Improving remotely-sensed crop monitoring by NDVI-based crop phenology estimators for corn and soybeans in Iowa and Illinois, USA

Field Crops Research 238 (2019) 113–128

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Improving remotely-sensed crop monitoring by NDVI-based crop phenology estimators for corn and soybeans in Iowa and Illinois, USA Bumsuk Seoa,b, Jihye Leeb,c, Kyung-Do Leed, Sukyoung Honge, Sinkyu Kangb,

T

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a

Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research/Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany b Department of Environmental Science, Kangwon National University, Gangwondaehak-ro 1, 200-701 Chuncheon, Republic of Korea c National Center for AgroMeteorology, 1, Gwanak-ro, Gwanak-gu, 08826, Seoul, Republic of Korea d Climate Change & Agroecology Division, National Institute of Agricultural Sciences, Wanju, Republic of Korea e Soil and Fertilizer Management Division, National Institute of Agricultural Sciences, Wanju, Republic of Korea

ARTICLE INFO

ABSTRACT

Keywords: Crop growth monitoring Crop phenology Crop growth timing Crop production Crop growth anomaly

Weather-related risks in crop production are not only crucial for farmers but also for market participants and policymakers since securing food supply is an important issue for society. Although crop growth condition and phenology represent essential information regarding such risks, extensive observations of these variables are virtually non-existent in many parts of the world. In this study, we developed an integrative approach to remotely monitor crop growth at a large scale. For corn and soybeans in Iowa and Illinois in the United States (2003–2015), we monitored crop growth and crop phenology with earth observation data and compared it against the United States Department of Agriculture National Agricultural Statistics Service (NASS) crop statistics. For crop phenology, we calculated three phenology metrics (i.e., start of season, end of season, and peak of season) at the pixel level from the MODIS 16-day Normalized Difference Vegetation Index (NDVI). For growth condition, we used two distinct approaches to acquire crop growth condition indicators: a process-based crop growth modeling and a satellite-NDVI-based method. Based on their pixel-wise historical distributions, we monitored relative growth strength and scaled-up that to the state-level. The estimates were compared with the crop progress and condition data of NASS. For the state-level phenology, the avg. root-mean-square-error (RMSE) of the estimates was 8.6 days for the all three metrics after bias correction. The absolute mean errors for the three metrics were smaller than 2.6 days after bias correction. For the condition, the state-level 10-day estimates showed moderate agreements with the observations (avg. RMSE = 10.02%). Notably, the condition estimates were sensitive to the severe degradation in 2003, 2012, and 2013 for both crops. In 2010, 2011 and 2013, unusually high errors occurred at the very beginning stage of growth (DOY 140–150), which attenuated over time. As the cumulative biomass and NDVI showed little change in comparison to the period mean biomass and NDVI for the spikes, this seems to be an error associated with variations in growth timing. Overall, the model using accumulated NDVI (S5) is preferable due to its performance and methodological simplicity. The proposed approach enables us to monitor crop growth for any given period and place where long-term statistics are available. It can be used to assist crop monitoring at large scales.

1. Introduction Crop production has become an important issue in recent years because of the growing concern about global food security under ongoing climate changes (Asseng et al., 2013; Rosenzweig et al., 2014; Siebert and Ewert, 2014). Loss of crop productivity due to climate, insects, or pests is of great concern to stakeholders not only in agricultural production but also in the international and domestic market

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(Atzberger, 2013; Lehecka, 2014). Crop growth condition is a term referring to the overall biophysical and biochemical properties (and their interactions) that contribute to the successful growth of a crop. Crop growth condition is affected by both environmental variables (e.g., climate, weather, and soil conditions) and management activities (e.g., fertilization and irrigation). It is an important concept in crop monitoring, but it is considered difficult to quantify as (1) it does not have a generally agreed definition and (2) it changes rapidly over space

Corresponding author. E-mail address: [email protected] (S. Kang).

https://doi.org/10.1016/j.fcr.2019.03.015 Received 17 October 2017; Received in revised form 19 February 2019; Accepted 21 March 2019 Available online 15 May 2019 0378-4290/ © 2019 Elsevier B.V. All rights reserved.

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and time. Nonetheless, it is critical for crop management and yield estimation. Large-scale crop conditions monitoring based on satellite data provides a useful decision support tool for policy making and international trade. Many researchers strive to integrate the various effects of environmental and climatic conditions and consequently predict the final effects on crop growth (Doraiswamy et al., 2004; Johnson, 2014; Han et al., 2012; Wu et al., 2013). In the late 1970s, the Large Area Crop Inventory Experiment (LACIE) program jointly implemented by National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), and United States Department of Agriculture (USDA) monitored growth condition of wheat over the US (MacDonald et al., 1975). Since 1986, weekly crop condition information has been published by USDA National Agricultural Statistics Service (NASS). Field surveyors hired by NASS are reporting crop growth stages and conditions following the provided guidelines every week (National Agricultural Statistics Service, United States Department of Agriculture, 2017e). China has provided crop monitoring data based on field observations since 1998 (Wu et al., 2013). These monitoring systems primarily based on field surveys are valuable to decision makers and analysts within government bodies for the better management of agricultural production and grain administration. These are qualitatively and quantitatively precisely with respect to field conditions but often costly and prone to human bias. Moreover, they do not provide spatially explicit information on crop growth condition and timing. Efforts to supplement them with remote sensing, statistical models, and climate models have been made due to such cost and operational difficulties of field surveys (Allen, 1990; Johnson, 2014; Stern et al., 2012; Yu et al., 2012). Satellite remote sensing is promising as a tool for monitoring crop growth, with many studies demonstrating high correlations between satellite-based estimates and field crop condition. Along with the development of remote sensing technologies, satellites have become the most important data source to monitor large-scale crop growth situation. In many regions of the world, extensive efforts to build monitoring systems have been pursued and many have built them based on remote sensing. In the United States (US), USDA has developed VegScape (https://nassgeodata.gmu.edu/VegScape/) based on remote sensing and monitors crop growth of the entire US in near real-time (Yang et al., 2013). Group on earth observations (GEO) services Global Agricultural Monitoring system (GLAM; https://cropmonitor.org) providing nearly real-time information on major crop growth condition in the 27 major crop production countries (i.e., G-20 plus 7 countries) (Whitcraft et al., 2015). The Global Information and Early Warning System (GIEWS; http://www.fao.org/giews) of United Nations uses various crop monitoring models in the estimation of crop growth condition and yield forecasts. Monitoring systems with specific purposes have developed. The Famine Early Warning Systems Network (FEWS NET) (http:// www.fews.net) by the US Agency for International Development (USAID) provides early warning and analysis on acute food insecurity for East and West Africa. In China, Cropwatch (http://cropwatch.com. cn) has provided crop monitoring data based on remote sensing at the global scale (Wu et al., 2013). Crop phenology (i.e., timing of crop growth) represents physiological crop growth stages and crucial information in crop growth monitoring. In remote sensing applications, it is often estimated via seasonal variations in greenness. The shape of a vegetation index curve (Delbart et al., 2005; Fischer, 1994; Beck et al., 2006) is the most widely used data source for crop phenology estimation. According to the shape of a curve, diagnostic parameters (e.g., inflection point) are extracted to estimate the timing of crop growth. To analyze the shape of the vegetation index curve, the global (White et al., 1997) and relative threshold method (Kim et al., 2012), the function fitting method (Zhang et al., 2003), and the frequency analysis method (Sakamoto et al., 2010) have been developed. Among these, function fitting methods have been used most widely in the last years with lots of enhancements in the form of

