MODIS Evapotranspiration Research Articles

Evaluation of MODIS-based estimates of water-use efficiency in Amazonia

ABSTRACTThis study aimed to assess the spatial-temporal patterns of water-use efficiency (WUE) obtained through MODIS gross primary productivity (GPP) and evapotranspiration (ET) products (MOD17 for GPP and MOD16 for ET) in the Upper Tapajos and Curua-Una River basins, located in the oriental flank of the Amazon region, and to validate the results with flux tower measurements within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project. The spatial variation of WUE was primarily related to the larger presence of forested areas in the Upper Tapajos River basin (western part) compared with the Curua-Una River basin (eastern part), which is situated within the so-called arc of deforestation. Temporally, WUE showed a pronounced seasonal pattern, varying with the dry and wet seasons in the region. A decrease of ~3% in WUE was observed during the dry season, which was related to the low water availability and increased vapour pressure deficit during the dry period, which induces stomatal closure, leading to a decline in the photosynthetic rate. Comparison between the WUE estimates obtained by MODIS data and observations from the LBA towers showed an average error of 17%, varying between ~12% and ~28% for the different sites. MODIS WUE depends on the accuracy of both GPP and ET estimation. In this sense, we highlight that improvements in both MODIS GPP and ET products are necessary and should focus on reducing the uncertainties related to the biophysical vegetation parameters and meteorological data that serve as input information in the algorithms.

Corrigendum to “Reliability of MODIS Evapotranspiration Products for Heterogeneous Dry Forest: A Study Case of Caatinga”

Corrigendum to “Reliability of MODIS Evapotranspiration Products for Heterogeneous Dry Forest: A Study Case of Caatinga”

Evaluation of Spatio-Temporal Patterns of Remotely Sensed Evapotranspiration to Infer Information about Hydrological Behaviour in a Data-Scarce Region

Information about the hydrological behaviour of a river basin prior to setting up, calibrating and validating a distributed hydrological model requires extensive datasets that are hardly available for many parts of the world due to insufficient monitoring networks. In this study, the focus was on prevailing spatio-temporal patterns of remotely sensed evapotranspiration (ET) that enabled conclusions to be drawn about the hydrological behaviour and spatial peculiarities of a river basin at rather high spatial resolution. The prevailing spatio-temporal patterns of ET were identified using a principal component analysis of a time series of 644 images of MODIS ET covering the Wami River basin (Tanzania) between the years 2000 and 2013. The time series of the loadings on the principal components were analysed for seasonality and significant long-term trends. The spatial patterns of principal component scores were tested for significant correlation with elevations and slopes, and for differences between different soil texture and land use classes. The results inferred that the temporal and spatial patterns of ET were related to those of preceding rainfalls. At the end of the dry season, high ET was maintained only in areas of shallow groundwater and in cloud forest nature reserves. A region of clear reduction of ET in the long-term was related to massive land use change. The results also confirmed that most soil texture and land use classes differed significantly. Moreover, ET was exceptionally high in natural forests and loam soil, and very low in bushland and sandy-loam soil. Clearly, this approach has shown great potential of publicly available remote sensing data in providing a sound basis for water resources management as well as for distributed hydrological models in data-scarce river basins at lower latitudes.

Validation of evapotranspiration and its long-term trends in the Yellow River source region

In this paper, the ground observations were compared to the ERA-Interim, NCEP-DOE AMIP-II Reanalysis, MODIS ET product, and emerging offline SEBS ET data sets in the Yellow River source region of the Tibet Plateau. In general, the slopes of linear least squares exhibit differences, with ERA-Interim, NCEP–DOE, MOD16, and SEBS slopes of 0.88 ± 0.05, 0.64 ± 0.07, 0.66 ± 0.17, and 1.24 ± 0.97 respectively. ERA-Interim was found superior with ground observations to others; therefore, it provided a good representation of the study area. Based on the ERA-Interim ET product, the Sen's slope estimator and the Mann–Kendall (MK) test were applied to quantify the significance of the shifts in trends, while the moving t-test and MK test characterized abrupt changes. The results show that the Yellow River source region experienced a statistical increase in evapotranspiration (ET) in the northern part and a decrease in the southern part of the region from 1979 to 2014 at rates of approximately 1.65 and −0.50 mm/yr, respectively. The shift in the annual ET trend was more pronounced, and abrupt changes were detected in the 1980s. Precipitation was the most dominant factor affecting ET variation, whereas surface temperature was the least influential.

