Soil erosion significantly impacts dam functionality by leading to reservoir siltation, reducing capacity, and heightening flood risks. This study aims to map soil erosion within a Geographic Information Systems (GIS) framework to estimate the siltation of the K'sob dam and compare these estimates with bathymetric observations. Focused on one of the Hodna basin's sub-basins, the K'sob watershed (1477 km2), the assessment utilizes the Revised Universal Soil Loss Equation (RUSLE) integrated with GIS and remote sensing data to predict the spatial distribution of soil erosion. Remote sensing data were pivotal in updating land cover parameters critical for RUSLE, enhancing the precision of our erosion predictions. Our results indicate an average annual soil erosion rate of 7.83 t/ha, with variations ranging from 0 to 224 t/ha/year. With a typical relative error of about 13% in predictions, these figures confirm the robustness of our methodology. These insights are crucial for crafting mitigation strategies in areas facing high to extreme soil loss and will assist governmental agencies in prioritizing actions and formulating effective soil erosion management policies. Future studies should explore the integration of real-time data and advanced modeling techniques to further refine these predictions and expand their applicability in similar environmental assessments.

Water use efficiency (WUE) is an important metric for assessing regional sustainability and the carbon-water balance of ecosystems. Drought is a significant stressor affecting the carbon-water cycle. Nevertheless, the response of WUE to different types of droughts remains poorly understood. This study analyzed the variability characteristics of WUE, meteorological (SPEI), hydrological (SRI), and agricultural (SSMI) droughts. Additionally, it investigated the response of WUE to these three types of droughts over the Mongolian Plateau (1982-2021). The results indicated a non-significant decreasing trend in WUE, and a remarkable drying trend in SPEI, SRI, and SSMI. WUE was positively correlated with three drought indices, with average R (WUE, SPEI), R (WUE, SRI), and R (WUE, SSMI) at 0.060, 0.051, and 0.028, respectively; this indicated that WUE decreased as drought intensified. Regional WUE proved most sensitive to SPEI and was observed in coniferous forests, broadleaf forests, meadow steppes, typical steppes, and alpine grasslands. Sand land vegetation and desert steppes showed the highest sensitivity to SRI, whereas shrubs and croplands exhibited the highest sensitivity to SSMI. More importantly, SRI caused more severe losses in WUE than SSMI and SPEI. These findings offer valuable insights for future carbon sink management and drought risk assessment.

Accurate monitoring of the leaf area index (LAI) and aboveground biomass (AGB) using remote sensing at a fine scale is crucial for understanding the spatial heterogeneity of vegetation structure in mountainous ecosystems. Understanding discrepancies in various retrieval strategies considering topographic effects or not is necessary to improve LAI and AGB estimations over mountainous areas. In this study, the performances of the look-up table method (LUT) using radiative transfer model (RTM), machine learning algorithms (MLAs), and hybrid RTM integrating RTM and MLAs based on Landsat surface reflectance (SR) before and after topographic correction were compared and analyzed. The results show that topographic correction improves the accuracies of retrieval methods involving RTM more significantly than the MLAs, meanwhile, it reduces the performance variability of different MLAs. Based on the topographically corrected Landsat SR, the random forest (RF) combined with RTM improves the retrieval accuracy of RTM-based LUT by 7.7% for LAI and 13.8% for AGB, and reduces the simulation error of MLA by 15.1% for LAI and 20.1% for AGB. Compared with available remote sensing products, the hybrid RTM based on Landsat SR with topographic correction has better feasibility to capture LAI and AGB variation at 30 m scale over mountainous areas.

Ephemeral gully headcut erosion contributes significantly to global land degradation and increased sediment yields, but the underlying driving factors and prediction models remain poorly understood. We conduct a comprehensive quantitative analysis of ephemeral gully headcut erosion in the Loess Plateau using an optimal parameters-based geographical detector (OPGD) model, leveraging high-resolution remote sensing images. Our findings reveal a varied ephemeral gully head advance rate spanning 0.04-5.54 m yr-1 between 2009 and 2021 (average 1.37 m yr-1), with over 58% of the erosion rates falling between 0.50 and 2.00 m yr-1. Catchment area emerges as the primary driving factor, with an explanatory power of 61%. Moreover, the interactions between catchment area and slope degree, rainfall erosivity, and fractional vegetation coverage (FVC) had explanatory powers exceeding 80%. Furthermore, we developed a robust prediction model for ephemeral gully head advance rates based on the results from the OPGD model, incorporating the FVC factor. The validation of our model yielded a high coefficient of determination (R2 = 0.92 m yr-1) and low root mean square error (RMSE = 0.31 m yr-1). Our study offers new insights into ephemeral gully headcut erosion control in the Loess Plateau and serves as a valuable reference for loess regions worldwide.

