Equivalent Water Thickness (EWT) is a key indicator of grassland water status, productivity, and drought resistance. However, it is poorly understood regarding the spectral response mechanisms of EWT during canopy senescence. In this paper, field measurements and the PROSAIL-D radiative transfer model were applied to explore the mechanism and monitoring method for the EWT throughout the forage senescence process in alpine grasslands in the Qinghai–Tibet Plateau. Results indicate that the forage EWT decreased from 0.0075 to 0.0060 g·cm⁻² during the senescence and coincided with pronounced spectral changes. The spectral reflectance increased by approximately 5% in the visible region and 11% in the SWIR region, while NIR reflectance decreased initially from 37% and subsequently recovered to 32%. Among four EWT-sensitive bands (1240, 1450, 1650, and 1940 nm), reflectance at 1450 and 1940 nm showed saturation at high EWT levels, whereas the 1240 and 1650 nm bands maintained stable near-linear responses across all stages. It was further revealed that chlorophyll degradation, dry matter accumulation, and increasing soil exposure altered reflectance at 860 and 1650 nm based on Extended Fourier Amplitude Sensitivity Test (EFAST), thereby affecting the robustness of vegetation indices. Among six evaluated vegetation indices, NDWI(1240,1650) achieved the highest accuracy (R² = 0.94, P < 0.05, RMSE = 0.00124 g·cm⁻²), improving accuracy by 9.3% and reducing error by 34.4% compared with NDII. These findings demonstrate that the performance of vegetation water indices is fundamentally controlled by spectral response stability, and support the use of NDWI(1240,1650) for satellite-based monitoring of grassland water status during canopy senescence.

The ecological security of the Qinghai–Tibet Plateau (QTP) alpine transitional region plays a critical role in regional and global climate regulation. Conventional Remote Sensing Ecological Index (RSEI) assessments, however, rely on single-date imagery and often fail to account for cumulative climatic effects. To address this limitation, we refined the traditional RSEI through a multi-principal component weighting scheme that retains over 95% of the cumulative variance. Using imagery from five discrete periods between 2000 and 2020, we evaluated the spatiotemporal evolution of regional ecological quality in the alpine transitional region in Gannan. Results show an overall improvement: mean RSEI increased from 0.63 to 0.69, Good/Excellent areas expanded from 65.6% to 79.9%, and the interquartile range decreased from 0.19 to 0.14. Spatially, ~59% of the area improved, whereas ~41% degraded, primarily in western transition zones and urban peripheries. As an evaluation of internal index contributions, NDVI remained the dominant structural component. Climatic constraints shifted from temperature dominance between 2005 and 2010 to precipitation dominance by 2020, with all interactions exhibiting synergistic enhancement. Cumulative climatic variables during the growing season, with an optimal seven month accumulation window, substantially outperformed single month variables in explaining ecological variations. Specifically, mean q-values increased by 0.12 for temperature and 0.18 for precipitation, yielding a 15 to 25 percent relative improvement, which validates the necessity of incorporating cumulative effects. This framework provides a robust foundation for long-term monitoring and differentiated management of fragile alpine ecosystems.

Estimating the fundamental matrix (FM) using the known corresponding points is a key step for three-dimensional (3D) scene reconstruction, and its uncertainty directly affects camera calibration and point-cloud calculation. The symmetric epipolar distance is the most popular error criterion for estimating FM error, but it depends on the accuracy, number, and distribution of known corresponding points and is biased. This study mainly focuses on the error quantitative criterion of FM itself. First, the calculated FM process is reviewed with the known corresponding points. Matrix differential theory is then used to derive the covariance equation of FMs in detail. Subsequently, the principal component analysis method is followed to construct the scalar function as a novel error criterion to measure FM error. Finally, three experiments with different types of stereo images are performed to verify the rationality of the proposed method. Experiments found that the scalar function had approximately 90% correlation degree with the Manhattan norm, and greater than 80% with the epipolar geometric distance. Consequently, the proposed method is also appropriate for estimating FM error, in which the error ellipse or normal distribution curve is the reasonable error boundary of FM. When the error criterion value of this method falls into a normal distribution curve or an error ellipse, its corresponding FM is considered to have less error and be credible. Otherwise, it may be necessary to recalculate an FM to reconstruct 3D models.

Near‐surface air temperature exhibits local spatial differences in complex terrain, and the native spatial resolution of ERA5‐Land near‐surface air temperature cannot directly meet application requirements. Taking Sichuan Province as the study area, this study developed a statistical downscaling and bias‐correction workflow for daily ERA5‐Land near‐surface air temperature based on observations from 6045 stations during 2020–2024, ERA5‐Land 2 m air temperature, high‐resolution land‐surface predictors, and ERA5/ERA5‐Land Reanalysis‐Derived Auxiliary Predictors (RDAPs). The performance of Elastic Net (EN), Bayesian‐Optimized Random Forest (BO‐RF), and Random‐Search‐based Long Short‐Term Memory network (RS‐LSTM) under different combinations of model and predictor set was compared across multiple evaluation dimensions. The results showed that all three models performed better after RDAPs were introduced. BO‐RF with RDAPs and RS‐LSTM with RDAPs were the two best‐performing combinations, with the lowest RMSE values for T _avg , T _max , and T _min on the independent final validation set of 0.94°C, 1.40°C, and 1.12°C, respectively. The two best‐performing combinations also showed relatively stable monthly RMSE reductions, with no evident month‐to‐month fluctuations in accuracy; for all three temperature variables, the proportions of validation stations with RMSE improvement exceeded 97%. Grouped Permutation Feature Importance (GPFI) further indicated that the near‐surface humidity and surface thermal state group and the pressure–wind–circulation background group were the RDAP predictor groups with relatively high contributions, whereas the terrain group was the main contributing group among the high‐resolution land‐surface predictors. However, the error reduction of the best‐performing combinations remained limited for sporadic extreme samples.