The Gravity Recovery and Climate Experiment (GRACE) satellite provides an unprecedented tool for monitoring large-scale terrestrial water storage (TWS) changes. Yet, its coarse resolution restricts its effectiveness in areas with complex hydrogeological environments, such as southwestern China. To address this limitation, we propose a novel method to improve the spatial resolution of GRACE observations. Our approach leverages a deep learning downscaling model that integrates generative adversarial networks (GANs) and transformer attention mechanisms to derive the spatial patterns of TWS variations. The model incorporates the estimated total water storage changes from GRACE and some hydrological variables—including the digital elevation model (DEM), soil moisture, evapotranspiration, temperature, and precipitation—to enhance the resolution and accuracy of GRACE data. By implementing this method, we successfully increased the spatial resolution of GRACE observations from 0.25° to 0.05°. The advanced neural network downscaling model can accurately characterize local water storage variations, with Nash–Sutcliffe efficiency (NSE) values ranging from 0.58 to 0.92. Moreover, this model not only significantly increases the spatial resolution but also maintains the spatial distribution, offering valuable insights for regional water resources management and fostering small-scale hydrological research. The results have profound implications for sustainable water resources management and climate change assessment.

In regions with complex terrain, high-spatial-resolution downward surface shortwave radiation (DSSR) is critical for monitoring mountain ecological processes and for environmental management. However, currently available DSSR products are often too coarse (from a kilometer to tens of kilometers) to capture the spatial heterogeneity of DSSR in topographically complex regions. To address this issue, this study proposes a new downscaling method for all-sky instantaneous DSSR, employing a machine learning (ML) method, top-of-atmosphere reflectance, and topographic data. The method is used to downscale the 5-km Himawari-8 (H-8) DSSR product to the Sentinel 10 m scale. A region of Southwest China was chosen as a case study. Validated by field measurements from nine stations in 2020, the downscaled DSSR showed improvements in the mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE) of 32.74%, 9.31%, and 6.34%, respectively, when compared with the original product. The downscaled DSSR can be generated in all-sky conditions. In general, this method successfully captures high-resolution DSSR over complex terrain and should be helpful for related studies.

Quantifying and monitoring land surface evapotranspiration (ET) is an essential task for understanding the earth’s water, energy, and carbon cycles. ET, specifically plant transpiration ($T$), is closely linked to the photosynthesis, which is coupled through stomatal function. However, the mechanistic links between sun-induced chlorophyll fluorescence (SIF) information indicating canopy photosynthetic activity and$T$are complex and difficult to derive empirically. An empirical SIF-$T$relationship at ecosystem scale was developed and coupled to the two-source energy balance model (TSEB-SIF) to estimate the ET and its components,$T$and soil evaporation,$E$. By comparing model predictions with observations from an irrigated cropland site located in a semiarid region, the TSEB-SIF model shows a slightly better performance to the TSEB model in estimating ET, especially under water deficit conditions. Moreover, the TSEB-SIF model more reliably partitioned the$T$from ET, while the TSEB model tended to overestimate the contribution of$T$to ET.

The validation of remote sensing land surface temperature (LST) data necessitates a comparison between satellite retrieval outcomes andin situobservations. The efficiency ofin situobservations can be ameliorated via analysis and modeling, whereby the heterogeneity ofin situobservations on temporal and spatial scales is central to the analysis. A fresh algorithm has been developed to optimize deployment by relying on the standard deviation of spatial heterogeneity. The validation outcomes indicated that the coefficient of determination (R2) of the five typical surface features at three time points was 0.66, with a root mean square error (RMSE) of 1.99 °C and a mean absolute error (MAE) of 1.62 °C. Moreover, the spatiotemporal heterogeneity character of typical surface features displayed different features, and the LST variation curves of each typical surface feature displayed a similar pattern under sunny conditions. The application of the Savitzky–Golay filtering method reduced errors by 4% of the total errors caused by random errors inin situobservations. With the analysis of the spatiotemporal characteristics of in-situ observation. First, the number of required sites algorithm computed a minimum sampling number of 4. Second, the analysis of the means algorithm computed the 5 optimal points. Additionally, the multipointin situobservations were regularized by standard scores. The optimization of the selected points could be executed to improve the results by eliminating the "distance" points, which are located further away from the multipointin situobserved LST statistical mean. Our outcomes will deepen the comprehension of the spatiotemporal character ofin situobserved LST and enhance the efficiency of equipment with equivalent accuracy.