Annual crop monitoring is a key parameter for managing agricultural strategies. Several studies have relied on remote sensing products such as the normalized difference vegetation index (NDVI) as a vegetation dynamic metric. However, the dependence of optical data on weather conditions limits its availability. In this study, we reconstruct the NDVI time series of wheat fields using the moving averages of the Sentinel-1 normalized VH/VV cross-polarization ratio (IN) and the interferometric coherence in VV polarization over wheat selected fields in a semiarid site in Tunisia during two seasons, from 2018 to 2020. The crop cycle is divided into two periods: before and after the heading phase, which occurs in approximately the middle of March. Due to the volume-scattering impact, the second phase is divided into the ripening and maturation phase (NDVI >= 0.4) and senescence phase (NDVI = 0.4 compared to their performance during the aforementioned periods. The proposed approach was tested on different wheat fields. The NDVI estimations are characterized by RMSE values varying between 0.12 and 0.19. The use of RF and SVR outperformed the curve-fitting methods with an RMSE equal to 0.12. The present findings revealed the high accuracy of the proposed approach to estimate the missing values of wheat fields NDVI values during the vegetation development period until heading and the senescence phase. The presence of the mutual effect of the vegetation water content and its volume complicated the NDVI estimation using the C-band data.

The recently launched Landsat-9 has an important mission of working together with Landsat-8 to reduce the revisit period of Landsat Earth observations to eight days. This requires the data of Landsat-9 to be highly consistent with that of Landsat-8 to avoid bias caused by data inconsistency when the two satellites are simultaneously used. Therefore, this study evaluated the consistency of the surface reflectance (SR) and land surface temperature (LST) data between Landsat-8 and Landsat-9 based on five test sites from different parts of the world using synchronized underfly image pairs of both satellites. Previous cross-comparisons have demonstrated high consistency between the spectral bands of Landsat-8 and Landsat-9, with differences of around 1%. However, it is unclear whether this low deviation will be amplified in subsequent multiband calculations. It is also necessary to determine whether the difference is consistent across different land cover types. Therefore, this study used a three-level cross-comparison approach to specifically examine these concerns. Besides the commonly used band-by-band comparison, which served as the first-level comparison in this study, this approach included a second-level comparison based on the calculations of several indicators and a third-level comparison based on a composite index calculated from the indicators obtained in the second-level comparison. This three-level approach will examine whether the difference found in the first-level per-band comparison would change after the subsequent calculations in the second- and third-level comparisons. The Remote Sensing based Ecological Index (RSEI) was used for this approach because it is a composite index integrating four indicators. The results of this three-level comparison show that the first-level per-band comparison exhibited high consistency between the two satellites' SR data, with an average absolute percent change (PC) of 1.88% and an average R2 of 0.957 across six bands in the five test sites. This deviation increased to 2.21% in the third-level composite index-based comparison, with R2 decreasing to 0.956. This indicates that after complex calculations, the deviation between the bands of the two satellites was amplified to some extent. However, when analyzing specific land cover types, notable differences emerged between the two satellites for the water category, with an average absolute PC ranging from 18% to 35% and an R2 of lower than 0.6. Additionally, there were also nearly 5% differences for the built-up land category, with an average R2 value of lower than 0.7. The comparison of LST data between both satellites also reveals that the Landsat-9 LST is on average 0.24 degrees C lower than Landsat-8 LST across the five test areas but can be 0.58 degrees C lower in built-up land-dominated areas and 0.42 degrees C higher in desert environments. Overall, the SR and LST data between Landsat-8 and Landsat-9 are consistent. However, their performance varies depending on different land cover types. Caution is needed particularly for water-related research when utilizing both satellites simultaneously. Significant discrepancies may also arise in the areas characterized by deserts and built-up lands.

