Evapotranspiration (ET) links the water cycle with the energy balance and serves as a key driving process for ecosystem functioning and water resource management. Canopy conductance (Gc) plays a central role in regulating transpiration, but many models inadequately represent its regulatory mechanisms and show varying applicability across different land cover types. This study develops a remote-sensing ET estimation approach suitable for large scales and diverse land cover types and proposes an improved canopy conductance model for daily latent heat flux (LE) estimation. By integrating the canopy radiation transfer concept from the K95 model into the multiplicative Jarvis framework, an improved canopy conductance model is developed that includes limiting effects from photosynthetically active radiation (PAR), vapor pressure deficit (VPD), air temperature (T), and soil moisture (θ). Eighteen combinations of limiting functions are designed to evaluate structural performance differences. Using observations from 79 global flux sites during 2015–2023 and integrating multi-source datasets, including ERA5, MODIS, and SMAP, a two-stage parameter optimization was applied to determine the optimal limiting function combination for each land cover type. And nine sites from nine different land cover types were selected for independent spatial validation. Temporal validation within the optimization sites shows that, at the daily scale, the model achieves a Kling–Gupta efficiency (KGE) of 0.82, a correlation coefficient (R) of 0.82, and a Root Mean Square Error (RMSE) of 27.83 W/m2, demonstrating strong temporal stability. Spatial validation over independent holdout sites achieved KGE = 0.84, R = 0.84, and RMSE = 22.53 W/m2. At the 8-day scale, when evaluated over the holdout sites, the model achieves KGE = 0.87, R = 0.88, and RMSE = 18.74 W/m2. Compared with the K95 and Jarvis models, KGE increases by about 34% and 15%, while RMSE decreases by about 38% and 12%, respectively. Relative to the MOD16 and PML-V2 products, KGE increases by about 32% and 16%, while RMSE decreases by about 33% and 17%, respectively. Comprehensive comparisons show that explicitly coupling canopy structure with multiple environmental constraints within the Jarvis framework, together with structure optimization across land cover types, can markedly improve large-scale remote-sensing ET retrieval accuracy while maintaining physical consistency and physiological rationality. This provides an effective pathway and parameterization scheme for producing ET products applicable across ecosystems.

Photosynthesis, a vital process for carbon exchange between biosphere and atmosphere, has been integrated into terrestrial biosphere models (TBMs). TBMs employ upscaling methods such as big-leaf (BL), two-leaf (TL), or multi-layer (MTL) models to simulate canopy-scale photosynthesis, with MTL model theoretically offering the highest accuracy due to its detailed canopy representation. The comparative efficacy of these models in simulating gross primary production (GPP), however, remains uncertain. This study provides a systematic assessment of how the MTL differs from the BL and TL models in GPP estimation across global flux tower sites. The results indicate that both the MTL and TL models performed better than BL model in simulating canopy GPP, reducing root mean square error (RMSE) by 32.23% and 26.51%, respectively, with the MTL (2.25 g C m- 2 d- 1) demonstrating a slightly improved accuracy compared to the TL model (2.44 g C m- 2 d- 1). Incorporating foliar clumping reduced the overestimation of GPP with mean error (ME) decreasing by 32%, 28.74%, and 6.94% for MTL, TL, and BL models, respectively; however, the specific impact varied among the models. The MTL model excelled in enabling layered simulations of photosynthesis, allowing for the identification of vertical heterogeneity in environmental responses. Nonetheless, its improvement in accuracy over simpler models like the TL model was limited without highly precise data on vertical structure. This study highlights that improved canopy structure data from LiDAR technologies, such as GEDI, is crucial for realizing the full potential of MTL models for accurately simulating carbon fluxes.

