Remote Sensing

The Water Cloud Model (WCM) provides a simple and effective framework for quantifying vegetation canopy absorption and scattering components and has been widely applied to active microwave-based soil moisture retrieval on vegetated areas. However, under conditions of dense vegetation, the WCM neglects soil–vegetation interaction scattering, which limits its retrieval accuracy. To mitigate this limitation, this study analyzes the active microwave radiative transfer process under vegetated conditions and proposes an approach to explicitly quantify soil–vegetation interaction scattering by incorporating the first-order soil–vegetation-scattering component into the WCM, thereby enhancing the performance of the WCM at high vegetation coverage. The effectiveness of the proposed model is validated using in situ observations from three study areas with different vegetation characteristics: (a) a pure farmland area, (b) a mixed landscape with small forest and shrubland patches and large cropland areas, and (c) a mixed landscape with large forest and shrubland patches and small cropland areas. Data from 2020–2022 were used for model training and parameter calibration, while independent datasets from 2023 and 2024 were employed to validate the model performance. In both the model training and validation phases, the proposed model improved the soil moisture retrieval accuracy across all study areas while exhibiting slight differences in the backscatter simulation performance. During the model training period, the root-mean-square error (RMSE) between simulated and measured backscatter in study area (a) increased slightly by 1.9%, whereas it decreased by 2.79% and 2.0% in study areas (b) and (c), respectively. In terms of soil moisture retrieval, the RMSEs in study areas (a), (b), and (c) decreased by 6.66%, 1.18%, and 6.03%, respectively. In the validation experiments, for the year 2023, the RMSEs of simulated versus observed backscatter in study areas (a), (b), and (c) were reduced by 9.6%, 1.51%, and 4.35%, respectively, while the corresponding soil moisture retrieval RMSEs decreased by 12.6%, 4.53%, and 7.24%. For the year 2024, the backscatter RMSE in study area (a) increased by 6.07%, whereas it decreased by 2.17% and 6.47% in study areas (b) and (c), respectively; meanwhile, the soil moisture retrieval RMSEs were reduced by 2.81%, 3.69%, and 9.45%, respectively. In summary, this study improves the accuracy of active microwave remote sensing-based soil moisture retrieval in areas with different vegetation cover by explicitly quantifying soil–vegetation interaction scattering.

Angular reflectance effects are essential for radiometric calibration and the interpretation of remotely sensed data, yet remain difficult to characterize under realistic field conditions. This study presents a UAV-based approach for high-angular-resolution hyperspectral HDRF measurement over the Dunhuang Radiometric Calibration Site (DRCS). Six circular UAV flights were conducted at viewing zenith angles from 10° to 60° using a non-imaging hyperspectral sensor, with continuous ground-based irradiance measurements used to derive HDRF values in the 400–850 nm range. Ross–Li model fitting achieved high accuracy (R² > 0.968), while residual analysis identified systematic discrepancies associated with forward-scattering geometry and secondary illumination from nearby solar towers, with local residuals up to 4.5%. These results highlight the value of dense angular sampling and rapid UAV-based measurements for interpreting field-measured HDRF and for the informed application of reflectance models in calibration environments.

Due to the complexity inherent in river ice dynamics, the utilization of remote sensing imagery represents the most crucial and effective method currently available for monitoring changes in river ice. In the Inner Mongolia segment of the Yellow River during winter, two distinct types of ice surfaces are observed: juxtaposed ice and consolidated ice. Additionally, certain areas of open water remain unfrozen. Rapid identification and classification of extensive ice formations and open water zones along this lengthy river section constitute critical information for informed decision-making in ice prevention and management strategies within the Yellow River basin. This paper takes the formation and characteristic analysis of different types of ice in the Yellow River channels in Inner Mongolia as the starting point. It employs a support vector machine (SVM) as the classifier and introduces an optimized model for classifying river ice types using high-resolution Sentinel-2 optical imagery. The model utilizes multi-band spectral features, along with multi-spectral fusion indices such as the normalized difference snow index (NDSI) and the normalized difference frozen surface index (NDFSI), as feature vectors. This approach attains an overall accuracy of 94.91% in classifying different types of ice and can significantly contribute to river ice monitoring by offering robust theoretical support. In the winter of 2023–2024, the proportion of juxtaposed ice on the Yellow River section in Inner Mongolia changed from 45% to 55%, the proportion of consolidated ice changed from 30% to 40%, and the proportion of open water changed from 9% to 19%. This research investigates the characteristics of river ice formations and develops a classification methodology for river ice patterns utilizing high-resolution Sentinel-2 imagery in conjunction with a supervised classification algorithm. The findings of this study are intended to offer technical support for the expedited interpretation of ice conditions in the Yellow River, thereby serving as a scientific basis for precise monitoring and effective disaster prevention and management related to river ice phenomena.