Role of Climate Variability and Human Activity on Poopó Lake Droughts between 1990 and 2015 Assessed Using Remote Sensing Data

In 2015, an emergency state was declared in Bolivia when Poopó Lake dried up. Climate variability and the increasing need for water are potential factors responsible for this situation. Because field data are missing over the region, no statements are possible about the influence of mentioned factors. This study is a preliminary step toward the understanding of Poopó Lake drought using remote sensing data. First, atmospheric corrections for Landsat (FLAASH and L8SR), seven satellite derived indexes for extracting water bodies, MOD16 evapotranspiration, PERSIANN-CDR and MSWEP rainfall products potentiality were assessed. Then, the fluctuations of Poopó Lake extent over the last 26 years are presented for the first time jointly, with the mean regional annual rainfall. Three main droughts are highlighted between 1990 and 2015: two are associated with negative annual rainfall anomalies in 1994 and 1995 and one associated with positive annual rainfall anomaly in 2015. This suggests that other factors than rainfall influenced the recent disappearance of the lake. The regional evapotranspiration increased by 12.8% between 2000 and 2014. Evapotranspiration increase is not homogeneous over the watershed but limited over the main agriculture regions. Agriculture activity is one of the major factors contributing to the regional desertification and recent disappearance of Poopó Lake.

Spatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration

Continuous monitoring of actual evapotranspiration (ET) is critical for water resources management at both regional and local scales. Although the MODIS ET product (MOD16A2) provides viable sources for ET monitoring at 8-day intervals, the spatial resolution (1km) is too coarse for local scale applications. In this study, we propose a machine learning and spatial temporal fusion (STF)-integrated approach in order to generate 8-day 30m ET based on both MOD16A2 and Landsat 8 data with three schemes. Random forest machine learning was used to downscale MODIS 1km ET to 30m resolution based on nine Landsat-derived indicators including vegetation indices (VIs) and land surface temperature (LST). STF-based models including Spatial and Temporal Adaptive Reflectance Fusion Model and Spatio-Temporal Image Fusion Model were used to derive synthetic Landsat surface reflectance (scheme 1)/VIs (scheme 2)/ET (scheme 3) on Landsat-unavailable dates. The approach was tested over two study sites in the United States. The results showed that fusion of Landsat VIs produced the best accuracy of predicted ET (R2=0.52–0.97, RMSE=0.47–3.0mm/8days and rRMSE=6.4–37%). High density of cloud-clear Landsat image acquisitions and low spatial heterogeneity of Landsat VIs benefit the ET prediction. The downscaled 30m ET had good agreement with MODIS ET (RMSE=0.42–3.4mm/8days, rRMSE=3.2–26%). Comparison with the in situ ET measurements showed that the downscaled ET had higher accuracy than MODIS ET.

Estimation and Assessment of Spatial Variations in Water Availability in West Flowing River Basin of Kutch, Saurashtra and Marwar (WFR-KSM basin) using Geospatial Technology

The availability and distribution of water varies nationally and regionally. The availability of fresh water is stressed due to growing population coupled with sustainable developmental efforts, the uneven distribution of water resources over time & space, their modification through human use & abuse and many others. The study area is WFR-KSM basin which lies between 67°52’ to 75°19’ E longitudes and 20°53’ to 26°57’ N latitudes, covering areas in Rajasthan, Gujarat and whole of Diu. Hydrologically, it is divided into six sub basins and 268 watersheds (India-WRIS). The annual and monsoon season water availability in the basin, its sub basins and watersheds for water year 2013-14 was calculated by SCS curve number method using IMD 0.25 degree rainfall, NRSC NR Census LULC, NBSS & LUP soil data and MODIS evapotranspiration data. The water availability in the basin was estimated to be 34.67 BCM annually and 47.4 BCM in monsoon season ranging from 0 to 505 MCM annually and from 0 to 647.23 MCM in monsoon season in the watersheds of the basin. The spatial interpretation of water availability in watersheds indicated more water availability in the southern part of the basin and less water availability in the watersheds lying in northern part of the basin.