Cloud base temperature (CBT) is a crucial factor determining the surface downward longwave radiation (SDLR) under cloudy conditions. Theoretically, CBT‐based parameterized models offer more accurate representations of cloud radiation effects and SDLR compared to simplistic models. However, they have poor performance in practical retrievals and have less development and application than other models. This study aims to pinpoint the shortcomings of existing CBT‐based models, quantify model errors, and evaluate the impact of key parameter errors on SDLR retrieval results. Using simulated datasets based on radiative transfer models and ground‐based remote sensing datasets, we conducted a detailed analysis of four CBT‐based models. Our findings reveal that current model formulations inadequately capture the contributions of the atmosphere and cloud, leading to overestimation of the former and underestimation of the latter. However, these errors can partially offset each other. Under accurate parameter conditions, mean SDLR errors are within 10 W/m 2 for Diak, Gupta‐Cal, and Wang models, and approximately −5 W/m 2 for the Schmetz model. The influence of cloud base height (CBH) and cloud fraction (CF) is significant and complex. When errors in CBH and CF are combined, CF error exerts a dominant influence. Surface downward longwave radiation error is insensitive to CBH error when CF is underestimated, while the impact of CBH error on SDLR estimation is notable when CF is overestimated. Regardless of CBH error, SDLR error is sensitive to CF error. Furthermore, when model errors are combined with cloud parameter errors, model errors may amplify or partially offset the impacts of parameter errors.

Cloud base height (CBH) is one of the most uncertain parameters in surface downward longwave radiation (SDLR) estimation. Climatology statistical models of cloud vertical structure (CVS), which provide 1-degree grid averages or latitude zone averages of CBH and cloud thickness (CT), have been frequently applied to improve coarse-resolution SDLR estimation. This study aims to develop a regional CVS climatology statistical model containing CT and CBH statistics at a kilometer scale, using CloudSat, CALIPSO, and MODIS data, and to explore its potential in kilometer-scale CBH and SDLR estimations. The RMSE of CBH estimated from the new CVS model ranges from 0.4 to 2.6 km for different cloud types when validated using CloudSat/CALIPSO data. CBH RMSEs are 2.20 km for Terra data and 1.99 km for Aqua data when validated against ground measurements. The simple Minnis CT model greatly overestimated CBH, while the new CVS model produced much better results. Using CBH from the new CVS model, the RMSEs of estimated cloudy SDLR are 26.8 W/m2 and 29.2 W/m2 for the GuptaSDLR and Diak-SDLR models, respectively. These results are significantly better than those from the Minnis CT model and are comparable to those from the more advanced Yang-Cheng CT model. Moreover, the RMSEs of all-sky SDLR range from 22.6 to 21.5 W/m2 with resolution from 1 km to 20 km. These findings indicate that the regional CVS model is feasible for high-resolution CBH and SDLR estimation and can be effectively combined with other CBH estimation methods. This study provides a novel approach for estimating SDLR by integrating active and passive satellite data.

Downward shortwave radiation (DSR) is critical for understanding global radiation budget and climate dynamics. Traditional estimation of DSR commonly relied on horizontal surface assumptions, and thus neglecting critical topographic factors such as slope, aspect, and terrain-induced shadowing. This study proposed a novel hybrid method specifically designed for DSR estimation across the tibetan plateau (TP). The proposed method integrates multisource datasets comprising paired daily MODIS atmospheric products, hourly ERA5 reanalysis data, and digital elevation model (DEM). Direct solar irradiance on tilted surfaces was estimated from horizontal-surface direct radiation, accounting for solar elevation angle on the tilted surface and terrain shadowing effects. Diffuse solar irradiance was corrected by incorporating sky view factor and terrain-reflected radiation contributions. Compared with in situ measurements, the method demonstrated superior performance, with root mean square error (RMSE) values of 169.47 W/m(2) (hourly, excluding nighttime) and 41.56 W/m(2) (daily), exceeding the performance of benchmark datasets (ERA5-Land: 52.14 W/m(2); GLASS: 46.38 W/m(2); MCD18A1: 43.39 W/m(2); Himawari-8: 55.36 W/m(2)). The proposed method reveals a stronger robustness over rugged surfaces compared with the state-of-the-art satellite-retrieved DSR products.

