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Studying and evaluating desertification is essential due to its potential occurrence as a result of both natural and anthropogenic processes. Precise forecasting of forthcoming climate change perils is crucial for devising policies, action strategies, and mitigation measures at both the local and global scales. Remote sensing facilitates the examination, monitoring, and forecasting of several aspects of desertification. Throughout the years, many methodologies have been employed to investigate desertification through the utilization of Remote Sensing (RS). This study investigated the worldwide prevalence and temporal sequence of research that utilized remote sensing (RS) to investigate desertification. In addition, the study assessed the primary approaches and factors employed in the examination of desertification through the analysis of remote sensing data. The application of remote sensing (RS) in the investigation of desertification can be traced back to 1991. Between 2015 and 2020, an annual average of over 40 publications were published, indicating a substantial rise in the utilization and accessibility of remote sensing (RS) technology to monitor desertification. However, there is a significant disparity in the amount of research conducted in different fields. Asia demonstrates a substantially higher quantity of studies in contrast to America or Africa. China has conducted the highest number of research on desertification using remote sensing (RS) techniques. The Thematic Mapper (TM) sensor is the principal source of satellite data, specifically Landsat pictures. The primary techniques utilized for studying desertification are classification and monitoring of alterations. Furthermore, remote sensing methods commonly employ land cover/land use change and vegetation, together with its attributes such as the Normalised Difference Vegetation Index (NDVI), as the primary factors for studying desertification.

Rapid urban expansion has intensified interactions between human disturbance and urban ecological processes, creating an urgent need for robust and urban-sensitive assessment tools. To improve the applicability of conventional remote sensing ecological evaluation in cities, this study develops an Urban Remote Sensing Ecological Index (URSEI) by incorporating an Urbanization Index (UI) into the RSEI-based PCA framework. Multi-temporal Landsat observations acquired during the peak vegetation season were used to construct annual ecological indicators, thereby improving the temporal representativeness of ecological assessment. Taking Hangzhou, China, as a case study, URSEI was applied to examine ecological quality dynamics inside and outside the Ecological Conservation Redline (ECR) from 2010 to 2024, together with temporal trend characteristics, indicative persistence patterns, and meteorological associations. The results show that URSEI generally achieved higher first principal component contribution rates than RSEI, suggesting stronger integration of ecological information within the PCA framework. UI exhibited the strongest negative correlation with URSEI among the stress-related indicators, highlighting the importance of explicitly representing urbanization-related disturbance in urban ecological assessment. Citywide ecological quality displayed a fluctuating but weakly improving tendency over the study period, while the ECR consistently maintained higher URSEI values than the overall urban area. However, most detected temporal changes were statistically non-significant, indicating that ecological conditions remained broadly stable rather than showing pronounced improvement or degradation. Temperature-related thermal conditions were predominantly negatively associated with URSEI, whereas precipitation showed mainly positive relationships and a stronger association with URSEI among the climatic variables examined. Overall, URSEI provides an urbanization-aware framework for long-term ecological monitoring and offers a useful basis for ecological management and sustainable planning in rapidly urbanizing regions.

Satellite-observed greening in arid regions is often interpreted as ecological restoration success, yet this assessment may conflate natural recovery with agricultural expansion. We developed an Arid Remote Sensing Ecological Index (ARSEI) incorporating a Comprehensive Salinity Index (CSI) to address systematic biases in the traditional RSEI when applied to irrigated drylands. ARSEI scores were validated against MODIS Net Primary Production (NPP) (R2>0.75 at the regional scale), confirming its reliability in capturing ecosystem productivity, while CSI effectively maps the upper-bound of surface salinization potential dictated by intrinsic soil properties. Applied to China’s Mu Us Sandy Land (2000–2024), the ARSEI reveals that 2327 km2 of sandy land—54% of current cropland—was converted to agriculture, creating “assessment-induced false greening” signals. While the traditional RSEI increased monotonically (+135%), the ARSEI shows a nuanced pattern with plateau (2010–2015) and decline (2015–2020) phases, reflecting salinization risks masked by high crop NDVI. Optimal Parameters-Based Geographical Detector analysis demonstrates that Land Cover × Precipitation interactions (q = 0.28) drive spatial heterogeneity through irrigation-mediated water redistribution. The ARSEI provides a dialectical evaluation framework: acknowledging agricultural greening’s economic benefits while monitoring subsurface degradation risks. This study offers a critical methodological advance for sustainable land assessment in global drylands undergoing agricultural intensification.