functions (Beck et al., 2006) and the fitting algorithms (Klosterman et al., 2014; Beck et al., 2006). To date, the accuracy of the elaborated function fitting methods is high (> 90%) especially in homogeneous vegetation cover (i.e., where the spatial resolution of remote sensing data is sufficiently fine to avoid severe within-pixel land cover mixture). For heterogeneous landscapes, down-scaling methods are developed to provide higher resolution input data than the field size (e.g., Gao et al., 2017); these methods are often computationally expensive. When a ‘shock’ hits a crop field, crops try to adapt to the environment to a certain extent, and visible signs entail afterward. Therefore, it is necessary to attenuate the immediate impact of the signals; monitoring of crop growth using remote sensing can be useful in that sense. There were studies which used satellite data and process-based mechanistic models to simulate individual plant growth (Hoefsloot et al., 2012; Ines et al., 2013). These models utilize environmental variables such as solar irradiance, temperature, and soil moisture and simulate the cause of degradation of crop growth (Sacks and Kucharik, 2011; Shen et al., 2013). Growth condition can also be indirectly monitored through indices such as normalized difference vegetation index (NDVI) (Yang et al., 2011) or growing degree days (Doraiswamy et al., 2004; Kim et al., 2012). In this indirect estimation, the crop growth of a target year is compared with the historical growth profiles. To estimate growth condition, Yang et al. (2011) compared a variety of secondary indices (e.g., Vegetation Condition Index) of a target year to normal year values in the 48 conterminous US states. These data were informative to field condition; however, the results varied by the measure they used. Therefore, secondary indices have to be sensibly chosen for acquiring accurate crop growth information, which adds additional complexity to the monitoring system. Whereas the developed crop condition assessment models showed potential for remote sensing for condition assessment, the performance of the remote sensing model has not been thoroughly compared against ground observations. To our knowledge, the only case study (Yu et al., 2012) was the comparison of the growth status was corn with the NASS statistics only for a single year in Iowa, USA. Growth condition and timing of vegetation are essential in monitoring. Plants adapt to harsh environmental conditions. Under water or light stress, plants adjust allocation and restrict unnecessary activities to balance internal resources (Blum, 1985; Chapin, 1991), which often lead to late emergence. Regarding these adaptive responses, it is nontrivial to decompose signals reflecting crop phenology and condition in spectral or radiometric remote sensing data. The interaction between them has often been ignored since it is difficult to differentiate signals reflecting the growth condition and the timing of vegetation. In remote sensing applications, this unaccounted interaction could be a substantial source of errors in estimating each phenomenon. Thus, this interaction needs to be investigated in crop growth monitoring applications further. To investigate such an inter-linkage, bio-physically elaborated modeling would be advantageous. With the goal of advancing crop condition assessment using satellites across large areas, we developed a method to provide information on the crop growth timing and the growth condition based on remote sensing. In particular, we focused on the ability of methods to reproduce temporal variation of the NASS crop progress and condition (CPC) statistics. For corn and soybeans in Iowa and Illinois in the US, we estimated relative crop growth condition (i.e., relative strength of input signals for a certain period in consecutive years) and progress (i.e., percent growth timing per growth stage) from 2003 to 2015. These were estimated by remote sensing data and then compared against the NASS observations provided at the state-level. The growth timing was estimated with the satellite NDVI and the growth condition was determined with the NDVI and the simulated biomass of the crops using a quasi-biophysical crop growth model. We compared the NDVI- and biomass-based predictors to clarify whether it is advantageous to use a biophysical model for crop condition since it accounts for a plant's adaptive responses to environmental forcing. We used robust climate 114

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Fig. 1. Study regions in Iowa and Illinois shown as a false-color image based on NASS cropland data layer (CDL) crop mask information (56 m).

and spectral data for the model to diagnose field condition under any weather condition reliably. Finally, estimated growth condition and timing were converted to the state-level data to provide a summary. To evaluate the potential of our model, we compared the results with the historical state-level condition and phenology database of USDA-NASS.

2.2.1. Satellite data Growth condition was estimated using a biomass growth model, which requires the following robust climate input data: the daily minimum and mean temperature, the daytime mean vapor pressure deficit (VPD), and the incident irradiance. We used the QC information provided by MODIS products and the 37 GHz brightness temperature data provided by radar sensors to gap-fill the MODIS data. We produced high-resolution (1–5 km) robust weather data by fusing MODIS spectral and temperature data and the microwave satellite data following (Jang, 2013; Jang et al., 2014). Note that we used this climate data generation to be based on robust climate data with a spatial resolution finer than the resolution of meteorological data (0.5×0.67°) in the MODIS GPP product (Running and Zhao, 2015). We generated input daily meteorological data: minimum (Tmin), maximum (Tmax), mean (Tavg) air temperature, average dew point temperature (Tdew), average VPD (VPDavg), and downward shortwave radiation (Rsd). The required MODIS and AMSR data products are described in Table 1. For phenology estimation, the Terra (MOD13A2) and Aqua (MYD13A2) 16-day NDVI data were used. Using a time difference of 8 days at a resolution of 500 m, we merged the two 16-day NDVI data to produce a pseudo 8-day NDVI time series (Seo et al., 2016). The NDVI data was used in phenology estimation routine, which produces phenology metrics in day of year (DOY). Note that all satellite input data were resampled into the 1-km base grid using a bilinear filtering algorithm.

2. Materials 2.1. Study area The study was conducted in Iowa and Illinois in the midwestern region of the US (Fig. 1). These two states are within the Corn Belt region, which accounts for more than 10% of US corn and soybean production (National Agricultural Statistics Service, United States Department of Agriculture, 2017e). In 2012, for example, Iowa was the highest in corn (17.4%) and soybeans production (13.7%) among the US states. It was also the largest in harvest area for corn (14.8%) and soybeans (12.2%) in the year. The average annual temperatures are 8.6 °C in Iowa and 10.9 °C in Illinois. The average annual precipitations are 815.1 mm yr−1 for Iowa and 915.7 mm yr−1 for Illinois, with continental climate with cold and hot summers; Dfa and Dfb in Köppen–Geiger classification (National Oceanic and Atmospheric Administration, 2018).

2.2.2. NASS crop progress and condition (CPC) We collected the NASS crop progress and condition data for Iowa and Illinois for the 13 years from 2003 to 2015. The USDA-NASS CPC database provides weekly information on the growth condition and timing of crops across the US from April to November (National Agricultural Statistics Service, United States Department of Agriculture,

2.2. Data Optical and radar satellite data, NASS CPC data, and NASS cropland layer data (CDL) data were used. The types and characteristics of these data are summarized in Table 1. 115

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Table 1 Data sources used in this study. All the satellite data sets were resampled into the target 1 km grid. Product

Variables

Temporal/spatial resolution

Target period

Solar zenith angle, latitude, longitude Aerosol optical thickness Cloud fraction Cloud optical thickness Air temperature Dew-point temperature Land Cover Type Normalized Difference Vegetation Index (NDVI) Fraction of Photosynthetically Active Radiation (FPAR) Albedo

5 min/1 km 5 min/10 km 5 min/5 km 5 min/1 km 5 min/1 km

2003–2015

1 year/500 m 16 days/500 m 8 days/1 km 16 days/1 km

Brightness temperature at 37 GHz

1 day/25 km

2003–2010 2011–2012 2013–2015

Crop mask Percent progress for each growth stage Percent crop growth condition grades

1 year/30 or 56 mc 1 week/state

2003–2015

a

MODIS spectral data MYD03 MYD04_L2 MYD06_L2 MYD07_L2 MCD12Q1 MOD/MYD13A2 MYD15A2 MCD43B3

Radar datab AMSR-E L2A Brightness temperatures SSM/I L3 Brightness temperatures AMSR-2 L3 Brightness temperatures USDA-NASS Cropland data layer (CDL) Crop progress and condition (CPC) a

Data are collected by MODIS Terra (MOD) products at 1:30 and 13:30 and by Aqua (MYD) at 10:30 and 22:30 local time. Three different brightness temperature products were used because of the discontinuation of the AMSR-E sensor. c Spatial resolution of CDL data has been changed between 30 m and 56 m due to data availability (National Agricultural Statistics Service, United States Department of Agriculture, 2018). b

