Microwave remote sensing emerged as a valuable technique for monitoring soil moisture (SM) due to the lower sensitivity to weather conditions and the ability to penetrate vegetation and surface layers. Given the latest advancements and changes in the field of microwave remote sensing for SM estimation, there is a need for a comprehensive and evidence-based evaluation of this technology from the extended published literature. Following the Center for Evidence-Based Conservation (CEBC) guidelines, we have selected and performed meta-analysis on 133 peer-reviewed research articles. The results show that microwave remote sensing has moderate to good accuracy in estimating SM (R2 ranged from 0.25 to 0.98, and RMSE from 0.005 to 0.141 m3/m3). Both active and passive sensors have unique spatial and temporal advantages, and while their use in combination is promising, the number of studies identified is limited, and the evidence for improved performance is inconclusive; therefore, further research and investigation are needed. Machine learning (ML) models, especially neural networks, significantly improved accuracy, especially when combined with semi-empirical models (median R2: 0.78 and median RMSE: 0.027 m3/m3). Regionally, microwave remote sensing has great potential for accurate monitoring in arid regions where SM is critical for resource management, with higher publication numbers and accuracy than in humid regions. As reported by the selected manuscripts, the performance inconsistency demonstrates the complexity of microwave remote sensing for SM estimation, which is mainly related to the sensor type, resolution level, and estimation method. This study delves into the state of the art of microwave remote sensing for SM measurement, highlighting performances, research gaps, and limitations.

Reducing vegetation disturbance in remote sensing images enhances lithology classification accuracy. This study utilized Gaofen-2 (GF-2), Sentinel-2A, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Gaofen-3 (GF-3) satellite images of Duolun County, Inner Mongolia. A vegetation coverage-based image filtering method was introduced to minimize vegetation interference in multispectral images, while an improved water cloud model mitigated interference in SAR backscattering images. Subsequently, a 63-dimensional feature sequence was extracted from the vegetation-suppressed images. A comparison experiment using the Support Vector Machine (SVM) classifier, both before and after vegetation suppression, was conducted. Results indicated that vegetation suppression improved the Overall Accuracy (OA) by 2.05% and the Kappa coefficient by 0.02. Specifically, the OA and Kappa coefficient for the 63-dimensional features post-suppression reached 91.52% and 0.90, respectively.

The African savannah elephant (Loxodonta africana) migrate in landscapes with patchily distributed food resources in semi-arid environments. GPS collar data in combination with the Minimum Convex Polygon approach (100% MCP) can be utilised to investigate elephant home ranges and spatial ecology. Mapping of suitable habitats in landscapes with isolated and patchy resources housing threatened and endangered species like the African savannah elephant is critical for conservation of their natural habitat. This study aimed to: (i) investigate the seasonal ranging patterns of the African savannah elephants and (ii) model the preferred habitat of the African savannah elephants in Mana Pools National Park (MNP) in Zimbabwe. Minimum Convex Polygon method was employed to delineate elephant home ranges and the MaxEnt algorithm was used to model their habitat preferences. There were significant differences (p < 0.05) in the size of the home ranges across all the three demarcated seasons (wet, transitional and dry). Elephant habitat preference is mainly driven by the presence, quantity and quality of palatable vegetation close to the Zambezi River in the Mana Pools National Park. GPS telemetry provides smart data for understanding elephant behaviour and movement patterns in semi-arid environments across seasons.

Desert shrubs are the dominant vegetation type in arid deserts and serve as crucial elements in sand retention, biodiversity maintenance, and carbon sequestration. However, due to their patchy and scattered distributions and spectral resemblance to herbaceous plants, desert shrub mapping relies on high-resolution imagery, which is less accessible for large-scale mapping. Here, a set of desert shrub mapping indices for Sentinel-2 (DSMIS) and for universal medium-resolution imagery (DSMIL) is developed to distinguish desert shrubs with dense vimen canopies. The index exploits the canopy structure and spectral characteristics, which have a sparse and multilayered canopy and a high proportion of desiccated branches, resulting in a consistently low reflectance in the red-edge to near-infrared range. The effectiveness of DSMI indices was examined in Ordos, China. In the experiment, an optimal threshold of 10.3 was obtained via DSMIs with Sentinel-2, which achieved an overall accuracy of 91.6% and identified a minimum desert shrub coverage of 0.23. In comparison, a threshold of 9.7 was obtained by DSMIL with Landsat-8, achieving an accuracy of 90.1% and identifying a minimum coverage of 0.17. The performance of DSMI was superior to that of the commonly used random forest, and this index could further improve classification as a complement to machine learning methods. The late stage of the nongrowing season was identified as the optimal period for desert shrub mapping with the index. DSMI also performed well at two test sites with diverse desert shrub dominant species and growing conditions. This study provides a novel index for desert shrub mapping and a practical tool for monitoring desertification in arid desert regions. It also offers a new perspective for historical dynamic studies of desert shrubs and other land cover types where only optical data are available.

BackgroundClimate change is increasing temperatures, frequency of heatwaves, and erratic rainfall, which threatens human biology and health, particularly in already extreme environments. Therefore, it is important to understand how environmental heat stress measures are tied to human water needs and thermoregulation under increasingly hot conditions.AimTo test how ambient temperature, heat index, and wet bulb globe temperature (WBGT) relate to hydration status and thermal heat perception in a hot, semi-arid environment.Subjects and methodsUrine samples, perceived heat stress, and anthropometrics were collected among Daasanach semi-nomadic pastoralists (n = 187 children, n = 231 adults) in northern Kenya. Environmental heat stress measures were recorded at sample collection; samples' urine specific gravity (USG) was measured.ResultsMultiple linear and logistic regressions indicate that all environmental heat stress measures were associated with USG, odds of dehydration, and heat perception. Ambient temperature performed marginally better than WBGT, and both performed better than heat index. These associations were stronger among children than adults.ConclusionIn a hot, semi-arid climate, ambient temperature and WBGT accurately predict human water needs and heat stress, with children more vulnerable to dehydration. To mitigate consequences of extreme heat, local bioculturally-appropriate hydration (e.g. tea) and cooling (e.g. shade) strategies should be encouraged.