With the growing demand for energy and the limitations of fossil fuel resources, the utilization of renewable energy sources has become a vital and sustainable solution. However, identifying optimal locations for the development of these resources remains a major challenge in energy planning. Accurate spatial potential assessment can play a critical role in enhancing efficiency and reducing production costs. This study aims to present a scenario-based framework for assessing solar and wind energy potential in Pakistan. A total of 19 spatial criteria were used, categorized into evaluation and constraint factors. The full consistency method (FUCOM) was applied to weight the criteria, while the ordered weighted averaging (OWA) method was employed to model various potential scenarios. The results revealed that global horizontal irradiation (GHI) and proximity to transmission lines are the most significant factors for solar energy, whereas wind speed and wind power density are crucial for wind energy potential. Scenario analysis indicated that, under the AND scenario, the area with very high potential for solar and wind energy is 8005.72 km2 and 968.98 km2, respectively. These values increase to 63,607.52 km2 and 16,288.32 km2 under the OR scenario. The spatial agreement map for the simultaneous development of solar and wind energy showed an overlap of 461.42 km2 in the AND scenario and 11,836 km2 in the OR scenario. These findings highlight the importance of scenario-based decision-making approaches and accurate spatial evaluations in the development of multiple renewable energy plant sites under various investment and policy conditions. Moreover, the proposed framework can serve as a practical model for simulating and assessing renewable energy development potential in other regions of the world.

The quantitative characterization of the thermal conditions in the Tibetan Plateau has long been a focal point of global research. Downward shortwave radiation, as a crucial component, plays an important role in numerous land surface processes while also serving as a significant indicator of the plateau’s thermal state. In order to gain a more comprehensive understanding of the Earth’s radiation budget in the Tibetan Plateau region, this study undertook an evaluation of six radiation products (ISCCP-FH, CERES-SYN, GLASS DSR, Himawari-8, MCD18A1, and ERA5). Two sets of ground measurements (downward shortwave radiation values from 10 CMA sites and 6 sites provided by the National Tibetan Plateau Data Center) in 2015 and 2016 were used as validation data to verify the accuracy of the remote sensing products. The results show that in the Tibetan Plateau region, CERESC products show the highest accuracy among the six data products with a bias (relative bias) of −7.57 W/m2 (3.46%), RMSE (relative RMSE) of 32.77 W/m2 (14.99%), and coefficient of determination of 0.80. Among all products, only the ERA5 products overestimated the value of downward shortwave radiation in the Tibetan Plateau region with a bias (relative bias) of 15.62 W/m2 (7.14%). By employing a spatial resolution upscaling approach, we assessed the influence of varying spatial resolutions on the validation accuracy, with the results indicating minimal impact. Through an analysis of the impact of cloud factors and aerosol factors on the validation accuracy, it is deduced that ERA5, Himawari-8, and MCD18A1 products are significantly influenced by cloud factors, whereas the CERES-SYN product is notably affected by aerosol factors.

Accurate surface soil moisture (SM) data are crucial for agricultural management in Jiangsu Province, one of the major agricultural regions in China. However, the seasonal performance of different SM products in Jiangsu is still unknown. To address this, this study aims to evaluate the applicability of four L-band microwave remotely sensed SM products, namely, the Soil Moisture Active Passive Single-Channel Algorithm at Vertical Polarization Level 3 (SMAP SCA-V L3, hereafter SMAP-L3), SMOS-SMAP-INRAE-BORDEAUX (SMOSMAP-IB), Soil Moisture and Ocean Salinity in version IC (SMOS-IC), and SMAP-INRAE-BORDEAUX (SMAP-IB) in Jiangsu at the seasonal scale. In addition, the effects of dynamic environmental variables such as the leaf vegetation index (LAI), mean surface soil temperature (MSST), and mean surface soil wetness (MSSM) on the performance of the above products are investigated. The results indicate that all four SM products exhibit significant seasonal differences when evaluated against in situ observations between 2016 and 2022, with most products achieving their highest correlation (R) and unbiased root-mean-square difference (ubRMSD) scores during the autumn. Conversely, their performance significantly deteriorates in the summer, with ubRMSD values exceeding 0.06 m3/m3. SMOS-IC generally achieves better R values across all seasons but has limited temporal availability, while SMAP-IB typically has the lowest ubRMSD values, even reaching 0.03 m3/m3 during morning observation in the winter. Additionally, the sensitivity of different products’ skill metrics to environmental factors varies across seasons. For ubRMSD, SMAP-L3 shows a general increase with LAI across all four seasons, while SMAP-IB exhibits a notable increase as the soil becomes wetter in the summer. Conversely, wet conditions notably reduce the R values during autumn for most products. These findings are expected to offer valuable insights for the appropriate selection of products and the enhancement of SM retrieval algorithms.

