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.

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.

Accurate quantification of ecosystem water use efficiency (eWUE), defined as the ratio of gross primary production (GPP) and evapotranspiration (ET), is vital to deepen our understanding of global water and carbon cycles. However, the influence of varying abiotic and biotic factors on GPP and ET is still not thoroughly understood, and thus accurate estimation of GPP and ET is still challenging, which may introduce uncertainties into eWUE. Here, we applied the random forest (RF) machine learning model to directly estimate the 8-day observed eWUE collected from the 197 globally distributed flux sites involved in the FLUXNET2015 dataset. Additionally, the RF model was also intercompared with the widely used Moderate Resolution Imaging Spectroradiometer (MODIS) and Penman-Monteith-Leuning version 2 (PMLv2) products. Our results show that the RF model could well reproduce the 8-day observed eWUE, as indicated by the root mean square error (RMSE) = 1.01 g C Kg-1H2O, the coefficient of determination (R2) = 0.66, and the mean prediction error (Bias) = 0.00 g C Kg-1H2O. More importantly, the RF model showed considerable improvements over the MODIS and PMLv2 products in simulating 8-day eWUE, with decreasing the RMSE by 1.03 g C Kg-1H2O and 0.86 g C Kg-1H2O, increasing the R2by 0.65 and 0.49, and reducing the Bias by 0.64 g C Kg-1H2O and 0.32 g C Kg-1H2O, respectively. This study indicates a promising avenue for using machine learning models to simulate eWUE directly.

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.

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.

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.

The buffer algorithm's urban heat island intensity is challenging to analyze urban heat islands' socioeconomic drivers, which undoubtedly increases the difficulty of making urban heat island mitigate policy. To address this question, in this study, we combine administrative borders data with satellite remote sensing data to comprehensively depict the 8-day urban heat islands intensity of 286 cities in China from 2001–2018 and analyze Spatio-temporal characteristics. We find that 90.7% of cities have urban heat islands during the daytime, becoming 91.6% at nighttime. There is a significant spatial clustering effect for both nighttime and daytime urban heat islands, and the temporal trend shows that urban heat islands have a greater degree of mitigation at nighttime compared to daytime. This study extends the methodology for characterizing urban heat islands and provides a China surface urban heat island (CSUHI) dataset for future interdisciplinary research.

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.

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.

Generally, atmospheric correction is a key process before the temperature and emissivity separation (TES). In view of the difficulty and accuracy of acquiring synchronous atmospheric profiles, several in-scene atmospheric correction algorithm have been proposed, one of which is the in-scene atmospheric compensation (ISAC) algorithm. Though this algorithm introduces a good way to find the black-body pixels for enhancing the practicability, it is limited by the spatial resolution of hyper-spectral sensors. This paper tries to apply this method to the thermal band data of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), since the spatial resolution can preferably satisfy the assumption of homogeneous atmosphere and more homogeneous black-body pixels will be found. The results show that the proposed algorithm is capable of retrieving atmospheric parameters with promising accuracies.

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.

The angular effect is a primary obstacle for wide applications of land surface temperature (LST) products. Current directional thermal radiation models do not fully consider the difference between visible/near infrared and thermal radiative, i.e. thermal inertial effect, and are not practical enough. Therefore, this study proposed a practical method for angular normalization of LST products based on the component temperature and fractional vegetation cover (FVC). Analyzing with simulated data indicated that the proposed method could improve the LST retrieval accuracy caused by angular effect from 1.2 K to 0.8 K. In addition, the retrieval accuracy of component temperature would affect the performance of the proposed method whereas the retrieval accuracy of component emissivity had almost no effect on the performance.

