sites are located in desert or desert-scrub systems. Fully capturing the spatial heterogeneity and rapid temporal responses of dryland ecosystem productivity and water exchange, as well as placing these systems in a global context, requires the expansion of these observational networks. Here, we describe two observational networks that would be particularly useful to expand given their utility for up-scaling ecosystem processes with remote sensing: 1) eddy covariance flux towers and 2) ground-based optical and thermal measurements at the canopy level, including SIF and PhenoCam instruments. Eddy covariance allows estimation of high-frequency land-atmosphere energy and gas exchanges – including CO2 and water fluxes – at the landscape level, which can be used to estimate ecosystem-level GPP and ET. Though the flux network is relatively sparse (both spatially and temporally) due to the expensive instrumentation, time-consuming construction and maintenance, and relatively short funding cycles, flux towers are present across most biomes and are featured in multiple networks that target different research questions. These networks include: FLUXNET (Baldocchi et al., 2001), AmeriFlux (Novick et al., 2018), ILTER (Mirtl et al., 2018), the Critical Zone Observatory network (CZO) (White et al., 2015), the Australian Terrestrial Ecosystem Research Network (TERN), and NEON (Schimel et al., 2007). While flux tower sites are relatively dense in western North America, Australia, and parts of Asia, especially given the existence of regional networks like OzFlux, AsiaFlux, SECA, and NMEG (see §3.4), there is very limited representation of large and important drylands in Africa, South

5.2. Leveraging observations from geostationary satellites The relatively long revisit period of LEO satellites (e.g., every 16 days for Landsat) limits the ability of these satellites to quantify changes occurring at the diurnal scale. High temporal frequency observations from geostationary satellites, which have served primarily as inputs for weather forecasting (Kurzrock et al., 2018), overcome many of the temporal limitations of LEO sensors. For example, NOAA's latest generation of geostationary satellites – the Geostationary Operational Environmental Satellite-R Series (GOES-R) – carry the Advanced Baseline Imager (ABI), which is able to complete a full disk scan (i.e., covering North and South America) along with three scans of the conterminous United States (CONUS) every 15 min (Schmit et al., 2005). The Advanced Himawari Imager (AHI) carried by the latest Japanese geostationary satellites Himawari-8/9 is capable of finishing a full disk scan of the Asia-Pacific region every 10 min, with 30 s scanning frequencies in certain landmark areas. These geostationary sensors, 14

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America, the Middle East, and central Asia (Fig. 1A). When working towards better constraining the global carbon, water, and energy cycles, the expansion of flux towers into these key regions should be a high priority, especially since dryland fluxes are highly “unique” (i.e., not easily extrapolated from one place to another) (Haughton et al., 2018) and algorithms are only as good as their underlying calibration data (Glenn et al., 2011, 2010). Optical and thermal measurements at the canopy level — especially sensors and imagers mounted on flux towers — are able to link site productivity to satellite-derived VIs by using high frequency repeat measurements (Browning et al., 2017). The Phenological Eyes Network (PEN) and PhenoCam network have been used to detect seasonal variation in evergreen needleleaf and shrubland systems (Brown et al., 2016; Noormets, 2009), while the nascent FluoNET network of groundbased SIF sensors offers the potential to detect sub-daily variation in SIF, which can be directly linked to flux tower GPP and ET estimates (Yang et al., 2018). Many of the flux tower networks mentioned above have PhenoCam systems (Richardson et al., 2018), but FluoNET currently includes only a single dryland site (the MPJ piñon-juniper site in New Mexico). Expanding in situ networks of SIF and LST sensors would be particularly valuable, since these could provide novel insight into physiological changes related to GPP and ET, respectively, occurring at sub-hourly timescales (Fig. 9). Further, parameters critical to dryland ecosystem function, including vegetation water stress and water-use efficiency, could be derived when these techniques are co-located in space and time (Fig. 9). Thus, prioritizing the rapid expansion of these observational networks can enable monitoring of plant physiological changes at short time scales and improve scalability from plot to pixel.

