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.