Net surface shortwave radiation (NSSR) is a key parameter for the estimation of surface energy budget. This paper proposes a method to directly estimate the NSSR from simulated Chinese Gaofen-5 (GF-5) data without using any ancillary information. Firstly, the narrowband reflectances of visible/near infrared channels at the top of the atmosphere (TOA) were converted to the TOA broadband albedo. Secondly, by categorizing the land surface into three types, the NSSR was estimated under clear and cloudy skies separately based on the relationship between TOA broadband albedo and the Earth's surface absorbed shortwave radiation. The estimation error of the absorption coefficient for each land type is lower than 0.05. Finally, by employing a look-up-table acquired in the process of narrowband-to-broadband conversion, and the parameters in the NSSR estimation model for each land type, the performance of the proposed method was evaluated, where the root mean square errors (RMSEs) were 25.85 (13.97) W/m2, 20.39 (7.97) W/m2, and 40.54 (11.26) W/m2for land, ocean and snow/ice surfaces for clear (cloudy) skies, respectively.

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.

As the main process parameter of water and energy exchange, evapotranspiration (ET) is defined as the water being converted from liquid to gaseous and from land surface to atmosphere. Potential evapotranspiration (ETO) is defined as the evapotranspiration when water supply is sufficient of the land surface and reflect the ability of the surface to supply moisture. In this study, we explored the relationship between annual averaged ET (ET/ETO) and annual averaged 36.5 GHz emission, and provided a new train of thought of how to use passive microwave data to estimate annual averaged evapotranspiration. We found a non-linear relationship with a R2 of 0.52 between annual averaged 36.5 GHz emission and observed annual evapotranspiration at 28 flux tower sites of Asia and North America. We estimated ET and ETO of China and found a linear relationship with a R2 of 0.51 between the annual averaged (ET/ET0)1/2and the annual averaged 36.5 GHz emission at 9 flux tower sites of China.

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.

Linking the terrestrial water cycles, carbon cycles and energy exchange, evapotranspiration (ET), which combines the surface evaporation and plant transpiration, is a key land surface parameter in water and heat balance of land, lake or river surface, and is central to earth system science. In this study, based on the MERRA reanalysis dataset and MODIS NDVI and LAI product, a support vector machine was used to estimate the land surface ET at sites and global scales. The results showed that, the support vector machine model probably could explain 60%–80% of the land surface ET change at 242 global FLUXnet sites when ten indicators while 56%–79% when five indicators were used to drive the model. For different vegetable cover sites, compared with EC observations, the results of evergreen broadleaf forest was worse than others.

Directional anisotropy limits the application of land surface temperature (LST) and a simplified parametric model to effectively estimate directional brightness temperature (DBT) in the thermal infrared is critical. This study used a widely validated four-stream scattering by arbitrarily inclined leaves (4SAIL) model as a benchmark to evaluate the performance of the kernel bidirectional reflectance distribution function (BRDF) model and the three-kernel-model. Results showed that the two kernel-driven models can fit the DBT well and the maximum root mean square error (RMSE) is 0.13°C. The kernel BRDF model has a wider application scope including canopies of uniform, spherical, plagiophile and planophile LIDF with low LAI and hotspot. When LIDF is planophile and plagiophile, two models can reach the best fitting effect and the worst effect is the canopy with erectrophile LIDF. Under a specified LIDF, the relationship between fitting accuracy and LAI is negative while hotspot parameter is positive.

Evapotranspiration (ET) is one of the most significant components in the water and heat transfer between land and atmosphere. This paper develops an improved constant evaporative fraction (EF) method through a theoretical derivation to improve the upscaling of remote sensing instantaneous latent heat flux (LE) to daily scale. Preliminary results show that our improved constant EF upscaling method can significantly reduce the underestimation of the daily LE upscaled using the conventional constant EF upscaling method. More validation work will be conducted to test the robustness of our improved EF method for the upscaling of remote sensing instantaneous LE estimates to daily scale.

Quantification of land surface evapotranspiration (ET) at daily or longer time scales is of great significance in agricultural ecosystem and hydrologic cycle. Temporal upscaling of instantaneous remote sensing-based ET to daily or longer time scales is generally only based on a single instantaneous estimate. A test is made to use two instantaneous ET estimates for the daily upscaling. The results show that the temporal upscaling using two instantaneous ET estimates is superior to that using only single instantaneous ET estimate for the constant extraterrestrial solar radiation ratio (Rp) method, the constant global solar radiation ratio (Rg) method, and the constant evaporative fraction (EF) method. The largest improvement of daily ET estimation occurs when instantaneous ET in the morning is combined with that in the afternoon for the Rpand Rgmethods, while the for EF method the optimal combination comprises of two moments in the afternoon.