Estimating high resolution evapotranspiration from disaggregated thermal images

Accurate evapotranspiration (ET) estimations based on surface energy balance from remote sensing require information in the thermal infrared (TIR) domain, normally provided with an insufficient spatial resolution. In order to estimate ET in heterogeneous agricultural areas, we inspect in this paper the use of disaggregation techniques applied to two different sensors, such as MODIS (daily revisit cycle and 1km spatial resolution in the TIR domain) and Spot 5 (5days revisit cycle and 10m spatial resolution in the VNIR bands but no TIR band). Spot 5 images were used as a proxy for upcoming Sentinel-2. The Simplified Two-Source Energy Balance (STSEB) model was used for the estimation of ET. Since no Sentinel-2 images were available yet, images from the Spot 5 Take 5 experiment were used for testing this approach. Results assessment was conducted at two different levels: field scale (using ground data), and scene scale (using Landsat 7-ETM+ images as a reference). Validation of both disaggregated land surface temperature (LST) and derived surface energy fluxes was performed. Mean absolute deviations of ~2°C in disaggregated LST were observed at both field and scene scales. At field scale, relative errors of 22% and 19% were obtained for ET at instantaneous and daily scales, respectively. At scene scale, the four components of the surface energy balance equation were obtained with relative errors of 3, 14, 11 and 8% for net radiation, evapotranspiration, sensible heat flux and soil heat flux, respectively, compared to Landsat. The results obtained were compared to the use of the MODIS LST at its original resolution (1km), which was used also to obtain the surface energy fluxes. As the surface heterogeneity increases the errors in both MODIS LST and ET become more and more significant, compared to the use of the disaggregated images. Although reference images at 10m spatial resolution were not available at this stage for a more robust comparison, this paper shows the potential of the use of disaggregated LST to estimate ET at 10m spatial resolution, which is especially attractive in highly heterogeneous areas.

Accuracy comparison of remotely sensed evapotranspiration products and their associated water stress footprints under different land cover types in Korean peninsula

Robust spatial information of evapotranspiration from multiple land cover types is deemed critical for several applications in agriculture and water balance studies. Energy balance models, used in association with satellite observations, are beneficial to map spatial variability of evapotranspiration which is mainly governed by different vegetation practices and local environmental conditions. This study utilize the Surface Energy Balance System model to estimate actual evapotranspiration and water scarcity footprints under complex landscape of Korean peninsula using Moderate-Resolution Imaging Spectroradiometer satellite data for a complete hydrological year of 2012. The modeled evapotranspiration was compared with flux tower measurements obtained from a subhumid cropland and temperate forest sites for the accuracy assessment. This accuracy comparison at daily scale had good agreement yielding reasonable coefficient of determination (0.72, 0.51), bias (0.41 mm day−1, 1.01 mm day−1) and root mean squared error (0.92 mm day−1, 1.53 mm day−1) at two observation (cropland, forest) sites, respectively. Furthermore, the monthly aggregated evapotranspiration from Surface Energy Balance System showed promising results than those of obtained from Moderate-Resolution Imaging Spectroradiometer based readymade global evapotranspiration product, i.e., MOD16, when both products were compared with unclosed and closed flux tower measurements. However, the variations in monthly evapotranspiration obtained from both products were significantly controlled by several climate factors and vegetation characteristics. Water stress mapping at regional and monthly scale also revealed strong contrast between the products of two approaches. Highest mean water stress (0.74) was observed for land use areas associated with evergreen forest and under sparsely vegetation condition by using estimated evapotranspiration from Surface Energy Balance System while an extreme mean water stress value of 0.56 by using end product of MOD16 evapotranspiration was raised from cropland regions. Overall, this study revealed the performance and suitability of two distinctive remote sensing approaches for characterizing the footprints of water fluxes in the Korean peninsula and provides a baseline for the policy makers to setup the sustainable use of existing water resources in this and other similar regions.

