Estimation Of Actual Evapotranspiration Research Articles

Estimation of actual evapotranspiration in barley crop through a generalized linear model

Evapotranspiration is a key variable of the water cycle. Its calculation requires several ground data that frequently are not available. This study contains a detailed method and measurements of meteorological and energy balance variables that can be used to estimate the daily actual evapotranspiration (ETa). A linear generalized model is obtained to calculate the ETa from common variables measured in meteorological stations. The method showed a good performance over a barley crop of easthern Argentine Pampas and can be applied and tested in other great plains.Measurements of soil-plant-atmosphere are includedThe routines to reproduce the method are includedThe generalized method allows the calculation of daily ETa over crops and was tested over barley crops

Unraveling human influence on evapotranspiration over East Asian monsoon river basins by using GRACE/GRACE-FO data and land surface models

With intensified human influences on the terrestrial water cycle, evapotranspiration (ET) is changed even at a large river basin scale. However, there is no consensus about the significance of the human-induced ET change across river basins due to the uncertainties from the estimations of both observed and natural ET at a large scale. In this study, the human influence on ET is estimated over seven large river basins in the East Asian monsoon region during 2003–2019, where the actual ET estimations based on the water budget method using the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On terrestrial water storage change and the natural ET estimations based on land surface modeling are compared. The state-of-the-art global reanalyses and the Variable Infiltration Capacity (VIC) land surface model are validated against naturalized runoff from the water resources bulletins, and the VIC model is found to outperform global reanalysis data over most southern river basins. Compared with the selected natural ET model data that has a good performance of naturalized runoff, the annual mean human-induced ET in the East Asian monsoon region is estimated to range from 38 ± 28 to 100 ± 76 mm/year (8 ± 6% to 12 ± 9% of the total ET) during 2003–2019. The reservoir evaporation contributes to 5–18% of the human-induced ET change especially over the Huai river basin. Our results reveal that human-induced ET change is more significant over northern semiarid basins than southern humid basins.

Comparative Evaluation of Simplified Surface Energy Balance Index-Based Actual ET against Lysimeter Data in a Tropical River Basin

In the past decades, multispectral and multitemporal remote sensing has been popularly used for estimating actual evapotranspiration (ETc) across the globe. It has been proven to be a cost-effective tool for understanding agricultural practices in a region. Today, because of the availability of different onboard sensors on an increasing number of different satellites, land surface activity can be captured at fine spatial and time scales. In the present study, three multi-date satellite imageries were used for the evaluation of remote sensing-based estimation of actual evapotranspiration in paddy in the command area of the tropical Kangsabati river basin. A surface energy balance model, the Simplified-Surface Energy Balance Index (S-SEBI), was applied for all three dates of the Rabi season (2014–2015) for the estimation of actual evapotranspiration. The crop coefficient was calculated using the exhaustive survey data collected from the command area and adjusted to local conditions. The ETc estimated using the S-SEBI-based model was compared with the Food and Agriculture Organization Penman–Monteith (FAO-56 PM) method multiplied by the adjusted local crop coefficient and lysimeter data in the command area. The coefficient of determination (r2) was applied to examine the accuracy of the S-SEBI model with respect to lysimeter data and the FAO-56 PM-based ETc. The results showed that the S-SEBI model performed well with the lysimeter (r2 = 0.90) in comparison with FAO-56 PM, with an r2 of 0.65. In addition to this, the S-SEBI-based ET estimates correlated well with the FAO-56 PM, with r and RMSE values of 0.06 and 1.13 mm/day (initial stage), 0.85 and 0.48 mm/day (development stage), and 0.77 and 0.52 (maturity stage) for paddy, respectively. The S-SEBI-based ETc estimate varied with different stages of crop growth and successfully captured the spatial heterogeneity within the command area. In general, this study showed that the S-SEBI method has the potential to calculate spatial evapotranspiration and provide useful information for efficient water management. The results revealed the applicability and accuracy of remote sensing-based ET for managing water resources in a command area with scarce data.

