As a key ecological zone of terrestrial ecosystem, the Belt and Road Initiative (BRI) region has experienced a significant change of vegetation coverage in recent years. Using the GIMMS NDVI3g, this study investigated the patterns of spatiotemporal variation of vegetation coverage in the BRI region during the period 1982-2015. The Theil-Sen Median trend analysis and Mann-Kendall method were used to analyze the data, followed by the calculation of Hurst index in order to analyze future trends of vegetation coverage. In addition, possible environmental factors affecting this variation were identified by using the partial correlation analysis and residual analysis methods. The results of the study showed that (1) the normalized difference vegetation index (NDVI) of the study area revealed a slow decrease during 1982-2015, with the linear tendency being −0.1%/10a. During this period, a stable increase was detected before 1997 (linear tendency 1.4%/10a), followed by a sharp decline after 1997 (linear tendency −1.8%/10a). (2) In spatial, the areas with increased vegetation NDVI are mainly distributed in Europe, India and China, whereas the regions with decreased vegetation NDVI are mainly distributed in northern Russia, Central Asia, Southeast Asia, the Malay Islands, and northeast China, of which, the magnitude of decrease in the north of Russia is particularly remarkable. This phenomenon indicates that vegetation activities in high latitude regions declined, such as coniferous forest of subfrigid zone and tundra vegetation. (3) The same characteristic of vegetation coverage change were stronger than the reverse characteristics. A total of 89.2% of the study area’s vegetation will change in the same way as in the past, with a continuously increasing area accounting for 34.0% and a continuously decreasing area accounting for 21.6%. (4) Although climate change may play a key role in vegetation growth trends on a long-term scale, human activities are also an important factor driving vegetation change in the BRI area, especially for areas with increased vegetation coverage such as China, India, and Europe.

In this letter, we propose a dual-temporal dual-channel (DTDC) algorithm for soil moisture retrieval by using time-series observations from the Sentinel-1 C-band synthetic aperture radar. This algorithm utilizes the ancillary information of vegetation water content derived from optical images and assumes no variation on the surface roughness during the two consecutive radar measurements. Therefore, with the DTDC backscatter observations, four equations could be established using forward models, while three unknowns (the two consecutive soil moisture values and one roughness parameter) could be solved simultaneously by minimizing a cost function. The algorithm was tested with a series of Sentinel-1 dual-channel (VV + VH) data over croplands (sugarcane and cassava) of Northeast Thailand with an upscaling resolution of 1 km. Results show that the proposed algorithm could well capture the temporal change of soil moisture with root-mean-square errors within 0.06 m ³ /m ³ when ignoring days with precipitation, and could achieve a similar spatial pattern of soil moisture as detected from the Soil Moisture Active Passive mission, indicating the Sentinel-1 might be a proper tool for agricultural water management.

Accurate and spatially-distributed precipitation information is vital to the study of the regional hydrological cycle and water resources, as well as for environmental management. To provide high spatio-temporal resolution precipitation estimates over insufficient rain-gauge areas, great efforts have been taken in using the Normalized Difference Vegetation Index (NDVI) and other land surface variables to improve the spatial resolution of satellite precipitation datasets. However, the strong spatio-temporal heterogeneity of precipitation and the “hysteresis phenomenon” of the relationship between precipitation and vegetation has limited the application of these downscaling methods to high temporal resolutions. To overcome this limitation, a new temporal downscaling method was proposed in this study by introducing daily soil moisture data to explore the relationship between precipitation and the soil moisture increment index. The performance of this proposed temporal downscaling was assessed by downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from a monthly scale to a daily scale over the Hekouzhen to Tongguan of the Yellow River in 2013, and the downscaled daily precipitation datasets were validated with in-situ measurement from 23 rainfall observation stations. The validation results indicate that the downscaled daily precipitation agrees with the rain gauge observations, with a correlation coefficient of 0.59, a mean error range of 1.70 mm, and a root mean square error of 5.93 mm. In general, the monthly precipitation decomposition method proposed in this paper has combined the advantage of both microwave remote sensing products. It has acceptable precision and can generate precipitation on a diurnal scale. It is an important development in the field of using auxiliary data to perform temporal downscaling. Furthermore, this method also provides a reference example for the temporal downscaling of other low temporal resolution datasets.

