Latent heat flux (LE) is pivotal in the regional water-energy nexus, exemplifying complex interplays between atmosphere and land surface. Thermal infrared (TIR) land surface temperature (LST) offers direct and vital information for estimating LE through the single-source energy balance method. Nevertheless, variations in the viewing angles of remote sensing sensors can introduce angular effects in the retrieval of LST, potentially causing significant incompatibility issues in estimating LE. To alleviate this uncertainty, we adopt a viable approach by using two composited LSTs derived from the integration of soil and vegetation component temperatures from Sentinel-3 SLSTR, combined with fraction vegetation coverage (FVC) obtained from both the GEOV2 FVC product and MODIS LAI-derived estimates. This composited LST was subsequently used as one of the inputs of a single-source energy balance system (SEBS) model driven by measured meteorological and ERA5 reanalysis data in Heihe River Basin in China during 2016-2022, respectively. The results demonstrate that two types of composited LST reduced the root mean square error (RMSE) of estimated LE by 4.8 W/m2and 8.8 W/m2, respectively, by using measured meteorological data; and using ERA5 meteorological data, the RMSE was reduced by 6.8 W/m2and 11.0 W/m2, respectively. Regardless of the meteorological data and FVC used, the RMSE for all stations assessed in the study decreased. This indicates that by partially mitigating the angular effects of TIR LST, improvements in TIR-based surface LE estimation can be achieved over regional scales.

The maximum surface relative humidity (hs,max) is a key factor that affects surface vapor pressure (es), which governs evapotranspiration (ET) rates. Existing empirical parameterizations lack a robust physical basis and exhibit limited generalizability. To address these limitations, we propose a remote sensing–based framework for the first global-scale estimation ofhs,max, which integrates physical constraints and data-driven methods. The framework first back-calculatedhs,maxby leveraging coupling mechanisms in the surface energy balance equation, using ET and meteorological data from 195 global flux tower sites. Subsequently, XGBoost–based inversion models were constructed separately for different land surface types, with site-level meteorological variables and the back-calculatedhs,maxas inputs. The models were applied to global remote sensing and reanalysis datasets, enabling the global-scale estimation of monthlyhs,maxfrom 2001 to 2020. Results demonstrate that thehs,maxmodels for different land surface types achieved high accuracy, with a mean root mean square error (RMSE) of 0.079 and a correlation coefficient (R) of 0.92. A contribution analysis using SHAP (Shapley Additive Explanations) reveals that relative humidity (RH) is the dominant predictor, while secondary factors vary by land surface type. The global monthlyhs,maxestimates for 2001–2020 exhibit distinct climate-driven spatial patterns. Temporal variability is low (CVhs,maxestimation, providing essential parameter support for studies of land–atmosphere interactions.

Remote sensing-based evapotranspiration (ET) has been widely used in the study of global climate change, water resources management and precision agriculture. However, due to the relative coarser spatial resolution of thermal infrared data obtained by remote sensing, the retrievals of fine resolution ET through different remote sensing-based models were full of challenge. In this paper, a general ET downscaling method based on the land surface temperature-vegetation index (Ts-VI) triangle was proposed. 990 m resolution ET datasets obtained by aggregating 90 m surface energy balance algorithm for land (SEBAL)-derived estimates from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over a spatial dimension of 9.9 km by 9.9 km around the AmeriFlux US-Ne1 site were downscaled to 90 m by using this new proposed Ts-VI-based ET downscaling method. Compared with the original 90 m ASTER ET, the 90 m downscaled ET results had a mean absolute error (MAE) of 19.2~40.2 W/m2, a root mean square error (RMSE) of 28.8~52.0 W/m2and a bias of 1.7~2.4 W/m2.

Evapotranspiration (ET) plays an important role in water resources, crop management and other fields. Thermal infrared (TIR) land surface temperature (LST) provides essential information for estimating ET by using single-source energy balance method. Nevertheless, variations in the viewing angles of remote sensing sensors can introduce angular effects in the retrieval of LST, potentially causing significant incompatibility issues in estimating ET. To alleviate this uncertainty, we adopt a viable approach by using two composited LSTs derived from the integration of soil and vegetation component temperatures based on Sentinel-3 SLSTR and two kinds of fraction vegetation coverage data. This composited LST was subsequently applied in a single-source energy balance system (SEBS) model driven by measured and ERA5 reanalysis meteorological data in Heihe River Basin in China, respectively. The results demonstrate that our improved approach with two kinds of composited LST reduced the root mean square error (RMSE) of estimated ET by 4.84 W/m2 and 8.81 W/m2 using measured meteorological data driven model; and using ERA5 meteorological data driven model, the RMSE was reduced by 6.78 W/m2 and 10.97 W/m2, with a reduction observed at each site.