A remote sensing and modeling integrated approach for constructing continuous time series of daily actual evapotranspiration

Satellite remote sensing-based surface energy balance (SEB) techniques have emerged as useful tools for quantifying spatialized actual evapotranspiration at various temporal and spatial scales. However, discontinuous data acquisitions and/or gaps in image acquisition due to cloud cover can limit the applicability of satellite remote sensing (RS) in agriculture water management where continuous time series of daily crop actual evapotranspiration (ETc act) are more valued. The aim of the research is to construct continuous time series of daily ETc act starting from temporal estimates of actual evapotranspiration obtained by SEB modelling (ETa eb) on Landsat-TM images. SEBAL model was integrated with the FAO 56 evaporation model, RS-retrieved vegetative biomass dynamics (by NDVI) and on-field measurements of soil moisture and potential evapotranspiration. The procedure was validated by an eddy covariance tower on a vineyard with partial soil coverage in the south of Sardinia Island, Italy. The integrated modeling approach showed a good reproduction of the time series dynamics of observed ETc act (R2 =0.71, MAE=0.54 mm d-1, RMSE=0.73 mm d-1). A daily and a cumulative monthly temporal analysis showed the importance of integrating parameters that capture changes in the soil-plant-atmosphere (SPA) continuum between Landsat acquisitions. The comparison with daily ETc act obtained by the referenced ET fraction (ETrF) method that considers only weather variability (by ETo) confirmed the lead of the proposed procedure in the spring/early summer periods when vegetation biomass changes and soil water evaporation have a significant weight in the ET process. The applied modelling approach was also robust in constructing the missing ETc act data under scenarios of limited cloud-free Landsat acquisitions. The presented integrated approach has a great potential for the near real time monitoring and scheduling of irrigation practices. Further testing of this approach with diverse dataset and the integration with the soil water modeling is to be analyzed in future work.

Practical analysis of remote sensing estimations of water use for major crops throughout the Urmia Lake basin

Remote sensing techniques are used to estimate the high spatial and temporal hydrological variable of evapotranspiration. This is certainly considered a step forward, compared to alternate point measurements. It is important, however, to properly interpret these estimations to practical information for water managers. In this study, results of two separate remote sensing studies (FAO-IHE and RSRC) on estimation of actual evapotranspiration for major crops in the Urmia Lake basin were compared with widely accepted theoretical estimations of corresponding Irrigation Requirements (IR) using CROPWAT. CROPWAT estimations represent the irrigation requirements of crops under no stress, whereas RS techniques are used to estimate actual crop water use which may be under nonideal growing situations. Comparison of irrigation requirements and actual water use for different crops at various locations within the Urmia Lake basin can provide practical information on the status of field water managements, i.e., sufficiency of applied irrigation. Such analysis provides grounds for possible improvement on water management in a highly competitive water used region. The RS crop water estimations results were crosschecked with CROPWAT estimations of irrigation requirements, as the reference range, using ground data adopted from 21 scattered synoptic weather stations across the basin, representing the widely variable crop-water demand in the region and corresponding effective precipitations. FAO-IHE used the SEBAL and RSRC used the METRIC algorithm to produce ETa maps. The available land-use maps with 30-m by 30-m pixels were used for designation of the type of field crops. RSRC maps were produced using 1-km by 1-km daily satellite images. At this scale, a mixed combination of crops occurs in one pixel. To provide a more realistic comparison, the relative area of each crop cited from the land use maps was used to compare the ETa estimates by RSRC with the weighted average of irrigation requirements. The results showed the actual water use estimated from remote sensing images were generally higher than Irrigation Requirements. The difference can be attributed to overirrigation which is a common practice in the region. RS estimated actual crop-water use in rainfed lands was also compared with temporal local precipitations. In some areas, actual crop-water use in rainfed lands was more than the corresponding effective precipitation estimates. This observation cannot practically be justified and can be drawback in the preparation of land-use maps. Practical conclusions were drawn from comparison of actual crop-water use and theoretical estimates. Locations of possible over-/under-irrigation were identified, for which improved water management can provide means for saving water for the restoration of the desiccating Urmia Lake. The feasibility and versatility of RS techniques have motivated researchers to increase adoption of this method, provided that the uncertainties can be resolved.