Accurate estimates of downward surface shortwave radiation (DSSR) are critical for hydrological, biogeochemical, and ecological studies and remote sensing-based estimation of DSSR is an important way to derive DSSR at different spatio-temporal ranges. However, current estimation algorithms usually somewhat rely on atmospheric parameters or in-situ measurements, further blocking the application of these methods. Inspired by the emerging DSSR reanalysis data from the model simulation, this study proposed an integrated method by initializing the estimation model with ERA5 reanalysis data and further refining the estimation through iterative training. The random forest regression method was applied in the estimation model to build the connection between DSSR with the MODIS top-of-atmosphere reflectance, cloud flag, geometry information, elevation, latitude, and coefficient of Sun-Earth distance as input features. To separately consider the impact from cloud cover, the estimation model was established for clear-sky and cloudy-sky conditions, respectively. The proposed method was applied to estimate instantaneous DSSR of MODIS daytime overpasses in the Southwest part of China in 2020. Comparison between the estimates of the initialized model and the finalized model shows that the iterative process improves the DSSR estimates on both spatial distribution and accuracy. Validated by the measurements from nine sites in the study area, the DSSR estimates of the finalized model show a 0.02 higher correlation coefficient (CC) and 7.35 W m-2lower root mean squared error (RMSE) than that of the initialized model. To better evaluate the performance of the proposed method, three popular DSSR products including ERA5, MCD18A1, and Himawari-8 were introduced to make an inter-comparison with the estimation of this study. The validation results showed that the all-sky DSSR estimated in this study had the best accuracy, with a CC of 0.90, a mean bias error of 37.80 W m-2, a RMSE of 125.30 W m-2, and a relative root mean squared error of 42.73%. Obvious improvements can be observed under cloudy-sky and clear-sky conditions, respectively. Because of the simplicity and reliable performance of the proposed method, it shows good potential for DSSR estimation.

Land surface temperature (LST) is a crucial parameter in the study of Land Surface processes. Currently, there are great progresses in LST retrieval based on thermal infrared (TIR) remote sensing. However, TIR-based LST suffers from serious spatial discontinuities due to clouds. Although there are methods developed to address this issue, the methods show high uncertainty in days with extremely clouds. Therefore, this study proposed an integrated method to reconstruct cloudy LSTs using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and the China Land Data Assimilation System (CLDAS) LST. This method was separated into two parts according to the ratio of clear-sky pixels (RCP). On days with RCP more than 30%, a random forest reconstruction method was used to establish the complicated relationship between LST and its predicting variables, including solar radiation factor, vegetation index, water index, topographic information and latitude, and then applied to cloudy pixels to derive LSTs. For the rest days, the CLDAS LST was selected to assist the reconstruction via downscaling it to 1 km and then merged with clear-sky data to generate spatially continuous results. The proposed method was applied to the Southwest China and generate daily LST product in 2019. Validation with ground measurements demonstrated a high accuracy with the correlation coefficient changing from 0.73 to 0.88. Additionally, the reconstructed LST dataset exhibits similar temporal variability as existing all-weather satellite-based and reanalysis LST products. The findings reveal that this method shows good potential in generating gap-free LST dataset, especially for the mountain regions with heavy clouds.

Accurate information about the airport's throughput is crucial for monitoring traffic flow, evaluating the development status of aviation industry. In this letter, we used the Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer (NPP-VIIRS) and the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light (NTL) data as effective proxies for evaluating and monitoring the airport's throughput power in China mainland. The results show that there is a significant positive correlation between the NTL intensity and the airport's throughput ( R2> 0.85). The NPP-VIIRS NTL data have been proved to not only distinguish the airport area and nonairport area but also build airport's NTL feature space. This letter reveals that the NTL images provide powerful remote sensing data sources to model the spatiotemporal dynamics of airport's throughput of China mainland at a large spatial scale.

