- Email: [email protected]
LandMOD ET mapper: A new matlab-based graphical user interface (GUI) for automated implementation of SEBAL and METRIC models in thermal imagery
Accepted Manuscript LandMOD ET mapper: A new matlab-based graphical user interface (GUI) for automated implementation of SEBAL and METRIC models in thermal imagery Nishan Bhattarai, Tao Liu PII:
S1364-8152(19)30120-3
DOI:
https://doi.org/10.1016/j.envsoft.2019.04.007
Reference:
ENSO 4439
To appear in:
Environmental Modelling and Software
Received Date: 3 February 2019 Revised Date:
7 April 2019
Accepted Date: 10 April 2019
Please cite this article as: Bhattarai, N., Liu, T., LandMOD ET mapper: A new matlab-based graphical user interface (GUI) for automated implementation of SEBAL and METRIC models in thermal imagery, Environmental Modelling and Software (2019), doi: https://doi.org/10.1016/j.envsoft.2019.04.007. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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LandMOD ET Mapper: a new Matlab-based graphical user interface (GUI) for automated
Nishan Bhattarai1, Tao Liu2
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implementation of SEBAL and METRIC models in thermal imagery
School for Environment and Sustainability, University of Michigan, Ann Arbor MI 48109
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Geographic Data Science, Oak Ridge National Laboratory, Oak Ridge, TN 37830
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*Correspondence to: [email protected]
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Highlights A new Matlab-based GUI “LandMOD ET Mapper” was developed.
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The toolbox implements SEBAL/METRIC model automatically using thermal imagery.
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Daily ET estimates from the toolbox were comparable with observed ET.
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Abstract Remote sensing based surface energy balance (SEB) models, such as SEB Algorithm for Land (SEBAL) and Mapping evapotranspiration (ET) at high Resolution with Internalized Calibration
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(METRIC) are widely used to monitor ET. However, their operational use is challenged by
model complexity and the tedious process of selecting hot and cold pixels. We developed a new Matlab-based ET mapping toolbox named “LandMOD ET Mapper” for automated
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implementation of these models to facilitate their widespread applications among new users. The toolbox is demonstrated using one Landsat and one Moderate Resolution Imaging
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Spectroradiometer (MODIS) Terra image covering three AmeriFlux sites in Nebraska, U.S. Daily ET estimates were comparable with the observed ET for five days when near cloud-free Landsat and MODIS Terra images were available in the 2006 maize growing season. The toolbox provides a viable ET mapping option to new and inexperienced users with reduced time
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and labor demands.
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Keywords: LandMOD ET Mapper, SEBAL, METRIC, Matlab, Landsat, MODIS
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Introduction
The transfer of water vapor from Earth’s surface to the atmosphere, also referred to as Evapotranspiration (ET), is an important phenomenon within the land-atmosphere interface that
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regulates the Earth’s energy and water cycles. Quantifying ET is considered a challenging task given the complex nature of heat and energy exchanges between the land and the atmosphere. Over the last few decades, the evolution of several remote sensing tools, especially the
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development and applications of several land surface temperature (LST or Ts) driven surface energy balance (SEB) algorithms has significantly improved our ability to monitor the process of
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ET (Kalma et al., 2008; Liou and Kar, 2014). In thermal remote sensing based SEB model, LST is used to define the surface moisture conditions and solve the SEB equation (Eq. (1)) (Kalma et al., 2008; Liou and Kar, 2014). Rn = LE + G + H
(1)
respectively.
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where Rn, G, H, and LE are net radiation, soil heat, sensible, and latent heat fluxes,
The Surface Energy Balance Algorithm for Land (SEBAL) (Bastiaanssen et al., 1998) and
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Mapping ET at high Resolution with Internalized Calibration (METRIC) (Allen et al., 2007b) are two widely used SEB based ET models on thermal sensors, such as Landsat and MODIS (Allen
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et al., 2011; Bastiaanssen et al., 2005; Bhattarai et al., 2016). SEBAL pioneered the concept of selecting the hot and cold pixels (referred to as anchor pixels hereafter) within a remotely sensed image to contextually derive ET across all pixels within the image. METRIC is a simple extension of SEBAL model in which reference ET (Allen et al., 1998) is used to internally calibrate H and was first developed for applications in the US (Allen et al., 2011; Allen et al., 2007a; Allen et al., 2007b). However, METRIC now has been successfully tested and applied
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other parts of the world (Khand et al., 2017; Madugundu et al., 2017; Santos et al., 2008; Spiliotopoulos et al., 2017). Despite their wide popularity, SEBAL and METRIC models are limited to experienced users due to the requirement of selecting anchor pixels within an image.
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Recently, there have been significant efforts to fully automate anchor pixels selection in these models (Allen et al., 2013; Bhattarai et al., 2017; Morton et al., 2013). For example, Allen et al (2013) used a simple statistical approach to group out candidate pixels and automatically
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identified hot and cold pixels in the METRIC. Bhattarai et al. (2017) proposed an optimized way of using an exhaustive search algorithm (ESA) to automate the anchor pixel selection process. A
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current effort is underway to automatically derive Landsat-based ET using METRIC model on the Google EarthEngine (GEE) based application (EEFlux) based on Allen et al (2013) and Morton et al. (2013). However, the GEE source code is not publicly available (Allen et al., 2015) and ET maps are only available for the US with no options to update coarse-resolution (~ 12.5
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km) input meteorological forcing, which typically degrades the performance of SEB models (Bhattarai et al., 2018). An R package (Olmedo et al., 2016) is currently available to run METRIC, which do not use the optimized version of hot and pixel selection and some predefined
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area of interests (AOI). A python version of the METRIC model is currently under development (https://github.com/hectornieto/pyMETRIC), which also has an option of automating hot and
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cold pixel using Bhattarai et al. (2017). Similarly, a python version of SEBAL (Jaafar and Ahmad, 2019) is currently under development. However, a matlab version of fully automated SEBAL and METRIC models is not currently available. An attempt to create matlab toolbox for SEBAL application was made by Atasevera et al. (2013); however, the process was not fully automated and is not available to the public.