Thin cirrus clouds can reduce land surface long-wave transmission and re-emit energy at a colder temperature and thus making it difficult to estimate downwelling surface longwave radiation (DSLR) from satellite data. In this study, a simulation database is established in terms of radiances observed at the top of the atmosphere (TOA), cloud optical thickness (COT), atmosphere water vapor content (WVC) and height of the cirrus bottom (HCB) and DSLR. And the back propagation (BP) artificial neural network (ANN) was used to estimate DSLR from remotely sensed data for cirrus cloudy skies. Results show that the BP model with TOA thermal radiance, COT, WVC and HCB as inputs provides a practical and efficient tool for remote sensing applications to estimate DSLR under thin cirrus clouds with root mean square error (RMSE) of 11.66 W/m2.

This paper attempts to retrieve leaf water content (LWC) by developing new vegetation indices from the combination of the near-infrared (NIR) and middle-infrared (MIR) spectral reflectances. The expanded vegetation leaf model PROSPECT-VISIR and the widely validated four-stream scattering by arbitrarily inclined leaves (4SAIL) model are employed to simulate canopy reflectance in 0.4–5.7 μm region with various leaf water content scenarios. Change of standard deviation of the canopy reflectance with respect to wavelength is used to analyze the sensitive of the spectral reflectance to the LWC. The results show that the spectral reflectances at 1.405μm, 1.875μm, 2.015μm, and 4.375μm are most sensitive to the change of LWC, and the difference vegetation index (DVI) combined by spectral reflectances in 1.405μm and 4.375μm is the best index to retrieve LWC with root mean square error (RMSE) of 0.0008 g/cm2.

Land surface temperature is an important driving force in the exchange of water, heat, and even CO2at the surface-atmosphere interface in the desert regions. The rapid and continuous measurements of land surface temperature are meaningful to the ecological and environmental researches. A physically based single-frequency and double-polarization algorithm for retrieving land surface temperature is developed in this study. The 18.7 GHz vertically polarized emissivities are firstly estimated from the Polarization Ratio (PR, defined as the ratio of the horizontal to vertical brightness temperature at the same frequency) at 18.7 GHz. And then the estimated emissivities can be directly used to retrieve land surface temperature without considering the atmospheric effect. A preliminary validation is done in the Taklimakan desert. The retrieved land surface temperatures are compared to the infrared land surface temperature products for all the year of 2007 with a Root Mean Square Error (RMSE) of 3.05 K.

Evapotranspiration (ET) is one of the key variables in the water and energy exchange between land surface and atmosphere. This paper develops an end-member-based two-source approach for estimating land surface ET (i.e., the ESVEP model) from remote sensing data, considering the differing responses of soil water content at the upper surface layer to soil evaporation and at the deeper root zone layer to vegetation transpiration. The ESVEP model first diverges the soil-vegetation system net radiation into soil and vegetation components by considering the transmission of direct and diffuse shortwave radiation separately from the transmission of longwave radiation through the canopy, then calculates the four dry/wet soil/vegetation end-members with the diverged soil and vegetation net radiations, and last separates soil evaporation from vegetation transpiration based on the two-phase ET dynamics and the four end-member temperatures. The model can overall produce reasonably good surface energy fluxes and is no more sensitive to meteorology, vegetation, and remote sensing inputs than other two-source energy balance models and surface temperature versus vegetation index ($T_{R}$ -VI) trapezoid models. A reasonable agreement could be found with a small bias of ±8 W/$\text{m}^{2}$ and a root-mean-square error within 60 W/$\text{m}^{2}$ (comparable to accuracies published in other studies) when both model-estimated sensible heat flux and latent heat flux from MODIS remote sensing data are validated with ground-based large aperture scintillometer measurements.