8.0% (poor), 24.9% (fair), 48.0% (good) and 15.7% (excellent), and the mean of the standard deviations (SD) of the all grades was 5.7%. The seasonal variations of the reported growth conditions for corn in Iowa and Illinois are illustrated in Fig. 3. The harzadous events occurred in the study area (Table 2) corresponded with the changes in the observed grade proportions (Fig. 3a and b). In 2012, as drought conditions developed in the growing season, the proportions of ‘very poor’ and ‘poor’ grades drastically increased for both corn and soybeans in the CPC observations. In 2013, moderate drought hit the area again (June–August), which is pronounced in the crop conditions as shown by the increased proportions of ‘poor’ and ‘very poor’ grades around DOY 260 (late August). Disastrous conditions such as soybean disease in 2003 and flooding in 2008 were occasionally less pronounced in the reported crop condition grades. Hazardous climatic or agricultural events during the period (2003–2015). To compare estimated crop conditions with the historical hazardous events in the study area, we summarized major events impacting corn and soybeans production during the period in Table 2, primarily based on the NASS reports. In 2003, charcoal root rot [Macrophomina phaseolina (Tassi) Goidanich] was massively epidemic in the US after the flowering season. The disease is more damaging to soybeans than to corn (Mueller et al., 2016) and it was assessed to have caused 30–50% of soybeans yield loss (Yang and Navi, 2005; Wrather and Koenning, 2006). In July 2005, lack of precipitation deteriorated crop conditions in Iowa and Illinois (Changnon and Changnon, 2006). In April 2007, a severe frost and freezing event swept across the US (Gu et al., 2008). The event was preceded by unusually warm conditions in early spring, thus hampering natural vegetation as well as many crops. Flooding in early 2008 (May–June) was started from winter snow melting. The magnitude of the flooding was historic and caused serious damages to crop fields (Smith et al., 2013). However, the impact of the flooding was not pronounced in the NASS CPC observations (Fig. 2). In 2012, a severe drought hit the area (Al-Kaisi et al., 2013; Hoerling et al., 2014) from June to August due to extreme dryness and heat across the central and eastern Corn Belt. The years exhibiting delayed planting were 2008, 2011, and 2013. In these years, cold and wet weather conditions slowed planting by decreasing suitable days for fieldwork during the planting season (National Agricultural Statistics Service, United States Department of Agriculture, 2017b,c).

2017d) and the annual summary is published every year (National Agricultural Statistics Service, United States Department of Agriculture, 2017e). During the crop season, the bulletin of crop condition is published every week. The weekly data are created by a surveyor visiting the panel parcels in the county, estimating the ratio of the area of cropland belonging to each item (e.g., condition grade), and summing them on a weekly basis. Crop progress and condition ratios are reported at the parcel-level without spatial distributions. Note that the information is subjective to observers’ judgment and may lack consistency among different statistical units (i.e., panel parcel). In the NASS CPC database, the crop progress and condition data are provided at the state-level (National Agricultural Statistics Service, United States Department of Agriculture, 2017d). For crop progress, weekly progress percentages of crops ‘planted’, ‘emerged’, ‘silking’, ‘doughing’, ‘dented’, ‘mature’, and ‘harvested’ for corn and crops ‘planted’, ‘emerged’, ‘blooming’, ‘setting pods’, ‘dropping leaves’, and ‘harvested’ for soybeans are reported (National Agricultural Statistics Service, United States Department of Agriculture, 2017e). For condition data, the cumulative percentages for each of the growth condition grades are given. The data include non-probability observations from approximately 3600 reporters in the whole U.S. (National Agricultural Statistics Service, United States Department of Agriculture, 2017e; Fackler and Norwood, 1999). The growth condition is determined in five levels based on the subjective judgment of the reporters about the growth conditions of the field crops (National Agricultural Statistics Service, United States Department of Agriculture, 2017e). The ‘very poor’ grade means a serious situation in which farming is likely to fail, and when it is not expected to meet the annual needs of crops, the ‘poor’ grade a severe condition due to drought or disease. It is given when the yield potential is expected to decrease. The ‘fair’ grade is likely to reduce crop yields but are not certain. The ‘good’ grade refers to a situation in which normal crop production is expected, and the soil moisture and insect damage are insignificant. The ‘excellent’ grade is given when production is expected to be better than normal, which implies that the crop stresses and pest damage are trivial. Observations are made at the field level by surveyors using the guideline, which describes a set of assessment criteria (Fackler and Norwood, 1999). Since the grades are assigned through the subjective evaluation of the researchers, the ratios for each grade vary from year to year (Fig. 2). The average of the ratios for the study period was 3.4% (very poor), 116

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Fig. 2. Historical proportions of the five crop condition grades of National Agricultural Statistics Service (NASS) database in Iowa and Illinois for (a) corn and (b) soybeans (2003–2015). The state-level annual mean values were calculated from the weekly reported state-level proportions.

Fig. 3. Observed crop condition grade proportions plotted against the Day of Year (DOY) (2003–2015): (a) corn in Iowa, (b) corn in Illinois, (c) soybeans in Iowa, and (d) soybeans in Illinois. The observed weekly state-level values from NASS crop conditions and progress (CPC) databases were converted to 10-day values using a spline filter. Data gaps in 2003 and 2012 exist due to the lack of the weekly NASS observations.

proportions of CDL pixels contained in each 1-km pixel. In this study, the pixels only with more than 50% target crop fraction were selected to estimate the phenology and condition of corn and soybeans. The mean proportions of the selected pixels per county were 25.4% and 13.5% for corn and soybean, respectively. The average proportion of the selected pixels (i.e., CDL > 50%) out of all crop pixels (i.e., CDL > 0%) was 32.0% for corn and 24.5% for soybeans.

2.2.3. NASS cropland data layer (CDL) data We spot the cultivation area using the USDA-NASS crop cover data (National Agricultural Statistics Service, United States Department of Agriculture, 2017a). USDA-NASS provides CDL data for each year based on satellite imagery and field survey data (Boryan et al., 2011). For each year, the crop mask is provided either in 30 m or 56 m grids, which are primarily based on Landsat Thematic Mapper and Advanced Wide Field Sensor specifications (AWiFS) sensors, respectively (https://www. nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php) (Lark et al., 2017). Due to the native pixel size of the AWiFS data (56 m) and the Landsat data (30 m), in our study area, the resolutions of the CDL data was 30 m (2003–2005 and 2008 onwards) and 56 m (2006–2007) (see further details in https://www.nass.usda.gov/Research_and_ Science/Cropland/metadata/meta.php). In this study, the source CDL data were reprojected and resampled to the base MODIS grid (1-km). We then created fractional crop cover for corn and soybeans based on

3. Methods We estimated crop growth timing and condition using globally available satellite data and NASS CPC database. We assessed growth timing and condition of the crops for every 10-day during the monitoring period of the NASS CPC reports: April (DOY 140) to November (DOY 320). The phenology and condition were first estimated at the pixel level and then aggregated at the state-level into proportions (%). 117

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Table 2 Major hazard events in corn and soybeans farming in the study area (2003–2015) based on the crop summary and agricultural overview reports of NASS crop conditions and progress databases (United States Department of Agriculture, 2017; National Agricultural Statistics Service, United States Department of Agriculture, 2017b,c). Year

Event

Timing

Impact

Note

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Disease Heavy rain Drought Frost, Drought, Hail, Snow Drought, Insects Storms, Flooding Heat and dryness Heat and heavy rain Delayed planting, Heat, Dryness Drought, Freeze Delayed planting, Drought – Delayed planting

May–June July May, July, September, October June–July, August June September August April, July, October June–August, September May, June–August April May

Severe Slight Moderate Moderate Slight Severe Slight Moderate Moderate Severe Moderate Slight Slight

More damaging to soybeans Cool weather from July–August Severe to soybeans Very warm year and early but less snow Soybeans were affected by Aphids. Early frost in September Slowed harvest in October Delayed harvest Sudden death of soybeans in August, Early harvest Dry and windy in October Early harvest Slowed progress in June Crop progress was fast –

The estimated state-level grade proportion was compared with the weekly NASS condition data (%) for the study period (2003–2015). The growth timing was estimated using a logistic curve fitting algorithm. The condition was determined by comparing a predictor value to the normal year distribution. Using NASS cropland data layer NASS CDL data, we selected relatively pure corn and soybean pixels (corn and soybeans > 50%) for minimizing errors due to the within pixel variability (i.e., signals from different land cover types) as described in Section 2.2.3.