Clouds are a critical factor in regulating the climate system, and estimating cloudy-sky Surface Downward Longwave Radiation (SDLR) from satellite data is significant for global climate change research. The models based on cloud water path (CWP) are less affected by cloud parameter uncertainties and have superior accuracy in SDLR satellite estimation when compared to those empirical and parameterized models relying mainly on cloud fraction or cloud-base temperature. However, existing CWP-based models tend to overestimate the low SDLR values and underestimate the larger SDLR. This study found that this phenomenon was caused by the fact that the models do not account for the varying relationships between cloud radiative effects and key parameters under different Liquid Water Path (LWP) and Precipitable Water Vapor (PWV) ranges. Based upon this observation, this study utilized Fengyun-4A (FY-4A) cloud parameters and ERA5 data as data sources to develop a new CWP-based model where the model coefficients depend on the cloud phase and cloud water path range. The accuracy of the new model’s estimated SDLR is 20.8 W/m2 for cloudy pixels, with accuracies of 19.4 W/m2 and 23.5 W/m2 for overcast and partly cloudy conditions, respectively. In contrast, the accuracy of the old CWP-based model was 22.4, 21.2, and 24.8 W/m2, respectively. The underestimation and overestimation present in the old CWP-based model are effectively corrected by the new model. The new model exhibited higher accuracy under various station locations, cloud cover scenarios, and cloud phase conditions compared to the old one. Comparatively, the new model showcased its most remarkable improvements in situations involving overcast conditions, water clouds with low PWV and low LWP values, ice clouds with large PWV, and conditions with PWV ≥ 5 cm. Over a temporal scale, the new model effectively captured the seasonal variations in SDLR.

Evapotranspiration（ET） is a critical component of hydrological and energy balance models and plays an important role in the groundwater monitoring and agricultural irrigation, however heterogeneous surface can lead to spatial scale errors in estimates of latent heat flux using remote sensing data. Using Sentinel data as the research data, the EFAF（Evaporative Fraction and Area Fraction） method and the temperature downscaling method were used to correct the errors of the latent heat flux, and the differences between the two methods were compared. The results show that the accuracy of the EFAF（Evaporative Fraction and Area Fraction） method and the temperature downscaling method are comparable, the coefficient of determination（R2） is about 0.86, the Mean Bias Error（MBE） is about 18 W/m2, the Root Mean Square Error（RMSE） is about 64 W/m~2. The accuracy of both methods is higher than that of the uncorrected latent heat flux, which has a certain effect on correcting the error of latent heat flux caused by heterogeneous land surface. The distribution of latent heat flux estimated by the EFAF method at the pixel scale is consistent with the land classification data, and at the regional scale with the uncorrected latent heat flux distribution. The latent heat flux estimated by the temperature downscaling method is highly similar to the distribution of the land surface temperature at the pixel scale, and its spatial details are richer and the local features are obvious.