Evapotranspiration (ET) is the combination process of the surface evaporation and plant transpiration which occur simultaneously, and it links the terrestrial water cycles, carbon cycles and energy exchange. In this study, based on the observations from 242 global FLUXnet sites, with daily average temperature, relative humidity, wind speed, incident solar radiation, NDVI and observed ET as input data, we used a support vector machine to estimate the land surface daily ET at nine different vegetation type sites. The results show that, for all vegetation type sites, when the predicted ET was validated with the eddy covariance measurements, the support vector machine algorithm underestimates the ET and probably could explain 71%-86% of the land surface ET change.

Ambient irradiance is pivotal to be considered for field measurements of soil emissivity with Portable Fourier Transform Infrared Spectro-radiometer (102F). Usually, a diffusely reflecting gold plate which has a near-Lambertian behavior was used to acquire the ambient irradiance. Because of the measurements of soil and ambient irradiance are not synchronized, It can generate errors on determination of soil emissivity, especially for the erratic cloud and instantaneous wind which can make the ambient irradiance a sharp change. In this study, four conditions about the ambient radiances were 30% underestimated, 50% underestimated, 30% overestimated and 50% overestimated to assess the impacts of ambient irradiance on determination of soil emissivity. Preliminary research shows that ambient irradiance has more impacts on determination of soil emissivity in 8-10um than it in 10-13um. In 8-10um, the relative difference of soil emissivity can be more than 0.005 when the ambient irradiance was 30% overestimated. And it can reach up to 0.01 when the ambient irradiance was 50% overestimated. The error magnitudes are related to soil types. By contrast, the impacts of ambient irradiance are not obviously in 10-13um. Similar results can be seen in the ambient irradiance were underestimated conditions.

This paper analyzes the influence of the anomalous temperature occurred at the near surface boundary layer of the atmosphere on the land surface temperature (LST) retrieval with the generalized split-window algorithm (GSW). The coefficients in the GSW algorithm corresponding to a series of overlapping ranging of the mean emissivity, the atmospheric water vapor content, and the LST are derived using a statistical regression method from the numerical values simulated with an accurate atmospheric radiative transfer model MODTRAN 4 over a wide range of atmospheric and surface conditions. The simulation analysis shows that the LST can be estimated by the GSW algorithm with the root mean square error (RMSE) increasing by larger than 0.2 K when atmospheric anomalous profiles are involved. Taking into account the angular dependence of the top of the atmosphere radiance, six different viewing zenith angles (VZAs) are used in the simulations. Results show that the RMSEs become larger when the VZAs change form 0°to 60°.

An algorithm has been developed for retrieving instantaneous microwave land surface emissivity using brightness temperature and precipitable water vapor data. Unlike previous algorithms, the new technique does not need infrared land surface temperature as the input data, and overcomes the limitation of previous algorithms under cloudy conditions. Compared with the values from physical retrieval algorithm, the result demonstrates that this new algorithm has a Root Mean Square Error of 0.038 and a bias of 0.012. Although the accuracy is worse than 1%, this new algorithm presents the potential to obtain the instantaneous microwave land surface emissivity under both cloud-free and cloudy conditions, which can be applied in some weather prediction models.

Soil moisture plays an important role in the process of energy exchange and water cycle. Soil moisture also provides critical information in agriculture, including crop growth and drought. In this study, the comparison between NASA AMSR-E soil moisture product and ground-based measurement are performed in terms of (1) measurement depths of soil moisture, and (2) satellite overpass times. The results show that the NASA AMSR-E soil moisture product can be used to monitor time-series variation in soil moisture. Compared to the AMSR-E product from descending overpasses, the AMSR-E product from ascending overpasses has better ability in monitoring soil moisture variation. Also the AMSR-E product has better ability in monitoring soil moisture at the depth of 0-10 cm than 10-20 cm.