Loess Plateau evapotranspiration intensified by land surface radiative forcing associated with ecological restoration

Evapotranspiration (ET) is a key variable coupled with water and energy availability and vegetation coverage. Since the year around 2000, several ecological restoration programs have been enacted within the Loess Plateau, China. The restoration programs have promoted vegetation greening and air cleaning, which has changed land surface albedo and solar radiation and consequently altered the land surface radiative forcing. Evidence has showed that ET is impacted by climate change and land use/cover change, but little is known about the effect of the radiative forcing on ET. This study reports the contribution of the surface radiative forcing during the growing season associated with ecological restoration to changes in potential ET (PET) and actual ET (AET) in the Loess Plateau. A differential attribution method based on the Penman-Monteith equation is proposed to isolate the contribution of radiative forcing. Accurate estimates of PET and AET are obtained from a sophisticated land surface hydrological model driven by long-term dynamic albedo, solar radiation, vegetation index, and meteorological forcings. We find that during 2003-2018, the surface albedo significantly decreased with increased solar radiation, which increased radiative forcing by 3.68 W m−2. These changes in radiative forcing increased PET and AET by 1.02 mm yr−1 and 0.12 mm yr−1, respectively. Despite spatial variabilities in radiative forcing, PET and AET intensified in approximately 50% (3.03 × 105 km2) and 60% (3.63 × 105 km2) of the area of the Loess Plateau, respectively. However, the PET and AET intensified by radiative forcing accounted for 8% and 5% of the total change in PET and AET, respectively. Although the average contribution of radiative forcing to the Loess Plateau is relatively small, the accumulated impact is non-negligible; it may mediate hydrological cycle and result in substantial water resource stress, particularly in areas implementing intensive ecological restoration.

On the interchangeability of Landsat and MODIS data in the CMRSET actual evapotranspiration model – Comment on “Monitoring irrigation using Landsat observations and climate data over regional scales in the Murray-Darling Basin” by David Bretreger, In-Young Yeo, Greg Hancock and Garry Willgoose

In a recent paper published in this journal, Bretreger et al. (2020) estimate irrigation water use from satellite remotely sensed estimates of actual evapotranspiration in five irrigated districts of the Murray-Darling Basin (southeast Australia). They used three models that scale crop reference evapotranspiration with vegetation indices acquired by recent Landsat satellites: (i) IrriSAT, (ii) Kamble and (iii) CMRSET. In their paper, irrigation water use computed with CMRSET generally overestimated observed irrigation water use, sometimes fivefold. Based on these results, Bretreger et al. (2020) discouraged the use of CMRSET for irrigation monitoring. In this comment, we reproduce the experiments in Bretreger et al. (2020), and demonstrate that their overestimation was because they used an incorrect Landsat band in their implementation of CMRSET. CMRSET was originally calibrated using both MODIS-derived Enhanced Vegetation Index (EVI) and Global Vegetation Moisture Index (GVMI). To calculate GVMI, a shortwave infrared (SWIR) band with a wavelength of ∼1.6 μm was used, which for MODIS is named SWIR2, and for Landsat is named SWIR1 (i.e., different sensors have different bands and a different number of bands and SWIR1 in MODIS has a wavelength of ∼1.2 μm). In their Landsat CMRSET implementation, Bretreger et al. (2020) computed GVMI using the Landsat SWIR2 band, which has a wavelength of ∼2.1 μm (see their Table 3). We show that CMRSET implemented with the Landsat SWIR1 band (i.e., with a wavelength of ∼1.6 μm, so being the correct Landsat band to calculate GVMI) yields similar results (both for temporal patterns and magnitude) when compared to the other two remote sensing actual evapotranspiration models in Bretreger et al. (2020). For the irrigation districts, these similar results meant that the mean water year (i.e., July to next June from 2010 to 2017) actual evapotranspiration mean absolute relative difference was 15.3% (with a 5.4%–26.5% range) and the water year irrigation water use mean absolute relative difference was 4.1% (with a 9.8%–18.9% range). Conversely, we show that a Landsat CMRSET implementation using the incorrect Landsat band for the calculation of GVMI (i.e., with a wavelength of ∼2.1 μm) led to a large overestimation agreeing with the CMRSET results reported by Bretreger et al. (2020), both for water year actual evapotranspiration (mean absolute relative difference of 64.6%, with a 51.1%–83.6% range) and irrigation water use (mean absolute relative difference of 44.1%, with a 31.4%–65.1% range). The use of the incorrect Landsat band means that the Bretreger et al. (2020) recommendation specific to their Landsat CMRSET implementation is invalid. A demonstration of Bretreger et al.’s (2020) incorrect Landsat CMRSET implementation and the correct one can be accessed at: https://jorgepena.users.earthengine.app/view/comment-on-bretreger-2020-joh-paper.Despite Bretreger et al.’s (2020) shortcomings in their implementation of the Landsat CMRSET actual evapotranspiration model, timely monitoring of irrigation water use via satellite remote sensing is required at Landsat resolutions (i.e., 30 m) or higher due to the heterogeneous nature of irrigation in most agricultural landscapes, and we applaud them for pursuing this line of research.