Accurate quantification of terrestrial gross primary production (GPP) is essential for enhancing our in-depth understanding of the global carbon budget and climate change [1] [2] . The two-leaf light use efficiency (TL-LUE) model, considering more deeply the disparities of photosynthesis capacity between sunlit and shaded leaves, has been proven to be a more efficient and potent approach than the big-leaf light use efficiency (BL-LUE) model for global GPP simulations [3] . However, the TL-LUE model is theoretically applicable for sunny days and fails to reflect the real configuration of the canopy under overcast and cloudy days, since the direct radiation could be shaded by clouds and thus all the leaves within the canopy are in reality shaded leaves (i.e., no sunlit leaves should exist). This mismatch between the theory and reality could definitely introduce a certain degree of systematic errors into the GPP simulations by the TL-LUE model. Here, we proposed a revised two-leaf light use efficiency (RTL-LUE) model for improving GPP estimation through better quantifying the sunlit and shaded leaf area index (LAI) under different sky conditions.

Drought risk assessment is one of the most important basic research topics on the quantitative understanding of the mechanism of drought risk and scientifically reducing the adverse effects of drought, which is of great significance in the theory and practice of developing coping strategies and drought management plans. In this paper, the drought risk on a global scale was quantified according to the hazard, exposure, and vulnerability of drought from 2020 to 2099. In addition, the trends of drought risk variation under two different representative concentration pathways (RCP45 and RCP85) scenarios are analyzed and compared. According to the variation character of drought risk in different scenarios, it is divided into 7 types, and the specific differences of each type are discussed. The results show that (1) the areas with high drought risk are primarily concentrated in populated and high precipitation variability places, such as Pakistan, western India, and central North America. (2) When the greenhouse gas concentration rises from RCP45 to RCP85, the drought risk in about 36.88% of the world will worsen, which is primarily concentrated in southern North America, southeastern South America, southern Africa, southern Oceania, southern Asia, and western Europe.

Accurate quantification of gross primary production (GPP) in agroecosystems not only improves our ability to understand global carbon budget but also ensures basic human survival supplements. Here, we improved the MODIS-GPP algorithm by two main perspectives: (1) taking the seasonal variations of maximum light use efficiency (LUE) into modeling consideration; (2) separately parameterizing maximum LUE with a recently proposed vegetation index (VI) NIRv during vegetative stage and senescence stage. Performances of the revised and traditional MODIS-GPP algorithms were tested at three FLUXNET crop sites planted with maize and soybean. The revised model was well validated, indicated by the root mean square error (RMSE), coefficient of determination$(\mathrm{R}^{2})$and Bias being 2.33$\text{gC m}^{-2}$day${}^{-1}, 0.91$and 0.48$\text{gC m}^{-2}$da y -l for maize, respectively, and being 1.51$\text{gC} \mathrm{m}^{-2}\text{day}^{-1},0.91$and 0.43$\text{gC m}^{-2}$day$-1$for soybean, respectively. Overall, compared to the traditional MODIS-GPP algorithm, the proposed algorithm reduced RMSE by 29.6% and 27.4%, increased$\mathrm{R}^{2}$by 10.9% and 10.9%, and reduced Bias by 41.5% and 36.8% for maize and soybean, respectively. This paper demonstrates that incorporating seasonal fluctuations of maximum LUE into MODIS-GPP algorithm and distinguishing the different photosynthesis rates among vegetative and senescence stages significantly benefit the retrieval accuracy of daily model-estimated GPP.

Due to the spatial heterogeneity within a relatively small distance of urban areas, it is necessary to consider the complex land cover types and three-dimensional geometric structure of urban surface. This study introduces the sky view factor (SVF) to calculate the equivalent emissivity of urban surface. In addition, the thermal radiation of adjacent pixels to target pixels is also considered to establish the urban radiative transfer model. The Landsat-8 collection-2 level-2 science product was taken to validate the proposed urban radiative transfer model. The area within the Fourth Ring Road of Beijing was regarded as the study area, then the land surface temperature retrieval algorithm was applied to estimate urban surface temperature (UST). The results of the UST retrieval algorithm were evaluated by comparing brightness temperature (BT) at the top of atmosphere (TOA) simulated by the Discrete Anisotropic Radiative Transfer (DART) model. The root mean squared error (RMSE) between brightness temperatures estimated by the urban radiative transfer model and those simulated by DART model was less than 0.21 K.

