Mapping evapotranspiration using MODIS and MM5 Four-Dimensional Data Assimilation

Evapotranspiration (ET) is significant and necessary to understand the hydrological cycle and to assess water resource at any region. The Moderate Resolution Imaging Spectroradiometer (MODIS) offers promising techniques to monitor regional or global ET patterns. Under cloudy conditions, however, some pixels contain missing data that hamper the continuous monitoring of ET. In this study, MODIS atmospheric and land products were used ET estimates under clear and partial clear sky condition, and atmospheric data produced by the Four-Dimensional Data Assimilation (FDDA) between MODIS products and the Fifth Generation Meso-scale Meteorological Model (MM5) and MODIS land products were used ET estimates under cloudy sky condition. MODIS ET under clear and cloudy sky conditions showed a good agreement with nine flux tower observations in Northeast Asia. These results indicate that MODIS can be applied to monitor ET with reasonable accuracy.

Evapotranspiration (ET) estimates are particularly needed for monitoring the available water of arid lands. Remote sensing data offer the ideal spatial and temporal coverage needed by irrigation water management institutions to deal with increasing pressure on available water. Low spatial resolution (LR) products present strong advantages. They cover larger zones and are acquired more frequently than high spatial resolution (HR) products. Current sensors such as Moderate-Resolution Imaging Spectroradiometer (MODIS) offer a long record history. However, validation of ET products at LR remains a difficult task. In this context, the objective of this study is to evaluate scaling properties of ET fluxes obtained at high and low resolution by two commonly used Energy Balance models, the Surface Energy Balance System (SEBS) and the Two-Source Energy Balance model (TSEB). Both are forced by local meteorological observations and remote sensing data in Visible, Near Infra-Red and Thermal Infra-Red spectral domains. Remotely sensed data stem from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS sensors, respectively, resampled at 100 m and 1000 m resolutions. The study zone is a square area of 4 by 4 km2 located in a semi-arid irrigated agricultural zone in the northwest of Mexico. Wheat is the dominant crop, followed by maize and vegetables. The HR ASTER dataset includes seven dates between the 30 December 2007 and 13 May 2008 and the LR MODIS products were retrieved for the same overpasses. ET retrievals from HR ASTER products provided reference ET maps at LR once linearly aggregated at the km scale. The quality of this retrieval was assessed using eddy covariance data at seven locations within the 4 by 4 km2 square. To investigate the impact of input aggregation, we first compared to the reference dataset all fluxes obtained by running TSEB and SEBS models using ASTER reflectances and radiances previously aggregated at the km scale. Second, we compared to the same reference dataset all fluxes obtained with SEBS and TSEB models using MODIS data. LR fluxes obtained by both models driven by aggregated ASTER input data compared well with the reference simulations and illustrated the relatively good accuracy achieved using aggregated inputs (relative bias of about 3.5% for SEBS and decreased to less than 1% for TSEB). Results also showed that MODIS ET estimates compared well with the reference simulation (relative bias was down to about 2% for SEBS and 3% for TSEB). Discrepancies were mainly related to fraction cover mapping for TSEB and to surface roughness length mapping for SEBS. This was consistent with the sensitivity analysis of those parameters previously published. To improve accuracy from LR estimates obtained using the 1 km surface temperature product provided by MODIS, we tested three statistical and one deterministic aggregation rules for the most sensible input parameter, the surface roughness length. The harmonic and geometric averages appeared to be the most accurate.

Estimation of daily evapotranspiration over Africa using MODIS/Terra and SEVIRI/MSG data

Validation of MODIS 16 global terrestrial evapotranspiration products in various climates and land cover types in Asia

A universal triangle method for evapotranspiration estimation with MODIS products and routine meteorological observations: Algorithm development and global validation

Daily evapotranspiration (ET) is of great significance among various practical applications including water management, drought monitoring, and climate change study. The ET estimations from remotely sensed models are usually instantaneous values. Various upscaling methods have been developed to extrapolate the instantaneous ET to daily scale. In the applications of these methods, the accuracy of daily ET estimation relies on both the accuracy of instantaneous ET calculation and the upscaling methods. This paper used the decoupling model to estimate daily ET directly, according to the constancy of the decoupling coefficient (Ω) in the model in a diurnal cycle, without the calculation of instantaneous ET. The estimated daily ET was compared with the Eddy covariance measurements which were corrected by the Bowen ratio method to close the energy imbalances. The result from field data alone showed that the coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of daily ET estimation was 0.860, with a root-mean-square error (RMSE) of 18.2 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and a bias of −4.7 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Combining MODIS data and field data, the estimated daily ET had a R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.860, a RMSE of 21.6 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and a bias of −5.0 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2 </sup> . Therefore, it is feasible and effective to obtain daily ET using remote-sensing based instantaneous Ω to replace daily value in the decoupling model.