Mu Us Sandy Land is a typical semi-arid vulnerable ecological zone, characterized by vegetation degradation and severe desertification. Effectively identifying desertification changes has been a topical environmental issue in China. However, most previous studies have used a single method or remote sensing index to monitor desertification, and lacked an efficient and high-precision monitoring system. In this study, an optimal monitoring scheme that considers multiple indicators combination and different machine learning methods (Classification and Regression Tree-Decision Tree, CART-DT; Random Forest, RF; Convolutional Neural Networks, CNN) was developed and used to analyze the spatial–temporal patterns of desertification from 2000 to 2018 in Mu Us Sandy Land. The results showed that: (a) The random forest model performed best for monitoring desertification based on medium and low-resolution remote sensing images, and the four-index combination (Albedo, NDVI, LST and TGSI) obtained the highest classification accuracy (OA = 87.67%) in Mu Us Sandy Land. Surprisingly, the model accuracy of the three-index combination (NDVI, LST and TGSI) (OA = 85.74%) is comparable to the four-index combination. (b) The TGSI index used to characterize soil information performs well, while the LST is not conducive to the extraction of desertified land in several desertification monitoring indicators. (c) Since 2000, the area of extremely severe desertified land has shown a reversal trend; however, there is significant interannual fluctuation in the total and light desertification land area affected by extreme climate. This research provides a novel approach and a valuable reference for monitoring the evolution of desertification in regional studies, and the results improve the research system of desertification and provide a data basis for desertification cause analysis and prevention.

Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in arid regions (especially in deserts) because of inaccurate remote sensing LST products. In this study, LST was downscaled by a random forest model between LST and multiple remote sensing indices (such as soil-adjusted vegetation index, normalized multi-band drought index, modified normalized difference water index, and normalized difference building index) in an arid region with an oasis–desert ecotone. The proposed downscaling approach, which involves the selection of remote sensing indices, was evaluated using LST derived from the MODIS LST product of Zhangye City in Heihe Basin. The spatial resolution of MODIS LST was downscaled from 1 km to 500 m. Results of visual and quantitative analyses show that the distribution of downscaled LST matched that of the oasis and desert ecosystem. The lowest (approximately 22 °C) and highest temperatures (higher than 37 °C) were detected in the middle oasis and desert regions, respectively. Furthermore, the proposed approach achieves relatively satisfactory downscaling results, with coefficient of determination and root mean square error of 0.84 and 2.42 °C, respectively. The proposed approach shows higher accuracy and minimization of the MODIS LST in the desert region compared with other methods. Optimal availability occurs in the vegetated region during summer and autumn. In addition, the approach is also efficient and reliable for LST downscaling of Landsat images. Future tasks include reliable LST downscaling in challenging regions.

This study was conducted to examine the capability of topographic features and remote sensing data in combination with other auxiliary environmental variables (geology and geomorphology) to predict CEC by using different machine learning models ((random forest (RF), k-nearest neighbors (kNNs), Cubist model (Cu), and support vector machines (SVMs)) in the west of Iran. Accordingly, the collection of ninety-seven soil samples was performed from the surface layer (0–20 cm), and a number of soil properties and X-ray analyses, as well as CEC, were determined in the laboratory. The X-ray analysis showed that the clay types as the main dominant factor on CEC varied from illite to smectite. The results of modeling also displayed that in the training dataset based on 10-fold cross-validation, RF was identified as the best model for predicting CEC (R2 = 0.86; root mean square error: RMSE = 2.76; ratio of performance to deviation: RPD = 2.67), whereas the Cu model outperformed in the validation dataset (R2 = 0.49; RMSE = 4.51; RPD = 1.43)). RF, the best and most accurate model, was thus used to prepare the CEC map. The results confirm higher CEC in the early Quaternary deposits along with higher soil development and enrichment with smectite and vermiculite. On the other hand, lower CEC was observed in mountainous and coarse-textured soils (silt loam and sandy loam). The important variable analysis also showed that some topographic attributes (valley depth, elevation, slope, terrain ruggedness index—TRI) and remotely sensed data (ferric oxides, normalized difference moisture index—NDMI, and salinity index) could be considered as the most imperative variables explaining the variability of CEC by the best model in the study area.

