Evapotranspiration Monitoring Research Articles

Biocrusts intensify grassland evapotranspiration through increasing evaporation and reducing transpiration in a semi-arid ecosystem

Biological soil crusts (biocrusts) are living ground covers that result from intimate associations between soil particles and organisms, which can modify soil properties and support fundamental ecosystem functions in drylands. Despite numerous studies concerning effects of biocrusts on soil hydrological processes, their impacts on grassland evapotranspiration have remained unanswered. By using micro-lysimeters, we conducted in-situ monitoring of evapotranspiration of bare soil vs. cyanobacterial-moss mixed biocrusts, gramineous grass vs. gramineous grass with biocrusts, and leguminous grass vs. leguminous grass with biocrusts over two months in a semi-arid ecosystem. Results suggested that compared to the bare soil, the biocrusts significantly increased soil evaporation rate by 20.9 %. Underneath gramineous and leguminous grasses, the biocrusts increased the average evapotranspiration rate by 9.4 % and 13.4 %. The evaporation of biocrusts contributed 8.1–10.6 % to the average evapotranspiration rate and 8.2–12.6 % to the cumulative evapotranspiration amount. Additionally, the biocrusts increased soil moisture by 12.5 % and decreased temperature by 3.4 % at 10 cm depth compared to the bare soil. However, the biocrusts beneath grasses led to a reduction in soil moisture by 10.0 % and 20.0 %, while causing an increase in temperature by 9.9 % and 2.9 %, respectively. Enhanced evaporation of the biocrusts could be mainly attributed to their higher water retention ability in comparison to the bare soil. Meanwhile, beneath grasses, the biocrusts decreased soil water content in deeper layers, potentially limited grass transpiration by reducing water availability for grass roots. Based on these results, we concluded that the biocrusts intensify grassland evapotranspiration through increasing soil evaporation and reducing grass transpiration. Our findings highlight that biocrusts play a crucial role in modulating surface water fluxes, potentially exert remarkable effects on dryland vascular plants development and ecosystem stability.

Intelligent monitoring of surface evapotranspiration in the Heihe River Basin based on SEBS modelling

[Objective] The Heihe River Basin, located in northwestern China, is the second largest inland basin in China, and its water resources play a crucial role in the ecology, agriculture and human life of the region. The aim of this study is to investigate the spatial and temporal variations of surface evapotranspiration in the Heihe River Basin and the potential impacts of these variations on water resource management. [Methods] To achieve this goal, we applied the PM-based dual-source model, a meteorological model for estimating global surface ET, which takes into account a variety of factors such as temperature, humidity, wind speed and downward solar shortwave radiation. By analysing the meteorological data and remote sensing data of the Black River Basin, we first investigated the spatial and temporal distribution of surface evapotranspiration. [Conclusion] The results show that surface evapotranspiration shows obvious seasonal and regional variations and is significantly affected by meteorological conditions. The inversion of surface ET in the Heihe River Basin by this dual-source model needs to be improved, and the trend of ET values calculated by the model is relatively small compared with the actual values.

Reconstructing monthly 0.25° terrestrial evapotranspiration data in a remote arid region using Bayesian-driven ensemble learning method