NDVI curve, an 8-day NDVI time series was generated by combining MODIS Terra and Aqua 16-day NDVI products following the method proposed in Seo et al. (2016). We model a seasonal NDVI time series as a function of time [NDVI(t)] using two logistic curves: one for the early part and the other for the latter part of the growth period [Eq. (1)] (Beck et al., 2006). NDVI(t ) = wNDVI + (mNDVI

wNDVI)

1 × (1 + exp( mS × (t

S ))

+

1 1 + exp(mA × (t

A))

1 ,

(1)

3.1. Crop phenology estimation

where wNDVI is the winter NDVI, mNDVI is the maximum NDVI during the growing year, S and A are inflection points, mS and mA are the NDVI changing rates at the inflection points; S is later we called ‘SOS’, A is ‘EOS’, and timing of mNDVI is ‘POS’. A derivative-based method was used to extract those parameters from the fitted logistic curve (Filippa et al., 2017). This approach does not require threshold parameters predetermined, thus free from parametrization. For the processing, we used the GNU R packages “greenbrown” (Forkel et al., 2013) and “phenopix” (Filippa et al., 2017). Note that the phenology extraction process employed here used relatively pure pixels, identified using NASS cropland data layer CDL data. Only the pixels with > 50% corn and soybeans fraction were selected to exclude signals from different land cover types.

Remote sensing of phenology in this study is based on seasonal changes in greenness reflecting plant growth. We estimated crop phenology metrics at the pixel level and converted them to a state-level percent progress curve. The estimated state-level progress was compared with the weekly NASS CPC progress (%) for the study period (2003–2015). 3.1.1. Pixel-level phenology estimation For each pixel, we estimated the start of season (SOS; date of emergence), peak of season (POS; date of maximum growth), and end of season (EOS; date of senescence) of corn and soybeans using the MODIS Normalized Difference Vegetation Index (NDVI) (Fig. 4). For a seasonal

3.1.2. State-level crop progress The state-level progress of SOS, POS, and EOS were interpolated from the pixel-level SOS, POS, and EOS for every ten days. The previous studies (Wardlow et al., 2006; Sakamoto et al., 2010; Gao et al., 2017) estimated crop growth period using vegetation indices and compared them against the growth stage development data of NASS, but only for the median dates of each crop growth stage. In contrast, we compared the progress estimates for each 10-day interval from DOY 140 to 320 with the state-level NASS observations. The pixel-level crop phenology metrics were aggregated at the statelevels resulting in state-level progress (%). For the comparison, the weekly NASS progress (%) was converted to day of year correspond to progress (%) using a spline method and a logistic curve fitting. Among the NASS progress data, we selected three stages for each crop: ‘emerged’, ‘silking’, and ‘mature’ for corn and ‘emerged’, ‘setting pods’, and ‘dropping leaves’ for soybeans. In the preliminary analysis, these stages were selected by finding the most similar growth stages to remotely sensed SOS, POS, and EOS. The SOS progress data were compared with the reported emergence dates. The POS progress data were compared to the ‘silking’ percentage for corn and ‘setting pods’ for soybeans. The EOS progress dates were compared with the ‘mature’ dates for corn and ‘dropping leaves’ for soybeans.

Fig. 4. Example of the phenology metrics (dotted line) derived by the doublelogistic method applied to a seasonal 8-day NDVI curve (solid black line). 118

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Even though the selected NASS growth stages are highly correlated with the estimated growth periods, there remain structural biases between them. The NASS progress is based on field observations of surveyors, whereas the estimates are based on spectral properties of crops. We accounted for these structural biases by a bias correction with a ‘leave-one-out’ cross-validation scheme; the bias was calculated for each year without the target year value for the entire period. In other words, the structural bias was defined by the average difference of the progress estimates excluding the target year. For each year, the amount of bias correction was determined by the mean error (ME) of the estimates of the rest of the years. After bias correction, root-mean-squareerror (RMSE) and ME were calculated using the NASS data of the target year. The results of the comparison were summarized using mean or median of the annual statistics depending on the distribution of data (i.e., median for a skewed distribution).

NASS condition grades. In the study period, the average proportion of each grade in the NASS condition for both crops in Iowa and Illinois was 4.0% (very poor), 8.3% (poor), 24.7% (fair), 47.1% (good), and 15.9% (excellent) (Fig. 2). Note that NASS grade proportions are imbalanced and inconsistent between years, crops, and states as they are determined by sensory observations (Fackler and Norwood, 1999). To determine reasonable threshold values (i.e., reference quantiles for the five grades) while avoiding the overfitting of the model, we used the ‘leave-one-out’ scheme. The reference quantiles (Q) were defined by the proportions of the NASS grades of the remaining years (i.e., excluding the target year) in each state for each crop for each 10-day interval. For an observation vector x, the distribution-free quantile Qp of a threshold probability p was estimated following (Hyndman and Fan, 1996) (Eq. (2)).

Qp = (1

3.2. Crop growth condition monitoring

where

) x j + x j + 1,

(2)

m = (p + 1)/3, j = np + m , = np + m j,

The growth condition of the crops was assessed for every 10-day during the monitoring period of the NASS CPC reports (DOY 140–320). The grade was determined by comparing a predictor value of a target year to the historical distribution of the predictor values for each pixel. Such pixel-wise grades were aggregated at the state-level and converted into grade proportions (%). The estimated state-level grade proportion was compared with the weekly NASS condition data (%) for the study period (2003–2015).

and xj is the jth order value and n is the size of x. Then thresholds were calculated for each year at each pixel and were used to grade the growth condition of each pixel. Note that the calculations were conducted using the function ‘quantile’ in GNU R 3.4.1. For plotting empirical distribution of the data in a seemingly continuous form, we used empirical cumulative distribution function (Eq. (3)) via “ecdf” function of the “stats” package in R. For observations x = {x1, ..., xn}, the fraction of observations less or equal to a threshold t is

3.2.1. Pixel-level growth condition For each time step, a predictor value was compared to the empirical distribution and a five-level grade was given based on the thresholds (Fig. 5). As noted above, the average year in this study was defined as the entire study period excluding the target year. Compared with the previous studies in which a set of secondary indices was used (e.g., Yu et al., 2012), our approach does not require the selection of an optimal secondary index such as Vegetation Condition Index (Kogan, 1995) as in Liu and Kogan (2002). Instead, in our approach, it is crucial to determine appropriate thresholds as the accuracy of the grading is highly sensitive to them. To objectively determine the thresholds, we referred to the historical proportions of the

Fn (t ) =

1 n

n

1[xi

t ].

i=1

(3)

3.2.2. State-level growth condition State-level growth condition grade proportions were produced for each 10-day from DOY 140 to 320 for Iowa and Illinois. The state-level growth condition (%) is defined by the proportion of the pixel-level grades. We then compared these 10-day estimates with the observed state-level NASS observations. For this, the weekly NASS proportions were converted to 10-day values using the spline method. The difference between the reported and the estimated grade proportions was compared using RMSE and ME. 3.3. Biomass growth modeling Above- and below-ground biomass are good phenotypic indicators for crop stress responses. In this study, the biomass of crops was simulated by combining remote sensing and a quasi-mechanistic model. The simulation was done in two steps. First, a data fusion algorithm introduced in Jang (2013) and Jang et al. (2014) was used to generate robust climate and spectral signals using spectral and radar sensors. Allweather meteorological data were acquired from MODIS and AMSR images (Table 1). Note that the various AMSR products were independently related to the MODIS products at the pixel-level and resulted in the regression models (Jang et al., 2014). The main inputs of the crop growth model are shortwave radiation, fraction of photosynthetically active radiation absorbed by plants (FPAR), VPD, and air temperature (Ta). Energy variables such as shortwave radiation and temperature determine the level of energy available to crop growth and controlling variables FPAR and VPD adjust the amount of primary production. The FPAR was used to infer the amount of actual solar energy absorbed by the crops. Second, the daily NPP and biomass increment of the crops were estimated using a light use efficiency (LUE) model with a simple respiration algorithm in the 1-km grid. The crop

Fig. 5. Example of the condition grade determination (blue square) using the historical distribution of the biomass values (red squares) with regard to the threshold values (dotted lines). In this example, the 5th, 25th, 75th, and 95th percentiles are used as thresholds for the five condition grades. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 119

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growth routine of CroPBY is basically an extension of the MODIS 17 GPP/NPP algorithm (Running and Zhao, 2015). The key addition in our algorithm compared to the MODIS 17 algorithm is that we used crop-specific growth and respiration parameters for corn and soybean on top of MODIS Biome Property Look-Up Table (BPLUT) parameters. The spatial distribution of a crop was identified using the NASS CDL crop mask. The model parameters were calibrated based on meteorological data and biomass data measured at the Eddy-flux towers in the study area.