Schwinning and Sala, 2004; Wang et al., 2015), and high spatial and temporal heterogeneity (Aguiar and Sala, 1999; Reynolds et al., 2007). Due to the inherent tradeoff between spatial and temporal resolution in satellite imagery, observations and products from a single sensor often cannot adequately resolve complex dryland dynamics. Integrative analytical techniques combined with cross-scale approaches are necessary to assess the influence of fine-scale processes on broad scale patterns in drylands (Browning et al., 2015, 2012). Data fusion methods that integrate data from multiple sensors show great promise for characterizing vegetation dynamics in drylands. For example, a regression tree modeling approach that combined observations from Landsat's Operational Land Imager (OLI) and Sentinel-2's Multispectral Instrument (MSI) significantly improved representation of dryland phenology (Pastick et al., 2018). Additionally, new techniques and sensors (highlighted in §5.1–5.2) show promise for incorporation into dryland-specific algorithms that overcome challenges in estimating GPP and ET. Pioneering studies have shown a linear relationship between remotely sensed SIF and GPP (Frankenberg et al., 2011; Guanter et al., 2012; Joiner et al., 2011) that appears to remain robust across a range of environmental conditions (Verma et al., 2017) and across biomes (Sun et al., 2017). Recent efforts have confirmed that in precipitation pulse driven drylands, SIF more accurately characterizes intra- and inter-annual GPP dynamics than VIs (Smith et al., 2018; Zuromski et al., 2018), though the SIF:GPP relationship is still being explored and debated (Damm et al., 2015) and in-depth investigations encompassing diverse dryland ecosystem types are crucially needed. Nonetheless, dryland-specific algorithms, whether empirical or processbased, could be vastly improved by inclusion of new structural and/or functional parameters derived from various combinations of cuttingedge remote sensing techniques (Fig. 9), but these efforts will require extensive dryland-specific field observations as discussed above.

5.4. Developing dryland-specific algorithms The “uniqueness” of dryland carbon and water fluxes (Haughton et al., 2018) means that algorithms trained using relatively sparse global networks with a dearth of dryland sites (e.g., FLUXNET; Fig. 1) will likely perform poorly when extrapolated to dryland regions in which they were not trained, as has been demonstrated with the MODIS GPP and ET products (e.g., Biederman et al., 2017) (Fig. 8). New models and algorithms must therefore be developed to adapt to the unique climatic, hydrological, and ecological characteristics of dryland systems, including their seasonality (Barnes et al., 2016; Smith et al., 2018), sensitivity to limited moisture (Lauenroth and Bradford, 2009;

5.5. Data assimilation, model-data fusion, and model development A new generation of satellite and near-surface remote sensing technologies (Chan et al., 2016; Köehler et al., 2018; Stavros et al., 2017) presents opportunities for coupling remote sensing with terrestrial ecosystem models through model benchmarking (comparing model output to observations to evaluate model skill), data assimilation (DA; statistically nudging model states or parameters to reduce modeldata mismatches), and model development (modifying the model logic

Fig. 11. A conceptual overview of how multi-scale remote sensing techniques could be combined using model-data fusion techniques to better constrain terrestrial ecosystem model estimates and forecasts of carbon, water, and energy balance. 15

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and processes) (Fig. 11). Ideally, these approaches should occur iteratively, with significant model–data mismatch serving as an indicator for targeted model development. This is a particularly important frontier for dryland ecology since models have historically underperformed in these regions (e.g., Dahlin et al., 2015; Fox et al., 2018; MacBean et al., 2015; Traore et al., 2014). For instance, benchmarking the performance of the Community Land Model against satellite derived-LAI led Dahlin et al. (2015) to modify the drought deciduous phenology component of this global land surface model to prevent anomalous green-up during dry seasons. In a further example using this model, Fox et al. (2018) recently explored the potential for DA to improve carbon cycling dynamics for a dryland ecosystem using both simulated and real satellite observations of biomass and LAI. When assessing DA using simulation data and thus controlling for model error, the study found large reductions in model error in C dynamics forecasts; whereas DA using real satellite observations was less successful due to exceptionally large differences between the model output and the real satellite observations – a clear indication that, for this particular dryland ecosystem, model development is required before DA can be effectively implemented to constrain model C dynamics. Moving forward, we recommend close collaboration between remote sensing and terrestrial ecosystem modelers to more rapidly identify model structural deficiencies and thus enable more accurate model estimates and forecasts of dryland ecosystem functioning.