This study presents a Bayesian-driven ensemble learning framework to minimize errors in evapotranspiration (ET) data assimilation, thereby generating a continuous-scale ET estimation with improved accuracy for the data-scarce arid Central Asia (CA). Five state-of-the-art gridded ET products were extensively tested and utilized to reconstruct new long-term ET data at grid scale, including two Global Land Data Assimilation System (GLDAS)-Land Surface Models (LSM) (Catchment and NOAH), two reanalysis products (European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) and Modern-Era Retrospective Analysis for Research and Applications – ver. 2 (MERRA2)), along with the Global Land Evaporation Amsterdam (GLEAM) satellite ET product ver. 3.6b. Taylor Skill Score was employed to evaluate the input gridded ET datasets, using in situ measurements across four arid biomes — cropland, shrubland, farmland, and grassland —as benchmarks. Bayes-Markov Chain Monte Carlo (Bayes-MCMC)-derived logarithmically transformed predictors from the posterior ensemble anomalies were standardized to resolve the mismatches in the cumulative distribution functions of ET data relating to the in situ observations. We found that the constrained residual errors were more profound between 45th and 95th percentiles for the rescaled total ET anomalies across all the input ET datasets at 95 % confidence interval. Moreover, the weighted ensemble estimates derived from the Bayes-MCMC were instrumental in correcting ET anomalies, leading to the robust reconstruction of a more reliable ensemble mean ET data for Central Asia (CAETens) at monthly temporal and 0.25° spatial resolutions, spanning from 2003 to 2020. Overall, CAETens exhibited superior performance, achieving the highest averaged Kling-Gupta Efficiency of 0.75 and registering the lowest Root Mean Square Error of 6.85 mm/m compared to the standalone gridded ET products. Comparisons of spatial distributions of fine-scale features, and evaluations using standard metrics revealed that all ET products varied significantly (p < 0.05) by biome types. Remarkably, only CAETens exhibited a reasonable low positive bias and pronounced ability to resolve the complex spatiotemporal patterns associated with the input gridded ET products. The study bridges the gap in continuous ET monitoring for sustainable water resources management in CA.

Surface temperature adjustment in METRIC model for monitoring crop water consumption in North China Plain

Evapotranspiration is the real water consumption element in agricultural water management. In order to improve the efficiency of agricultural water use and maintain the sustainable development of agriculture, more and more attention has been paid to the improvement of monitoring and management of farmland evapotranspiration. In this study, MODIS data and the Temperature Vegetation Dryness Index (TVDI) were used in the METRIC model to estimate evapotranspiration for winter wheat and summer corn in the North China Plain. In order to decrease the error caused by the spatial heterogeneity of air temperature in the estimation of evapotranspiration, based on the characteristics of cold pixels with surface temperatures close to their air temperature, the TVDI was used to assist the selection of cold pixels and obtain the spatial variation trend of air temperature with latitude from all cold pixels in the study area. The influence of the spatial heterogeneity of air temperature on the surface temperature at the time of the satellite overpass was corrected accordingly, thereby the estimation accuracy of evapotranspiration was improved. A METRIC-TVDI model was established and the evapotranspiration of winter wheat and summer corn in Hebei Plain was estimated from 2017 to 2019. The average evapotranspiration of the winter wheat over these three years were 361 mm, 327 mm and 354 mm respectively, and that of summer corn were 394 mm, 388 mm and 405 mm respectively. Based on the farmland water balance, the irrigation water consumption (evapotranspiration minus precipitation) of winter wheat and summer corn rotation were 324 mm, 134 mm and 360 mm respectively. This kind of crop rotation pattern consumed more groundwater for irrigation, especially the winter wheat. In order to realize the balance of groundwater between exploitation and replenishment in Hebei Plain, more water-saving irrigation technologies should be adopted to reduce the evapotranspiration and improving water use efficiency.

GeeSEBAL-MODIS: Continental-scale evapotranspiration based on the surface energy balance for South America