estimated values. The period mean statistics were derived using the error for each of the target years. 4. Results The crop phenology and condition were estimated and compared with the NASS CPC reports in Iowa and Illinois at the state-level (2003–2015). 4.1. Crop phenology estimation

3.4. Scenarios

The distributions of the three estimated crop phenology metrics (SOS, POS, and EOS) before the bias correction are presented in Fig. 6. The timings of SOS, POS, and EOS were substantially earlier in 2012 than in other years. The phenological dates were later than average in 2008, 2009, and 2013. For soybeans specifically, in 2003 the estimated SOS was substantially slower than the average. Within-year variations are indicated by the height of peaks, and between-year variations are indicated by the position of peaks. As a whole, all three metrics were distributed with smaller variations in the normal years (e.g., 2006 and 2011) than in the problematic years (e.g., 2008 and 2013). Specifically, the POS had a smaller within-year variability (reddish) than SOS and EOS. The density distributions of the estimated phenological dates illustrate their considerable inter-annual variations. The between-year variations of the metrics were large (dislocations in Fig. 6). Note here that the distributions of the phenological dates, especially the height of the peaks, were affected by the geographical shapes of the states (i.e., Illinois has a longer north-tosouth axis). Although the differences in peak height between the two states are pronounced in the figure, the positions of the peaks were similar to each other. To correct the systemetic differences between the phenology estimates and the NASS progress data, the mean difference using the ‘leaveone-out’ scheme, later called ‘structural bias’, were calculated (Table 4). The biases for corn were 16.1 days (SOS), 10.1 days (POS), and 6.5 days (EOS). For soybeans, the biases were 5.4 days (SOS), −3.9 days (POS), and −1.0 days (EOS). The POS and EOS of corn were later than the corresponding ‘silking’ and ‘mature’ stages, and those of soybeans were earlier than ‘setting pods’ and ‘dropping leaves’ stages. Although the biases were non-trivial, the strong correlations between the estimated phenological dates and the observations (avg. r = 0.96 for corn and soybeans) suggest that the estimated phenological dates are informative about crop phenology in the study area (Fig. A2). We corrected the structural bias of the estimated growth periods and compared the corrected estimates with the observed growth period of NASS at the state-level (Table 4 and Fig. A1). After the bias correction, the phenology estimates fit well with the NASS observations. For both crops, the median RMSE of the all three state-level progress were < 10.3 and |ME| < 2.7 days. When comparing two crops, the average of the median RMSE of the three estimates was larger for corn (9.3 days) than for soybeans (8.0 days). Time series of the state-level crop phenology estimates after the bias correction are presented in Fig. 7 for corn in Iowa; estimates for corn in Illinois (Fig. S6), for soybeans in Iowa (Fig. S7), and soybeans in Illinois (Fig. S8) are provided in the supplementary document. Overall, the estimated phenology (dotted lines) correspond well with the observed values (asterisk). Although they agreed with the observations, there were years in which growth was earlier or lagged, especially for SOS. In 2004, for example, the estimated SOS (dotted line) was considerably earlier than the observed period (asterisk). For 2010, the estimated phenology was overall later than the observations. In 2004, especially the estimated SOS was earlier than the observations for both crops.

We prepared six predictor sets to compare model performance based on different predictors and accumulation schemes. For all predictors, input data were organized as raster images for the 10-day intervals from DOY 140 to 320 (19 in total). We prepared the accumulated predictors with and without consideration of the phenology. The accumulated predictor (X) of a ith pixel at time t was defined as t

Xiacc ,t =

Xi, d ,

(4)

d = SOS ref

where Xi,d is the period value of the predictor and SOSref is the reference SOS of the pixel (t > SOSref). For the scenarios with no consideration of phenology, the reference SOS date was fixed at DOY 140. For the scenarios considering variable emergence dates, SOSref was determined by the estimated SOS of the pixel. The biomass predictor sets contained the simulated crop biomass time series for the target period (DOY 140–320) at each pixel (Table 3). The predictor ‘dBM (i.e., delta biomass)’ is defined as biomass increment between two adjacent time points, which equals to the NPP during the period. The predictor sets ‘BM (i.e., biomass)’ and ‘BMp (i.e., biomass with phenology)’ are the simulated crop biomass at a time point. In a mathematical sense, ‘BM’ and ‘BMp’ are the summation of ‘dBM’, or the period mean NPP; they only differ in the starting date of accumulation. The predictor sets ‘NDVI’, ‘aNDVI (i.e., accumulated NDVI)’, and ‘aNDVIp (i.e., accumulated NDVI with phenology)’ contained the corresponding NDVI time series. Likewise, ‘aNDVI’ and ‘aNDVIp’ were the accumulations of ‘NDVI’. The predictors ‘dBM’ and ‘NDVI’ reflect the short-term response of crop vegetation at a given time point. In contrast, the accumulated predictors are intended to reflect the overall condition of a crop as the accumulation scheme suppresses erroneous signals caused by short-term noise (e.g., cloud contamination), thus improving the robustness of the estimates. We used RMSE to compare differences between the NASS condition grades and the estimated grades, which are relevant but unequal in the definitions. Thus, please note we conceived the gap between a reported observation and an estimation as a “difference” rather than an “error”. The ME was applied to measure a bias between the reported and Table 3 Scenarios are generated by combining the three options: predictor, accumulation, and phenology. Note that the prefix “d” is for delta (Δ) and “a” for accumulation and the suffix “p” for phenology. In S2 (BM) and S5 (aNDVI), the starting day of accumulation was DOY 140 without considering phenologic variations. In S3 (BMp) and S6 (aNDVIp), we used NDVI-based start-of-season (SOS) data to adjust the starting day of accumulation for each pixel. For all scenarios, the start and end date of the evaluation are DOY 140 and DOY 320, respectively. Scenario

Predictor

Accumulation

Phenology

S1 S2 S3 S4 S5 S6

Biomass Biomass Biomass NDVI NDVI NDVI

Incremental Period mean Period mean Period mean Accumulated Accumulated

× × ○ × × ○

(dBM) (BM) (BMp) (NDVI) (aNDVI) (aNDVIp)

4.2. Crop growth condition Model performance by predictor and accumulation scheme. We 120

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Fig. 6. The phenology metrics SOS (left), POS (center), and EOS (right) for the study period (2003–2015) are displayed for corn in the first (Iowa) and the second (Illinois) rows and soybeans in the third (Iowa) and the fourth (Illinois) rows; the more yellowish the color, the larger the variation was in the year, and the more reddish the color, the smaller the variation. Note that we only used the pixels (1 km) with higher than 50% of the target crop fraction in the NASS CDL data (30 and 56 m). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

modeled the pixel-wise crop growth condition in five grades and aggregated them at the state-level to obtain grade proportions (%) (Tables 5 and 6); spatial plots are provided in Figs. S1 and S2 in the supplementary document. The mean RMSE of the corn and soybeans condition estimates were 11.4% and 9.6%, and average absolute ME (|ME|) were 4.4% and 2.0%, respectively. In all scenarios, the average ME were < 1% and the average |ME| were < 5.2%. The |ME| ranged from 3.43 to 5.11% for corn and from 1.79 to 2.32% for soybeans. Based on the mean RMSE for the all grades, the estimation was the closest to the NASS data when using S5 (aNDVI) for corn (avg. RMSE = 10.77%) and S4 (NDVI) for soybeans (avg. RMSE = 9.26%). Based on the RMSE for the ‘very poor’ and ‘poor’ grades, dBM (S1), BM (S2), and aNDVIp (S6) were more capable of capturing crop anomalies than the other scenarios. Reproducing temporal patterns of crop growth condition. The seasonal changes of the estimated crop conditions are presented in Fig. 8 for corn in Iowa; Estimated conditions for corn Illinois (Fig. S3), soybeans in

Iowa (Fig. S4), and soybeans in Illinois (Fig. S5) are provided in the supplementary document. Overall the estimations based on NDVI predictors [(e) S4, (f) S5, and (g) S6] has a greater similarity in temporal patterns of the observed values than the estimates based on the biomass predictors [(b) S1, (c) S2, and (d) S3]. For the particularly problematic years [2003 (disease) and 2012 (drought)], all the scenarios indicated anomalies in crop condition. In both years, the proportion of the poor grades increased from DOY 200 [Fig. 8(a)], and the estimated results were consistent with this finding [Fig. 8(b)–(g)]. In another problematic year 2013, the temporal patterns between NASS and the estimated results were dissimilar. In the NASS data, growth condition in the second half (DOY240–280) was worse than the normal years (i.e., the high proportions of ‘fair’ and ‘poor’ grades), while in the estimates the growth condition of the first half of the growth period (DOY 160–200) was particularly bad. The accumulation scheme used in the scenarios S2–S3 and S5–S6 stabilized signals thus helped the models to become less sensitive to 121

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Table 4 Summary of the average ‘leave-one-out’ errors (days) of the estimated phenology metrics for corn and soybeans in Iowa and Illinois (2003–2015). The mean biases between the NASS observed and the estimated state-level phenology metrics were calculated for each year and then averaged. The S.D. of biases indicates the standard deviation of the biases. After the bias correction, the median RMSE and ME were calculated between the NASS phenology data and the bias-corrected phenology estimates.