Well design and coordinated implementation of validation activity is necessary to evaluate the accuracy of remote sensing product. However, validation is not a straightforward task and remain many challenges. A generally recognized difficult issue is the inconsistence between sparse observations and remote sensing pixels, strong spatial and temporal variations of surface variables, and the intrinsic heterogeneity of land surfaces. Thus, to develop, design and conduct reasonable validation schemes and activities to acquire ground truth at pixel scale over heterogeneous land surfaces is urgently needed. This contains, from the perspective of measurement, integrating various ground observations collected at multi-scale, in order to validate different types of RSPs from site to network, especially for those land surface variables with strong spatial-temporal variations. To this end, a dedicated validation initiative has been launched in China since 2011. The main scientific objectives and research contents are to develop mathematical approaches for spatial sampling optimization to acquire the ground truth at pixel scale over heterogeneous land surfaces, to form a series of recognized and practicable technical specifications to guide validation of various RSPs, and to establish a prototype of national validation network for long term operation. Specific validation activities, such as HiWATER, were conducted from site to network, through multi-scale observations collected from multi-platform and multi-source sensors, to experimentally examine those proposed methodologies and guidelines. Following the experience of these validation exercises, we are coordinating a Chinese validation network to use standardized and recognized technical specifications in implementing future validation attempts, aiming to extend validation exercises from point scale to regional scale and to national scale across different zones.

The systemic validation works were carried out at a watershed scale based on the ground-based observation data of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER). Three validation strategies, scaling-up, spatial representation analysis, footprint analysis were used based on different data acquirement techniques. Some studies were performed and four types of remote sensing products were validated. This paper makes a general introduction on the validation results based on these systematic validation activities, which aims to support the integrated study of the water-ecosystem-economy in the Heihe River Basin.

This study describes the use of ground-based albedometer measurement based on the automatic weather stations (AWS) for validating MCD43A3 and GLASS albedo products over heterogeneous landscapes in Heihe river Basin, China. Because the footprint of ground observed albedo was far less than the spatial resolution of albedo products, high-resolution albedo imageries were used as an upscaling bridge to reduce the scale discrepancy. Based on this scheme, we present the results from an accuracy assessment of MODIS and GLASS. The validation results show that MODIS and GLASS have RMSEs less than 0.05 over large areas and over a full year of measurements.

The ground-based sites are critical for assessing the uncertainties and evaluating the accuracy of the satellite-derived land surface temperature (LST) products by using the temperature-based validation (T-based) method. However, there is often a risk of the spatial mismatch between the observations of the satellite-sensor and the ground validation sites. Therefore the heterogeneity analysis of the ground-based sites, which can indicate the representativeness of the sites' observation, has been a basic and key stage in the whole process. The purpose of this paper is to analyses the heterogeneity of the ground sites based on the variogram function. In this study, the variograms of the eighteen sites in the Heihe River Basin (HRB) based on NDVI data were calculated to discuss the heterogeneities of these sites. Then according to the heterogeneity analysis results, these sites were leveled ground observation scale for validating satellite-derived LSTs.

The validation of remote sensing land surface temperature (LST) products is vital for their broad application. Conventional validation methods use ground-based measurements to evaluate the LSTs retrieved from remote sensing data. However, it is difficult to directly validate low or medium spatial resolution LST products because of the scale mismatch between in situ and remote sensing observations. In this letter, we compare two ground-based in situ observations with Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km-resolution LST products (MOD11A1). We focus on the differences in scale between the ground-based measurements and the MODIS observations. A comparison of the results obtained during the daytime with those obtained at night indicates that stronger heterogeneity leads to a greater scale-mismatch effect. Because the LST heterogeneity influences the mismatch effect, semivariance is employed to analyze the heterogeneity of the MODIS 1-km mixed-pixel data using the LSTs retrieved from the high-resolution Thermal Airborne Spectrographic Imager and the Advance Spaceborne Thermal Emission and Reflection Radiometer data.

An evaluation of the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) products is essential for their use in scientific studies. We evaluated the accuracy of MODIS NDVI data that were derived from the MOD09GQ and MYD09GQ products using ground-based measurements from nearly one complete growing season (from May 2 to September 28, 2013) for six land-use types in the upper and middle reaches of the Heihe River Basin. The spatial heterogeneity and scale effects of the NDVI were analyzed using TM8 images of the observation sites. A comparison of the field measurements showed that the MODIS NDVI data were correlated with the in situ data and had an R2of 0.60-0.98. The semivariance analysis results showed that scale effects were common for MODIS data at a pixel resolution of 250 m for corn, potato, rape, and barley crops. Upscaling the in situ NDVI data using high spatial resolution satellite images can improve the validation accuracy of pixel-level NDVI. The results from this letter provide ground-based NDVI data that are required for the calibration and validation of satellite observations and products.