Monitoring actual evapotranspiration (ET) is critical for the accurate assessment of water availability and water resources management, especially in areas with dry climates and frequent droughts. The Surface Energy Balance Algorithm for Land (SEBAL) has been used over several land and climate conditions, and is able to estimate ET at field scale with high accuracy. However, model complexity and subjective parameterization have hindered its operationalization, until the recent development of the geeSEBAL model, which implements the SEBAL model on the Google Earth Engine platform. Here, we present a unique methodology for a continental-scale application of SEBAL, called geeSEBAL-MODIS, that employs novel land surface temperature normalization techniques, enabling the application of contextual ET models to very large scales. We introduce a dynamic ET dataset for the entire South American continent, between 2002 and 2021, at 500 m spatial and 8 days temporal resolution. The satellite-based data were compared against daily ET measured at 27 flux towers as well as water balance-based annual ET from 29 large river basins. geeSEBAL-MODIS data were also compared to eight state-of-the-art global ET datasets. At local scale, geeSEBAL-MODIS demonstrated a satisfactory performance (correlation (r) = 0.65, Kling-Gupta Efficiency (KGE) = 0.64, Mean Absolute Error (MAE) = 0.83 mm day−1 (24.7 %) and Root Mean Squared Error (RMSE) = 1.07 mm day−1 (31.8 %)), with negligible bias. At basin scale, geeSEBAL-MODIS generally underestimated ET (bias = -85 mm year−1, r = 0.65, KGE = 0.47, MAE = 107 mm year−1 (10.1 %) and RMSE = 137 mm year−1 (12.9 %)). Compared to other global datasets, geeSEBAL-MODIS demonstrated better performance over multiple South American biomes, climates and land cover types. The developed dataset also provides lower errors (local monthly RMSE = 23.0 mm month−1 and basin annual RMSE = 138 mm year−1) and when compared to the performance of the global datasets (local monthly RMSE between 23.9 and 30.1 mm month−1 and basin annual RMSE between 161 and 308 mm year−1). The analyses demonstrate that geeSEBAL-MODIS can be used as a tool for monitoring climate change and human-related impacts on ET. The geeSEBAL-MODIS model opens the path for high accuracy global ET monitoring at moderate to high resolution, supporting advances in water resources management around the globe.

Integration of microwave satellite soil moisture products in the contextual surface temperature-vegetation index models for spatially continuous evapotranspiration estimation

The contextual surface temperature-vegetation index (TVX) models have been widely used for the retrieval of soil moisture (SM) and evapotranspiration (ET). One of the key premises for their application is to determine quantitatively the theoretical boundaries of this contextual TVX space. Usually these theoretical boundaries are determined based on land surface energy balance principle. Although sound in physical mechanism, the complex parameterization involved has hampered its remote sensing (RS) applications. Our recent studies show that ground-based SM observations can be used for a quick and continuous retrieval of these theoretical boundaries. The main disadvantage lies in its reliance on in-situ SM observations. Fortunately the development of microwave RS technology has made it possible to monitor SM at both regional and global scale. Under this background, a practical framework based on the contextual TVX models was proposed in this paper for continuous ET estimation through the combination of microwave-based SM products and MODIS (Moderate Resolution Imaging Spectroradiometer) products. Daily actual ET over the Southern Great Plains of the United States of America was calculated as the product of net radiation and evaporative fraction (EF). The focus of this framework was to investigate how optical and microwave RS can be combined to achieve a quick and continuous retrieval of EF independently of ground observations. Specifically, EF was retrieved from the contextual TVX models, the theoretical boundaries of which were calibrated at the annual scale based on an optimization scheme. Assuming the seasonal variation of the theoretical dry edge follows a cosine function of solar zenith, the essence of this optimization scheme is to search for the optimal amplitude parameter that maximizes the correlation between microwave-based SM observations and soil moisture index retrieved from the contextual TVX models. Two microwave-based SM products including SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) were adopted to demonstrate this optimization scheme. The correlation coefficient, root mean square error and bias of EF estimates calibrated by SMAP were 0.700, 0.146 and 0.047, and those achieved by SMOS were 0.663, 0.174 and 0.168, respectively. The optimization scheme has not only made it possible to conduct a continuous monitoring of ET based entirely on RS observations but also achieved the downscaling of microwave satellite SM products. Thus the combination of optical and microwave RS holds great potential for coupled and continuous estimation of SM and ET over large scale.