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In this paper, we develop a new matlab-based graphical user interface (GUI) toolbox for the application of fully automated versions of SEBAL and METRIC models in Landsat, MODIS, or other thermal sensors. With this GUI we aim to address some of the existing challenges in
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SEBAL/METRIC implementations, such as the tedious process of anchor pixels selection, need of expertise to run the models, uncertainty in weather inputs, and bring the model within the domain of new and inexperienced users. Methods
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2.1 Brief descriptions of the SEBAL and METRIC models
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The residual form of SEB equation is used in SEBAL and METRIC models (i.e. LE = Rn-G-H). Rn is computed using the net radiation balance equation (Eq. 2), while G is computed as a small
Rn = Rs (1– α) + εoRld - Rlu
(2) (3)
H = (ρacpdT)/rah
(4)
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G = Rn (Ts-273.15)/α (0.0038α + 0.0074α2)(1 - 0.98NDVI 4)
proportion of Rn (Eq. 3) (Bastiaanssen, 2000).
Details on Rn and G estimations can be found in Allen et al. (2007b). The key component of
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SEBAL and METRIC model is self-calibration of H using an iterative process based on
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manually or automatically selected hot (dry and bare agricultural field) and cold (well-irrigated croplands) pixels. A major difference between SEBAL and METRIC is that LE at the cold pixel is equal to available energy in SEBAL (or H = 0), while in METRIC cold pixel ET is considered to be 5% greater than alfalfa reference ET (Allen et al., 2007b). In both models, H is estimated by simultaneously solving equations for H, aerodynamic resistance for heat transfer (rah), and frictional velocity (u*), as described in Allen et al. (2007b), Allen et al. (2011), and Bhattarai et al. (2016). 6
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2.2 The LandMOD ET mapper The LandMOD ET mapper GUI is ET mapping toolbox (Fig 1) designed to automatically implement SEBAL and METRIC models efficiently by users with any level of prior model
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experiences. The Matlab software and license (and preferably version 2017a and beyond) are required to open the GUI; however, no prior Matlab coding experience is required to run the tool. The automation of hot and cold pixel uses a modified version of Bhattarai et al. (2017) that
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eliminates the need of having a reference weather station within the image. Instead, gridded weather data or scalar values (applicable for small regions) are used. The automation of hot and
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cold pixel starts with the selection of candidate pixels based on simple decision tree classifier and exhaustive search used to select a subset of pixels that could potentially be selected as hot and cold pixels. The final hot and cold pixel is based on the ranking of each pixel based on its LST and NDVI values. The pixel with highest LST and lowest NDVI value is selected as the hot
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pixel, while the pixel with the lowest LST and highest NDVI is taken as the cold pixel. The automatically selected hot and cold pixels are used to internally calibrate H and follow the stabilization correction procedures explained in Allen et al. (2011). LE is then used to estimate
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Evaporative fraction (in SEBAL) or reference ET fraction (ETrF), which is assumed to be constant during the day to produce daily ET maps, as described in Allen et al. (2011) and
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Bhattarai et al. (2017).
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Fig.1. The LandMOD ET mapper GUI in Matlab 2.2.1 Structure of the LandMod ET Mapper The toolbox can be divided into two sections: 1) model inputs and 2) model outputs. The model input part is further divided into 5 subsections (Image metadata inputs, surface and cloud cover information, weather inputs, remote sensing inputs, and weather and image information). Several parameters and constants (i.e. Processing parameters/constants and hot and cold pixel 8
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selection parameters) are automatically populated based on satellite type (Landsat or MODIS). The remotely sensed inputs include LST, NDVI, albedo, and emissivity, which are either available as derived products (e.g. https://modis.gsfc.nasa.gov/data/dataprod/; last visited
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October 10, 2018) or computed using individual bands (Jiménez‐Muñoz and Sobrino, 2003; Liang, 2001; Tasumi et al., 2007). The main reason for using these derived remotely sensed products rather than the individual bands as input is because there are several methods to derive
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LST, albedo, and emissivity from Landsats (Bhattarai et al., 2016; Sobrino et al., 2004; Tasumi et al., 2007; Teixeira et al., 2009). Such an option makes the toolbox more applicable for other
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thermal sensors from which these parameters could be derived (e.g. Advanced Spaceborne Thermal Emission and Reflection Radiometer, ASTER). Detailed descriptions of all other inputs and outputs are provided in the file “About LandMOD ET Mapper.pdf” provided with the toolbox (see Data and code availability). Because LST is the key input in these models, it is
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required that LST input is in GeoTIFF format, from which Matlab will extract referencing information to write outputs in the same format. The model outputs can be saved in defined output folder (i.e., output path) and output files are named based on the parameter (e.g., “le” for
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LE appended with model type and yeardoy of the input image). The toolbox also allows for the visualization of key model outputs (LE and daily ET).
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2.2.2 Application of the LandMod ET Mapper For the demonstration purpose, we applied LandMod ET Mapper in a highly studied AmeriFlux site in Nebraska that covers three eddy covariance (EC) towers: US NE1 (lat: 41.1651 and lon: 96.4766), USNE2 (lat: 41.16487 and lon: -96.4701), and USNE3 (lat: 41.17967 and lon: 96.43965) over croplands (Fig. 2). Readers are referred to Verma et al. (2005) for details on instrumentation, operation and maintenance, data processing, and quality control of EC tower at
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these sites. Hourly and daily weather data (solar radiation, temperature, wind speed, relative humidity) were obtained from five Automated Weather Data Network (AWDN) managed by the High Plains Regional Climate Center (HPRCC) (https://hprcc.unl.edu/awdn.php; last visited on
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October 15, 2018). Inverse distance weighting (IDW) method was used to create weather grids
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based on instantaneous and daily weather variables from the five AWDN stations (Fig. 2).
