Tongren Xu

Soil moisture (SM) plays a pivotal role in regulating terrestrial energy-water exchanges and exerts substantial influence on agricultural productivity. In this study, a high-resolution soil moisture (HRSM) dataset (16 m) was generated by integrating multi-source remote sensing data from SMAP, HJ-2, Sentinel-2, and Gaofen-6, together with the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model. The data assimilation (DA) method was implemented for assimilating HRSM within the Ensemble Kalman Filter (EnKF) framework using the Noah-MP model at a spatial resolution of 1 km. To enhance the spatial detail of SM, HRSM and its relative uncertainties derived from the three-cornered hat (TCH) method were used to update the observation error and Kalman gain in the EnKF framework, thereby improving SM profile estimates at a 16 m resolution. The performance of the DA method was evaluated against in situ measurements during the spring drought period in central Yunnan Province, China. The results show that assimilating HRSM (DA_HRSM) significantly improves surface and root-zone SM estimates in the Noah-MP model. The simulated SM from the DA_HRSM method demonstrates lower relative uncertainty. Compared to the assimilation of SMAP SM, the DA_HRSM method provides higher-resolution spatial features of SM and enhances spatial heterogeneity across 20 irrigation districts. The DA_HRSM method effectively captured the spring drought in central Yunnan, demonstrating good agreement with the Palmer Drought Severity Index (PDSI). The result highlights the advantages of incorporating high-resolution SM data into agricultural and drought monitoring systems.

Accurate representation of terrestrial water storage is essential for water resource management and climate adaptation, yet model‑observation inconsistencies hinder global water cycle assessment. Here we show, using a unified multi‑scale framework, that seven model families exhibit scale‑dependent performance against Gravity Recovery and Climate Experiment observations across global, climate-zone, and multiple basin scales (large, medium, small). Globally, hydrological models show the highest temporal correlation (0.94) but underperform spatially (0.32), whereas the satellite observation‑constrained assimilation system balances both dimensions with higher spatial agreement (0.73). Across climate zones, the assimilation system maintains robust performance, while physical models degrade in polar regions (-0.40). From large to small basin scales, performance deteriorates systematically: correlations decline from 0.67 to 0.33, while the assimilation system maintains consistency and higher directional accuracy (80.65%). These findings demonstrate that the satellite observation-constrained assimilation system dramatically reduces inconsistencies, providing essential guidance for climate adaptation and water management strategies.

Unmanned aerial vehicle (UAV)-based fine-scale evapotranspiration (ET) estimation is becoming increasingly critical in precision agricultural water management. However, existing UAV-based ET estimation studies often directly transfer satellite-based ET models and parameterization schemes to fine-scale UAV data, which hampers accurate fine-scale ET estimation. Here, we use machine learning (ML)-based alternative estimation schemes to estimate key parameters of aerodynamic roughness length (z_0m) and excess resistance (kB^-1) in the surface energy balance system (SEBS) ET model. In addition, we use a computational fluid dynamics (CFD) model to provide downscaled meteorological data for the SEBS model. Compared to physical parameterization schemes, ML-based estimates of z_0m and kB^-1 show improved accuracy, reducing the mean root mean square error (RMSE) for z_0m from 0.07 m to 0.04 m, and for kB^-1 from 4.58 to 2.41. Validation against eddy covariance (EC) systems with a source area of hundreds of meters shows that ML-based estimates of latent heat flux (LE) have an RMSE of 39.94 W/m², which is superior to the RMSE of 77.44 W/m² achieved by physical parameterization schemes. ML-based LE estimates also show comparable accuracy with an RMSE of 41.94 W/m² when using CFD-based meteorological data. A comparison with an optical-microwave scintillometer (OMS) system with a source area spanning kilometers confirmed the importance of CFD-based meteorological data and reduced the mean relative error (MRE) for LE from 26.53% (using site-observed meteorological data) to 22.28%. Our proposed robust framework improves the accuracy of UAV-based ET estimates, thus helping to bridge the scale gap between satellite remote sensing and site-based observations.

The inhibition of foliar respiration by light is a crucial yet often overlooked component in estimating ecosystem respiration. However, current estimations of the light inhibition of ecosystem respiration are biased by ignoring the effects of moisture factors. We developed a novel physics‐constrained machine learning method to quantify the extent of light inhibition (Re_li) driven by multiple factors in global ecosystems. Our findings revealed significant seasonal variations in light inhibition rate aligned with vegetation growth. Temperature predominantly influenced variations in Re_li, and the temperature‐Re_li relationship was regulated by vapor pressure deficit rather than soil water content. A reassessment of global ecosystem respiration revealed that current Earth system models (ESMs) overestimate ecosystem respiration in mid‐to‐high latitude dryland regions, with a global average light inhibition strength of 0.51 (±0.16). Knowledge from this study provides an accurate understanding of light inhibition driven by temperature and moisture coupling in simulating carbon cycle.

The uncertainty of ground truth values at the pixel scale obtained via upscaling directly affects the credibility of remote sensing product validation. This paper provides an in-depth analysis of the sources of uncertainty in ground truth evapotranspiration (ET) at the pixel scale. This uncertainty is quantitatively evaluated, and the methods for its control are discussed. The results indicate that the uncertainty from upscaling methods is highest, followed by that from auxiliary data, with that from instrument measurements being the smallest. The relative accuracy of the ground truth ET at the pixel scale for the LAS1-LAS4 (LAS5-LAS7) regions is 89.15%-90.16% (81.56%-82.58%). The accuracy for homogeneous surfaces is relatively high at approximately 90%-93%, whereas for moderately and highly heterogeneous surfaces, it is lower, varying from approximately 81-92%. To control uncertainty, precise instrument calibration, strategic positioning, the use of diverse constraints, and robust modeling are recommended to increase measurement accuracy and prediction reliability. This uncertainty study includes analyses of different sources of uncertainty and quantitatively evaluates the uncertainty of ground truth values over different heterogeneous underlying surfaces. The results can be used to objectively evaluate the accuracy of remote sensing products, thus advancing studies of the uncertainty of ground truth values at the pixel scale and enhancing the scientific, reliable and systematic validation of remote sensing products. This approach can greatly promote the validation of remote sensing ET products over heterogeneous surfaces.

The validation of remotely sensed evapotranspiration (ET) products is important for the development of ET estimation models and the accuracy of the scientific application of the products. In this study, different ET products such as HiTLL, MOD16A2, ETMonitor, and SoGAE were compared using multi-source remote sensing data and ground-based data to evaluate their applicability in the Heihe River Basin (HRB) during 2010–2019. The results of the comparison with the site observations show that ETMonitor provides a more stable and reliable estimation of ET than the other three products. The ET exhibited significant variations over the decade, characterized by a general increase in rates across the HRB. These changes were markedly influenced by variations in land use and topographical features. Specifically, the analysis showed that farmland and forested areas had higher ET rates due to greater vegetation cover and moisture availability, while grasslands and water bodies demonstrated lower ET rates, reflecting their respective land cover characteristics. This study further explored the influence of various factors on ET, including land use changes, NDVI, temperature, and precipitation. It was found that changes in land use, such as increases in agricultural areas or reforestation efforts, directly influenced ET rates. Moreover, meteorological conditions such as temperature and precipitation patterns also played crucial roles, with warmer temperatures and higher precipitation correlating with increased ET. This study highlights the significant impact of land use and climatic factors on spatiotemporal variations in ET within the HRB, underscoring its importance for optimizing water resource management and land use planning in arid regions.