Constraining water limitation of photosynthesis in a crop growth model with sun-induced chlorophyll fluorescence

Water fulfils key roles in maintaining a plant's biological activity. Water shortage induces stomatal closure, causing a reduction in photosynthesis and transpiration rates. Sun-induced chlorophyll fluorescence (SIF) emission is sensitive to subtle, stress-induced variations in non-photochemical quenching and in photosynthetic electron transport, caused by e.g., a fluctuation in the water availability. Based on this sensitivity, a framework for calibrating a water stress function in a crop growth model using ground-based SIF observations is proposed. SIF time series are simulated by coupling the AgroC crop growth model to the Soil Canopy Observations Photosynthesis Energy (SCOPE) model. This allowed parametrizing the water stress function in the AgroC crop growth model, resulting in improved estimates of actual evapotranspiration and net ecosystem exchange over a sugar beet stand during stressed periods. The improvement in the estimation of the water and carbon fluxes by AgroC during the summer months highlights the ability of canopyscale SIF observations to serve as a remote sensing metric to indicate the intensity of a stress condition. We argue that our framework, linking SIF emission to stress functions, can be used to extract information concerning drought stress from the Fluorescence Explorer (FLEX) satellite, scheduled for launch in 2024.

Retrieving gap-free daily root zone soil moisture using surface flux equilibrium theory

Root zone soil moisture (RZSM) is a dominant control on crop productivity, land-atmosphere feedbacks, and the hydrologic response of watersheds. Despite its importance, obtaining gap-free daily moisture data remains challenging. For example, remote sensing-based soil moisture products often have gaps arising from limits posed by the presence of clouds and satellite revisit period. Here, we retrieve a proxy of daily RZSM using the actual evapotranspiration (ETa) estimates from Surface Flux Equilibrium Theory (SFET). Our method is calibration-less, parsimonious, and only needs widely available meteorological data and standard land-surface parameters. Evaluation of the retrievals at Oklahoma Mesonet sites shows that our method, overall, matches or outperforms widely available RZSM estimates from three markedly different approaches, viz. remote sensing data based Atmosphere-Land EXchange Inversion (ALEXI) model, the Variable Infiltration Capacity (VIC) model, and the Soil Moisture Active Passive (SMAP) mission RZSM data product. When compared with in-situ observations, unbiased root mean square difference of retrieved RZSM were 0.03 (m3 m−3), 0.06 (m3 m−3), and 0.05 (m3 m−3) for our method, the ALEXI model, and the VIC model, respectively. Better performance of our method is attributed to the use of both SFET for the estimation of ETa and non-parametric kernel-based method used to relate the RZSM with ETa. RZSM from our method may serve as a more accurate and temporally-complete alternative for a variety of applications including mapping of agricultural droughts, assimilation of RZSM for hydrometeorological forecasting, and design of optimal irrigation schedules.