Table 5 Average cross-validation RMSE of growth condition estimation in Iowa and Illinois by scenario (2003–2015). The mean values were calculated from the 10day time points in the growing season of a year (i.e., DOY 140–320; 19 in total) and then averaged over the target period. RMSE for Corn (%) S1 dBM

S2 BM

S3 BMp

S4 NDVI

S5 aNDVI

S6 aNDVIp

Very poor Poor Fair Good Excellent

6.56 6.79 11.53 13.24 17.37

6.50 11.76 16.18 15.05 5.70

11.03 16.13 20.06 5.51 5.69

15.29 16.95 5.96 6.22 9.52

15.70 6.36 5.49 11.07 15.21

7.11 6.36 10.51 15.43 17.95

Avg.

11.10

11.04

11.68

10.79

10.77

11.47

Corn Stage

NASS stage

Mean bias

S.D. of bias

Median RMSE

Median ME

SOS POS EOS

“Emerged” “Silking” “Mature”

16.1 10.1 6.5

2.8 7.6 0.8

8.0 9.8 10.2

0.8 1.1 −0.5

Soybeans Stage

NASS stage

Mean bias

S.D. of bias

Median RMSE

Median ME

SOS POS EOS

“Emerged” “Setting pods” “Dropping leaves”

5.4 −3.9 −1.0

0.9 0.5 4.6

7.1 8.5 7.7

1.5 −1.2 2.6

RMSE for Soybeans (%)

instantaneous shocks. Actually, in the observed crop condition, the short-lived changes in these predictors were often not pronounced. All the scenarios were able to diagnose observed crop growth anomalies particularly in the problematic years (2003, 2011, 2012, and 2013). However, there were years in which our model produced false alarms (i.e., the estimated condition was bad, whereas there was no sign of a lean year in the observed condition): 2007, 2008, 2014, and 2015. In some of the years (2007, 2014, and 2015), the false alarm was more pronounced in the scenarios using biomass, whereas it was not clearly shown in the scenarios using NDVI, suggesting that NDVI-based indicators are less prone to false alarms than biomass-based indicators. There were years, both types of scenarios produced false alarms. In 2008, the estimated grade proportions indicated a degraded crop

S1 dBM

S2 BM

S3 BMp

S4 NDVI

S5 aNDVI

S6 aNDVIp

Very poor Poor Fair Good Excellent

5.93 6.39 11.02 11.72 11.33

5.95 12.28 13.57 11.95 6.75

11.14 13.80 12.88 6.28 5.67

13.16 12.80 5.97 5.76 8.60

12.58 6.59 5.55 11.33 11.86

6.41 6.33 11.02 13.84 10.79

Avg.

9.28

10.10

9.95

9.26

9.58

9.68

condition, whereas the observations suggested little sign of a lean year. The timing of an alarm was occasionally unmatched with the observations. In 2013, the increased proportions of the ‘poor’ and ‘very poor’ grades occurred early in the growing season for the estimates but in the late season for the observations. Even though rather subtle, in the slightly lean year 2011, the biomass-based scenarios (S2 and S5) indicated condition degradation in the early season (S1–S3), whereas it occurred later in the season in the fields. Overall, the NDVI-based scenarios were more capable of capturing the timing of the degradation. In the years with false alarm or incorrect timing of an alarm, hazardous

Fig. 7. NASS reported (asterisk) and the estimated (dotted line) percent crop progress of corn in Iowa (2003–2015). The state-level progress was estimated based on pixel-wise crop phenology using MODIS 16-day NDVI products. 122

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indices after adjusting the structural bias. The bias is hard to avoid in any phenology estimation method based on mathematical properties since the field observations are sensory. The high correlation between the phenology estimates (before the bias-correction) and the observations (avg. r = 0.96) suggests that the sphenology metrics provide consistent information on the growth period of corn and soybean. Although our phenology model still exhibits a (structural) bias when compared with the NASS statistics; note the bias was removed using the leave-one-out scheme before the final comparison. We would claim that this approach is justifiable as the variation of the annual bias was small (avg. SD = 2.7 days). However, to apply this approach to an area in which ground-observed phenology is missing, we need to enhance the method. For such a, we need to find more reliable fitting methods and curve forms yielding smaller systematic and random errors (Filippa et al., 2017). The timing of estimation is another potential issue in phenology estimation. The double-logistic function fitting requires the entire seasonal curve, which will become an issue when we need to determine the emergence dates in the middle of a season. Any other function fitting method being used for phenology estimation shares this issue. To enable earlier estimation, the detection method should be operational with only a fraction of a seasonal curve. We may need to use multiple phenology estimation methods simultaneously; simpler phenology estimation methods can be useful in this case (e.g., a threshold method, as introduced in White et al., 1997). For benefiting from the higher temporal resolution, we created a pseudo 8-day product by combining two 16-day products (see details in Section 2.2.1). We shall note that there exist better quality NDVI data (8-day at 250 m) from the collection 6 MOD/MYD13Q1 products, released from 2015. The use of the MODIS and other new products will increase the transparency and the interoperability of the approach and save data pre-processing time.

Table 6 Average cross-validation ME of growth condition estimation in Iowa and Illinois by scenario (2003–2015). The mean values were calculated from the 10-day time points in the growing season of a year (i.e., DOY 140–320; 19 in total) and then averaged over the target period. ME for Corn (%) S1 dBM

S2 BM

S3 BMp

S4 NDVI

S5 aNDVI

S6 aNDVIp

Very poor Poor Fair Good Excellent

−3.32 0.86 3.03 7.22 −9.23

0.76 1.27 7.37 −6.08 −1.23

1.49 7.69 −10.76 −2.15 2.21

7.39 −6.70 −3.13 1.54 3.84

−6.31 −1.58 0.84 2.48 7.18

−3.12 1.93 1.16 7.35 −12.00

Avg. ME Avg. |ME|

−0.29 4.73

0.42 3.43

−0.31 4.85

0.59 4.52

0.52 3.68

−0.93 5.11

ME for Soybeans (%) S1 dBM

S2 BM

S3 BMp

S4 NDVI

S5 aNDVI

S6 aNDVIp

Very poor Poor Fair Good Excellent

−3.87 0.65 2.99 1.40 0.10

0.70 3.32 1.35 −1.61 −4.61

3.40 1.42 −0.85 −4.60 0.14

1.22 −1.46 −3.88 0.16 2.25

−1.45 −4.02 0.60 2.63 1.85

−3.93 0.53 3.48 1.71 0.37

Avg. ME Avg. |ME|

0.25 1.80

−0.17 2.32

−0.10 2.08

−0.34 1.79

−0.08 2.11

0.43 2.00

events occurred such as flooding (2008) and delayed planting and drought (2011 and 2013). Furthermore, there were seemingly artefactual errors in some of the years shown as spikes in the ‘poor’ and ‘very poor’ grade proportions. The spikes at the beginning of the period found in 2011, 2013, and 2014 when planting was delayed as noted in Table 2, occurred for the scenarios with a naive accumulation scheme [i.e., NPP accumulation in S2 (BM) and NDVI accumulation in S5 (aNDVI) with no phenology]. These spikes at the beginning of the growing season disappeared in S3 and S6, in which we used SOS dates to adjust the date that the accumulation began.