Temporal upscaling of instantaneous evapotranspiration: An intercomparison of four methods using eddy covariance measurements and MODIS data

Thailand is characterized by typical tropical monsoon climate, and is suffering serious water related problems, including seasonal drought and flooding. These issues are highly related to the hydrological processes, e.g., precipitation and evapotranspiration (ET), which are helpful to understand and cope with these problems. It is critical to study the spatiotemporal pattern of ET in Thailand to support the local water resource management. In the current study, daily ET was estimated over Thailand by ETMonitor, a process-based model, with mainly satellite earth observation datasets as input. One major advantage of the ETMonitor algorithm is that it introduces the impact of soil moisture on ET by assimilating the surface soil moisture from microwave remote sensing, and it reduces the dependence on land surface temperature, as the thermal remote sensing is highly sensitive to cloud, which limits the ability to achieve spatial and temporal continuity of daily ET. The ETMonitor algorithm was further improved in current study to take advantage of thermal remote sensing. In the improved scheme, the evaporation fraction was first obtained by land surface temperature—vegetation index triangle method, which was used to estimate ET in the clear days. The soil moisture stress index (SMSI) was defined to express the constrain of soil moisture on ET, and clear sky SMSI was retrieved according to the estimated clear sky ET. Clear sky SMSI was then interpolated to cloudy days to obtain the SMSI for all sky conditions. Finally, time-series ET at daily resolution was achieved using the interpolated spatio-temporal continuous SMSI. Good agreements were found between the estimated daily ET and flux tower observations with root mean square error ranging between 1.08 and 1.58 mm d−1, which showed better accuracy than the ET product from MODerate resolution Imaging Spectroradiometer (MODIS), especially for the forest sites. Chi and Mun river basins, located in Northeast Thailand, were selected to analyze the spatial pattern of ET. The results indicate that the ET had large fluctuation in seasonal variation, which is predominantly impacted by the monsoon climate.

Evapotranspiration (ET) is a key component in the hydrological cycle, responsible for removing almost two thirds of all precipitation over land. Globally modeling ET is of great importance in determining near surface energy and water fluxes, especially in remote areas with limited available data. In the present study, 'actual' daily ET is directly modeled globally based on the Penman-Monteith equation using only remotely sensed data, eliminating the need for a hydrological model to determine the available moisture. Model estimates are validated against 39 Eddy Covariance Flux towers mostly in North America. It is shown that here, air temperature (Ta) and relative humidity (RH) are the main factors in estimating ET. In order to serve the global application of the model, daily Ta is estimated using remotely sensed Land Surface Temperature (LST) from the MODerate resolution Imaging Spectroradiometer (MODIS) product on the Aqua and Terra satellites. Other physical variables such as LAI, albedo, solar elevation angle, latitude, elevation, etc. are also used as auxiliary predictors in determining Ta. Several linear and non-linear regression methods are used, and it is shown that Artificial Neural Networks (ANN) can learn the most variabilities in the training data set and predict the targets most accurately. Similar to Ta, RH is estimated using remotely sensed data. Soil moisture from Soil Moisture Active Passive (SMAP) product is also used as a predictor in estimating RH. Again, several linear and non-linear regression methods are used, and it is shown that the most accurate method to estimate RH is Support Vector Regression (SVR). ET estimates will finally be modified using the improved air temperature and relative humidity.

Monitoring of crop water consumption, also known as actual evapotranspiration (ETa), is crucial for the prudent use of limited freshwater resources. Remote-sensing-based algorithms have become a popular approach for providing spatio-temporal information on ETa. Satellite-based ETa products are widely available. However, identifying an adequate product remains a challenge due to validation data scarcity. This study developed an assessment process to identify superior ETa products in agricultural areas in Egypt. The land cover product (MCD12Q1) from Moderate Resolution Imaging Spectroradiometer (MODIS) was evaluated and used to detect agricultural areas. The performances of three ETa products, namely: Earth Engine Evapotranspiration Flux (EEFlux), USGS-FEWS NET SSEBop ETa monthly product, and MODIS ETa monthly product (MOD16A2), were evaluated. The ETa values of these products were compared to previous ETa observations and evaluated using the integrated Normalized Difference Vegetation Index (iNDVI) on a seasonal and annual basis. Finally, the irrigation efficiency throughout Egypt was calculated based on the annual Relative Water Supply (RWS) index. Results reveal that the SSEBop monthly product has the best performance in Egypt, followed by the MOD16A2. The EEFlux overestimated ETa values by 36%. RWS had a range of 0.96–1.47, indicating high irrigation efficiency. The findings reported herein can assist in improving irrigation water management in Egypt and the Nile Basin.