Annual Actual Evapotranspiration Estimation via GIS Models of Three Empirical Methods Employing Remotely Sensed Data for the Peloponnese, Greece, and Comparison with Annual MODIS ET and Pan Evaporation Measurements

Actual evapotranspiration (ETa) has been insufficiently investigated in Greece. This study aimed to estimate annual ETa by empirical methods (Turc, modified Turc, and Coutagne) for the Peloponnese, Greece, a Mediterranean testbed, between 2016–2019, four of the warmest years since the preindustrial era, and compare them to MODIS ET. Furthermore, measurements of annual pan evaporation (Epan) were performed for two Class A pan stations in the Peloponnese with different reliefs and conditions. The empirical methods and statistical formulae (RMSD, MB, and NMB) were developed as models in ArcMap. The outcomes of the Turc method resembled MODIS ET ranges for all years, followed by those of Coutagne. The estimates by the modified Turc method were almost identical to MODIS ET. Therefore, the modified Turc method can be used as an alternative to MODIS ET (and vice versa) for the Peloponnese for 2016–2019. Moreover, the Epan at Patras University station (semiurban, low elevation) exhibited an upward trend resembling the trends of the empirical methods over the study years, whereas the Epan at Ladonas station (higher elevation, lakeside) required investigation on a monthly time scale. Additionally, the gradual decrease of pan-water icing at Ladonas in December (from 20 d in 2016 to 0 d in 2019) could imply an undergoing decrease in snowpack storage retention across the mountains of the Peloponnese.

Multiple sources of uncertainties in satellite retrieval of terrestrial actual evapotranspiration

Since evapotranspiration (ET) is the intrinsic link between global energy and water cycle, remote sensing-based models have been developed for regional and global scale ET on heterogeneous land surface over the past four decades. In view of the significantly different physical mechanisms and mathematical expressions among remote sensing ET models as well as data availability and quality control process among the model input products, it is necessary to investigate the uncertainties of the multiple sources in actual ET estimation. Here, three remote sensing ET models, including the PT-DTsR model, the PM-mod model and the PML model, were simultaneously driven by three meteorological reanalysis products, resulting in nine calculation schemes to analyze the combined effect of the models and the input datasets. The Sobol’ sensitivity method was also adopted for identifying the influential model parameters and in turn understanding the model process. The results indicated that estimates from nine calculation schemes showed great differences in the magnitude and temporal variation, explaining 20–50% of ET variability over all sites. Additionally, schemes compared with both uncorrected and corrected energy balance observations, as well as schemes using meteorological variables from three reanalysis products and Eddy Covariance tower observations, verified that the uncertainties in latent heat flux data observations caused by the energy budget mis-closure problem and spatial scale mismatch have propagated into the ET estimation. Our study is a beneficial reference for the uncertainties in remote sensing-based methods, and thus can provide guidance for the future development of ET models.

Estimation of Actual Evapotranspiration in Panam Canal Command Using Remote Sensing and Geographical Information System (GIS)

Estimation of reference evapotranspiration (ET0) and actual evapotranspiration (ETc) is a key factor for estimation of crop water requirement, water balance and irrigation scheduling. The FAO-56 Penman–Monteith equation has been accepted universally for estimating of reference evapotranspiration (ET0). Considering the high spatial variation of meteorological phenomenon and limited availability of such dense network for data collection, application of remote sensing and GIS has gained momentum for estimation of ET0 and ETc over the larger area with accurately and efficiently. For estimation of ET0 and ETc, the most widely applied MOD16 remote sensing images as well as Landsat 8 remote sensing images are applied in this study of Panam canal command area which is located in the semi-arid middle Gujarat region. Initially, FAO-56 PM method is used to estimate ET0 and MOD16 data is used to estimate ET0, whereas Landsat 8 is used to estimate land surface temperature and then by using the regression equation to estimate maximum and minimum temperature to find out ET0 for the study area. Based on result obtained, it was found that Landsat 8 remote sensing-based data have better capacity to estimate actual evapotranspiration compared to the MOD16 remote sensing data. The better performance of Landsat 8 data compared to MOD16 data is due to the reason that it has better spatial resolution(30m) compared to MOD16 (1 km) remote sensing image and can represent the actual field conditions of farm fields which are generally smaller.