5.2. Remote sensing of crop condition In previous studies, Yu et al. (2012) estimated the growth condition of corn in Iowa based on various vegetation indices and reported the RMSE of 13–29% for a single year. In our study, the avg. RMSE of the estimated crop condition grades for all scenarios was 10.2%. The evaluation of results for the multiple study years indicates that our method is reasonable in performance. Most of the improvement was associated with the empirical threshold method, as this approach allowed us to avoid the optimal threshold finding problem. However, there remained a large unexplained (10%), which is likely to be associated with the characteristics of the ground measured NASS condition data. The NASS data are the outcome of a non-probability survey based on visual observations of field surveyors (United States Department of Agriculture, 2017; Fackler and Norwood, 1999). As they are sensory observations, they are more sensitive to visible signals of crop stress and strain. The surveyors use a priori designed heuristics (e.g., a proportion of drought-impacted area) and are also in contact with local farmers. Therefore, less visible signs of the degradation of crops would be ignored, for example., diseased plants are often only noticeable after a time delay (Yang and Navi, 2005). Nonetheless, the NASS database is the highest quality dataset of the same kind in the world. It is unique in the sense that it is collected by experienced field reporters, who can notice anomalies earlier than satellite or aerial sensors. The data is also robust and keeps its consistency over time and space. Importantly, it has been continued for more than a few several years. Therefore, it is possible to extract the characteristics of the condition measurements. To better reproduce NASS condition, it is, therefore, necessary to consider not only remotely sensible vegetation characteristics but also the crop condition traits captured by human's eyes. A major assumption in our study, as well as in many previous crop condition studies using remote sensing, is that crop condition is

5. Discussions In this study, we estimated the growth timing and condition of corn and soybeans using satellite NDVI and model-derived biomass, for which robust weather input data were generated by combining optical and radar sensor data. The estimates were compared with NASS crop progress and condition data in Iowa and Illinois, USA. For crop phenology, three phenology metrics (SOS, POS, and EOS) were obtained from MODIS 16-day NDVI. The mean SD of the state-level phenology estimates was 2.9 days, and Pearson's r was 0.96, indicating that satellite NDVI provided consistent information on crop growth timing. After bias correction, the mean RMSE of the estimated phenology was 8.6days, and absolute ME was < 1 day. For crop condition, we assigned five grades referring to the empirical distribution of a predictor (i.e., biomass and NDVI) at each pixel for every 10-day interval from DOY 140 to DOY 320. The overall mean RMSE of the grade proportions was 10.2%. The temporal patterns of the NASS condition data were best reproduced using NDVI with accumulation (S5), and the use of phenology information reduced errors at the beginning of seasons, although it did not improve the overall performance. The estimated grade proportions were informative in the lean years; however, a few years remained with false alarms or incorrect timing of alarms. 5.1. Remote sensing of crop phenology We estimated the growth season of the crops by the phenology 123

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Fig. 8. Observed and estimated proportions of the crop condition grades for corn in Iowa are plotted against the day of year (DOY) (2003–2015): (a) NASS CPC data, (b) estimates based on period mean biomass increment (dBM; S1), (c) period mean biomass (BM; S2), (d) period mean biomass with phenology adjustment (BMp; S3), (e) period mean NDVI (S4), (f) accumulated NDVI (S5), and (g) accumulated NDVI with phenology adjustment (S6). The estimated proportions were aggregated at the state-level for comparison.

measurable and biomass growth or vegetation indices are sufficient indicators of the crop condition. However, crop growth condition is an abstract term that has no standard physical definition (Fackler and Norwood, 1999). In vegetation science, a condition can be conceived by stress and strain (Blum, 2015). Vegetation stress and strain are not equivalent; the former is instantaneous pressure on an individual plant under harsh external conditions, whereas the latter is an irreversible modification of plant organs as a result of long-lasting stresses. The strain is also related to the adaptation of crops. For example, a decrease in the number of leaves is a signal of crop strain due to water shortage. In contrast, period mean values of vegetation indices and FPAR indicate temporarily decreased photosynthetic activity due to crop stress. Therefore, remote sensing of crop condition should better specify their target processes, otherwise fail to capture it. However, current remote sensing techniques (based on low- to medium-resolution images) are unable to distinguish the two phenomena. Correspondingly, remote sensing of crop condition often relies on multi- or hyper-spectral properties of crops without recognizing the source of the variations in the signal. For example, low NDVI could be due to decreased photosynthetic ability per unit leaf area as well as die-backs of leaves or whole plants. In this case, if leaf area index is used in parallel with NDVI to model the condition, the outcome would be more specific about the source of the signal and provide more useful diagnostic information. We initially hypothesized that the biomass-based models would

perform better than the NDVI-based models, assuming that biophysical modeling can improve the reliability of the crop condition estimates. Contrary to that, the results of the biomass-based models were mostly indifferent to the NDVI-based models. Thus, when biomass modeling is infeasible, accumulated vegetation indices can be an alternative. For instance, the integration of NDVI and Enhanced Vegetation Index (EVI) are considered as proxies for crop net primary production and biomass growth (Sims et al., 2006; Sjöström et al., 2011). The indifference can be due to the fact the predictors we used in this study are often highlycorrelated: FAPAR is largely linearly correlated with the NDVI and affected by model/estimation errors (Waldner et al., 2015). However, we would argue that biomass-based models are still an important option as they have more flexibility for further developments. In our biomass modeling, the accumulated crop damage during the growth period is incorporated through MODIS FPAR, which indicates the amount of absorbed incoming photosynthetically active radiation. VPD helps to determine moisture stress in crops. These features reflect the growth as a result of solar energy, as well as the developmental status of crops and the influence of regulatory factors such as water stress. The unconsidered growth factors can be plugged in by modifying the model structure. The biomass model is also advantageous that it can be used to determine the growth condition at any timing in a season.

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5.3. Interaction between crop phenology and condition

5.4. Future remote sensing of crop condition

Crop phenology is closely related to growth stress (Chapin, 1991; Sacks and Kucharik, 2011). In this study, we independently estimated and evaluated the crop growth status and growth period. In fact, two factors were closely related (Bolton and Friedl, 2013), and we need to find ways to integrate both analyses. The inter-comparison of the phenology and condition estimates in our results suggests that severe environmental conditions affected condition and phenology simultaneously, and the interaction between them played a role; the unusually large errors in crop condition estimates were related with the anomalies in crop phenology. If the growth period is later than normal, the cumulative biomass of the same year at the same period may have been low even though the actual growth conditions were moderately good. For example, in the case of 2013, the `very poor' grade was especially overestimated at the beginning of the growing season (Fig. 8), indicating that the SOS of that year was also significantly delayed [Fig. 7(a)]. Considering the estimated growth stage in the determination of biomass accumulation timing can help to resolve this issue. For example, the timing of the maximum growth differs among years. Plants that start to grow later than usual have a smaller leaf area and thus photosynthesize less, which will be reflected in spectral properties. If the growth timing is far from optimum, the level of maximum growth of the crop may be limited. On the other way around, if the growth condition were not well, the growth timing would be altered. Therefore, it is important to find a way to synthesize the information on the growing season and the growing condition for improving model performance. Another limitations of the proposed growth condition and the method of estimating the growth period is the requirement of land cover data. In this study, we used CDL data to identify crop coverage, but the uncertainty remains very high if the exact information does not exist. In general, the land cover data has a long update interval, and it is difficult to reflect the change in crop cultivation status from year to year. Therefore, accurate crop classification may be difficult to obtain depending on the study area. The CDL data for the US region cannot be used for early prediction because the data for the year are distributed in the first half of the following year. To overcome the limitations of these land cover data, Sakamoto et al. (2014) classified crop covers endogenously at DOY 215 and 263 (i.e., early July and late September, respectively). The within-season crop classification developed recently is noteworthy as well (Inglada et al., 2015). We shall note also that new Sentinel-2 datasets provide information basis for overcoming the abovementioned difficulties (Bontemps et al., 2015). When an early classification of land cover is infeasible, it is possible to extract per-crop phenology using additional information such as the maximum vegetation index or the shape of a vegetation index curve. (e.g., Asner and Lobell, 2000; Ozdogan, 2010) – e.g., unsupervised classification of the pixels in a target area into several groups and analyze the growth timings of each group. Our model mainly requires satellite data that are in the public domain, crop statistics data, and crop mask data. For validating the results, crop growth timing and condition measurements are needed. Regarding the data input, a caveat of our model is the dependence on fine-scale crop statistics for modeling structural bias (phenology) and threshold (condition). The main information we need from statistics is determined by genetic, environmental, and agro-technical characteristics. To increase the applicability of the model, we need to use medium- to large-scale aggregated crop statistics for a target area. Even roughly, regional crop growth timing and condition statistics are available for most parts of the world through regional or global organizations, e.g., UN-FAO. For applying our model to an area lacking crop statistics, (Bayesian) multi-level modeling should be considered as an option (Iizumi et al., 2009; Nandram et al., 2013).