Many large rivers around the world no longer flow to their deltas, due to ever greater water withdrawals and diversions for human needs. However, the importance of riparian ecosystems is drawing increasing recognition, leading to the allocation of environmental flows to restore river processes. Accurate estimates of riparian plant evapotranspiration (ET) are needed to understand how the riverine system responds to these rare events and achieve the goals of environmental flows. In 2014, historic environmental flows were released into the Lower Colorado River at Morelos Dam (Mexico); this once perennial but now dry reach is the final stretch to the mighty Colorado River Delta. One of the primary goals was to supply native vegetation restoration sites along the reach with water to help seedlings establish and boost groundwater levels to foster the planted saplings. Patterns in ET before, during, and after the flows are useful for evaluating whether this goal was met and understanding the role that ET plays in this now ephemeral river system. Here, diurnal fluctuations in groundwater levels and Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to compare estimates of ET specifically at 3 native vegetation restoration sites during 2014 planned flow events, and MODIS data were used to evaluate long‐term (2002–2016) ET responses to restoration efforts at these sites. Overall, ET was generally 0–10 mm d−1 across sites, and although daily ET values from groundwater data were highly variable, weekly averaged estimates were highly correlated with MODIS‐derived estimates at most sites. The influence of the 2014 flow events was not immediately apparent in the results, although the process of clearing vegetation and planting native vegetation at the restoration sites was clearly visible in the results.

Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data

Central Asia, located in the hinterland of the Eurasian continent, is characterized with sparse rainfall, frequent droughts and low water use efficiency. Limited water resources have become a key factor restricting the sustainable development of this region. Accurately assessing the efficiency of water resources utilization is the first step to achieve the UN Sustainable Development Goals (SDGs) in Central Asia. However, since the collapse of the Soviet Union, the evaluation of water use efficiency is difficult due to low data availability and poor consistency. To fill this gap, this paper developed a Water Use Efficiency dataset (WUE) based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Gross Primary Production (GPP) data and the MODIS evapotranspiration (ET) data. The WUE dataset ranges from 2000 to 2019 with a spatial resolution of 500 m. The agricultural WUE was then extracted based on the Global map of irrigated areas and MODIS land use map. As a complementary, the water use amount per GDP was estimated for each country. The present dataset could reflect changes in water use efficiency of agriculture and other sectors. The published data are available at http://www.dx.doi.org/10.11922/sciencedb.j00076.00012.

Evaluations of MODIS and microwave based satellite evapotranspiration products under varied cloud conditions over East Asia forests

&amp;lt;p&amp;gt;Land surface temperature (LST) from remote sensing has been widely used to estimate regional and local scale evapotranspiration (ET). However, remotely sensed LST viewed by the same sensor from different angles would lead to different LST retrievals and this would lead to the deviation in ET estimations with LST as input. The terrestrial surface bidirectional reflectance distribution function (BRDF) are commonly inverted against multiple cloud-free, atmospherically-corrected directional reflectance values that sufficiently sample the anisotropy caused by different view angles. The MODerate-resolution Imaging Spectroradiometer (MODIS) product MCD43A1 contains three-dimensional (3D) data sets and can provide weighting parameters for the models used to derive the Albedo. The MODIS MCD43A4 is reflectance product providing reflectance data adjusted using a bidirectional reflectance distribution function (BRDF) to model the values as if they were taken from nadir view and solar zenith. Here we intend to operate the correction of the angle effect in LST with these two MODIS BRDF related products in ET estimation. The two products are used to provide reflectance with consistent view angle and with solar zenith of satellite sensor and 0&amp;amp;#176; solar zenith, respectively, and then corresponding fractional vegetation cover (FVC) are calculated with two kinds of corrected reflectance, respectively. Combining the soil temperature (T&amp;lt;sub&amp;gt;s&amp;lt;/sub&amp;gt;) and vegetation temperature (T&amp;lt;sub&amp;gt;v&amp;lt;/sub&amp;gt;) components which are separated from MODIS LST and have no directional effects with the corrected FVC, the nadir LST (with solar zenith of satellite sensor and 0&amp;amp;#176;solar zenith, respectively) were obtained. Finally, ET were estimated with the surface energy balance system (SEBS) model using the remote sensed LST and the two kinds of corrected LST as input, respectively. The results showed that compared to ET measurements, the ET estimations with two kinds of corrected LST as input performed much better than that with uncorrected LST as input, and ET estimation with corrected LST in which FVC are calculated from MCD43A1 had highest accuracy.&amp;lt;/p&amp;gt;