Estimation of actual evapotranspiration and its components in an irrigated area by integrating the Shuttleworth-Wallace and surface temperature-vegetation index schemes using the particle swarm optimization algorithm

Daily actual evapotranspiration (ET) and its components – soil evaporation (E) and vegetation transpiration (T) – play a key role in water resource management of irrigated areas. Nevertheless, due to large uncertainties in the parameterization of the resistances in irrigated areas, traditional ET models do not always provide accurate ET estimates, especially for its components. This uncertainty is mainly due to the difficulty of determining the empirical parameters accurately of soil and canopy resistances which is debated for a long time and the error in input variables. This paper proposed an optimized Shuttleworth-Wallace model (SW) using the particle swarm optimization (PSO) algorithm to integrate the original SW with the surface temperature-vegetation index (Ts-VI) triangle models (named Shuttleworth & Wallace_Temperature Vegetation Index model, SW_TVI).The performance of the SW_TVI model was significantly improved by optimizing the soil and canopy resistances in the original SW model using discontinuous regional ET estimates from the Ts-VI model under clear-sky conditions. Compared with the original SW model, the root mean squared error (RMSE) and mean absolute deviation (MAD) of the SW_TVI model were reduced by more than 30%, against in situ measurements in the Heihe River Basin. The Bias of the T/ET ratio was also significantly reduced from -22.3% for the original SW model to -5.5% for the SW_TVI model. The annual contributions of E and T to ET were about 20% and 80%, respectively, and had a strong seasonal variation in the typical irrigated area of China. In summary, the SW_TVI model shows three outstanding advantages: (1) it estimates daily continuous E, T, and total ET with high accuracy; (2) it is very robust and insensitive or slightly sensitive to most input variables and empirical parameters; (3) it is independent from ground data. This new SW_TVI model will benefit water resources management in irrigated areas, particularly in arid and semi-arid regions.

Introduction to ‘A tribute to Edward P. Glenn (1947–2017): A legacy of scientific environmental assessment and applications in hydrological processes’

AbstractThis special issue (SI) ‘A Tribute to Edward P. Glenn (1947‐2017): A legacy of Scientific Environmental Assessment and Applications in Hydrological Processes’ is a celebration of the extensive work of Dr. Ed Glenn that was instrumental across multiple sub‐disciplines of hydrology. The SI highlights four primary areas of hydrological processes that are cornerstones of Ed Glenn's over four decades of research. These contributions cover the following specialties: (i) Hydrology in the Colorado River Delta; (ii) Riparian ecosystem water use; (iii) Riparian Plant ecophysiology and ecohydrology; and (iv) Methods and models to characterize evapotranspiration. Since Ed was passionate about the dryland delta at the end of the Colorado River, we begin with four research studies that focus on this special region on the U.S.–Mexico border which encompasses four states (Baja and Sonora in Mexico and California and Arizona in United States) as well as tribal communities in the transboundary area. The Colorado River delta reaches the Northern Gulf of California in the Sea of Cortez which has been designated as a UNESCO international biosphere reserve (‘Reserva de la Biosfera El Pinacate y Gran Desierto de Altar’), which includes the Upper Gulf of California and Delta of the Colorado River (‘Reserva de la Biosfera Alto Golfo de California y Delta del Río Colorado’). Ed spent the majority of his last three decades on water balance studies and on ground‐based transpiration quantification for validation of satellite and airborne remote sensing methods. We wrap up the special issue with contributions related to improving satellite and airborne remote sensing estimation of actual evapotranspiration. It is our pleasure to summarize the 16 research studies contributed to the special issue to honour Ed Glenn's research interests.