Seasonal Flooding Impact on Land Surface Temperature(LST) in Response to Vegetation Index Using Spectral Radiance Model in Kirkuk, Iraq

Change detection of land use is essential in management and planning decisions. Land Surface Temperature (LST) has a significant importance in many geoscience aspects, such as heat transportation between land and atmosphere, state of corps, heat flux, greenhouse effect both locally and regionally, surface moisture and surface energy budget estimation, the monitoring of urban heat island, evapotranspiration and environmental biogeochemistry process simulation. A geographic information system was efficiently utilized to observe the changes in land use different categories and to map annual and seasonal LST variations. In this study, Land sat 5 and 8 images for the years 2010, 2017 and 2019 were used to study the flooding imact on the LST. The Mono-Window algorithm and sepectral rediance model on Landsat-5 TM and OLI were used to estimate LST. Taking 2010 as a reference, an empirical study shows that the No-vegetation category percentage was 9.28 % and 11.03% in the 2017 and 2019 summer seasons, respectively, while it was -1.48% and -0.46% in winter. Vegetation change was -60.9% and -50.04% in the 2017 and 2019 summer seasons, respectively, while it was 28.28% and -6.11% in winter. Water area change was -65.8% and 84.1% in the 2017 and 2019 summer seasons, respectively, while it was -38.5% and 59.89% in winter. The results indicate that the water area had greatly decreased during 2017 which led to increase the LST and the floods in 2019 led to decrease the LST by 5-8 degrees. The temperature trend analysis reveals a definite negative association between LST and flooded area, representing this study’s significant finding.

Evapotranspiration of Winter Wheat in the Semi-Arid Southeastern Loess Plateau Based on Multi-Source Satellite Data

Continuous monitoring of evapotranspiration (ET) at high spatio-temporal resolutions is vital for managing agricultural water resources in arid and semi-arid regions. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to calculate the ET of winter wheat between the green-up and milk stages in Linfen Basin, a typical, semi-arid area of the Loess Plateau, at temporal and spatial resolutions of 30 m and 8 d, respectively. We then analyzed the impact of meteorological factors on ET and its variation during the main growth period of winter wheat. The fused ET data displayed the spatial details of the OLI ET data better and could accurately reflect ET variation and local sudden variations during the main growth period of winter wheat. Moreover, winter wheat ET in rain-fed areas is more heavily influenced by meteorological factors, and the effect is more direct. Affected by the synergistic effect of wind velocity, precipitation, and temperature, the ET of winter wheat in rain-fed area was lower in the green-up stage. Then, ET gradually increased, reaching its maximum in the heading–grain filling stage. At the jointing stage, temperature had a significant effect on ET. A combination of precipitation and temperature had the greatest impact on the ET of winter wheat in the heading–filling stage. In the milk stage, meteorological factors had a minor impact on ET. This study serves as a reference for ET in winter wheat in semi-arid areas and its influencing meteorological factors, which can assist in drought mitigation and regional food security strategies.

Correction to: Local-scale monitoring of evapotranspiration based on downscaled GRACE observations and remotely sensed data: An application of terrestrial water balance approach

Correction to: Local-scale monitoring of evapotranspiration based on downscaled GRACE observations and remotely sensed data: An application of terrestrial water balance approach

Local-scale monitoring of evapotranspiration based on downscaled GRACE observations and remotely sensed data: An application of terrestrial water balance approach

Local-scale monitoring of evapotranspiration based on downscaled GRACE observations and remotely sensed data: An application of terrestrial water balance approach

Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite earth observations

Evapotranspiration (ET) is an essential ecohydrological process linking the land surface energy, water and carbon cycles, and plays a critical role in the earth system. ET remains one of the most problematic components of the water cycle to be determined due to the heterogeneity of the landscape and the complexity of driving factors. The satellite-based earth observation is expected to provide ET information at large-scales. However, accurate global ET information, with spatially and temporally continuous coverage at moderate-to-high resolution, is still scarce. In this paper, a combined model, called ETMonitor, with multi-process parameterizations, was improved and applied to estimate the global ET, mainly using the biophysical and hydrological parameters/variables retrieved from satellite observations. The ETMonitor model was improved in several aspects in this study to generate the global ET datasets during 2000–2019 at daily/1-km resolution, including: 1) adopting high temporal resolution surface water cover and snow/ice cover as input, to simulate the impact of their seasonal change on ET variation; 2) parameterizing the impact of soil moisture on plant transpiration and soil evaporation using high resolution soil moisture, which was downscaled to 1-km resolution from the coarse resolution data retrieved from microwave remote sensing observation; 3) involving a better soil heat flux estimation to reduce its impact on the uncertainty of estimated ET; 4) being calibrated based on global ground flux observations to achieve better accuracy. The estimated daily ET was validated based on the global in situ observations at site scale across various ecosystems, with overall high correlation (0.75), low bias (0.08 mm d-1), and low root mean square error (0.93 mm d-1). It had good ability to partition total ET to plant transpiration and soil evaporation indicated by the good agreement with the ground isotope measurements in a growing season at one site in the northwest China. The estimated global ET was cross-validated by comparing with other existing ET products, and it showed the global ET estimated by ETMonitor could capture the expected global ET patterns both in space and in time. It also indicated the superiority of the ET product by ETMonitor in the following aspects: capability in capturing the seasonal dynamics of waterbody evaporation and sublimation; better performance in capturing the spatial variation of ET in the irrigated cropland regions and mountain regions with complex terrain than other global ET products, e.g., the GLEAM and MOD16 ET products; capability of ET component partitioning at high spatial (1-km) and high temporal (daily) resolutions with good accuracy. The estimated plant transpiration, soil evaporation, canopy rainfall interception loss, and water body evaporation and snow sublimation accounted for 61.54 % (±0.44 %), 19.08 % (±0.54 %), 13.54 % (±0.49 %), and 5.84 % (±0.24 %), respectively, of the total ET on global average. The ETMonitor global ET dataset could provide important information on studies of global terrestrial water and energy cycles and climate change studies, and water resources management at global and regional scales.

Application of the two-source energy balance model with microwave-derived soil moisture in a semi-arid agricultural region

Evapotranspiration (ET) is a critical component of the water, carbon and energy cycles of the land surface. Remote sensing-based models provide the possibility of mapping ET from field to regional and even global scales. A key boundary condition of the Two-Source Energy Balance (TSEB) model for computing ET is land surface temperature (LST). The proposed modification to TSEB is to use a combination of near surface soil moisture (SM) derived by microwave and LST as dual boundary conditions along with a more physically-based transpiration formulation for computing ET. Both TSEB and TSEB-SM were applied to an irrigated agricultural area with surrounding semi-arid sparse natural vegetation. In general, TSEB-SM model performance when compared to measured fluxes was similar to the TSEB model. However, the TSEB-SM model computed more reliable estimates of LE under dry soil surface/low vegetation cover conditions, primarily in the arid and semiarid natural ecosystems surrounding the irrigated agricultural area. These comparisons indicate that TSEB-SM may provide more reliable flux estimation and ET partitioning over sparsely vegetated areas compared to the traditional formulations used in TSEB. With reliable microwave remote sensing of soil moisture, TSEB-SM has potential for ET monitoring in both natural and agricultural landscapes.

Generating Synthetic Monthly Net Radiation Data Conditioned on Wind Speed

Aims: This study aimed at generating synthetic monthly net radiation data conditioned on wind speed over Port Harcourt, Benue, Kano and Enugu in Nigeria.&#x0D; Duration of Study: The daily maximum and minimum Relative-Humidity, maximum and minimum air temperature, solar radiation and wind speed data was obtained from the International Institute of Tropical Agriculture (IITA) Ibadan, Nigeria for the period of thirty-four (34) years (1977-2010).&#x0D; Method: The Penman-Monteith (FAO-56) step by step method was used to compute the daily net radiation. The autocorrelation function was used to establish the fact that net radiation and wind speed exhibit the Markov property. Lastly, a two – state Markov Chain model and an indicator function of energy balance and imbalance was developed and used in the course of this work.&#x0D; Results: The finding reveals that net radiation is surplus over the study areas. The Lag one autocorrelation coefficient confirms the fact that an actual day net radiation and wind speed state depends on the previous day’s state. The monthly steady state probabilities of surplus net radiation conditioned on low wind speed is higher compared to monthly steady state probabilities of surplus net radiation conditioned on high wind speed. The indicator function of energy balance or imbalance reveals that there is energy imbalance over study areas.&#x0D; Conclusion: The generated synthetic net radiation data conditioned on wind speed preserved the characteristics of actual net radiation data when compared as observed in the study. This data is essential in the study of climate change, weather monitoring, agricultural meteorology and estimation of evapotranspiration.