Fig. 2. Study area (processing grid and zoomed in views) shown on a false color composite of Landsat imagery acquired on yeardoy 2006215 A total of five near cloud-free Landsat and MODIS images (doy 199, 215, 267, 287, and 311,
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during the 2006 maize growing season were used in this study. The key remotely sensed inputs (data source or derivation methods) used in this study are listed in Table 1.
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Table 1. Sources of Key remotely sensed input used in the LandMod ET Mapper toolbox Variable Name MODIS Landsat* albedo
MOD09GA and Liang (2001)
Liang (2001)
LST
MOD11A1 (Wan et al., 2015)
Jiménez‐Muñoz and Sobrino (2003)
Emissivity
MOD11A1 (Wan et al., 2015)
Sobrino et al. (2004)
NDVI
MOD09GA (Vermote, 2015)
Masek et al. (2006)
* Landsat Surface Reflectance products courtesy of the U.S. Geological Survey Earth Resources Observation and Science Center
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2.3 Model Evaluation We aggregated half-hourly flux from the three AmeriFlux sites to convert into daily average LE and then daily ET (ET= LE/λ, where λ is the latent heat of vaporization, k J kg−1) for evaluation
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of the ET models based on Landsat and MODIS inputs. Daily ET from EEFlux was also used to compare the relative performance of the models for the five clear sky MODIS and Landsat image acquisition days. Models were evaluated based on commonly used statistical metrics, such as
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root mean squared error (RMSE), the coefficient of determination (R2), mean absolute error
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Results and Discussion
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(MAE), and percent bias error (PBIAS) (Moriasi et al., 2007).
3.1 ET maps from the LandMOD ET mapper toolbox
Fig. 3 demonstrates the ET mapping capability of the LandMOD ET mapper toolbox. Here Landsat and MODIS derived daily ET maps for the yeardoy 2006215 using the LandMODET
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mapper toolbox are shown. The difference in spatial resolution is clearly evident, as MODIS is coarser resolution and typically used for regional scale ET mapping, while Landsat can provide
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field-scale ET (30 m). However, the spatial pattern is similar across all models and scales.
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Fig. 3. Daily ET maps from SEBAL and METRIC models using the LandMOD ET mapper toolbox
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3.2 Performance Evaluation of Daily ET estimates from the LandMod ET mapper Comparison of daily ET estimates from the LandMod ET mapper and observed ET at the three AmeriFlux sites in NE suggests that the METRIC and SEBAL models are capable of predicting
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ET with reasonable accuracies (R2 from 0.78-0.89 and RMSE from 0.91-1.54 mm day-1; Fig. 4). In particular, both models performed much better when Landsat inputs were used, as shown in
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Fig. 4. For example, PBias values were within -7% (METRIC) and 3% (SEBAL) when Landsat images were used, as compared to the PBias values of -13% and -14% from SEBAL and METRIC, respectively, when MODIS images were used (Fig. 4). The better performance of the models when Landsat images were used could be attributed to the fact that these two models were initially developed for use in moderate resolution thermal images (i.e. Landsat, ASTER) (Waters et al., 2002), where the anchor pixels are likely to be better representatives of the
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extreme conditions (i.e. hot/dry and cold/wet) in the image and on the ground (i.e., ~30 m × 30 m in Landsat vs ~1 km × 1 km in MODIS). In addition, a pixel value extracted based on the location of a flux tower within an image have lesser chances of being contaminated from
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neighboring pixels in Landsat than in MODIS. Even in a large field representing a single land cover type (e.g., croplands in our case), spatial heterogeneity in LST (a key input in the SEBbased ET models) can exist based on the actual soil moisture conditions. For example, a MODIS
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LST pixel represents an aggregated LST values from small fields within the 1 km × 1 km area, which could be quite different than the actual LST on the small fields that have contrasting
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moisture conditions than neighboring pixels (Mallick et al., 2015; Mallick et al., 2014; Stoy et al., 2013). Hence, daily ET values from MODIS pixels may not be as representative of actual ground conditions (i.e., flux towers) as in Landsat. This could be a reason that despite the same models/tools were used, Landsat-derived ET estimates were found to be more accurate than
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those from MODIS. Other potential reasons include the differences in values and associated biases in other remotely sensed input variables, such as NDVI, albedo, and emissivity between
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the two sensors.
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Fig. 4. Evaluation of daily ET for the five near cloud-free Landsat image acquisition days in 2006 maize growing season from the METRIC and SEBAL models from the LandMOD ET toolbox (using Landsat and MODIS) and EEFlux based METRIC model using observed ET at three nearby AmeriFlux sites in NE. Notably, the toolbox-derived ET estimates from the Landsat images were closer to the observed ET than the EEFlux ET estimates that were derived using the same Landsat images
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(Fig. 4). This difference could be the differences in submodels used in EEFlux and LandMod ET mapper toolbox (e.g., LST, albedo, emissivity, and surface roughness derivations); however,
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model codes used in EEFlux were not publicly available for further investigation. Another key reason, also reported in recent studies (Bhattarai et al., 2018; Dhungel et al., 2019; Lewis et al., 2014), could be the difference between the weather inputs in these two ET modeling approaches. For example, EEFlux based METRIC model uses coarse resolution weather inputs, such as NLDAS and GRIDMET (Abatzoglou, 2013; Mitchell et al., 2004), while the high-quality weather station data from AWDN was used in the LandMod ET mapper toolbox. However,
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EEFlux captured the higher magnitude (> 7 mm day-1) of observed daily ET for the yeardoy 2006215 much better than the SEBAL and METRIC models in the LandMod ET toolbox. On this specific date, the mean daily ET from the three sites was 7.15 mm day-1, which was
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underestimated by 19 % and 22 % by the Landsat-based SEBAL and METRIC models,
respectively. This could be justified by the potential underestimation of ET at the hot pixel (i.e. ET assigned as zero or close to zero), as precipitation (~ 0.1 mm) preceded two days prior to the
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Landsat image acquisition date (i.e. 2006215). Because of the antecedent moisture in the soil, ET from the hot pixel may not be zero, which would underestimate ET from all pixels within the
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image. In addition, this could also increase evaporation from bare soil and leaves, which were not captured by these models. Conversely, for the same day, daily ET from EEFlux was within 1% of observed daily ET, which indicates that the antecedent moisture conditions may be better represented in the EEFLux based METRIC model. It could also be the positive biases in the solar
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radiation (and hence the daily ETr) from the 12.5 km NLDAS and the 4 km GRIDMET data (Bhattarai et al., 2018; Dhungel et al., 2019; Lewis et al., 2014) used in EEFlux. For example, daily ETr from NLDAS/GRIDMET data was nearly 50% greater than those from the AWDN
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station data for the yeardoy 2006215. This positive bias is also reflected in an overall overestimation (26%) of observed ET by EEFlux (Fig. 4). However, for cases when daily ET
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values were relatively low (< ~1.5 mm day-1), EEFlux underestimated daily ET. Under such conditions, the toolbox derived daily ET showed much better agreement with observed ET (Fig. 4). The inconsistency of EEFlux derived ET at a field scale (as in this study) could be attributed to biases in the coarse resolution gridded data (Bhattarai et al., 2018; Dhungel et al., 2019; Lewis et al., 2014) that were designed for more regional or country scale implementation. Our results suggest that while high-quality weather data is crucial for improving the performance of the
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SEBAL and METRIC models despite some uncertainties associated with these models (e.g., scale effects, representation of extreme conditions). In the LandMOD ET mapper toolbox, users can use high-quality reference weather station data to reduce potential biases associated with
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weather inputs in these models.