Synergistic Use of Multispectral Data and Crop Growth Modelling for Spatial and Temporal Evapotranspiration Estimations

The aim of this research is to explore the analysis of methods allowing a synergetic use of information exchange between Earth Observation (EO) data and growth models in order to provide high spatial and temporal resolution actual evapotranspiration predictions. An assimilation method based on the Ensemble Kalman Filter algorithm allows for combining Sentinel-2 data with a new version of Simple Algorithm For Yield (SAFY_swb) that considers the effect of the water balance on yield and estimates the daily trend of evapotranspiration (ET). Our study is relevant in the context of demonstrating the effectiveness and necessity of satellite missions such as Land Surface Temperature Monitoring (LSTM), to provide high spatial and temporal resolution data for agriculture. The proposed method addresses the problem both from a spatial point of view, providing maps of the areas of interest of the main biophysical quantities of vegetation (LAI, biomass, yield and actual Evapotranspiration), and from a temporal point of view, providing a simulation on a daily basis of the aforementioned variables. The assimilation efficiency was initially evaluated with a synthetic, large and heterogeneous dataset, reaching values of 70% even for high measurement errors of the assimilated variable. Subsequently, the method was tested in a case study in central Italy, allowing estimates of the daily Actual Evapotranspiration with a relative RMSE of 18%. The novelty of this research is in proposing a solution that partially solves the main problems related to the synergistic use of EO data with crop growth models, such as the difficult calibration of initial parameters, the lack of frequent high-resolution data or the high computational cost of data assimilation methods. It opens the way to future developments, such as the use of simultaneous assimilation of multiple variables, to deeper investigations using more specific datasets and exploiting the advanced tools.

Challenges in Reconciling Satellite-Based and Locally Reported Estimates of Wetland Change: A Case of Topographically Constrained Wetlands on the Eastern Tibetan Plateau

The coupling of rapid warming and wetland degradation on the Tibetan Plateau has motivated studies of climate influence on wetland change in the region. These studies typically examine large, topographically homogeneous regions, whereas conservation efforts sometimes require fine-grained information in rugged terrain. This study addresses topographically constrained wetlands on the Eastern Tibetan, where herders report significant wetland degradation. We used Landsat images to examine changes in wetland areas and Sentinel-1 SAR images to investigate water level and vegetation structure. We also analyzed trends in precipitation, growing season length, and reference evapotranspiration in weather station records. Snow cover and the vegetation growing season were quantified using MODIS observations. We analyzed estimates of actual evapotranspiration using the Atmosphere-Land Exchange Inverse model (ALEXI) and the Simplified Surface Energy Balance model (SSEBop). Satellite-informed analyses failed to confirm herders’ accounts of reduced wetland function, as no coherent trends were found in wetland area, water content, or vegetation structure. An analysis of meteorological records did indicate a warming-induced increase in reference evapotranspiration, and both meteorological records and satellites suggest that the growing season had lengthened, potentially increasing water demand and driving wetland change. The discrepancies between the satellite data and local observations pointed to temporal, spatial, and epistemological gaps in combining scientific data with empirical evidence in understanding wetland change on the Tibetan Plateau.

Estimation and Validation of Actual Evapotranspiration (ETa) of Maize Wheat Cropping System Using SSEBop Model Over IARI Research Farm, New Delhi, India

Evapotranspiration (ET) is an indispensable component of earth surface energy exchange studies and forms basis for various environmental applications. The present study attempts to estimate Landsat-8 satellite-based spatial distribution of crop ET using Operational Simplified Surface Energy Balance (SSEBop) model and validate through ET estimated using multilevel micrometeorological tower-based Bowen Ratio Energy Balance (BREB). The study was conducted over Indian Agricultural Research Institute farm during the summer (kharif maize) and winter (wheat) season of 2017–2018. The result of BREB showed the intra-seasonal variations in actual ET estimation ranging from maximum ET of 3.84 mm day−1 and 2.64 mm day−1 for maize and wheat during mid-stage to minimum ET of 1.88 mm day−1 for maize in maturity and 1.14 mm day−1 in an initial stage of wheat. It was observed that SSEBop slightly overestimated the crop ET; however, it showed the good agreement with BREB estimated ET (R2 = 0.76) with d-index of 0.92 and root-mean-square error of 0.48 mm day−1. It can be concluded that despite slight overestimation of ET by SSEBop, satellite-based SSEBop model can be recommended for crop ET estimation over large area and can be used for various water management studies.