For over 50 years, the Xianyang city on the Chinese loess plateau, has been deeply affected by land subsidence and its associated ground fissures. In this study, 67 Sentinel-1A images from 2015 to 2022 were analysed to obtain the spatio-temporal evolution of land subsidence. Subsidence centers identified in Yunyang town and Luqiao town exhibit annual subsidence rates of -31 mm/a and -26.7 mm/a, respectively, culminating in total subsidence of -258 mm in Yunyang town and -139 mm in Luqiao town. Moreover, time series analysis revealed that both towns exhibit pronounced seasonal subsidence patterns. It is believed that human cultivation and groundwater overuse, combined with the effects of active faults, have led to uneven land subsidence. Then, the gradient of land subsidence and its potential relation to ground fissure hazards were evaluated. The results highlight a strong spatial correlation between high subsidence gradient areas and dense ground fissure distribution. Therefore, we proposed for the first time to introduce the land subsidence gradient factors for the susceptibility mapping of ground fissures using the artificial neural network algorithm. The susceptibility zonation results show that integrating the gradient factor can improve the rationality of the susceptibility evaluation. The above understanding provides valuable support for disaster prevention and mitigation in urban areas affected by land subsidence and ground fissures in loess regions.

In rainfed and dryland agricultural areas with smallholder farms (less than 2 ha), crop diversity is high due to farmers' decisions and local climatic conditions, leading to a complex spatial-temporal distribution of crops. Monitoring and mapping crops is crucial for food security and implementing agricultural support programs. This study aims to map crop types across Senegal using Sentinel-2 satellite imagery and the limited ground reference data available, which has been increasing recently. The study compares conventional supervised classification algorithms to unsupervised classification algorithms using high-resolution satellite imagery. Crop type classification for 2020 in Senegal employed supervised machine learning algorithms, including Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) on the Google Earth Engine (GEE) cloud platform, and the unsupervised Iso-clustering classification algorithm with Spectral Matching Techniques (SMTs). Due to limited ground data, supervised classifiers achieved 45-55% accuracy, whereas the unsupervised semi-automatic approach achieved over 75% accuracy. The study indicates that supervised classifiers' performance depends on ground data quantity, while SMT shows good performance even with limited ground data. This SMT approach is valuable for classifying crop types in dryland areas with smallholder farms and diverse cropping patterns.

Sandstorms pose several challenges for solar power generation in desert environments, such as dust accumulation, surface abrasion, structural stress, long-term degradation, and potential deterioration. This study aims to address these challenges by proposing a sandstorm-resistant solar tracking system that does not require complex and costly protection mechanisms, cleaning, or maintenance practices. Instead, we present an innovative approach to protect solar systems from sandstorms by integrating wind data readings into conventional solar tracking tools. Initially, sandstorms are detected when the wind speed exceeds a predetermined threshold. Then, the tracker adjusts its position based on wind direction data to reduce exposure and prevent damage. The system will resume regular tracking once the wind speed drops below the specified threshold. Our research indicates that positioning the tracker at a 20 degrees angle and exposing it to the wind at an attack angle of 100 degrees can reduce wind pressure on solar panels and minimize dust accumulation, thereby safeguarding the panels during sandstorms. The results show that the proposed tracking system consumed only 0.26-2.24% of the generated energy throughout the day, which is lower than other solar tracking systems and protective mechanisms. This efficient design has the potential to influence the future of photovoltaic (PV) systems and contribute to climate change adaptation and the economic feasibility of PV systems in desert environments.

The uncontrolled proliferation of natural roads in arid regions has exacerbated regional land degradation and desertification, presenting substantial challenges to their accurate mapping owing to their dynamic and obscure features. Moreover, the high cost of data annotation restricts the availability of comprehensively labelled datasets, which are essential for advanced remote sensing processing and natural road detection. This study dedicated to implement a semi-supervised deep learning method for dirty road extraction in southern Mongolia. A new thematic semantic segmentation dataset of natural roads was established firstly to address scarcity of annotation datasets this region. A semi-supervised UniMatch structure was designed consequently. Operating with high-resolution GaoFen-2 images, this approach minimises the need for extensive manual annotation, achieving an IOU of 73.51% and MIOU of 86.37%. This method significantly reduces labour and time costs associated with manual and fully supervised methods. These observations provide a valuable data source and methodology for addressing natural road expansion in arid regions. They can aid governments in evaluating transportation infrastructure in remote areas, and analysing dirty road traffic impact on environment.