The high spatial-temporal variability of soil moisture necessitates monitoring at a high resolution in order to improve our understanding of Earth system processes. Current large-scale soil moistures inferred from the microwave satellites have limited spatial resolution, typically in the range of tens of kilometers. Recent studies have revealed that synthetic aperture radar (SAR) backscatter exhibits qualitative relationships with soil moisture, suggesting the potential for large-scale high-resolution mapping of soil moisture. Here, we proposed a method for directly estimating soil moisture content based on the Advanced Integral Equation Model (AIEM) and Mironov dielectric model. The approach involves establishing a series of semi-empirical models, independent of preceding surface roughness determination, using two Envisat ASAR alternating polarization (AP) model precision products. We generate a time series of high-resolution soil moisture using Envisat ASAR AP data acquired from 2004 to 2011, with an uncertainty of approximately 0.05 m ³ /m ³ . Our soil moisture retrievals demonstrate very good agreement with European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture products and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 re-analysis hourly products, even in the absence of synchronous ground measurements. Furthermore, our study reveals good temporal coherence between drought and heavy rainfall events, and SAR-derived soil moisture, which suggests a potential to capture heavy rainfall and drought events. We conclude that SAR-derived soil moisture is a more direct and efficient method in quantifying soil moisture at a high spatial resolution, making it more suitable for watershed scale hydrological studies.

With the launch of the ICESat-2 satellite, global-scale forest parameter monitoring has entered a new phase. However, the background noise in ICESat-2 lidar data significantly impairs the accuracy of signal photon extraction. This study introduces a direction-adaptive DBSCAN method for denoising ICESat-2 photon point clouds, integrating elevation histogram-based coarse denoising with adaptive clustering for fine denoising. The method is applied to data from the Gongbella River Nature Reserve. An innovative aspect of this approach is the introduction of elliptical tilt angle adaptation, which dynamically adjusts the elliptical orientation of the photon point cloud to determine the optimal tilt angle, thus optimizing the denoising effect and reducing computational and memory demands. The direction-adaptive DBSCAN algorithm improves denoising accuracy by dynamically adjusting the neighborhood radius based on the elliptic tilt angle and the distribution of the point cloud. Additionally, the density threshold selection is optimized using the Otsu method, enhancing the accuracy of distinguishing noise photons from signal photons. The method was validated using data from the Gongbella River Nature Reserve, showing significant improvements in denoising accuracy. Compared to existing methods, recall (R) increased by 6.91%, precision (P) improved by 8.82%, and both the F1-score and accuracy rose by 9.52%. The photon point cloud denoising algorithm demonstrated substantial accuracy improvements across multiple data strips, making it particularly effective for processing complex data from ICESat-2, with broad application potential. Both quantitative and qualitative analyses confirm that the algorithm outperforms traditional methods in signal-to-noise ratio and denoising performance, providing reliable technical support for extracting photon point cloud elevation data from forest surfaces and canopies.

Single tree detection is essential in forest resource surveys. Efficient, accurate, and rapid extraction of individual trees facilitates the timely acquisition of forest resource information. Traditional tree surveys rely on manual field measurements, which are limited in coverage, costly, and time-consuming. To address these issues, this study utilizes multispectral imagery captured by unmanned aerial vehicles (UAVs) as the data foundation and applies deep learning methods for tree species segmentation in the study area. An enhanced ECA-Unet model, derived from the U-net architecture, integrates a channel attention mechanism in the Decoder to augment the model’s accuracy and precision in small-scale UAV-based tree segmentation, attaining an overall accuracy of 85.87%. The performance of the suggested model was compared with that of conventional semantic segmentation models within the same study region. Compared with the original U-net model, the model in this study improved the mAP and Accuracy by 2.1% and 1.23%, respectively, in terms of tree species segmentation accuracy. Compared with the traditional semi-automatic forest crown segmentation method, the model in this paper can output forest single tree segmentation results more quickly and effectively. The findings indicate that the proposed technique yields optimal segmentation accuracy and performance for individual trees within the dataset, facilitating the rapid and exact differentiation of trees in wooded settings utilizing limited sample data.