CROP MODELING WITH LESS DATA: THE FAO MODEL FOR SOYBEAN YIELD ESTIMATION

Crop growth simulation models such as WOFOST and DSSAT are useful, but require several inputs that sometimes are not available, especially in developing areas. In addition, measured data is usually time and labor-intensive. In search of faster and easier methods for soybean estimates, this study presents a lower input requiring methodology for yield estimation. This study combines the FAO-33 yield model with the agro-ecological zone approach for soybean yield estimations using mostly indirect data. Sowing and harvest dates and yield were collected from 74 soybean commercial farms. Agrometeorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used. Fifty farms (66%) were used to calibrate the model and 24 farm areas (33%) were used for evaluation purposes. Two methodologies (FAO-56 and Thornthwaite and Mather) for water balance and actual evapotranspiration (ETa) estimations were used. The comparison of yield estimations and observations showed that the use of low data input to obtain reasonable accuracy, with a mean error of −310 kg ha−1 and a mean absolute percentage error of 23.3%.

Actual Evapotranspiration Estimates in Arid Cold Regions Using Machine Learning Algorithms with In Situ and Remote Sensing Data

Actual evapotranspiration (ETa) estimations in arid regions are challenging because this process is highly dynamic over time and space. Nevertheless, several studies have shown good results when implementing empirical regression formulae that, despite their simplicity, are comparable in accuracy to more complex models. Although many types of regression formulae to estimate ETa exist, there is no consensus on what variables must be included in the analysis. In this research, we used machine learning algorithms—through implementation of empirical linear regression formulae—to find the main variables that control daily and monthly ETa in arid cold regions, where there is a lack of available ETa data. Meteorological data alone and then combined with remote sensing vegetation indices (VIs) were used as input in ETa estimations. In situ ETa and meteorological data were obtained from ten sites in Chile, Australia, and the United States. Our results indicate that the available energy is the main meteorological variable that controls ETa in the assessed sites, despite the fact that these regions are typically described as water-limited environments. The VI that better represents the in situ ETa is the Normalized Difference Water Index, which represents water availability in plants and soils. The best performance of the regression equations in the validation sites was obtained for monthly estimates with the incorporation of VIs (R2 = 0.82), whereas the worst performance of these equations was obtained for monthly ETa estimates when only meteorological data were considered. Incorporation of remote-sensing information results in better ETa estimates compared to when only meteorological data are considered.

Simulation of soil water content through the combination of meteorological and satellite data

Recent investigations have supported the possibility of predicting soil water content (SWC) through a simplified soil water balance (WB) model fed with remotely sensed actual evapotranspiration (ETa) estimates. This approach, however, requires information on main soil features (i.e. depth, wilting point, field capacity) which are generally difficult to retrieve over large regions. The current paper proposes an alternative model which directly predicts SWC relying on the same logic of the recently proposed ETa estimation method, i.e. the combination of meteorological and normalized difference vegetation index (NDVI) datasets. The theoretical bases of the old and new SWC estimation methods are first described. Both methods are then applied in Central Italy using ancillary and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data having a spatial resolution of 250 m; the outputs obtained are assessed versus SWC measurements taken both continuously and instantaneously. In the former case three ecosystems are analyzed (i.e. the grassland of Amplero and the forests of Barbialla and Amiata), where SWC was measured by probes during three different years (2003, 2012 and 2018, respectively). The latter experiment concerns 362 sample sites where SWC gravimetric measurements were taken during a similar time span (2000–2018). In both cases SWC is first normalized into relative SWC (RSWC), which is more directly influential on vegetation conditions. The results of both experiments indicate that the two simulation methods perform similarly when the former is driven by adequate information on soil features. The main limitations of the two simulation approaches are due to the spatial resolution mismatch between SWC measurements and estimates, which has a relatively minor impact on the first three homogeneous areas but is decisive for the other sampled sites. In general, the new simulation method is capable of predicting RSWC with relatively high spatial and temporal resolution without the use of specific soil information.