Global Evapotranspiration Datasets Assessment Using Water Balance in South America

Evapotranspiration (ET) connects the land to the atmosphere, linking water, energy, and carbon cycles. ET is an essential climate variable with a fundamental importance, and accurate assessments of the spatiotemporal trends and variability in ET are needed from regional to continental scales. This study compared eight global actual ET datasets (ETgl) and the average actual ET ensemble (ETens) based on remote sensing, climate reanalysis, land-surface, and biophysical models to ET computed from basin-scale water balance (ETwb) in South America on monthly time scale. The 50 small-to-large basins covered major rivers and different biomes and climate types. We also examined the magnitude, seasonality, and interannual variability of ET, comparing ETgl and ETens with ETwb. Global ET datasets were evaluated between 2003 and 2014 from the following datasets: Breathing Earth System Simulator (BESS), ECMWF Reanalysis 5 (ERA5), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), MOD16, Penman–Monteith–Leuning (PML), Operational Simplified Surface Energy Balance (SSEBop) and Terra Climate. By using ETwb as a basis for comparison, correlation coefficients ranged from 0.45 (SSEBop) to 0.60 (ETens), and RMSE ranged from 35.6 (ETens) to 40.5 mm·month−1 (MOD16). Overall, ETgl estimates ranged from 0 to 150 mm·month−1 in most basins in South America, while ETwb estimates showed maximum rates up to 250 mm·month−1. ETgl varied by hydroclimatic regions: (i) basins located in humid climates with low seasonality in precipitation, including the Amazon, Uruguay, and South Atlantic basins, yielded weak correlation coefficients between monthly ETgl and ETwb, and (ii) tropical and semiarid basins (areas where precipitation demonstrates a strong seasonality, as in the São Francisco, Northeast Atlantic, Paraná/Paraguay, and Tocantins basins) yielded moderate-to-strong correlation coefficients. An assessment of the interannual variability demonstrated a disagreement between ETgl and ETwb in the humid tropics (in the Amazon), with ETgl showing a wide range of interannual variability. However, in tropical, subtropical, and semiarid climates, including the Tocantins, São Francisco, Paraná, Paraguay, Uruguay, and Atlantic basins (Northeast, East, and South), we found a stronger agreement between ETgl and ETwb for interannual variability. Assessing ET datasets enables the understanding of land–atmosphere exchanges in South America, to improvement of ET estimation and monitoring for water management.

An unmixing-based spatial downscaling fusion approach for the MODIS evapotranspiration product

Although the Moderate Resolution Imaging Spectroradiometer global evapotranspiration product (MOD16 ET) has been successfully applied to mesoscale evapotranspiration (ET) monitoring, its coarse spatial resolution (500-m) cannot meet the needs of locally scaled water management and environmental change research. Based on the correlation between surface ET differences and corresponding land cover types, and spectral mixture analysis theory, we propose an unmixing-based spatial downscaling approach for the MOD16 ET (500-m) product to obtain temporally continuous high spatial resolution 16-m ET data corresponding to the spatial resolution of the Chinese GaoFen Satellite No. 1 Wide Field-of-View camera (GF-1 WFV) images. Three different landscape areas in China are selected for an experimental study. The experimental results indicate that the downscaled 16-m ET has higher spatial details in spatiotemporal patterns than the MOD16 ET (500-m) product. The MOD16 ET product’s assessment of agreement indicates that the downscaled ET has high correlation and less error with the MOD16 ET product. The assessment of agreement with 29 in situ ET measurements shows that the downscaled ET has high correlation and less error with in situ ET measurements. Our downscaling method is reliable and applicable in downscaling MOD16 ET product to meet the needs of local-scale water management and environmental change research.

