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This paper examines a feature-level fusion framework for detecting and mapping land degradation (LD) and enabling sustainable land management (SLM) in semi-arid areas using optical sensors and Synthetic Aperture Radar (SAR) satellite data. The objectives of this review were to (i) determine the trends and geographical location of land degradation mapping publications, (ii) to identify and report current challenges pertaining to mapping LD using multiscale remote sensing data, (iii) to recommend a way forward for monitoring LD using multiscale remote sensing data. The study reviewed 78 peer-reviewed research articles published over the past 24 years (1998–2022). Image fusion has the potential to be more useful in various remote sensing applications than individual sensor image data, making it more informative and valuable in the interpretation process. In addition, this review discusses the importance of SAR and optical image fusion, pixel-level techniques, applications, and major classes of quality metrics for objectively assessing fusion performance. The literature review alluded that the SAR and optical image fusion in the detection and mapping of land degradation and enabling sustainable land management has not been fully explored. Advanced techniques such as the fusion of SAR and optical satellite imageries need to be incorporated for the detection and mapping of LD, as well as the promotion of SLM in halting LD in South African drylands and around the world. We conclude that there is scope for further research on the fusion of SAR and optical images, as new micro-wave and optical sensors with higher resolution are introduced on a regular basis. The results of this review contribute to a better understanding of the applications of SAR and optical image fusion in future research in the severely degraded drylands of southern Africa. The fusion of SAR and optical data still remains an open challenge. The future of different remote sensing applications lies in this kind of fusion. Land degradation is one of the greatest challenges amongst the environmental problems in South Africa, causing a reduction in the capacity of the land to perform ecosystem functions and services that support society and development. Yet, in South Africa, there are no studies that have widely investigated the potential for a fusion of SAR and optical data to detect and map land degradation and SLM practices. This paper established a baseline for understanding the application of a fusion of SAR and optical data as rapid tools for mapping, monitoring, and evaluating LD, as well as the impacts of SLM practices in South Africa's degraded drylands. [ABSTRACT FROM AUTHOR]

Rational planning of soil resources based on their capabilities are needed for the sustainable use of agricultural lands. Land suitability classification is an important evaluation tool for the management of soil resources. This study aimed to evaluate the land suitability for wheat (Triticum aestivum) cultivation using an approach that integrates multi-criteria decision making (MCDA) analysis and geographic information systems (GIS). The study area cover 21146 ha land and is located within the land consolidation area in the Çumra Plain, located in Central Anatolia of Turkey, The physical, chemical and fertility properties of the soil samples collected from 342 points in the study area were used as parameters in the wheat suitability assessment. The relative weight values of the soil parameters were determined by the Analytical Hierarchy Process (AHP). Literature and expert opinion were used in the creation of the AHP matrices and the determination of the sub-criteria. The criteria with the highest weight values or which have the highest impact on wheat growth were soil texture (0.30) and pH (0.16), while the lowest weight values were given for micro elements (0.02). Land Suitability Assessment was applied to the maps of soil variables using weighted overlay analysis in the GIS environment by using the relative weights. Thus, the suitability of the study area for wheat cultivation was mapped. The results revealed that 74% of the study area was highly suitable (S1) and 24% was moderately suitable (S2) for wheat cultivation. The coefficient of determination (R2) was 0.81, which indicated a successful prediction of the GIS-MCDA hybrid approach for wheat suitability assessment. Integration of land suitability analyzes specific to plant variety in land consolidation projects can provide a more detailed perspective on the land in the design of planning studies. [ABSTRACT FROM AUTHOR]

Drought is a very complex natural phenomenon that affects the water supply and consequently impacts agricultural and socioeconomic activities, causing social and political crises. The objective of this study is to spatiotemporally analyze meteorological and agricultural droughts based on multiple indices in the Upper Paraíba River basin for the 2001–2017 period. The agricultural droughts were analyzed using four drought indices (NDVI, LST, VSWI, and VHI) based on data from the MODIS sensor on the TERRA and AQUA satellites, while the meteorological droughts were characterized using the standardized precipitation index (SPI) at three different time scales based on TRMM rainfall data. The values of LST, NDVI, VHI, VSWI, and SPI revealed how much vegetation is influenced by rainfall variability in this semiarid region of Brazil. The estimated results of VSWI and VHI show the years with extremely wet and dry conditions from spatial and temporal perspectives. The relationship between estimated drought indices and observed relative humidity showed that VSWI (R² = 0.78) was best fitted when compared to VHI (R² = 0.46). Thus, it can be concluded that the VSWI index is better adapted because it presented more consistent results for the behavior of agricultural droughts when compared to the VHI. The results of the SPI analysis were used for the identification of the main drought events that affected the study basin. This combined technique of different drought indices easily produced an alternative method for comprehensive drought assessment. [ABSTRACT FROM AUTHOR]