Vegetation plays a fundamental role within terrestrial ecosystems, serving as a cornerstone of their functionality. Presently, these crucial ecosystems face a myriad of threats, including deforestation, overgrazing, wildfires, and the impact of climate change. The implementation of remote sensing for monitoring the status and dynamics of vegetation ecosystems has emerged as an indispensable tool for advancing ecological research and effective resource management. This study takes a comprehensive approach by integrating ecosystem monitoring indicators and aligning them with the objectives of SDG15. We conducted a thorough analysis by leveraging global 500 m resolution products for vegetation Leaf Area Index (LAI) and land cover classification spanning the period from 2016 to 2020. This encompassed the calculation of annual average LAI, identification of anomalies, and evaluation of change rates, thereby enabling a comprehensive assessment of the global status and transformations occurring within major vegetation ecosystems. In 2020, a discernible rise in the annual Average LAI of major vegetation ecosystems on a global scale became evident when compared to data from 2016. Notably, the ecosystems demonstrating a slight increase in area constituted the largest proportion (34.23%), while those exhibiting a significant decrease were the least prevalent (6.09%). Within various regions, such as Eastern Europe, Central Africa, and South Asia, substantial increases in both forest ecosystem area and annual Average LAI were observed. Furthermore, Eastern Europe and Central America recorded significant expansions in both grassland ecosystem area and annual average LAI. Similarly, regions experiencing notable growth in both cropland ecosystem areas and annual average LAI encompassed Southern Africa, Northern Europe, and Eastern Africa.

Evapotranspiration (ET) is a key variable in terrestrial water, energy and carbon cycles. ET products have proliferated. This study evaluates ten monthly, globally available products, including one product from the upscaling of in situ observations (FLUXCOM), one ensemble product (SynthesisET), four remote sensing-based products (SSEBop, MOD16, Numerical Terradynamic Simulation Group (NTSG) and PT-JPLSM) and four prod-ucts from land surface models (Global Land Data Assimilation System (GLDAS), FLDAS, TerraClimate and Global Land Evaporation Amsterdam Model (GLEAM)). The assessments are conducted during the period of 2003-2013 using FLUXNET2015 eddy covariance datasets at the site scale, which are grouped by land cover, elevation and climate, and GRACE-based water balance ET at basin scales. The results indicate that all products show com-parable performance and that no single product shows the best performance. FLUXCOM and GLDAS have outstanding performances at both the site and basin scales, respectively. SynthesisET tends to have suboptimal performance at both validation scales, while FLDAS, SSEBop and TerraClimate show relatively poor results. Other products reproduced ET moderately well. The metrics of the two validation methods are compared. The results indicated that the performance of products at the basin scale is usually better than that at the site scale. The anomalies in specific regions and the trends among ET products are observed at both scales. In addition, the special relationship between the products and the validation methods will affect the credibility of their assess-ment results. This study contributes to the assessment of the performance of products to identify proper can-didates for hydrological analysis and improve the ET algorithm.

GF-1—GF-7 satellite series with 19 major payloads has been launched with the continuous implementation of the highresolution Earth Observation System（referred to as GF） in the past decade. This progress is vital in forming the multispectral and multimode observation capability of China’s Earth Observation System. Remote sensing data with high spatial, temporal, and spectral resolution have been obtained and widely used in scientific research and remote sensing applications. However, obtaining high-quality remote sensing information products from the original satellite data is a complicated scientific issue and faces huge challenges. Hence, the conversion chain from GF data to information must be urgently set up to reduce the remote sensing application threshold and improve the effectiveness of application services.The errors of remote sensing quantitative products are determined by accumulating a series of errors, such as sensor imaging error,calibration error, remote sensing data processing error, and quantitative inversion error. Thus, improving the accuracy of quantitative remote sensing products is a complex system engineering. Completing the whole process, including data processing, retrieval algorithm development, product generation, and validation independently, is challenging. Remote sensing algorithm test and product validation are the two crucial ways for the quality improvement of remote sensing products. Hence, this study proposes the technique system of GF common product generation and validation to improve the quality of GF remote sensing products further, thereby guaranteeing the improvement of the application quality and the extensive application area of GF remote sensing products. Lastly, the current progress of the GF common product validation and algorithm determination system platform is introduced and discussed.GF common products are required by more than two thematic remote sensing products. They can be validated using in situ observations.According to the GF common product system, the number of 39 + 6 products in seven categories are sorted out for the common requirements of multiple users, including geometric products, basic radiation products, land cover and land type products, energy balance products, vegetation products, water products, and atmosphere products. This study presents the technique flowchart of GF common product algorithm determination and product generation. The key technologies of algorithm testing, algorithm optimization, product generation, and validation are developed. Eleven national standards for remote sensing product validation are issued and implemented. Other group standards, such as GF common product generation, ground in situ observation, and validation of GF common remote sensing products, are being designed and compiled. Based on these validation technologies and the in situ data from the national network of GF remote sensing product validation field sites, the GF common product validation platform and product algorithm determination system platform can ensure the high quality of GF common products.Building such a technical system for GF common product generation and validation has great relevance for ensuring high accuracy and high quality to improve the efficiency of application services further. It requires the cooperation of multiple researchers from different units to research and develop common product retrieval algorithms. Moreover, the algorithm should be continuously tested to improve the accuracy of common products.