Incorporation of Net Radiation Model Considering Complex Terrain in Evapotranspiration Determination with Sentinel-2 Data

Evapotranspiration (ET) is the primary mechanism of water transformation between the land surface and atmosphere. Accurate ET estimation given complex terrain conditions is essential to guide water resource management in mountainous areas. This study is based on the ETWatch model driven by Sentinel-2 remote sensing data at a spatial resolution of 10 m incorporating a net radiation model considering the impact of a complex terrain. We tested our model with two years of data in two regions with a high relief near the Huairou (2020) and Baotianman (2019) weather stations. Regarding the validation results of the ET model, the coefficient of determination (R2) reached 0.84 in Huairou and 0.86 in Baotianman, while the root mean square error (RMSE) value reached 0.59 mm in Baotianman and 0.82 mm in Huairou. The validation results indicated that the model is applicable in regions with a complex terrain, and the ET results can capture topographic textures. In terms of the slope aspect, the ET value on south-facing slopes is higher than that on north-facing slopes in both study areas. Accurate ET monitoring in mountainous regions with a high relief yields a profound meaning in obtaining a better understanding of the characteristics of heat and water fluxes at different vegetation growth stages and underlying surface types, which can provide constructive suggestions for water management in mountainous areas.

Estimation of Evapotranspiration in the Yellow River Basin from 2002 to 2020 Based on GRACE and GRACE Follow-On Observations

Evapotranspiration (ET) plays an important role in the hydrological cycle of river basins. Studying ET in the Yellow River Basin (YRB) is greatly significant for the scientific management of water resources. Here, we made full use of the advantages of the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) gravity satellites for monitoring large-scale hydrological changes to calculate the terrestrial water storage anomaly (TWSA) and terrestrial water flux in the YRB from May 2002 to June 2020. Furthermore, combined with terrestrial water flux, precipitation, and runoff data, ET in the YRB was calculated based on the water budget equation and then compared with other traditional ET products. The mutation of annual mean ET was identified by the Mann–Kendall trend test method, and the seasonal and interannual variations of ET were explored. ET was closely related to precipitation. Annual mean ET exhibited a sudden change in 2011, with an insignificant downward trend from 2003 to 2010, followed by an increasing trend from 2011 to 2019, particularly after 2016. Compared with the traditional ET monitoring methods and products, the ET estimated by GRACE/GRACE-FO observations provides a new way to effectively obtain continuous and reliable ET data in a wide range of river basins.

Assessing Satellite, Land Surface Model and Reanalysis Evapotranspiration Products in the Absence of In-Situ in Central Asia

Shortfalls in regular evapotranspiration (ET) monitoring and evaluation pose a huge challenge to agricultural water resource distribution in arid Central Asia (CA). In this study, a first detailed regional assessment of GLEAM, ERA5, MERRA2, CLSM and NOAH ET products in CA was performed by systematically implementing the triple collocation (TC) method, in which about 36,936 grid cells for each ET data (within a six-triplet design) were collocated, at 0.25° and with monthly resolutions during 2003–2020. The reliability of the strategy adopted was confirmed in four arid biomes using standard evaluation metrics (R, RMSE and BIAS), and by spatiotemporal cross-validation of the six ET triplets across CA. Results show that the systematic TC method produced more robust ET product assessment metrics with reduced RMSEs compared to the initial ET product validation using in-situ, which showed weak-positive correlation and high negative bias-range (i.e., −21.02 ≤ BIAS &lt; 16 mm) in the four arid biomes of CA. The spatial cross-validation by TC showed that the magnitude of ET random errors significantly varies, and confirms the systematic biases with site-scale measurements. The highest ET uncertainties by CLSM (27.43%), NOAH (29.16%), MERRA2 (38.28%), ERA5 (36.75), and GLEAM (41%) were more evident in the shrubland, cropland, grassland, cropland again, and desert biomes, respectively. Moreover, error magnitudes in high altitudes (Tianshan Mountain range) are generally lower than in plain-desert areas. All ET products spatially captured ET dynamics over CA, but none simultaneously outperformed the other. These findings are invaluable in the utilization of the assessed ET products in supporting regional water resource management, particularly in CA.