3.3 Efficiency and future application of the LandMOD ET mapper
The total processing time taken by the toolbox is dependent on a number of factors, such as the
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size of the image, the option to run skin evaporation, and the number of models selected to run. For example, the processing time ranged from 6 seconds to 580 seconds to process both models
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on a 1km MODIS scene (204 × 310 array) and a 30 m Landsat scene (6141 × 5861 array) with similar spatial domain (yeardoy 2006215), when implemented on a 3.4 GHz i-7 processor computer with 48 GB of memory on a windows machine. Processing time for Landsat will be significantly reduced if smaller subsets are used. This should be considered a significant
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improvement in processing time since no prior knowledge of the models or manual selection of hot and cold pixels is required. A simple routine to batch process large number of images is presented in the tool description file “About LandMOD ET Mapper.pdf”. Although, Landsat and
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MODIS images were used in this study, the LandMOD ET mapper can be ideally used to other thermal sensors such as the ASTER (https://asterweb.jpl.nasa.gov/) and ECOSTRESS
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(https://ecostress.jpl.nasa.gov/), as the key remote sensing inputs in the toolbox are derived products from the bands (i.e., albedo, NDVI, LST, and emissivity). 4
Conclusion
We developed a Matlab based toolbox named LandMOD ET mapper to run SEBAL and METRIC on Landsat and MODIS images for mapping field to regional scale ET. The LandMOD ET mapper utilizes automated algorithms to fully automate the process of hot and cold pixel
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selection and implements all parameters of the surface energy balance to produce ET maps at the input resolution of the thermal image. Full automation of these models will allow new users to implement SEBAL and METRIC models in an efficient manner that can reduce the potential
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biases introduced by the manual components in the original models. Free distribution of
LandMOD ET and associated functions within the METRIC/SEBAL algorithms could be
considered as a step forward for widespread application of these models. Future studies will test
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the ability of LandMOD ET mapper on more complex terrains, heterogeneous landscapes, and
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diverse agro-climatic zones.
Software and data availability: Landsat, MODIS, and weather data were freely available. EEFlux data are available at https://eeflux-level1.appspot.com/. Matlab codes and instructions for running the LandModET Mapper are available in
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https://umich.box.com/s/b6ldlf7vvrrmt51mbp2u3l0xmqrl23cj. In the future, the LandModET Mapper toolbox will be freely available to the public through a Github public repository (https://github.com/nishan3/LandModETmapper).
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Acknowledgments
We thank High Plains Regional Climate Center, University of Nebraska (UNL), for providing
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weather data for free use in this study. Thanks to the U.S. Department of Energy’s Office of Science and the principal investigator for the NE flux sites, Andy Suyker, UNL for the flux data. References
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Lewis, C.S., Geli, H.M.E., Neale, C.M.U., 2014. Comparison of the NLDAS Weather Forcing Model to Agrometeorological Measurements in the western United States. Journal of Hydrology 510 385-392. Liang, S., 2001. Narrowband to broadband conversions of land surface albedo I: Algorithms. Remote Sensing of Environment 76(2) 213-238. Liou, Y.-A., Kar, S.K., 2014. Evapotranspiration estimation with remote sensing and various surface energy balance algorithms—A review. Energies 7(5) 2821-2849.
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Madugundu, R., Al-Gaadi, K.A., Tola, E., Hassaballa, A.A., Patil, V.C., 2017. Performance of the METRIC model in estimating evapotranspiration fluxes over an irrigated field in Saudi Arabia using Landsat-8 images. Hydrol. Earth Syst. Sci. 21(12) 6135-6151.
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Mallick, K., Boegh, E., Trebs, I., Alfieri, J.G., Kustas, W.P., Prueger, J.H., Niyogi, D., Das, N., Drewry, D.T., Hoffmann, L., Jarvis, A.J., 2015. Reintroducing radiometric surface temperature into the PenmanMonteith formulation. Water Resources Research 51(8) 6214-6243.
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Mallick, K., Jarvis, A.J., Boegh, E., Fisher, J.B., Drewry, D.T., Tu, K.P., Hook, S.J., Hulley, G., Ardö, J., Beringer, J., Arain, A., Niyogi, D., 2014. A Surface Temperature Initiated Closure (STIC) for surface energy balance fluxes. Remote sensing of Environment 141 243-261. Masek, J.G., Vermote, E.F., Saleous, N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., Lim, T.-K., 2006. A Landsat surface reflectance dataset for North America, 1990-2000. Geoscience and Remote Sensing Letters, IEEE 3(1) 68-72. Mitchell, K.E., Lohmann, D., Houser, P.R., Wood, E.F., Schaake, J.C., Robock, A., Cosgrove, B.A., Sheffield, J., Duan, Q., Luo, L., Higgins, R.W., Pinker, R.T., Tarpley, J.D., Lettenmaier, D.P., Marshall, C.H., Entin, J.K., Pan, M., Shi, W., Koren, V., Meng, J., Ramsay, B.H., Bailey, A.A., 2004. The multiinstitution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. Journal of Geophysical Research: Atmospheres 109(D7) n/a-n/a.