Evapotranspiration (ET) is one of the most significant components in the water and energy transfer between land surface and atmosphere at regional and global scales. this study aims to explore the underlying mechanism in the surface temperature versus fractional vegetation cover (Ts-Fr) space for regional ET and evaporative fraction (EF) estimation through a physically-based soil-vegetation-atmosphere transfer (SVAT) simulation. It also investigates the effect of vegetation type and physiology on the relationship between EF and Tsunder deep-layer water-saturated and water-stressed conditions. The preliminary results show that in the Ts-Frspace surface EF varies linearly with surface temperature when root zone layer is not water-stressed. However, the linear relationship may be different between one vegetation type and another. When root zone layer is water-stressed, the variation of root zone layer soil water content has a negligible effect on the canopy temperature but the EF can be significantly influenced.

This work estimated firstly net surface shortwave radiation (NSSR) from MODIS/Aqua data with six visible and near infrared channels by re-parameterizing the methodology proposed by Tang et al. (2006). Comparison of the estimated NSSR with those simulated actual one showed that the root mean square error (RMSE) is 34.1 W/m2. To validate the proposed parameterization scheme, some field measurements made at seven sites of the Surface Radiation Budget Network (SURFRAD) in October, 2008 were used. The result showed that the RMSE is 53.33 W/m2. To accurately capture the diurnal variation of NSSR for cloudy skies, a simple and practical linear regression model by combing the instantaneous NSSRs estimated from MODIS/Terra at local solar time 10:30 AM and MODIS/Aqua at 13:30 PM has been proposed to estimate the daily average net surface shortwave radiation (DANSSR). The results showed that the RMSE between the estimated DANSSR and those calculated from the seven SURFRAD measurements for cloudy days in 2008 is 42.59 W/m2.

This paper retrieves the Land surface temperature (LST) from MODIS mid-infrared data. Considering that the daytime mid-infrared satellite data contains both reflected radiance due to sun irradiance and emitted radiance from the surface and the atmosphere, this paper estimates the bidirectional reflectivity in mid-infrared channels firstly, and then derives the directional emissivity with the linear kernel-driven BRDF model. Finally based on the radiative transfer equations in mid-infrared channels, the LST is retrieved. The retrieved LSTs are preliminarily validated with the MODIS LST product MYD11B1. The results show that the root mean square error (RMSE) between the two estimated LST is below 1.9 K and the Bias is below 1.10 K. In addition, some in situ measurements are also used to validate the retrieved LST. The results show that the RMSE is 2.06 K and Bias is 0.73 K.

Based on analyzing the influence of atmospheric water vapor content (WVC) on coefficients determination in the generalized split-window (GSW) algorithm, it is found that the coefficients are relatively monotonic variable with the increasing of WVC, which were proposed to determine the coefficients as implicit linear functions. To improve the land surface temperature (LST) retrieval accuracy in the GSW algorithm, the WVC is proposed to determine the coefficients as an explicit parameter in this work. The results show that the proposed method can acquire relatively high accurate LST if WVC is known. The root mean square errors (RMSEs) between the actual LST and those estimated with the proposed method are lower than those retrieved with the coefficients in the GSW algorithm.

To compare and evaluate the performance of iterative spectrally smooth temperature and emissivity separation method (ISSTES) and linear emissivity constraint temperature and emissivity separation method (LECTES) on land surface temperature (LST) and land surface emissivity (LSE) estimation, the simulation data for hyperspectral infrared spectroradiometer are used. The results reveal that the LST can be retrieved within the accuracy of 1 K at various conditions for both methods. However, the 0.01 accuracy of LSE depends on the method selected and the noise level. The ISSTES method should be taken full consideration when it used to retrieve LSE for the warm and wet atmosphere. It is advised that the ISSTES method is used for cold and dry atmosphere and the LECTES method for warm and wet atmosphere. The noises in the ground measurements may be have more effects on the accuracies of LST and LSE than those in the atmospheric downwelling measurements.