This letter introduces the ecohydrological wireless sensor network (EHWSN), which we have installed in the middle reach of the Heihe River Basin. The EHWSN has two primary objectives: the first objective is to capture the multiscale spatial variations and temporal dynamics of soil moisture, soil temperature, and land surface temperature in the heterogeneous farmland; and the second objective is to provide a remote-sensing ground-truth estimate with an approximate kilometer pixel scale using spatial upscaling. This ground truth can be used for validation and evaluation of remote-sensing products. The EHWSN integrates distributed observation nodes to achieve an automated, intelligent, and remote-controllable network that provides superior integrated, standardized, and automated observation capabilities for hydrological and ecological processes research at the basin scale.

Remotely sensed data have long been seen as the best way to determine spatially distributed evapotranspiration (ET) fluxes owing to their spatial and temporal continuity. A simple biophysical model by using remotely sensed leaf area index data and the Penman-Monteith (PM) equation was introduced to calculate daily ET of grassland and farmland in the middle and upper reaches of Heihe River Basin. The modelled daily ET agrees well with measurements and the R2 is over 0.80. This study confirmed that the PM equation with MODIS LAI can provide reliable estimates of ET at daily time scales and with different ecosystems in arid and cold regions.

Land surface temperature (LST) is a key parameter in climatological and environmental studies [1]. The Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the NASA Terra and Aqua Earth Observing System satellites, can provide global temperature and narrowband emissivity data on a daily basis. However, when the surface is obscured by clouds, the variable cannot be measured directly by using satellite thermal infrared channels, which leads to many invalid value pixels in the MODIS LST products. Methods for calculating LST of the MODIS cloudy pixels are important, yet few studies have been done. The objective of this paper is to estimate the LST values of the cloudy-pixels using the neighboring-pixel approach (NP) and MODIS NSSR (net surface shortwave radiation) product. In this study, the Heihe River Basin was selected as a case study area. The estimation was validated using ground-measured data of Huazhaizi (HZZ) desert station which is covered by homogeneous desert steppe. The validation shows that the reconstruction values of MODIS LSTs can agree well with the ground-measured data, and the biggest absolute error is 2.6K.

A simultaneous airborne, satellite and ground based remote sensing experiment which is aiming to improve the observability, understanding, and predictability of hydrological and related ecological processes at catchmental scale is implemented in a typical inland river basin of northwest China. The experiment is composed of the cold region, forest, and arid region hydrological experiments as well as a hydro/meteorological elements and Doppler radar precipitation observation experiment. Airborne microwave radiometers at L, K and Ka bands, hyperspectral imager, thermal imager, and lidar are used. Various satellite data are collected. Based on these observations, the remote sensing retrieval models and algorithms of water cycle variables can be developed or improved, and a catchment-scale land/hydrological data assimilation system is going to be developed.

A field campaign was executed in a semi-arid grassland of northwest China from July 11th-July 15th, 2002. According to the VALERI (Validation of Land European Remote Sensing Instruments) sampling procedures, the leaf area index (LAI) were intensively measured within a homogenous 3/spl times/3 km/sup 2/ square by using LAI-2000 and TRAC instrument. A quarter scene of Landsat7 ETM+ with acquisition times close to the field campaign time was processed by proper geo-registration and atmospheric correction. Three kinds of spectral vegetation index including NDVI, SR and MSAVI in the sampling area were derived from the corrected ETM+ image. The two sets of LAI data measured with LAI-2000 and TRAC instrument at the same site were inter-compared. The relationships between the measured LAI and vegetation indices were investigated as well. The results elicit that the statistical relationships between measured LAI and the different vegetation indices are consistent. Among them, NDVI seems the most promising estimator for the extraction of LAI. In addition, the LAI-2000 seems to perform better for LAI measurement in the semi-arid grassland than the TRAC instrument.

The study area is Gaotai Oasis located in the middle of Heihe River Basin, China. The data are from band 8 of Landsat ETM+ images with a resolution of 15 m. The edge enhancement and unsupervised classification are used to extract corridor information. To estimate the extraction precision, four validation regions are selected for collecting 269 sampling points using Global Position System (GPS). The results show that the remote sensing method can obtain much more detailed corridor information than the method of digitizing from the topographic maps. The average extraction precision can reach 92.6%. Lastly, some problems under settlement are discussed such as extraction of different types of corridor information, the transverse corridor information and newly developed oasis corridor information.