Unmanned aerial vehicles (UAVs) can be used to monitor soil moisture (SM) in semiarid regions. We collected high resolution RGB imagery concurrently with hundreds of SM samples across nine sites over a latitudinal gradient in the western USA. We used RGB bands, texture metrics, and vegetation indices to predict SM using machine learning. The model showed a moderately acceptable accuracy (R-2 = 0.63 with cross validation, R-2 = 0.53 using an independent validation). Texture metrics ('mean' and 'entropy'), and the Excess Green (ExG) index had high predictive power while RGB bands performed poorly. Unlike Idaho and Montana, the spatial predictions for Utah and California showed high reliability (alpha < 0.01). Novel linear equations for the conversion of raw digital number (DN) values to reflectance are provided and facilitate remote sensing applications that build upon UAV-RGB imagery as a robust and cost-effective pathway to model SM for monitoring semiarid ecosystems.

Climate change, population growth, and economic development exacerbate water scarcity. This study investigates the impact of drought on water availability in the Belt and Road region using high-resolution remote sensing data from 2001 to 2020. The results revealed an average water availability (precipitation minus evapotranspiration) of 249 mm/year and a declining trend in the Belt and Road region. Approximately 13% of the Belt and Road region faces water deficits (evapotranspiration exceeds precipitation), primarily in arid and semi-arid regions with high drought frequency. The area in the water deficit is expanding, and the intensity of the water deficit is increasing. The annual trend of water availability is strongly related to the frequency of droughts, i.e. water availability decreases with increased drought frequency. Drought exacerbates seasonal water stress in approximately one-third of the Belt and Road region, mainly in Europe and northern Asia, where drought frequently occurs during seasons with low water availability. The more severe the drought, the larger the negative anomaly in water availability. The critical role of evapotranspiration in seasonal water availability variability is also highlighted. This research underscores the importance of understanding drought-induced changes in water availability, which is crucial for sustainable water resource management.

This paper investigates the impacts and dynamics of land use/land cover (LULC) change dynamics in mountainous catchments in semi-arid regions, focusing on drivers, methods, and hydrological impacts. This study reviews studies using the application of remotely sensed data and spatially modified data, highlighting advancements in LULC assessments through GIS integration and predictive modelling. Key drivers include agricultural expansion, population growth, urbanisation, and infrastructure development, which transform forests and grasslands into built environments, affecting ecosystem services and biodiversity. LULC changes significantly impact hydrology, leading to increased surface runoff, poor water quality, and disruptions in the hydrological cycle. Agricultural expansion also contributes to habitat fragmentation ad biodiversity loss. This study underscores the importance of sustainable land management and informed policy decisions to mitigate negative impacts and enhance ecological resilience in semi-arid regions.

This study investigates the distribution and impact of photovoltaic (PV) stations in the arid Northwest China, a crucial area for regional economic cooperation. A hierarchical extraction method combining RegNet and SAM models achieved 91.89% accuracy in PV station identification, while also showing a broader extraction coverage compared to existing datasets. In 2023, we identified 688 centralized PV stations, covering a total area of 719.28 km(2). Using the Continuous Change Detection and Classification (CCDC) algorithm along with Global Moran's I, we observed significant development in PV installations between 2013 and 2021, with smaller stations being more spatially dispersed. Ecological analysis revealed that PV stations were predominantly situated within the Gobi area/desert, with a minor proportion located in low-coverage grasslands. The impacts of PV stations on local temperature exhibited both locational and seasonal variations. Temperature variations between PV stations and their 1 km buffer zones were significant, with over half of the PV stations contributing a cooling effect on their surroundings (50.78% in summers and 58.79% in winters). Additionally, vegetation coverage increased with distance from the PV stations, which indicated a substantial ecological interaction, underscoring the potential advantages and complexities associated with PV deployment in arid ecosystems.

Two experiments were conducted over two growing seasons (2021/2022 and 2022/2023) in the South of Alamein Region (Moghra), Matrouh Governorate, Egypt, to evaluate the effects of soil covering, planting methods and sugar beet varieties on growth, yield, quality traits and water use efficiency (WUE). Treatments included two soil coverings (black polyethylene mulch vs. no mulch), two planting methods (manual vs. small planter) and four sugar beet varieties (two monogerm: Slama and Gustav, and two multigerm: Faten and Halawa). Principal component analysis (PCA) was used to identify key traits contributing to performance differences. Black polyethylene mulch significantly improved germination, growth, sucrose content, extractable sugar percentage, root yield, sugar yield and WUE compared to no mulch. Similarly, planting with small machinery outperformed manual planting. Among the varieties, Gustav and Halawa showed superior performance across seasons. PCA revealed that the first three components explained 92.15% of the total variation. PCA1 (55.3% variation) identified root yield and WUE for sugar yield as critical traits, while PCA2 (88.56% variation) highlighted leaf area index and sugar yield. These findings suggest that black polyethylene mulch, mechanized planting and high-performing varieties like Gustav and Halawa enhance sugar beet productivity and resource efficiency. Future research should explore eco-friendly alternatives to plastic mulch and evaluate the long-term impacts of mechanized planting and mulching in semi-arid agriculture.