Photosynthetically Active Radiation（PAR） is an important input of vegetation productivity models, and also a key parameter of terrestrial ecosystem models and biogeochemical models. The accuracy and availability of current global or regional products are still insufficient to better understanding Earth system. China has launched series of Gaofen（GF） Earth observation satellites and provide the possibility of high spatial resolution PAR products retrieval. In this paper, a new method based on a parametric model for remote sensing inversion of PAR was proposed. The surface albedo products and the aerosol optical depth products were retrieved from GF-1 satellites, the cloud optical thickness products were retrieved from Himawari-8 and FY-4 satellites. Under clear sky conditions, the attenuation of PAR by aerosol and Rayleigh scattering is mainly considered. The influence of cloud on incident radiation is mainly considered for cloudy skies, and the calculation is based on the Mie scattering theory of spherical particles. Surface received direct PAR for rugged surfaces were calculated with the input of incident angle, the slope and the aspect. The enhancement or attenuation effect of the scattered radiation were retrieved using the sky view factor the horizontal surface received diffuse radiation. The PAR products were compared and verified using the continuous observation data of the ground stations collected from the Hebei Huailai, the Heihe River Basin Surface Process Comprehensive Observation Network and Ganyansuo in Chengdu. The correlation coefficient, mean bias error and the root mean square error between the two datasets were 0.87, 1.56 W/m2 and 16.14 W/m2, respectively. The spatial resolution of the input atmospheric parameters（sub-satellite point 1 km） and the surface parameter resolution（16 m） of the PAR products have a large spatial scale difference. The atmospheric parameters with the resolution of 50 m provided by the GF-4 satellite will be used to further improve the spatiotemporal accuracy of GF PAR products, and more extensive and in-depth verification analysis will be carried out in our future study.

The Fraction of absorbed Photosynthetically Active Radiation（FPAR） is one of the key parameters in the light use efficiency model of the carbon cycle. High-spatiotemporal-resolution data have been provided for the inversion of quantitative remote sensing products since the launch of GF satellites. The FPAR products derived from GF satellite data provide precise and accurate input parameters for the analysis and evaluation of the ecosystem’s carbon cycle. In this study, a deep learning algorithm was developed to retrieve FPAR over China based on the simulated data of the radiative transfer model. The inputs are surface reflectance, cloud detection, and land cover products of GF-1 satellite data, whereas the output is FPAR. The FPAR product has a spatial resolution of 16 m and a temporal resolution of 10 days.This method uses the SAIL model to simulate output canopy FPAR and reflectance under various input variables, such as solar and observing angles and atmospheric conditions. The FPAR inversion model of GF-1 satellite data was obtained using a deep belief network.The long-term crop and grassland FPAR observation data in Huailai and Heihe were used to compare and validate the FPAR products, with a root mean square error of 0.15 and 0.17, respectively. The inversed FPAR is in good agreement with the measured FPAR in the low values,but lower than the measured FPAR in the high values. The radiative transfer model, the representativeness of the simulated data, and the preprocessing（calibration and geometric and atmospheric correction） of the GF-1 satellite data inevitably introduce some biases in the inversion process. This method uses the multidimensional atmospheric and surface variables as the input and the simulated vegetation canopy by the radiative transfer model parameters as the output. The simulated dataset, used as the training samples for deep learning, makes up for the errors in the deep learning training process caused by the insufficient number of training samples and incomplete observation data.The input of the inversion is only the surface reflectance product with the information of the sun angle and the observation angle. It lessens the difficulty of obtaining input parameters, reduces the influence of the error transmission of the input parameters, and is conducive to the realization of the commercial production of the product. The high FPAR is mainly distributed in the northeast, north, central, east, southwest,and south parts of China. The interannual variation of the FPAR time series, combined with the vegetation growing cycle and phenology, is high in spring and summer and low in autumn and winter.