In the present study, the quality of groundwater for drinking purposes was evaluated. The following parameters were analyzed using American Public Health Association standard method: pH, total hardness (TH), total dissolved solids (TDS), electrical conductivity (EC), bicarbonate (HCO3-), chloride (Cl-), sulphate (SO4 2-), fluoride (F-), calcium (Ca2+), magnesium (Mg2+), sodium (Na+) and potassium (K+). These values were compared with limits recommended by the World Health Organization (WHO) and the Bureau of Indian Standards (BIS) for drinking purposes. The cation and anion dominance of the study region groundwater samples were Na+ > Ca2+ > Mg2+ > K+ and HCO3- > Cl- > SO4 2- > F-, respectively. Bicarbonate was the dominant anion and Na+ was identified as the dominant cation in the groundwater of the study region. The Na+ and Cl- concentration of 43% and 37% of groundwater samples were found to be more than the acceptable limit of WHO in the study region. Most of the groundwater samples in the study region were categorized as a very hard category. The groundwater was highly affected by the fluoride and about 60% of groundwater samples were unfit for drinking purposes in the study region. [ABSTRACT FROM AUTHOR]

Piping erosion is one of the water erosions that cause significant changes in the landscape, leading to environmental degradation. To prevent losses resulting from tube growth and enable sustainable development, developing high-precision predictive algorithms for piping erosion is essential. Boosting is a classic algorithm that has been successfully applied to diverse computer vision tasks. Therefore, this work investigated the predictive performance of the Boosted Linear Model (BLM), Boosted Regression Tree (BRT), Boosted Generalized Linear Model (Boost GLM), and Deep Boosting models for piping erosion susceptibility mapping in Zarandieh Watershed located in the Markazi province of Iran. A piping inventory map including 152 piping erosion locations was prepared for algorithm training and testing. 18 initial predisposing factors (altitude, slope, plan curvature, profile curvature, distance from river, drainage density, distance from road, rainfall, land use, soil type, bulk density, CEC, pH, clay, silt, sand, topographical position index (TPI), topographic wetness index (TWI)) was derived from multiple remote sensing (RS) sources to determine the piping erosion prone areas. The most significant predisposing factors were selected using multi-collinearity analysis which indicates linear correlations between predisposing factors. Finally, the results were evaluated for Sensitivity, Specificity, Positive predictive values (PPV) and Negative predictive value (NPV), and Receiver Operation characteristic (ROC) curve. The best Sensitivity (0.80), Specificity (0.84), PPV (0.85), NPV (0.79), and ROC (0.93), were obtained by Deep Boosting model. The results of the piping erosion susceptibility study in agricultural land use showed that 41% of agricultural lands are very sensitive to piping erosion. This outcome will enable natural resource managers and local planners to assess and take effective decisions to minimize damages to agricultural land use by accurately identifying the most vulnerable areas. Hence, this research proved Deep Boosting model's ability for piping erosion susceptibility mapping in comparison to other popular methods such as BLM, BRT, and Boost GLM. [ABSTRACT FROM AUTHOR]

The ecological evolution and relevant factors in semi-arid and arid regions is a research focus on climate changes. One such region, the mountain-oasis-desert coupling system in Xinjiang, China, is sensitive to global climate changes because it has a fragile ecological environment. This study attempted to investigate the temporal-spatial variations of surface soil moisture, vegetation cover and their influencing factors of mountain-oasis-desert coupling system in Xinjiang from 1986 to 2015. The results showed that: (1) The surface soil moisture increased in most of the ecoregions of Xinjiang from 1986 to 2015 whereas the vegetation cover decreased in the same period. (2) The comparison of the correlations between soil moisture content (SMC) and the variables of precipitation and temperature revealed that precipitation is an important supplement source of SMC. The variation characteristics of surface soil moisture are closely related to the dry and wet soil conditions. (3) The comparison of the correlations between the normalized difference vegetation index (NDVI) and the variables of precipitation and temperature revealed that the different responses of vegetation to precipitation were related to the gradually decreased precipitation pattern in the mountain-oasis-desert system. Meanwhile, vegetation was found to be more sensitive to precipitation in deserts and oases and more sensitive to temperature in mountainous regions. In each ecoregion, the variation cycles of SMC and vegetation cover were slightly different, but it was found that SMC is more likely to be affected by temperature and precipitation rather than vegetation cover. Overall, surface soil moisture and vegetation cover products have a great potential to be effectively used in investigating landscape pattern variations, which is very important theoretically and practically for the mountain-desert-oasis system in arid areas. [ABSTRACT FROM AUTHOR]