Accurate retrieval of land surface temperature (LST) over urban areas is of great significance for urban thermal environment monitoring. In previous studies, most of the urban LST retrieval methods were developed based on the assumption of a flat surface without considering the influence of urban 3-D geometry structure, which has a significant impact on the retrieval accuracy of LST over urban areas. In this study, a radiative transfer equation (RTE)-based single-channel method was developed to retrieve LST with urban geometry effect correction from the Landsat 8 thermal infrared (TIR) data in band 10. The increase in adjacent radiance from the surrounding pixels and the decrease in atmospheric downwelling radiance caused by urban geometry structure were taken into account in this method. Because it is difficult to directly validate the retrieval accuracy of LST over urban areas usingin situLST measurements, the performance of the RTE-based LST retrieval method was evaluated via comparing brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the discrete anisotropic radiative transfer (DART) model and the urban RTE over three subregions. There is a good agreement between BT at the TOA simulated by the DART model and the urban RTE, with a root-mean-squared error (RMSE) of less than 0.25 K. The variations in LST retrieved with urban geometry effect correction over different local climate zones (LCZs) were analyzed. In general, built-up LCZs have relatively higher LST than land cover LCZs. The differences between LST retrieved without/with urban geometry effect correction over different LCZs are greater than 0.2 K. The largest average LST difference over built-up LCZs is approximately 0.9 K, whereas that over land cover LCZs is approximately 0.65 K. LST retrieved without/with urban geometry effect correction was used to calculate urban heat island intensity (UHII) in terms of the LCZ-based method. The results indicate that UHII calculated from LST with urban geometry effect correction is lower than that calculated from LST without urban geometry effect correction, with an average difference of approximately 0.5 K.

Gross Primary Production (GPP) and evapotranspiration (ET) play a critical role of the global carbon, water and energy cycle. Accurate quantification of the global GPP and ET could improve our ability to understand global climate change and energy budget. However, most of the GPP and ET remote sensing models fail to take the coupled relationship between vegetation transpiration (Et) and photosynthesis into consideration. More importantly, these models might ignore the difference of transpiration and photosynthesis rate in different groups of leaves (sunlit and shaded). Here, we coupled the estimates of daily GPP and ET at 84 global forest sites based on the Two-Leaf Light Use Efficiency model and the Penman-Monteith equation that were linked by the Ball-Berry conductance model. The developed model was well calibrated with the root mean square error (RMSE) and the coefficient of determination (R2) being 1.97 gC/m2 day and 0.77 for GPP respectively, and being 21.91 W/m2and 0.65 for ET, respectively. In the meantime, the validation results demonstrated the good performance of the coupled model, with the RMSE and R2 being 1.95 gC/m2 day and 0.77 for GPP, respectively, and being 21.34 W/m2and 0.66 for ET, respectively.

Evapotranspiration (ET) plays an important role in a large range of applications in the fields of agriculture, hydrology and meteorology. Thermal infrared (TIR) land surface temperature (Ts) is a valuable metric for constraining ET. However, remote sensing-based Ts values corresponding to a scene viewed by different angles thus leading to different retrievals would bring directional anisotropy effect on ET estimation. In this paper, the anisotropy effect was evaluated at first by simulating the directional Ts with an integrated model named SCOPE (soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance) in which the meteorological data at Yucheng site in China were used as driving data. Then ET were estimated with the surface energy balance system (SEBS) model using the simulated directional Ts. The ET differences with different directional Ts as input were analyzed for assessing the directional anisotropy effect on ET estimation. The analysis of the amplitude of anisotropy showed that the ET difference resulted from Ts anisotropy can reach up to 90 W/m2.

Evapotranspiration (ET) is an important variable in hydrological cycle and widely used in the study of water management and climate change. This paper developed a Random Forest (RF) model for global daily ET estimation over vegetated areas with 500 m spatial resolution using merely MODIS data. 255 in-situ sites from AmeriFlux network, FLUXNET Network and National Tibetan Plateau Data Center (TPDC) of China have been used to evaluate the RF model. Results indicated that the RF model-estimated global daily ET exhibited reasonable accuracy compared to the in-situ observations using MODIS datasets as inputs, with root mean square error (RMSE) between 0.52-0.97 mm/d over six different land-cover types representing forest, shrubland, cropland, savanna, grassland and wetland. The models generally achieved the best performance in shrubland, grassland and savanna, while provided the worst in wetland.