Development of remote sensing technology and the use of remote sensing data make it possible to apply crop growth models at large scales. Although the model showed fair performance in monitoring crop condition, there remains room for improvement. Above all, there is no agreed definition of crop conditions which prohibits comparative studies across countries and regions. The biggest caveat of this study is the deficiency of the rigorous definition of ‘crop condition’, which is the core concept of the analysis. Remotely sensed ‘crop condition’ is a subjective term and often operationally defined by the relative strength of remotely sensed data (e.g., NDVI) for a certain period in consecutive years, while the term ‘condition’ has multiple aspects across yield, biomass, and disease. Therefore, the remote-sensing-based monitoring tools are actually measuring not biophysical crop growth condition but just spectral properties of them. In this paper, there existed a gap between the categorical NASS condition reports and the relative spectral strength based on the remote sensing data. Therefore it was difficult to perfectly match to biophysical attributes of crops. As far as we concerned, all the remote-sensing-based crop growth condition monitoring shares this fundamental issue. We, therefore, must limit the value of our study in the sense that it showed that the remote sensing methods can be improved by incorporating crop growth timing (i.e., phenology). Toward monitoring more biophysical crop properties, alongside crop phenology, we need to incorporate biophysical and biochemical properties and their interactions consisting of crop condition. For instance, there are a number of limiting or reducing factors (van Ittersum et al., 2013) that were not considered in this study. For example, shortterm changes in leaf viability such as significant damage due to pests and fungi may not be diagnosed in our model (Guan et al., 2016; Yoshida et al., 2015). Attempts to acquire this information from fluorescence imaging needs to be considered (Joiner et al., 2014). In addition, the estimation of soil moisture based on the satellite image and monitoring of crop growth (Ines et al., 2013) can enhance the airdryness factor of our model via VPD. In this way, the accuracy of the estimation of the growth condition can be further improved by using other data that can supplement the cumulative biomass. To complement spectral products, we used radar products (i.e., AMSR-E) to increase climate data availability as well as the quality which enabled us to monitor crop biomass growth under any weather condition. Near-infrared band information (NIR) was used to estimate leaf nitrogen content, one of the critical factors in crop growth (Ollinger et al., 2013). Potential improvements can also come from the use of high-resolution images, which can alleviate the mixed pixel problems. Using high-resolution images as well as a fusion of multi-sensor data for crop growth monitoring would be an important future research topic. The crop phenology and conditions are estimated at the base 1-km grid. The data sources (MODIS) are based at 250 m and 500 m grids. Within a 1-km pixel, we have two types of variabilities: variability within the original MODIS pixels and between the MODIS pixels in the 1-km pixel. To cope with these variabilities, we used the pixels with > 50% target crop fractions, however not explicitly we modeled them. State-level aggregation and the pixel selection may have suppressed errors due to other vegetation (e.g., field edges, weeds) than a target crop. For crop phenology, the logistic fitting method we used is also robust to small errors. Still, the mixed-pixel problem caused by land cover heterogeneity is a critical issue in remote sensing of crop growth. The large estimation errors can happen in the mixed land-use areas (e.g., Perry and Cook Counties, Illinois) with complex heterogeneous agro-ecosystems. For example, the 1-km resolution of the input FPAR data would be a source of the error (Fritsch et al., 2012) as lowdensity agriculture is difficult to handle at the 1-km spatial resolution of MODIS (Reeves et al., 2005). To resolve the issue, upscaling of coarse resolution images (Zhu et al., 2010), unmixing of spectral signals (Asner and Lobell, 2000), or process-based partitioning of spectral signals (Zhang et al., 2014) can be used complementarily. Especially for input 125

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data, extraction of crop FPAR from total FPAR would lead to a substantial reduction in uncertainty.

timing and status in the currently existing crop monitoring systems. The results presented here, as well suggest that growth timing and condition can be captured by remote sensing approaches. Whether this level of accuracy is useful will depend on the location and the goal of a particular application. There are several opportunities for a better estimation of crop growth condition. It is essential to integrate crop conditions and crop phenology considering adaptive responses of plant organs to the greater depth. In remote sensing, although it is non-trivial to decompose variations in vegetation cover, condition, and phenology, such improvements could significantly increase the condition model performance. For early warning, the method should be applicable as early as possible, thus early retrieval of land cover information would be a crucial issue. We anticipate that the presented results will help scientific communities to monitor crop growth at large scales and contribute to decomposing variations in crop growth timing and condition.

6. Conclusions There is a pressing need for the accurate prediction of crop growth with relevance to extreme weather events threatening global food security. We demonstrated the improvement of the remotely-sensed sensing of crop growth strength for corn and soybeans in the central US. Overall, the results were informative to the state-level crop growth timing and growth anomalies. While the different predictors (i.e., biomass and NDVI) and accumulation schemes (i.e., with and without phenology) had comparative strengths and weaknesses, they were able to identify anomalies. However, there remains room for improvements such as false alarms and time delay in forecasting. Early detection of crop anomalies can influence greatly on food security policies. The crop growth at early stages of a growing season is important. If reliably diagnosed, such information can be useful to deal with food shortage or surplus in advance. Monitoring crop growth with remote sensing can provide information for cereal crop seedlings, as well as the changing pattern of their growth. In our study, we used remote sensing climate data instead of climate data products to supply timely information on crop growth. The waiting time for the MODIS and the AMSR products is less than ten days and the computing time for our algorithm was approximately three days on a consumer desktop (Intel i7 4.0 GHz, 64 Gb RAM) for the study site (295,741 km2). High performance computing is being developed increasingly faster, thus even to large study areas, it is feasible to apply our algorithms in two weeks, approximately. As shown in the comparison, vegetation index reflects immediate crop response whereas crop biomass reflects the accumulated impact of the crop stresses. The result suggests that the limitation of the vegetation condition index approach can be improved by introducing crop phenology estimator. Therefore, towards a better diagnosis of biophysical crop growth condition, we suggest incorporating the crop growth

Acknowledgements This research was supported by grants from the Agenda Program of the Rural Development Administration (PJ00997802) in the Republic of Korea. B. Seo was partially supported by the Helmholtz Association. S. Kang was partially supported by a grant from Kangwon National University. MODIS information obtained from (https://lpdaac.usgs. gov), maintained by the NASA Land Processes Distributed Active Archive Center (LP DAAC) at the USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, 2003. Data for the image were provided by NASA and USDA. We thank the anonymous reviewers for their constructive and substantial contributions to the peer review of this work and A. Boon from Edanz Group (www. edanzediting.com/ac) for editing a draft of the manuscript. B. Seo specially thanks to B. Reineking, C. Bogner, J. Tenhunen, D. Lee, and J. Rhee for their supervision.

Appendix A A.1 Appendix figures

Fig. A1. The ‘leave-one-out’ cross-validation ME and RMSE of the phenology estimates for corn and soybeans in Iowa and Illinois altogether (2003–2015); the estimates were compared against the NASS progress data at the state-level. Note that the estimates were bias-corrected as described in Section 3.1.2.

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Fig. A2. Comparison of observed NASS crop progress data (x-axis) and estimates (y-axis) for the three crop growth stages: SOS, POS, and EOS for the study period (2003–2015) before bias correction. The day of percent crop progress corresponds to a percent crop progress at each stage (e.g., a DOY when 50% of the fields/pixels in a state reached SOS). The opaqueness of the dots indicates the percent in progress; the thicker the color, the larger the value.

Appendix B. Supplementary data Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.fcr.2019.03.015.

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