Forest canopy fuel moisture content (FMC) is a critical factor in assessing the vulnerability of a specific area to forest fires. The conventional FMC estimation method, which relies on look-up tables and loss functions, cannot to elucidate the relationship between FMC and simulated data from look-up tables. This study proposes a novel approach for estimating FMC by combining enhanced vegetation index (EVI) and normalized difference moisture index (NDMI). The method employs the PROSAIL + PROGeoSAIL two-layer coupled radiation transfer model to simulate the vegetation index, the water index, and the FMC value, targeting the prevalent double-layer structure in the study area’s vegetation distribution. Additionally, a look-up table is constructed through numerical analysis to investigate the relationships among vegetation indices, water indices, and FMC. The results reveal that the polynomial equations incorporating vegetation and water indices as independent variables exhibit a strong correlation with FMC. Utilizing the EVI–NDMI joint FMC estimation method enables the direct estimation of FMC. The collected samples from Dali were compared with the estimated values, revealing that the proposed method exhibits superior accuracy (R2 = 0.79) in comparison with conventional FMC estimation methods. In addition, we applied this method to estimate the FMC in the Chongqing region one week before the 2022 forest fire event, revealing a significant decreasing trend in regional FMC leading up to the fire outbreak, highlighting its effectiveness in facilitating pre-disaster warnings.

Landslide detection in mountainous regions remains highly challenging due to complex terrain conditions, heterogeneous surface textures, and the fragmented distribution of landslide features. To address these limitations, this study proposes an enhanced object detection framework named YOLOv11-SAFM, which integrates a Spatially Adaptive Feature Modulation (SAFM) module, an optimized MPDIoU-based bounding box regression loss, and a multi-scale training strategy. These improvements strengthen the model’s ability to detect small-scale landslides with blurred edges under complex geomorphic conditions. A high-resolution remote sensing dataset was constructed using imagery from Bijie and Zhaotong in southwest China including GF-2 optical imagery at 1 m resolution and Sentinel-2 data at 10 m resolution for model training and validation, while independent data from Zhenxiong County were used to assess generalization capability. Experimental results demonstrate that YOLOv11-SAFM achieves a precision of 95.05%, recall of 90.10%, F1-score of 92.51%, and mAP@0.5 of 95.30% on the independent test set of the Zhaotong–Bijie dataset for detecting small-scale landslides in rugged plateau environments. Compared with the widely used Mask R-CNN, the proposed model improves precision by 13.87% and mAP@0.5 by 15.7%; against the traditional YOLOv8, it increases recall by 27.0% and F1-score by 22.47%. YOLOv11-SAFM enables efficient and robust automatic landslide detection in complex mountainous terrains and shows strong potential for integration into operational geohazard monitoring and early warning systems.

Accurately estimating sensible heat (H) and latent heat (LE) in mountainous areas is a significant challenge due to the influence of complex topography factors. Currently, most models have been developed to estimate surface heat flux for flat surfaces without considering the effect of complex geometric terrain structures. In this study, the Surface Energy Balance Algorithm for Land coupled with a mountainous net surface radiation (R_n) calculation method (MSEBAL) was proposed to accurately estimate H and LE in the upstream catchment regions of the Heihe River Basin (HRB). The R_n was estimated by correcting the solar incoming radiation components using topographic factors, including slope, aspect, sky view factor (SVF), and terrain configuration factor (TCF). The SEBAL and MSEBAL models were applied to satellite remote sensing data from Landsat 8 images and ground-observed datasets. In situ measurements from the eddy covariance (EC) system of the A’rou superstation were used to validate the estimation accuracy of R_n, LE, and H by MSEBAL. The results show that R_n, LE, and H estimated by the MSEBAL exhibit good consistency with the validation of in situ measurements. The R_n estimated by MSEBAL showed a decrease in RMSE from 164.32 to 51.31 W/m² and a reduction in absolute bias from 154.50 to 10.50 W/m² compared to SEBAL. The H and LE estimated by MSEBAL exhibit low RMSE and bias, with values of 31.96 and -17.93 W/m² for H, and 35.67 and -6.18 W/m2 for LE, respectively, compared to SEBAL. The spatial pattern of surface heat fluxes exhibited variations with complex terrain changes. H and LE were found to be higher at mountain peaks, while lower values were observed in valleys. Additionally, H and LE were greater on east and south-facing slopes that receive more solar radiation compared to west and north-facing slopes. This study provides an effective tool for estimating surface heat fluxes over mountainous regions.