Wet and dry patterns associated with ENSO events in the Sonoran Desert from, 2000–2015

The impact of ENSO events on generating wet and dry conditions in the Sonoran Desert is examined. Wet and dry conditions were identified from data of the Satellite Climate Extremes Index (SCEI), using MODIS satellite imagery for May–September 2000–2015. Additionally, MOD16 evapotranspiration products were considered as supplementary criteria. Negative SCEI values (dry conditions) correspond to radiation mechanism of regulation of prevailing surface temperature, whereas positive values (wet conditions) correspond to evapotranspiration. Five types of SCEI seasonal dynamics were identified in the Sonoran Desert. The link between these five types and ENSO events are discussed. Extreme moisture occurred in 2005 for four months, in 2010 only in May, and 2012 in May, then decreased in June–July, and transitions to wet condition in September. These events occurred in years when El Niño and La Niña were active. In summer 2005, an abrupt shift from El Niño to La Niña occurred, whereas in summer 2010, La Niña conditions were established after May. In August 2012, El Niño was interrupted and shifted to a La Niña-like episode for a month. Our results indicate that the likelihood of wet conditions during May–September increased in shifts from El Niño to La Niña conditions.

Dynamic changes in terrestrial net primary production and their effects on evapotranspiration

Abstract. The dramatic increase of global temperature since the year 2000 has a considerable impact on the global water cycle and vegetation dynamics. Little has been done about recent feedback of vegetation to climate in different parts of the world, and land evapotranspiration (ET) is the means of this feedback. Here we used the global 1 km MODIS net primary production (NPP) and ET data sets (2000–2014) to investigate their temporospatial changes under the context of global warming. The results showed that global NPP slightly increased in 2000–2014 at a rate of 0.06 PgC yr−2. More than 64 % of vegetated land in the Northern Hemisphere (NH) showed increased NPP (at a rate of 0.13 PgC yr−2), while 60.3 % of vegetated land in the Southern Hemisphere (SH) showed a decreasing trend (at a rate of −0.18 PgC yr−2). Vegetation greening and climate change promote rises of global ET. Specially, the increased rate of land ET in the NH (0.61 mm yr−2) is faster than that in the SH (0.41 mm yr−2). Over the same period, global warming and vegetation greening accelerate evaporation in soil moisture, thus reducing the amount of soil water storage. Continuation of these trends will likely exacerbate regional drought-induced disturbances and point to an increased risk of ecological drought, especially during regional dry climate phases.

Parameterizing ecosystem light use efficiency and water use efficiency to estimate maize gross primary production and evapotranspiration using MODIS EVI

Abstract Quantifying global carbon and water balances requires accurate estimation of gross primary production (GPP) and evapotranspiration (ET), respectively, across space and time. Models that are based on the theory of light use efficiency (LUE) and water use efficiency (WUE) have emerged as efficient methods for predicting GPP and ET, respectively. Currently, LUE and WUE estimates are obtained from biome-specific look-up tables and coarse resolution remote sensing data with large uncertainties. The major objective of this study was to parameterize eddy covariance tower-based ecosystem LUE (ELUEEC), defined as the ratio of tower-based GPP (GPPEC) to photosynthetically active radiation (PAR), and ecosystem WUE (EWUEEC), defined as the ratio of GPPEC to tower-based ET (ETEC), using the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived enhanced vegetation index (EVI) for predicting maize (Zea mays L.) GPP and ET, respectively. Three adjacent AmeriFlux maize sites with different rotations (continuous maize vs. annual rotation of maize and soybean, Glycine max L.) and water management practices (rainfed vs. irrigated) located near Mead, NE, USA were selected. The EVI tracked the seasonal variations of ELUEEC (R2 = 0.83) and EWUEEC (R2 = 0.74) across sites, indicating that EVI can be explicitly used as a measure of ELUEEC and EWUEEC. The predicted GPP (GPPELUE) using the parameterized ELUE model correlated well with GPPEC (slope = 1.0, R2 = 0.83, and RMSE = 2.85 g C m−2 d−1) and was significantly improved when compared to widely used models that estimate GPP by integrating EVI and climate variables (Greenness and Radiation, Temperature and Greenness, and Vegetation Index) and the standard MOD17 GPP product. Similarly, the predicted ET (ETEWUE) using the parameterized EWUE correlated well with ETEC (slope = 1.02, R2 = 0.62, and RMSE = 0.83 mm ET−1) and was significantly improved when compared to the standard MOD16 ET product. Preliminary data demonstrate that ELUE and EWUE can be parameterized using EVI, offering new methods for predicting GPP and ET.

Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches

This study presented a MODIS 8-day 1 km evapotranspiration (ET) downscaling method based on Landsat 8 data (30 m) and machine learning approaches. Eleven indicators including albedo, land surface temperature (LST), and vegetation indices (VIs) derived from Landsat 8 data were first upscaled to 1 km resolution. Machine learning algorithms including Support Vector Regression (SVR), Cubist, and Random Forest (RF) were used to model the relationship between the Landsat indicators and MODIS 8-day 1 km ET. The models were then used to predict 30 m ET based on Landsat 8 indicators. A total of thirty-two pairs of Landsat 8 images/MODIS ET data were evaluated at four study sites including two in United States and two in South Korea. Among the three models, RF produced the lowest error, with relative Root Mean Square Error (rRMSE) less than 20%. Vegetation greenness related indicators such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), and vegetation moisture related indicators such as Normalized Difference Infrared Index—Landsat 8 OLI band 7 (NDIIb7) and Normalized Difference Water Index (NDWI) were the five most important features used in RF model. Temperature-based indicators were less important than vegetation greenness and moisture-related indicators because LST could have considerable variation during each 8-day period. The predicted Landsat downscaled ET had good overall agreement with MODIS ET (average rRMSE = 22%) and showed a similar temporal trend as MODIS ET. Compared to the MODIS ET product, the downscaled product demonstrated more spatial details, and had better agreement with in situ ET observations (R2 = 0.56). However, we found that the accuracy of MODIS ET was the main control factor of the accuracy of the downscaled product. Improved coarse-resolution ET estimation would result in better finer-resolution estimation. This study proved the potential of using machine learning approaches for ET downscaling considering their effectiveness and ease of implementation. Future research includes development of the spatial-temporal fusion models of Landsat data and MODIS ET in order to increase temporal resolution of downscaled ET.

Modeling Surface Runoff and Evapotranspiration using SWAT and BEACH for a Tropical Watershed in North Vietnam, Compared to MODIS Products

Accurate estimation of surface runoff (Q) and evapotranspiration (ET) is a challenging task but an important research topic because both Q and ET play vital roles in the study of the hydrological cycle, of climate change, water resources, flood management and so on. In this paper we will present the modeling method to estimate the daily Q and ET for a medium-sized watershed in the tropical region of the North of Vietnam using the Soil and Water Assessment Tool (SWAT) and Bridging Event and Continuous Hydrological (BEACH) models. The models were calibrated and validated for the river discharge for SWAT and evaporation (E) for BEACH in a 12 year period from 2001 to 2012. The simulated ETs by the models were compared with the satellite-based ET of MODIS products. Our simulation results show that the SWAT and BEACH models are capable of satisfactorily reproducing (with the NSE > 0.62 and R 2 > 0.78) the stream-gauged river discharge and the observed E, respectively. Daily ET varied from 0.3 to 14 mm day -1 and was highest from May to August and lowest from December to March. Although the monthly and yearly MODIS ETs were slightly higher than those of SWAT and BEACH, a strong relationship between them was found with a standard deviation ranging from three to 40 mm. A light decrease of ET values in the 12 years can be seen in the result analyses; however a longer simulation time might be needed to ensure this trend.

Evaluation of Water Use Efficiency Derived from MODIS Products against Eddy Variance Measurements in China

Water use efficiency (WUE) is a useful indicator to illustrate the interaction of carbon and water cycles in terrestrial ecosystems. MODIS gross primary production (GPP) and evapotranspiration (ET) products have been used to analyze the spatial and temporal patterns of WUE and their relationships with environmental factors at regional and global scales. Although MODIS GPP and ET products have been evaluated using eddy covariance flux measurements, the accuracy of WUE estimated from MODIS products has not been well quantified. In this paper, we evaluated WUE estimated from MODIS GPP and ET products against eddy covariance measurements of GPP and ET during 2003–2008 at eight sites of the Chinese flux observation and research network (ChinaFLUX) and conducted sensitivity analysis to investigate the possible key contributors to the bias of MODIS products. Results show that MODIS products underestimate eight-day water use efficiency in four forest ecosystems and one cropland ecosystem with the bias from −0.36–−2.28 g·C·kg−1 H2O, while overestimating it in three grassland ecosystems with the bias from 0.26–1.11 g·C·kg−1 H2O. Mean annual WUE was underestimated by 14%–54% at four forest sites, 45% at one cropland site and 7% at an alpine grassland site, but overestimated by 66% and 9% at a temperate grassland site and an alpine meadow site, respectively. The underestimation of WUE by MODIS data results from underestimated GPP and overestimated ET at four forest sites, while MODIS WUE values are significantly overvalued mainly due to underestimated ET in the three grassland ecosystems. The maximum light use efficiency and fraction of photosynthetically-active radiation (FPAR) were the two most sensitive factors to the estimation of WUE derived from the MODIS GPP and ET algorithms. The error in meteorological data partly caused the overestimation of ET and accordingly underestimation in WUE in subtropical and tropical forests. The bias of MODIS-produced WUE was also derived from the uncertainties in eddy flux data due to gap-filling processes and unbalanced surface energy issue. Their contributions to the uncertainty in estimated WUE at both eight-day and annual scales still need to be further quantified.