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Morton, C.G., Huntington, J.L., Pohll, G.M., Allen, R.G., McGwire, K.C., Bassett, S.D., 2013. Assessing Calibration Uncertainty and Automation for Estimating Evapotranspiration from Agricultural Areas Using METRIC. Journal of the American Water Resources Association 49(3) 549-562. Olmedo, G.F., Ortega-Farías, S., de la Fuente-Sáiz, D., Fonseca-Luengo, D., Fuentes-Peñailillo, F., 2016. Water: Tools and functions to estimate actual evapotranspiration using land surface energy balance models in r. R Journal 8(2) 352-370.
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Verma, S.B., Dobermann, A., Cassman, K.G., Walters, D.T., Knops, J.M., Arkebauer, T.J., Suyker, A.E., Burba, G.G., Amos, B., Yang, H., Ginting, D., Hubbard, K.G., Gitelson, A.A., Walter-Shea, E.A., 2005. Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems. Agricultural and Forest Meteorology 131(1) 77-96. Vermote, E., 2015. MOD09A1MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V006, NASA EOSDIS Land Processes DAAC. Wan, Z., Hook, S., Hulley, G., 2015. MOD11A2 MODIS/Terra Land Surface Temperature/Emissivity 8Day L3 Global 1 km SIN Grid V006, NASA EOSDIS Land Processes DAAC, USGS Earth Resources Observation and Science (EROS) Center. https://lpdaac. usgs. gov (accessed 16 June 2016): Sioux Falls, SD, doi. Waters, R., Allen, R., Bastiaanssen, W., Tasumi, M., Trezza, R., 2002. Surface energy balance algorithms for land, Idaho implementation, advanced training and users manual. NASA, USA.
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S1364-8152(19)30120-3
DOI:
https://doi.org/10.1016/j.envsoft.2019.04.007
Reference:
ENSO 4439
To appear in:
Environmental Modelling and Software
Received Date: 3 February 2019 Revised Date:
7 April 2019
Accepted Date: 10 April 2019
Please cite this article as: Bhattarai, N., Liu, T., LandMOD ET mapper: A new matlab-based graphical user interface (GUI) for automated implementation of SEBAL and METRIC models in thermal imagery, Environmental Modelling and Software (2019), doi: https://doi.org/10.1016/j.envsoft.2019.04.007. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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LandMOD ET Mapper: a new Matlab-based graphical user interface (GUI) for automated
Nishan Bhattarai1, Tao Liu2
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implementation of SEBAL and METRIC models in thermal imagery
School for Environment and Sustainability, University of Michigan, Ann Arbor MI 48109
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Geographic Data Science, Oak Ridge National Laboratory, Oak Ridge, TN 37830
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*Correspondence to: [email protected]
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Highlights A new Matlab-based GUI “LandMOD ET Mapper” was developed.
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The toolbox implements SEBAL/METRIC model automatically using thermal imagery.
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Daily ET estimates from the toolbox were comparable with observed ET.
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Abstract Remote sensing based surface energy balance (SEB) models, such as SEB Algorithm for Land (SEBAL) and Mapping evapotranspiration (ET) at high Resolution with Internalized Calibration
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(METRIC) are widely used to monitor ET. However, their operational use is challenged by
model complexity and the tedious process of selecting hot and cold pixels. We developed a new Matlab-based ET mapping toolbox named “LandMOD ET Mapper” for automated
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implementation of these models to facilitate their widespread applications among new users. The toolbox is demonstrated using one Landsat and one Moderate Resolution Imaging
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Spectroradiometer (MODIS) Terra image covering three AmeriFlux sites in Nebraska, U.S. Daily ET estimates were comparable with the observed ET for five days when near cloud-free Landsat and MODIS Terra images were available in the 2006 maize growing season. The toolbox provides a viable ET mapping option to new and inexperienced users with reduced time
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and labor demands.
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Keywords: LandMOD ET Mapper, SEBAL, METRIC, Matlab, Landsat, MODIS
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Introduction
The transfer of water vapor from Earth’s surface to the atmosphere, also referred to as Evapotranspiration (ET), is an important phenomenon within the land-atmosphere interface that
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regulates the Earth’s energy and water cycles. Quantifying ET is considered a challenging task given the complex nature of heat and energy exchanges between the land and the atmosphere. Over the last few decades, the evolution of several remote sensing tools, especially the
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development and applications of several land surface temperature (LST or Ts) driven surface energy balance (SEB) algorithms has significantly improved our ability to monitor the process of
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ET (Kalma et al., 2008; Liou and Kar, 2014). In thermal remote sensing based SEB model, LST is used to define the surface moisture conditions and solve the SEB equation (Eq. (1)) (Kalma et al., 2008; Liou and Kar, 2014). Rn = LE + G + H
(1)
respectively.
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where Rn, G, H, and LE are net radiation, soil heat, sensible, and latent heat fluxes,
The Surface Energy Balance Algorithm for Land (SEBAL) (Bastiaanssen et al., 1998) and
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Mapping ET at high Resolution with Internalized Calibration (METRIC) (Allen et al., 2007b) are two widely used SEB based ET models on thermal sensors, such as Landsat and MODIS (Allen
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et al., 2011; Bastiaanssen et al., 2005; Bhattarai et al., 2016). SEBAL pioneered the concept of selecting the hot and cold pixels (referred to as anchor pixels hereafter) within a remotely sensed image to contextually derive ET across all pixels within the image. METRIC is a simple extension of SEBAL model in which reference ET (Allen et al., 1998) is used to internally calibrate H and was first developed for applications in the US (Allen et al., 2011; Allen et al., 2007a; Allen et al., 2007b). However, METRIC now has been successfully tested and applied
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other parts of the world (Khand et al., 2017; Madugundu et al., 2017; Santos et al., 2008; Spiliotopoulos et al., 2017). Despite their wide popularity, SEBAL and METRIC models are limited to experienced users due to the requirement of selecting anchor pixels within an image.