BSCI Biological Soil Crust Index CAM Crassulacean Acid Metabolism ChlF Chlorophyll Fluorescence CI Crust Index CLM Community Land Model CO2 Carbon Dioxide CONUS Conterminous United States CRU Climatic Research Unit CZO Critical Zone Observatory network DA Data Assimilation DART Data Assimilation Research Testbed DTR Diurnal Surface Temperature Range ECOSTRESS ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station ESA European Space Agency EVI Enhanced Vegetation Index ET Evapotranspiration E Evaporation FPAR Fraction Of Photosynthetically Active Radiation GEDI Global Ecosystem Dynamics Investigation GeoCarb Geostationary Carbon Cycle Observatory GESAVI Generalized Soil Adjusted Vegetation Index GOES-R Geostationary Operational Environmental Satellite-R Series GPP Gross Primary Productivity GRACE Gravity Recovery and Climate Experiment HiWATERHeihe Watershed Allied Telemetry Experimental Research Hyperspec Hyperspectral ILTER International Long-Term Ecological Research L Soil Adjustment Factor LAI Leaf Area Index LDASs Land Data Assimilation Systems LEO Low Earth Orbit LiDAR Light Detection and Ranging LSMs Land Surface Models LST Land Surface Temperature LUE Light-Use Efficiency MODIS MODerate Resolution Imaging Spectroradiometer MSAVI Modified Soil Adjusted Vegetation Index MSI Multispectral Instrument MSS MultiSpectral Scanner NASA National Aeronautics and Space Administration NEON National Ecological Observation Network NOAA National Oceanic and Atmospheric Administration NDVI Normalized Difference Vegetation Index NIR Near Infrared NLCD National Land Cover Database NMEG New Mexico Elevation Gradient NPP Net Primary Production OLI Operational Land Imager OSAVI Optimized Soil Adjusted Vegetation Index PEN Phenological Eyes Network PRI Photochemical Reflectance Index SEVIRI Spinning Enhanced Visible and InfraRed Imager SMOS Soil Moisture and Ocean Salinity IAV Interannual Variability ISS International Space Station R Red SAVI Soil Adjusted Vegetation Index SATVI Soil Adjusted Total Vegetation Index SEB Surface Energy Balance SECA Semiarid ECohydrology Array SIF Solar-Induced Chlorophyll Fluorescence TIR Thermal Infrared T Transpiration TERN Terrestrial Ecosystem Research Network TM Thematic Mapper

6. Conclusions Drylands have played an important role as a testbed for new remote sensing techniques since the earliest days of Earth observation. Through advances in remote sensing, often achieved in drylands (Fig. 4), these ecosystems have been revealed as vitally important to global carbon and water cycling (Humphrey et al., 2018; Poulter et al., 2014). This relatively recent revelation suggests that a renewed prioritization of dryland remote sensing is needed, especially given rapidly developing field-based remote sensing techniques and the upcoming diversity of observations that will be available from space. In an effort to strategically exploit these new opportunities, we identify the following research foci: 1) utilizing new and improved remotely sensed assets that provide enhanced structural and functional information beyond those retrievable from optical reflectance and traditional vegetation indices; 2) using new sensors deployed on geostationary satellites to monitor diurnal variation of dryland ecosystem water use, moisture stress, and photosynthesis that have eluded low-Earth orbit satellites; 3) maintaining and expanding ground observational networks (especially eddy covariance flux towers and ground-based sensor systems) to better represent the spatial and temporal heterogeneity of dryland systems; 4) developing algorithms that are specifically tuned for the unique features of dryland ecosystems; and 5) coupling remote sensing observations with process-based models using formal data assimilation techniques to enable improved ecological forecasts and further model development and parameterization. Together, these research directions have the potential to reveal new insights into dryland structural and functional dynamics, and to more fully characterize the role of drylands in Earth system functioning. Acronym dictionary ABI AHI AMSR-2 AOP APAR AVHRR BFAST Biocrust BRDF

Advanced Baseline Imager Advanced Himawari Imager Advanced Microwave Scanning Radiometer-2 Airborne Observation Platform Absorbed Photosynthetically Active Radiation Advanced Very High Resolution Radiometer Breaks for Additive Season and Trend Biological soil crust Bidirectional Reflectance Distribution Function 16

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TSAVI Transformed Soil Adjusted Vegetation Index UASs Unmanned Aerial Systems UNESCO United Nations Educational, Scientific and Organization USGS U.S. Geological Survey U.S. United States UV Ultraviolet VIs Vegetation Indices VOD Vegetation Optical Depth VPD Vapor Pressure Deficit

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Cultural

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