Thermal Infrared (TIR) cameras mounted on unmanned aerial vehicles (UAVs) provide low-cost, high spatial and temporal resolutions TIR data. This paper develops a novel single-channel algorithm adaptive to UAV TIR data. Atmospheric parameters were estimated using atmospheric reanalysis data and surface emissivity were acquired by the Portable Fourier transform thermal infrared spectrometer (102F). Then the effective atmospheric transmittance and emissivity were calculated owing to the broad spectral range of the UAV TIR channel. The results were validated using in-situ land surface temperature (LST) derived from SI-111 radiometers at an area of Baotou City, China. The root mean square error (RMSE) were 2.31K on 24 September and 1.82K on 26 September, which indicates that the proposed algorithm is a promising method to estimate LST from UAV TIR images.

Fine spatial resolution land surface temperature (LST) data derived from a thermal infrared remote sensing image are essential to the study of land surface energy, water and carbon cycles. As an alternative and effective way to obtain fine spatial resolution LST, a large number of LST downscaling methods have been proposed in recent decades to enhance coarse resolution LST to fine resolution. However, the drawbacks of the random selection of scaling factors and the establishment of statistical regression relationships are obvious. In this context, a general and physical LST downscaling method based on surface energy balance (DTsEB) is proposed in this study. Moderate Resolution Imaging Spectroradiometer (MODIS) LST data at 990 m spatial resolution were downscaled to 90 m by using this new proposed SEB-based LST downscaling method in this study. Compared with the concurrent 90 m resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST data, the downscaled results have a mean absolute error (MAE) of 1.37 K and a root mean square error (RMSE) of 1.84 K.

Evapotranspiration (ET) plays an important role in a variety of practical applications. Land surface temperature (LST) is a valuable metric for constraining ET. However, retrieved remote sensed LST values corresponding to a scene viewed by different angles thus leading to different retrievals would bring directional effects on ET estimation. Here the directional effects were assessed by developing an approach to provide LST with consistent view angle. The key of this approach was to provide nadir (0° view zenith) surface reflectance with bidirectional reflectance distribution function (BRDF) model to derive nadir fractional vegetation cover (FVC); simultaneously, the original remote sensed directional LST was separated to soil temperature (Ts) and vegetation temperature (Tv) components which have no directional effects. Then the nadir LST were obtained by combining the Tsand Tvusing corresponding nadir FVC. Finally, ET were estimated with the surface energy balance system (SEBS) model using the remote sensed directional LST and the obtained nadir LST as input, respectively, and the difference between them was analyzed for assessing the directional effects. Meteorological and remote sensing data at Yucheng site in China were used as test data. The results showed that the directional effect from LST would cause the escalation of Root Mean Square Error (RMSE) by 15.8 W/m2(5%) in ET estimations.

Evapotranspiration (ET), including soil evaporation and vegetation transpiration, is a vital component of the water cycle and energy exchange. The end-member-based soil and vegetation energy partitioning approach (ESVEP model) for estimating ET is the first model considering the differing responses of soil water content at the upper surface layer and at the deeper root zone layer. In this paper, we have improved the ESVEP model by 1) improving estimates of canopy resistance from three parts: stomatal conductance, cuticular conductance and leaf boundary-layer conductance; 2) adding the influence of atmosphere pressure and atmosphere temperature on resistance; 3) dividing aerodynamic resistance into convective resistance and radiative heat transfer resistance. Compared to the original ESVEP model, the improved algorithm is more applicable for different biome types and has a great potential in operational estimation of regional and global evapotranspiration. Due to the underestimation of soil temperature, the estimated ET is biased, which means that the biome properties and resistance are required to be further reparameterized in future study.

Passive microwave data are much less affected by clouds than TIR data for the retrieval of land surface temperature (LST), providing its unique advantages in global mapping of LST. In this study, a physically-based algorithm for LST retrieval was applied to AMSR2 global brightness temperature data. The performances of this algorithm applied on different land cover types were further evaluated against nighttime MYD11A1 thermal infrared LST products. The results showed that (i) the overall accuracy of the algorithm is about 5.42 K by root mean square error (RMSE) and 2.99 K by bias against MODIS LST during nighttime; (ii) the algorithm overestimates the LST over all land types. The overestimation is most evident over barren/sparsely vegetated surfaces. The algorithm shows that the algorithm has a robust performance comparing with MODIS LST and could be applied to estimate LST effectively.