Jointly Assimilating MODIS LAI and ET Products Into the SWAP Model for Winter Wheat Yield Estimation

Leaf area index (LAI) and evapotranspiration (ET) are two crucial biophysical variables related to crop growth and grain yield. This study presents a crop model-data assimilation framework to assimilate the 1-km moderate resolution imaging spectroradiometer (MODIS) LAI and ET products (MCD15A3 and MOD16A2, respectively) into the soil water atmosphere plant (SWAP) model to assess the potential for estimating winter wheat yield at field and regional scales. Since the 1-km MODIS products generally underestimate LAI or ET values in fragmented agricultural landscapes due to scale effects and intrapixel heterogeneity, we constructed a new cost function by comparing the generalized vector angle between the observed and modeled LAI and ET time series during the growing season. We selected three parameters (irrigation date, irrigation depth, and emergence date) as the reinitialized parameters to be optimized by minimizing the cost function using the shuffled complex evolution method-University of Arizona (SCE-UA) optimization algorithm, and then used the optimized parameters as inputs into the SWAP model for winter wheat yield estimation. We used four data-assimilation schemes to estimate winter wheat yield at field and regional scales. We found that jointly assimilating MODIS LAI and ET data improved accuracy (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.43, RMSE = 619 kg · ha <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ) than assimilating MODIS LAI data (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.28, RMSE = 889 kg · ha <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ) or ET data (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.36, RMSE = 1561 kg·ha <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ) at the county level, which indicates that the proposed estimation method is reliable and applicable at a county scale.

Water use efficiency of net primary production in global terrestrial ecosystems

The carbon and water cycles of terrestrial ecosystems, which are strongly coupled via water use efficiency (WUE), are influenced by global climate change. To explore the relationship between the carbon and water cycles and predict the effect of climate change on terrestrial ecosystems, it is necessary to study the WUE in global terrestrial ecosystems. In this study, the 13-year WUE (i.e., net primary production (NPP)/evapotranspiration (ET)) of global terrestrial ecosystems was calculated based on the Moderate Resolution Imaging Spectro-radiometer (MODIS) NPP (MOD17A3) and ET (MOD16A3) products from 2000 to 2012. The results indicate that the annual average WUE decreased but not significantly, and the 13-year mean value was 868.88 mg C m −2 mm −1. The variation trend of WUE value for each pixel differed greatly across the terrestrial ecosystems. A significant variation (P Oceania (1084.46 mg C m −2 mm −1)> Africa (893.51 mg C m −2 mm −1)> South America (893.07 mg C m −2 mm −1)> North America (870.79 mg C m −2 mm −1)> Asia (738.98 mg C m −2 mm −1) and warm temperate climates (1094 mg C m −2 mm −1)> snowy climates (862 mg C m −2 mm −1)> arid climates (785 mg C m −2 mm −1)> equatorial climates (732 mg C m −2 mm −1)> polar climates (435 mg C m −2 mm −1). Based on the WUE value and the present or future rainfall, the maximum carbon that fixed in one region may be theoretically calculated. Also, under the background of global climatic change, WUE may be regarded as an important reference for allotting CO 2 emissions offsets and carbon transactions.