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Recently, there have been significant efforts to fully automate anchor pixels selection in these models (Allen et al., 2013; Bhattarai et al., 2017; Morton et al., 2013). For example, Allen et al (2013) used a simple statistical approach to group out candidate pixels and automatically
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identified hot and cold pixels in the METRIC. Bhattarai et al. (2017) proposed an optimized way of using an exhaustive search algorithm (ESA) to automate the anchor pixel selection process. A
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current effort is underway to automatically derive Landsat-based ET using METRIC model on the Google EarthEngine (GEE) based application (EEFlux) based on Allen et al (2013) and Morton et al. (2013). However, the GEE source code is not publicly available (Allen et al., 2015) and ET maps are only available for the US with no options to update coarse-resolution (~ 12.5
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km) input meteorological forcing, which typically degrades the performance of SEB models (Bhattarai et al., 2018). An R package (Olmedo et al., 2016) is currently available to run METRIC, which do not use the optimized version of hot and pixel selection and some predefined
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area of interests (AOI). A python version of the METRIC model is currently under development (https://github.com/hectornieto/pyMETRIC), which also has an option of automating hot and
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cold pixel using Bhattarai et al. (2017). Similarly, a python version of SEBAL (Jaafar and Ahmad, 2019) is currently under development. However, a matlab version of fully automated SEBAL and METRIC models is not currently available. An attempt to create matlab toolbox for SEBAL application was made by Atasevera et al. (2013); however, the process was not fully automated and is not available to the public.
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In this paper, we develop a new matlab-based graphical user interface (GUI) toolbox for the application of fully automated versions of SEBAL and METRIC models in Landsat, MODIS, or other thermal sensors. With this GUI we aim to address some of the existing challenges in
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SEBAL/METRIC implementations, such as the tedious process of anchor pixels selection, need of expertise to run the models, uncertainty in weather inputs, and bring the model within the domain of new and inexperienced users. Methods
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2.1 Brief descriptions of the SEBAL and METRIC models
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The residual form of SEB equation is used in SEBAL and METRIC models (i.e. LE = Rn-G-H). Rn is computed using the net radiation balance equation (Eq. 2), while G is computed as a small
Rn = Rs (1– α) + εoRld - Rlu
(2) (3)
H = (ρacpdT)/rah
(4)
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G = Rn (Ts-273.15)/α (0.0038α + 0.0074α2)(1 - 0.98NDVI 4)
proportion of Rn (Eq. 3) (Bastiaanssen, 2000).
Details on Rn and G estimations can be found in Allen et al. (2007b). The key component of
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SEBAL and METRIC model is self-calibration of H using an iterative process based on
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manually or automatically selected hot (dry and bare agricultural field) and cold (well-irrigated croplands) pixels. A major difference between SEBAL and METRIC is that LE at the cold pixel is equal to available energy in SEBAL (or H = 0), while in METRIC cold pixel ET is considered to be 5% greater than alfalfa reference ET (Allen et al., 2007b). In both models, H is estimated by simultaneously solving equations for H, aerodynamic resistance for heat transfer (rah), and frictional velocity (u*), as described in Allen et al. (2007b), Allen et al. (2011), and Bhattarai et al. (2016). 6
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2.2 The LandMOD ET mapper The LandMOD ET mapper GUI is ET mapping toolbox (Fig 1) designed to automatically implement SEBAL and METRIC models efficiently by users with any level of prior model
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experiences. The Matlab software and license (and preferably version 2017a and beyond) are required to open the GUI; however, no prior Matlab coding experience is required to run the tool. The automation of hot and cold pixel uses a modified version of Bhattarai et al. (2017) that
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eliminates the need of having a reference weather station within the image. Instead, gridded weather data or scalar values (applicable for small regions) are used. The automation of hot and
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cold pixel starts with the selection of candidate pixels based on simple decision tree classifier and exhaustive search used to select a subset of pixels that could potentially be selected as hot and cold pixels. The final hot and cold pixel is based on the ranking of each pixel based on its LST and NDVI values. The pixel with highest LST and lowest NDVI value is selected as the hot
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pixel, while the pixel with the lowest LST and highest NDVI is taken as the cold pixel. The automatically selected hot and cold pixels are used to internally calibrate H and follow the stabilization correction procedures explained in Allen et al. (2011). LE is then used to estimate
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Evaporative fraction (in SEBAL) or reference ET fraction (ETrF), which is assumed to be constant during the day to produce daily ET maps, as described in Allen et al. (2011) and
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Bhattarai et al. (2017).
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Fig.1. The LandMOD ET mapper GUI in Matlab 2.2.1 Structure of the LandMod ET Mapper The toolbox can be divided into two sections: 1) model inputs and 2) model outputs. The model input part is further divided into 5 subsections (Image metadata inputs, surface and cloud cover information, weather inputs, remote sensing inputs, and weather and image information). Several parameters and constants (i.e. Processing parameters/constants and hot and cold pixel 8
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selection parameters) are automatically populated based on satellite type (Landsat or MODIS). The remotely sensed inputs include LST, NDVI, albedo, and emissivity, which are either available as derived products (e.g. https://modis.gsfc.nasa.gov/data/dataprod/; last visited
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October 10, 2018) or computed using individual bands (Jiménez‐Muñoz and Sobrino, 2003; Liang, 2001; Tasumi et al., 2007). The main reason for using these derived remotely sensed products rather than the individual bands as input is because there are several methods to derive
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LST, albedo, and emissivity from Landsats (Bhattarai et al., 2016; Sobrino et al., 2004; Tasumi et al., 2007; Teixeira et al., 2009). Such an option makes the toolbox more applicable for other
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thermal sensors from which these parameters could be derived (e.g. Advanced Spaceborne Thermal Emission and Reflection Radiometer, ASTER). Detailed descriptions of all other inputs and outputs are provided in the file “About LandMOD ET Mapper.pdf” provided with the toolbox (see Data and code availability). Because LST is the key input in these models, it is
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required that LST input is in GeoTIFF format, from which Matlab will extract referencing information to write outputs in the same format. The model outputs can be saved in defined output folder (i.e., output path) and output files are named based on the parameter (e.g., “le” for
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LE appended with model type and yeardoy of the input image). The toolbox also allows for the visualization of key model outputs (LE and daily ET).