Traditional pixel-based algorithms, considering only spectral information and ignore spatial information, have limitations to provide better accuracy of surface heat fluxes from high-resolution images. Based on the high-resolution satellite images, this paper systematically analyzes the feasibility of combining the object-based approach with traditional physical model to estimate surface heat fluxes.Sentinel-3 surface temperature and Sentinel-2 multi-spectral data were input to the energy balance Two-Source Energy Balance (TSEB) model to estimate the surface heat fluxes. Pixel-based TSEB model was firstly employed at 10m. An multi-layer experiments framework was constructed to explore the applicability of the object-based method. The object-based approach is introduced into TSEB model and the multi-scale segmentation algorithm of eCognition is used to segment the images and extract the surface objects. Two object -based strategies, estimating heat fluxes before or after aggregating objects properties, were used to analyze the influence of the different strategies on the results. Object-based method and different inversion strategies are compared with pixel -based results. The results show that, comparing with the pixel-based method, the object-based method is beneficial to map surface heat fluxes and can improve the estimation accuracy of the TSEB model, which is mainly influences by the vegetation-related parameters.

The radiation budget in polar regions plays an important role in global climate change study. This study investigates the performance of downward longwave radiation (DLR) of three satellite radiation products in polar regions, including GEWEX-SRB, ISCCP-FD, and CERES-SYN. The RMSEs are 35.8, 40.5, and 26.9 W/m(2) at all polar sites for GEWEX-SRB, ISCCP-FD, and CERES-SYN. The results in the Arctic are much better than those in the Antarctic, RMSEs of the three products are 34.7 W/m(2), 36.0 W/m(2), and 26.2 W/m(2) in the Arctic and are 38.8 W/m(2) and 54.8 W/m(2), and 28.6 W/m(2) in the Antarctic. Both GEWEX-SRB and CERES-SYN underestimate DLRs at most sites, while ISCCP-FD overestimates DLRs at most sites. CERES-SYN and GEWEX-SRB DLR products can capture most of the DLR seasonal variation in both the Antarctic and Arctic. Though CERES-SYN has the best results that RMSE within 30 W/m(2) in most polar sites, the accuracy of satellite products in polar regions still cannot meet the requirement of climate research. The improvement of satellite DLR products in polar regions mainly depends on the quality of improving input atmospheric parameters, the accuracy of improving cloud detection over the snow and ice surface and cloud parameters, and better consideration of spatial resolution and heterogeneity.

Evapotranspiration (ET) is an important part of surface–atmosphere interactions, connecting the transfer of matter and energy. Land surface heterogeneity is a natural attribute of the Earth’s surface and is an inevitable problem in calculating ET with coarse resolution remote sensing data, which results in significant error in the ET estimation. This study aims to explore the effect and applicability of the evaporative fraction and area fraction (EFAF) method for correcting 1 km coarse resolution ET. In this study we use the input parameter upscaling (IPUS) algorithm to estimate energy fluxes and the EFAF method to correct ET estimates. Five ground stations in the midstream and downstream regions of the Heihe River Basin (HRB) were used to validate the latent heat flux (LE) calculated by the IPUS algorithm and EFAF method. The evaluation results show that the performance of the EFAF method is superior to that of the IPUS algorithm, with the coefficient of determination (R2) increasing, the root mean square error (RMSE) decreasing, and the mean bias error (MBE) decreasing by 17 W/m2 on average. In general, the EFAF method is suitable for correcting the deviation in LE estimated based on Sentinel data caused by land surface heterogeneity and can be applied to obtain accurate estimates of ET.