Seasonal variation of evapotranspiration, Priestley-Taylor coefficient and crop coefficient in diverse landscapes

Priestley-Taylor equation (PT) and the Penman-Monteith equation (PM) are commonly used methods for regional evapotranspiration monitoring, using the PT coefficient (αa) and PM crop/ vegetation coefficient (Kc). This paper investigates the seasonal changes in αa and Kc at five sites in Australia and China, to understand the relationship between environmental conditions and evapotranspiration when applying different evaporation estimation methods. The research shows that higher actual evapotranspiration does not lead to higher αa and Kc values. αa and Kc perform similarly in cropland and forest environments in both China and Australia. Both αa and Kc continuously increase to a peak during the growing season and then decrease to their lowest values during the winter season. Considering Kc’s similar performance to αa and its greater data processing requirements, Kc has few advantages for estimating regional evapotranspiration. Applying the Priestley-Taylor equation with a regional α indicator will enhance the accuracy and reduce the workload when estimating regional evapotranspiration for similar landcover types based on remote sensing.

Mapping Daily Evapotranspiration at Field Scale Using the Harmonized Landsat and Sentinel-2 Dataset, with Sharpened VIIRS as a Sentinel-2 Thermal Proxy

Accurate and frequent monitoring of evapotranspiration (ET) at sub-field scales can provide valuable information for agricultural water management, quantifying crop water use and stress toward the goal of increasing crop water use efficiency and production. Using land-surface temperature (LST) data retrieved from Landsat thermal infrared (TIR) imagery, along with surface reflectance data describing albedo and vegetation cover fraction, surface energy balance models can generate ET maps down to a 30 m spatial resolution. However, the temporal sampling by such maps can be limited by the relatively infrequent revisit period of Landsat data (8 days for combined Landsats 7 and 8), especially in cloudy areas experiencing rapid changes in moisture status. The Sentinel-2 (S2) satellites, as a good complement to the Landsat system, provide surface reflectance data at 10–20 m spatial resolution and 5 day revisit period but do not have a thermal sensor. On the other hand, the Visible Infrared Imaging Radiometer Suite (VIIRS) provides TIR data on a near-daily basis with 375 m resolution, which can be refined through thermal sharpening using S2 reflectances. This study assesses the utility of augmenting the Harmonized Landsat and Sentinel-2 (HLS) dataset with S2-sharpened VIIRS as a thermal proxy source on S2 overpass days, enabling 30 m ET mapping at a potential combined frequency of 2–3 days (including Landsat). The value added by including VIIRS-S2 is assessed both retrospectively and operationally in comparison with flux tower observations collected from several U.S. agricultural sites covering a range of crop types. In particular, we evaluate the performance of VIIRS-S2 ET estimates as a function of VIIRS view angle and cloud masking approach. VIIRS-S2 ET retrievals (MAE of 0.49 mm d−1 against observations) generally show comparable accuracy to Landsat ET (0.45 mm d−1) on days of commensurate overpass, but with decreasing performance at large VIIRS view angles. Low-quality VIIRS-S2 ET retrievals linked to imperfect VIIRS/S2 cloud masking are also discussed, and caution is required when applying such data for generating ET timeseries. Fused daily ET time series benefited during the peak growing season from the improved multi-source temporal sampling afforded by VIIRS-S2, particularly in cloudy regions and over surfaces with rapidly changing vegetation conditions, and value added for real-time monitoring applications is discussed. This work demonstrates the utility and feasibility of augmenting the HLS dataset with sharpened VIIRS TIR imagery on S2 overpass dates for generating high spatiotemporal resolution ET products.