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2.2.2 Application of the LandMod ET Mapper For the demonstration purpose, we applied LandMod ET Mapper in a highly studied AmeriFlux site in Nebraska that covers three eddy covariance (EC) towers: US NE1 (lat: 41.1651 and lon: 96.4766), USNE2 (lat: 41.16487 and lon: -96.4701), and USNE3 (lat: 41.17967 and lon: 96.43965) over croplands (Fig. 2). Readers are referred to Verma et al. (2005) for details on instrumentation, operation and maintenance, data processing, and quality control of EC tower at
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these sites. Hourly and daily weather data (solar radiation, temperature, wind speed, relative humidity) were obtained from five Automated Weather Data Network (AWDN) managed by the High Plains Regional Climate Center (HPRCC) (https://hprcc.unl.edu/awdn.php; last visited on
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October 15, 2018). Inverse distance weighting (IDW) method was used to create weather grids
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based on instantaneous and daily weather variables from the five AWDN stations (Fig. 2).
Fig. 2. Study area (processing grid and zoomed in views) shown on a false color composite of Landsat imagery acquired on yeardoy 2006215 A total of five near cloud-free Landsat and MODIS images (doy 199, 215, 267, 287, and 311,
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during the 2006 maize growing season were used in this study. The key remotely sensed inputs (data source or derivation methods) used in this study are listed in Table 1.
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Table 1. Sources of Key remotely sensed input used in the LandMod ET Mapper toolbox Variable Name MODIS Landsat* albedo
MOD09GA and Liang (2001)
Liang (2001)
LST
MOD11A1 (Wan et al., 2015)
Jiménez‐Muñoz and Sobrino (2003)
Emissivity
MOD11A1 (Wan et al., 2015)
Sobrino et al. (2004)
NDVI
MOD09GA (Vermote, 2015)
Masek et al. (2006)
* Landsat Surface Reflectance products courtesy of the U.S. Geological Survey Earth Resources Observation and Science Center
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2.3 Model Evaluation We aggregated half-hourly flux from the three AmeriFlux sites to convert into daily average LE and then daily ET (ET= LE/λ, where λ is the latent heat of vaporization, k J kg−1) for evaluation
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of the ET models based on Landsat and MODIS inputs. Daily ET from EEFlux was also used to compare the relative performance of the models for the five clear sky MODIS and Landsat image acquisition days. Models were evaluated based on commonly used statistical metrics, such as
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root mean squared error (RMSE), the coefficient of determination (R2), mean absolute error
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Results and Discussion
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(MAE), and percent bias error (PBIAS) (Moriasi et al., 2007).
3.1 ET maps from the LandMOD ET mapper toolbox
Fig. 3 demonstrates the ET mapping capability of the LandMOD ET mapper toolbox. Here Landsat and MODIS derived daily ET maps for the yeardoy 2006215 using the LandMODET
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mapper toolbox are shown. The difference in spatial resolution is clearly evident, as MODIS is coarser resolution and typically used for regional scale ET mapping, while Landsat can provide
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field-scale ET (30 m). However, the spatial pattern is similar across all models and scales.
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Fig. 3. Daily ET maps from SEBAL and METRIC models using the LandMOD ET mapper toolbox
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3.2 Performance Evaluation of Daily ET estimates from the LandMod ET mapper Comparison of daily ET estimates from the LandMod ET mapper and observed ET at the three AmeriFlux sites in NE suggests that the METRIC and SEBAL models are capable of predicting
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ET with reasonable accuracies (R2 from 0.78-0.89 and RMSE from 0.91-1.54 mm day-1; Fig. 4). In particular, both models performed much better when Landsat inputs were used, as shown in
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Fig. 4. For example, PBias values were within -7% (METRIC) and 3% (SEBAL) when Landsat images were used, as compared to the PBias values of -13% and -14% from SEBAL and METRIC, respectively, when MODIS images were used (Fig. 4). The better performance of the models when Landsat images were used could be attributed to the fact that these two models were initially developed for use in moderate resolution thermal images (i.e. Landsat, ASTER) (Waters et al., 2002), where the anchor pixels are likely to be better representatives of the
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extreme conditions (i.e. hot/dry and cold/wet) in the image and on the ground (i.e., ~30 m × 30 m in Landsat vs ~1 km × 1 km in MODIS). In addition, a pixel value extracted based on the location of a flux tower within an image have lesser chances of being contaminated from
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neighboring pixels in Landsat than in MODIS. Even in a large field representing a single land cover type (e.g., croplands in our case), spatial heterogeneity in LST (a key input in the SEBbased ET models) can exist based on the actual soil moisture conditions. For example, a MODIS
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LST pixel represents an aggregated LST values from small fields within the 1 km × 1 km area, which could be quite different than the actual LST on the small fields that have contrasting
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moisture conditions than neighboring pixels (Mallick et al., 2015; Mallick et al., 2014; Stoy et al., 2013). Hence, daily ET values from MODIS pixels may not be as representative of actual ground conditions (i.e., flux towers) as in Landsat. This could be a reason that despite the same models/tools were used, Landsat-derived ET estimates were found to be more accurate than
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those from MODIS. Other potential reasons include the differences in values and associated biases in other remotely sensed input variables, such as NDVI, albedo, and emissivity between
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the two sensors.