Evapotranspiration (ET) is widely considered as one of the key parameters in a variety of practical applications. However, because of the contamination of cloud cover, the retrieval of daily ET from optical remote sensing data under cloudy sky conditions is full of challenge. In this paper, we reconstructed daily ET on cloudy days using the relationship between the potential evapotranspiration ratio (RPET) and the available water fraction (FAW). The field data from 8 Ameriflux sites were chosen to explore this relationship and applied for gap-filling of daily ET at these sites at first. Then MODIS data and meteorological data from Yucheng site in China were used to estimate daily ETs on clear days, and daily ETs on cloudy days at this site were reconstructed using the explored relationship between the RPETand the FAW, as an application of this reconstruction method. The results from 8 flux sites showed that daily ET reconstructions had good agreement with ET measurements, with the root mean square error (RMSE) less than 31.25 W/m2. Based on MODIS and filed data from the Yucheng site, the reconstructed daily ETs under cloudy sky conditions were also consistent with measured daily ETs, with a bias of 2.62 W/m2and an RMSE of 35.21W/m2.

Daily evapotranspiration (ET) is considered more significant in many practical applications, compared to the instantaneous ET obtained from remote-sensing based models. The constant reference evaporative fraction (EFn the ratio of actual to reference ET) method is one of the well preformed upscaling methods used to extrapolate instantaneous ET to daily timescales. The constant decoupling coefficient (Ω) method requires similar input data to the constant EFr method and can be used to calculate daily ET directly. This study evaluated the performances of the two methods underlying the estimation of daily ET. The results from field data only showed that (i) daily ET were both overestimated by two methods when compared to the uncorrected Eddy covariance (EC) measurements; (ii) the estimated daily ET had a good agreement with the measurement corrected by the Bowen Ratio (BR) method. Based on MODIS and filed data and when the ET measurements were corrected by the BR method, the results showed that (i) the constant EFr method overestimated daily ET by a bias of 5.6 W/m2and a root mean square error (RMSE) of 18.6 W/m2; (ii) the constant Ω method underestimated daily ET by a smaller bias of -4.8W/m2 and a RMSE of 22.5 W/m2.Therefore, the constant Ω method had a similar performance with the constant EFr method, and could be applied to estimate daily ET effectively.

Continuous monitoring of high spatial resolution evapotranspiration (ET) is critical for water resources management at both regional and local scales. This research employs a multi-sensor satellite data fusion approach (ESTARFM: Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) combined with a Two-Source N95 model and a constant evaporative fraction method to compute daily ET at 30 m spatial resolution. Two schemes are followed: the first scheme is to apply ESTARFM on the LST data to estimate daily ET at 30 m spatial resolution. The second scheme is to apply ESTARFM on the ET derived from MODIS and Landsat 8 images. The results show that the ET fused by both schemes is in good agreement with the reference ET data from the Landsat 8, while the first scheme (applying the ESTARFM on LST) is observed with more variations.

Daily Evapotranspiration (ET) is of great significance among various practical applications in the fields of water management, drought monitoring and climate change study. This paper utilized instantaneous decoupling coefficient to estimate daily LE (used interchangeably with ET in this paper) with atmospheric and surface resistances calculated from MODIS data. The field data were used only at first to identify the errors induced by the parameter retrieval from remote sensing data. The estimated daily LE was compared with measured data and the result showed that the coefficient of determination (R2) was 0.960, with a root mean square error (RMSE) of 12 W/m2and a bias of −4 W/m2. When MODIS data were involved in the calculation of decouple coefficient and resistances, the R2of the estimated daily LE was 0.949, with a RMSE of 33.1 W/m2and a bias of −17.9 W/m2. Therefore, it is feasible and effective to obtain daily LE using instantaneous decoupling coefficient from remote sensing data.

This paper presents a complement analysis for the wavelet transform method for separating temperature and emissivity (WTTES) with different wavelets, wavelet levels and biased atmospheric downwelling radiance. According to the results, the WTTES algorithm is quite insensitive to the choice of the wavelet. By comparing the retrievals with different wavelet levels, a wavelet level of n=3 or n=4 is more recommended in most cases. In addition, compared with the white noise, the WTTES algorithm is more sensitive to the atmospheric downwelling radiance with bias errors. For the profile with a bias error of 10%, the RMSE of the emissivity retrievals can be increased approximately 0.17%-2.33%, which depends on the specified water vapor content of the profile. However, different from the obvious errors on emissivity, the overall accuracies of the temperature retrievals under different atmospheric profiles are all less than 0.7K, which means the WTTES algorithm is still feasible to retrieve the temperature under the condition of biased moisture profiles.