Changes in irrigation water consumption in the Nile Delta of Egypt assessed by remote sensing

This paper quantified irrigation water consumption according to vegetation cover changes in the Nile Delta using remote sensing data. Soil in the Nile Delta is clay, so the common irrigation system is tradition surface irrigation with less than 60 % irrigation efficiency. Agricultural field consumes more than 80 % of water resources under surface irrigation. In the previous decades, vegetation cover decreased in old lands because of urbanization. Vegetation cover changing studied at years 2002, 2007 and 2013 using Landsat 7, Landsat 8 and SPOT 4 satellite data. Monthly MODIS evapotranspiration (ETa) data were used to estimate the quantity of water consumed by vegetation cover in the Nile Delta. MODIS products land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) were used to estimate water deficit index (WDI). The FAO-Penman-Monteith (FPM) model was used to calculate reference evapotranspiration (ETo). The crop coefficient (Kc) was estimated according to NDVI. WDI and potential crop evapotranspiration (ETc) were used to calculate actual crop evapotranspiration (ETa). A linear relation between ETa based on MODIS product and ETa based on estimated method was established with R 2 as high as 0.86. The Nile Delta agricultural water consumption changed as a result of vegetation cover changing. The vegetation cover changing were 691,870, 698,510 and 693,340 (ha), and it consumed 3.78*109, 4.21*109 and 3.78*109 (m3/year) at years 2002, 2007 and 2013 respectively according to MODIS ETa product. On the other hand, vegetation water consumption according to estimated ETa changed and the results were 4.7*109, 5.3*109 and 4.8*109 (m3/year) at years 2002, 2007 and 2013 respectively.

Comparison of three dual‐source remote sensing evapotranspiration models during the MUSOEXE‐12 campaign: Revisit of model physics

AbstractVarious remote sensing‐based terrestrial evapotranspiration (ET) models have been developed during the past four decades. These models vary in conceptual and mathematical representations of the physics, consequently leading to different performances. Examination of uncertainties associated with limitations in model physics will be useful for model selection and improvement. Here, three dual‐source remote sensing ET models (i.e., the Hybrid dual‐source scheme and Trapezoid framework‐based ET Model (HTEM), the Two‐Source Energy Balance (TSEB) model, and the MOD16 ET algorithm) using ASTER images were compared during the MUSOEXE‐12 campaign in the Heihe River Basin in Northwest China, aiming to better understand the differences in model physics that potentially lead to differences in model performance. Model results were first compared against observations from a dense network of eddy covariance towers and isotope‐based evaporation (E) and transpiration (T) partitioning. Results show that HTEM outperformed the other two models in simulating ET and its partitioning, whereas MOD16 performed worst (i.e., ET root‐mean‐square errors are 42.3 W/m2 (HTEM), 49.8 W/m2 (TSEB), and 95.3 W/m2 (MOD16)). On to model limitations, HTEM tends to underestimate ET under high advection due mostly to the underestimation of temperatures for the wet edge in its trapezoidal space. For TSEB, large uncertainties occur in determining the initial Priestley‐Taylor coefficient and the iteration procedure for ET partitioning, leading to overestimation/underestimation of T/E in most cases, particularly over sparse vegetation. Primary use of meteorological data for MOD16 does not effectively capture the soil moisture restriction on ET, and therefore results in unreasonable spatial ET patterns.

Multiscale Validation of the 8-day MOD16 Evapotranspiration Product Using Flux Data Collected in China

Accurate estimates of terrestrial evapotranspiration (ET) are critical and significant to the field for modeling water and energy transfer between the land surface and atmosphere. This paper evaluates the 8-day MOD16 actual ET product using the ground-based eddy covariance (EC) system and large aperture scintillometer (LAS) measurements collected from 2008 to 2011 at seven sites in North and Northwest China. Overall, the 8-day MOD16 ET reproduces the temporal patterns of both the LAS and EC measurements but tends to underestimate and overestimate these measurements at high and low ET levels, respectively. It is also of limited use when surface is irrigated because under such condition significant underestimation is observed. Using the LAS measurements that also include a large source area does not generate a better agreement than using the EC measurements which comprise a small source area. The 8-day MOD16 ET averaged over ${3} \times {3\;km}^{{2}}$ MODIS pixels agree better with both the EC and LAS measurements than that extracted at the ${1} \times {1}\;{km}^{{2}}$ MODIS pixel. For the EC validation, the relative bias and the relative root-mean-square error (RMSE) vary between 5% and $- 59\% $ and between 35% and 120%, respectively, for the LAS validation, the relative bias and the relative RMSE vary between 6% and $- 69\% $ and between 55% and 108%, respectively. The agreement between one site and another is not consistent and varies greatly. More validation work is recommended to testing the general applicability of the MOD16 ET product at a large number of sites worldwide.