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Fig. 4. Evaluation of daily ET for the five near cloud-free Landsat image acquisition days in 2006 maize growing season from the METRIC and SEBAL models from the LandMOD ET toolbox (using Landsat and MODIS) and EEFlux based METRIC model using observed ET at three nearby AmeriFlux sites in NE. Notably, the toolbox-derived ET estimates from the Landsat images were closer to the observed ET than the EEFlux ET estimates that were derived using the same Landsat images
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(Fig. 4). This difference could be the differences in submodels used in EEFlux and LandMod ET mapper toolbox (e.g., LST, albedo, emissivity, and surface roughness derivations); however,
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model codes used in EEFlux were not publicly available for further investigation. Another key reason, also reported in recent studies (Bhattarai et al., 2018; Dhungel et al., 2019; Lewis et al., 2014), could be the difference between the weather inputs in these two ET modeling approaches. For example, EEFlux based METRIC model uses coarse resolution weather inputs, such as NLDAS and GRIDMET (Abatzoglou, 2013; Mitchell et al., 2004), while the high-quality weather station data from AWDN was used in the LandMod ET mapper toolbox. However,
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EEFlux captured the higher magnitude (> 7 mm day-1) of observed daily ET for the yeardoy 2006215 much better than the SEBAL and METRIC models in the LandMod ET toolbox. On this specific date, the mean daily ET from the three sites was 7.15 mm day-1, which was
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underestimated by 19 % and 22 % by the Landsat-based SEBAL and METRIC models,
respectively. This could be justified by the potential underestimation of ET at the hot pixel (i.e. ET assigned as zero or close to zero), as precipitation (~ 0.1 mm) preceded two days prior to the
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Landsat image acquisition date (i.e. 2006215). Because of the antecedent moisture in the soil, ET from the hot pixel may not be zero, which would underestimate ET from all pixels within the
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image. In addition, this could also increase evaporation from bare soil and leaves, which were not captured by these models. Conversely, for the same day, daily ET from EEFlux was within 1% of observed daily ET, which indicates that the antecedent moisture conditions may be better represented in the EEFLux based METRIC model. It could also be the positive biases in the solar
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radiation (and hence the daily ETr) from the 12.5 km NLDAS and the 4 km GRIDMET data (Bhattarai et al., 2018; Dhungel et al., 2019; Lewis et al., 2014) used in EEFlux. For example, daily ETr from NLDAS/GRIDMET data was nearly 50% greater than those from the AWDN
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station data for the yeardoy 2006215. This positive bias is also reflected in an overall overestimation (26%) of observed ET by EEFlux (Fig. 4). However, for cases when daily ET
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values were relatively low (< ~1.5 mm day-1), EEFlux underestimated daily ET. Under such conditions, the toolbox derived daily ET showed much better agreement with observed ET (Fig. 4). The inconsistency of EEFlux derived ET at a field scale (as in this study) could be attributed to biases in the coarse resolution gridded data (Bhattarai et al., 2018; Dhungel et al., 2019; Lewis et al., 2014) that were designed for more regional or country scale implementation. Our results suggest that while high-quality weather data is crucial for improving the performance of the
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SEBAL and METRIC models despite some uncertainties associated with these models (e.g., scale effects, representation of extreme conditions). In the LandMOD ET mapper toolbox, users can use high-quality reference weather station data to reduce potential biases associated with
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weather inputs in these models.
3.3 Efficiency and future application of the LandMOD ET mapper
The total processing time taken by the toolbox is dependent on a number of factors, such as the
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size of the image, the option to run skin evaporation, and the number of models selected to run. For example, the processing time ranged from 6 seconds to 580 seconds to process both models
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on a 1km MODIS scene (204 × 310 array) and a 30 m Landsat scene (6141 × 5861 array) with similar spatial domain (yeardoy 2006215), when implemented on a 3.4 GHz i-7 processor computer with 48 GB of memory on a windows machine. Processing time for Landsat will be significantly reduced if smaller subsets are used. This should be considered a significant
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improvement in processing time since no prior knowledge of the models or manual selection of hot and cold pixels is required. A simple routine to batch process large number of images is presented in the tool description file “About LandMOD ET Mapper.pdf”. Although, Landsat and
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MODIS images were used in this study, the LandMOD ET mapper can be ideally used to other thermal sensors such as the ASTER (https://asterweb.jpl.nasa.gov/) and ECOSTRESS
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(https://ecostress.jpl.nasa.gov/), as the key remote sensing inputs in the toolbox are derived products from the bands (i.e., albedo, NDVI, LST, and emissivity). 4
Conclusion
We developed a Matlab based toolbox named LandMOD ET mapper to run SEBAL and METRIC on Landsat and MODIS images for mapping field to regional scale ET. The LandMOD ET mapper utilizes automated algorithms to fully automate the process of hot and cold pixel
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selection and implements all parameters of the surface energy balance to produce ET maps at the input resolution of the thermal image. Full automation of these models will allow new users to implement SEBAL and METRIC models in an efficient manner that can reduce the potential
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biases introduced by the manual components in the original models. Free distribution of
LandMOD ET and associated functions within the METRIC/SEBAL algorithms could be
considered as a step forward for widespread application of these models. Future studies will test
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the ability of LandMOD ET mapper on more complex terrains, heterogeneous landscapes, and
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diverse agro-climatic zones.
Software and data availability: Landsat, MODIS, and weather data were freely available. EEFlux data are available at https://eeflux-level1.appspot.com/. Matlab codes and instructions for running the LandModET Mapper are available in
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https://umich.box.com/s/b6ldlf7vvrrmt51mbp2u3l0xmqrl23cj. In the future, the LandModET Mapper toolbox will be freely available to the public through a Github public repository (https://github.com/nishan3/LandModETmapper).
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Acknowledgments
We thank High Plains Regional Climate Center, University of Nebraska (UNL), for providing
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weather data for free use in this study. Thanks to the U.S. Department of Energy’s Office of Science and the principal investigator for the NE flux sites, Andy Suyker, UNL for the flux data. References
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