Soil moisture is a key variable that influences the redistribution of the radiant energy and the runoff generation and percolation of water in soil. Knowledge of soil moisture temporal-spatial variations is important in a wide range of studies. This study aims to investigate the temporal-spatial variations of soil moisture using microwave polarization difference index (MPDI). The AMSR-E/Aqua Daily Global Quarter-Degree Gridded Brightness Temperature at 10.65 GHz channel was used to calculate the MPDI. In addition, the AMSR-E/Aqua Daily L3 Surface Soil Moisture was used in this study. The temporal and spatial patterns between the MPDI and soil moisture were analyzed. The results indicate that the temporal and spatial patterns of the MPDI are consistent with those of soil moisture. The MPDI reflects the temporal and spatial variations of soil moisture.

Land surface temperature (LST) is a critical parameter for numerical weather forecasting, drought monitoring, water resources management and global climate change studies. Because of the supercooled temperature, the cirrus cloud can significantly reduce the LST retrieved from thermal infrared data. This paper focused on analyzing and reducing the influence of thin cirrus cloud on the accuracy of LST retrieved using the generalized split-window (GSW) algorithm. A correction method was proposed with the LST retrieval error expressed as linear functions of cirrus optical depth (COD). The slopes of the linear functions were further written as the combination of the difference and mean of two used channels emissivities and cirrus cloud top height (CTH). The results showed that the LST retrieval accuracy could be significantly improved with root mean square error (RMSE) of LST changing from 14.4 K before LST error correction to 1.8 K after LST error correction for COD equivalent to 0.3.

The total atmospheric precipitable water vapor amount (TWV) is a key variable for the study of the Earth's climate. This paper develops an algorithm to estimate the TWV over clear skies from the Medium Resolution Spectral Imager (MERSI) data in the near-IR channels. The MODTRAN 4 code is used to simulate the top of the atmospheric radiances for the MERSI channels. The results show that the proposed algorithm is suitable to estimate TWV form the absorbing channel centered at 0.940 μm and the atmospheric window channels centered at 0.865 μm and centered at 1.030 μm by the radiances over the clear pixels, with relative differences in the range of 10%-15%.

Evapotranspiration (ET) controls the water and heat transfer between land surface and atmosphere at different temporal and spatial scales. Given the different structures of the spatial variability-based SEBAL and TS-VI triangle models but essentially the same definitions of the dry and wet pixels, this study aims to investigate through an analytical deduction and model applications how the SEBAL model and the TS-VI triangle method differ from each other in the regional evaporative fraction (EF) and ET estimation. Results show that the SEBAL model produces more satisfactory latent heat flux (LE) estimates than the Ts-VI triangle method when compared with ground-based large aperture scintillometer measurements at the Yucheng station. The SEBAL-derived EF and ET values for most pixels over the study area are larger than those derived by the TS-VI triangle method when the same group of dry and wet pixels is applied.

To evaluate the radiometric characteristics of the thermal infrared channels of Visible and InfraRed Radiometer (VIRR) aboard Chinese second generation polar-orbiting meteorological satellite FengYun-3B (FY-3B), the inter-calibration of those thermal infrared channels with high spectral resolution data acquired by the Atmospheric InfraRed Sounder (AIRS) aboard Aqua is carried out in this paper. Four steps, i.e. subsetting, collocating, transforming and regressing, were used to calculate the inter-calibration coefficients. The collocation data were picked out with a series of thresholds: the absolute viewing zenith angle differences less than 10°, the absolute viewing azimuth angle differences less than 20°, and absolute time differences less than 40 minute. The results on June 1st, 2012 reveal that the VIRR/FY-3B measurements are highly linearly related to the convolved AIRS/Aqua measurements. However, calibration discrepancies exist between VIRR and AIRS channels. When brightness temperatures in VIRR channels change from 270 K to 300 K under a normal condition, the AIRS-VIRR temperature adjustment linearly varies from -0.79 K to -2.32K for VIRR channel 4, from 0.14 K to -1.42 K for VIRR channel 5, respectively.