Land surface temperature is a significant source of energy budget and climate information, contributing to various environmental and biophysical processes. This research includes comparing the LSTs retrieved from the ASTER sensor using the Reference Channel method, the Emissivity Normalization method (NOR), TES method and Retrieving LSE by taking the proportion of vegetation cover coupled with NDVI and integrates it into the TES algorithm. The results of derived LST from the four algorithms compare with MODIS data of 7 control points having thermally homogenous sites. The analysis showed that the four algorithms are suitable for LST retrieval, whereby the proposed emissivity-derived NDVI algorithms exhibited the highest degree of accuracy (RMSE 0.145), and the NOR had the least accuracy (RMSE 0.403). The analysis shows that emissivity derived NDVI TES method more reliant on the upwelling, downwelling and transmittance and will achieve the best results compared to the other three algorithms. [ABSTRACT FROM AUTHOR]

Accurate soil organic matter (SOM) estimation could provide critical information to understand soil organic carbon sequestration, soil fertility, and the global carbon cycle. A nearest-neighbourhood autoregressive moving average (NN-ARMA) modelling technique along with linear regression has been used to predict localized soil SOM variation based on topographical characteristics and vegetation indices in semi-arid region of Saudi Arabia. Seven topographic variables derived using DEM, and twelve vegetation indices obtained from Landsat 8 used in the model. The best NN-ARMA model showed seven significant variables explaining 96.4% of the total variation of SOM, whereas the best linear regression model could explain 78.8% of the total variation of SOM. The results showed that NN-ARMA model gave better results compared to the linear regression model. Our research gave a better understanding of the possibility of accurate estimation of SOM using the NN-ARMA approach using topographical characteristics and vegetation indices easily acquired by RS sensors. [ABSTRACT FROM AUTHOR]

It is of great practical significance to thoroughly study the environmental changes and conduct desertification assessments of sandy lands to elucidate the formation and recovery mechanism of desertification. The purpose of this study is to evaluate the desertification process and its driving factors accurately and objectively, and compare the differences of desertification process in different areas. We used Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo (MCD43A3) and leaf area index (LAI) (MCD15A2H) products to calculate the desertification divided index (DDI) to reveal the spatial-temporal variation regularity of the four sandy lands in northern China during 2000–2017 as well as the correlation between desertification and precipitation. The results showed that the total desertification degree of sandy lands in northern China showed a downward trend during the study period. By grading the desertification level, it was found the decline trend of severe desertification and high desertification was relatively significant (p < 0.05). The spatial trend of the desertification level in sandy lands was significantly different, and the decreased area of severe desertification and high desertification in Mu Us sandy land was the largest (−0.042 × 104 km2 yr−1 and −0.052 × 104 km2 yr−1), while the interannual variation of the various desertification levels in Hulun Buir sandy land was small (±0.003 × 104 km2 yr−1). The precipitation and DDI were positively correlated in most areas (p < 0.05). Especially in Mu Us sandy land and Hunshandake sandy land, the proportional area of positive correlation between the precipitation and DDI was more prominent, accounting for 72.9% and 63.7%, respectively. This study had important decision-making significance for the effective management of sandy lands in arid/semiarid areas. [ABSTRACT FROM AUTHOR]

The present study assesses the spatio-temporal dynamics of land use/cover over a 28-year period in the upper Tana River Basin (TRB), Kenya using digital change detection techniques. The results indicate that during the last three decades, agricultural land and built-up area have increased by 32.57% (184,796 ha) and 26.35% (1460 ha) respectively, while open land, waterbodies and vegetation have decreased by 35.9%, 3.13% and 8.29% respectively. There was a huge expansion of agricultural land to marginal semi-arid and arid areas (lower part of the basin) over the period. The results of this study provide a better understanding of the spatial and temporal dynamics of the natural resources and form a basis for better planning and effective spatial organization. Such information can help various stakeholders including policy decision-makers in balancing development needs and river basin vital environmental systems protection and sustainability, especially in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Soil erosion is the most important factor in land degradation and influences desertification in semi-arid areas. A comprehensive methodology that integrates revised universal soil loss equation (RUSLE) model and GIS was adopted to determine the soil erosion risk (SER) in semi-arid Aseer region, Saudi Arabia. Geoenvironmental factors viz. rainfall (R), soil erodibility (K), slope (LS), cover management and practice factors were computed to determine their effects on average annual soil loss. The high potential soil erosion, resulting from high denuded slope, devoid of vegetation cover and high intensity rainfall, is located towards the north western part of the study area. The analysis is investigated that the SER over the vegetation cover including dense vegetation, sparse vegetation and bushes increases with the higher altitude and higher slope angle. The erosion maps generated with RUSLE integrated with GIS can serve as effective inputs in deriving strategies for land planning/management in the environmentally sensitive mountainous areas. [ABSTRACT FROM AUTHOR]