Soil moisture saturation index (SMSI) is an important indicator that demonstrates the status of the soil water content for drought monitoring. However, at present, most of the methods to calculate the SMSI from the in situ measurement data are inadequate or inaccurate. This paper proposed a simple method to determine the SMSI from the remotely sensed data. Combining the theory of thermal inertia and triangle method, the apparent thermal inertia and fractional vegetation cover can construct a triangular space. In this space, SMSI can be determined easily. Validation was performed with in situ measurements for 19 meteorological stations in the study area. Results indicated that the method can obtain the accurate soil water status that reflects the variation in soil moisture to some extent and is suitable for monitoring the regional surface soil moisture.

Land surface temperature and emissivity separation is a critical process for land surface temperature (LST) retrieval from hyperspectral thermal infrared data. This paper compared the iterative spectrally smooth temperature/emissivity separation (ISSTES) and the correlation based temperature/emissivity separation (CBTES) methods for land surface temperature and emissivities retrievals from simulated data under typical atmospheres and different land surface covers. The paper also compared both methods for retrieving low emissivities with simulated data. For typical land cover types, neglecting the instrumental noise, ISSTES is more accurate than CBTES with root mean square error (RMSE) of LSTs less than 0.0005K for the ISSTES and 0.1K for the CBTES. For low emissivity material, considering instrumental noise, both methods have large errors, but the CBTES performs much better.

To resolve uncertainties in evapotranspiration (ET) estimates caused by the retrieval error of remotely sensed data, this study develops an evaporative fraction (EF) parameterization based on surface energy balance and the assumption of generally invariant EF during the daytime. EF is deduced as a function of temporal change of surface temperatures, temporal change of air temperature, temporal change of net radiation, and fractional vegetation cover. The EF parameterization is evaluated by the simulated data from a soil-vegetation-atmosphere transfer model with a coefficient of determination (R2) of 0.786 and a root mean square error (RMSE) of 0.117. When the EF parameterization is used to estimate the daily ET of the Yucheng station in North China by in situ measurements, the estimated results are acceptable with an RMSE of 0.7 mm (relative RMSE of 25%) and an R2of 0.837.

Surface evaopotranspiration (ET) is one of the significant water and energy components in the land and atmosphere system. Remote sensing technology provides opportunity to map surface ET at large heterogeneous area. However, this ET is generally produced at the instantaneous scale. This paper investigated in the constant reference evaporative fraction upscaling method whether the use of a variable canopy resistance in the reference ET estimation from the Penman-Monteith equation could improve the daily ET estimate. Near-surface meteorological variables and eddy covariance system measurements used as the model inputs and ground-truth were collected from late April 2009 to late October 2011 at the Yucheng station in Northern China. Preliminary results showed that it was not an imperative step to use a more complex parameterization of canopy resistance to estimate the reference ET when the constant evaporative fraction method was applied to upscale the instantaneous ET to daily value.

This study firstly analyses the spatial representation of LAS (Large Aperture Scintillometer)-observed heat fluxes for a complex surface; and then evaluates the performance of SEBS model applied to a complex surface in comparison with in situ measurements. The results showed that LAS observation is indeed more stable than EC measurements even for complex surfaces, and the sensible heat flux from LAS is less than that from EC observations because of some land types with more evapotranspiration included into LAS footprint. SEBS overestimated latent heat flux at QYZ station in southern China because of the underestimation of H, but SEBS-estimated turbulent fluxes are more consistent with the LAS measurement.

Significance of Evapotranspiration (ET) has been realized in disciplines of hydrology, meteorology and agriculture from a number of studies. A parameterization based on the spatially contextual information of surface temperature-vegetation index, namely Ts-VI triangle method, is applied to estimate regional ET from remotely sensed data acquired at the Changwu agro-ecological experiment station. Surface net radiation (Rn) is estimated also from MODIS/Terra products. Ratio of soil heat flux (G) to Rnis determined using a linear combination of G/Rnat bare soil and fully vegetated surface. Reasonably good agreement between estimated and measured sensible heart flux from Large Aperture Scintillometer is observed with RMSD about 48 W/m2.