Soil salinity is a major issue causing land degradation in coastal areas. In this study, we assessed the land use and soil salinity changes in Djilor district (Senegal) using remote sensing and field data. We performed land use land cover changes for the years 1984, 1994, 2007, and 2017. Electrical conductivity was measured from 300 soil samples collected at the study area; this, together with elevation, distance to river, Normalized Difference Vegetation Index (NDVI), Salinity Index (SI), and Soil-Adjusted Vegetation Index (SAVI), was used to build the salinity model using a multiple regression analysis. Supervised classification and intensity analysis were applied to determine the annual change area and the variation of gains and losses. The results showed that croplands recorded the highest gain (17%) throughout the period 1984–2017, while forest recorded 3%. The fastest annual area of change occurred during the period 1984–1994. The salinity model showed a high potential for mapping saline areas ( R 2  = 0.73 and RMSE = 0.68). Regarding salinity change, the slightly saline areas (2 < EC < 4 dS/m) increased by 42% whereas highly saline (EC > 8 dS/m) and moderately saline (4 < EC < 8 dS/m) areas decreased by 23% and 26%, respectively, in 2017. Additionally, the increasing salt content is less dominant in vegetated areas compared with non-vegetated areas. Nonetheless, the highly concentrated salty areas can be restored using salt-resistant plants (e.g., Eucalyptus sp . , Tamarix sp.). This study gives more insights on land use planning and salinity management for improving farmers’ resilience in coastal regions.

Forest disturbances and restoration are key processes in carbon transmission between the terrestrial surface and the atmosphere. In boreal forests, fire is the most common and main disturbance. The reconstruction process for post-disaster vegetation plays an essential role in the restoration of a forest’s structure and function, and it also maintains the ecosystem’s health and stability. Remote sensing monitoring could reflect dynamic post-fire features of vegetation. However, there are still major differences in the remote sensing index in terms of regional feasibility and sensibility. In this study, the largest boreal primary coniferous forest area in China, the Greater Hinggan Mountains forest area, was chosen as the sampling area. Based on time series data from Landsat-5 TM surface reflectance (SR) and data obtained from sample plots, the burned area was extracted using the Normalized Burn Ratio (NBR). We used the pre- and post-fire difference values (dNBR) and compared them with survey data to classify the burn severity level. The Normalized Difference Vegetation Index (NDVI) (based on spectrum combination) and the Disturbance Index (DI) (based on Tasseled-Cap transformation) were chosen to analyze the difference in the degree of burn severity and vegetation restoration observed using various methods according to the sequential variation feature from 1986 to 2011. The results are as follows: (1) The two remote sensing indexes are both sensitive to fire and the burn severity level. When a fire occurred, the NDVI value for that year decreased dramatically while the DI value increased sharply. Alongside these findings, we observed that the rangeability and restoration period of the two indexes is significantly positively correlated with the degree of burn severity. (2) According to these two indexes, natural vegetation restoration was faster than the restoration achieved using artificial methods. However, compared with the NDVI, the DI showed a clearer improvement in restoration, as the restoration period the DI could evaluate was longer in two different ways: the NDVI illustrated great changes in the burn severity in the 5 years post-fire, while the DI was able to show the changes for more than 20 years. Additionally, from the DI, one could identify felling activities carried out when the artificial restoration methods were initially applied. (3) From the sample-plot data, there were few differences in forest canopy density—the average was between 0.55 and 0.6—between the diverse severity levels and restoration methods after 33 years of recovery. The average diameter at breast height (DBH) and height values of trees in naturally restored areas decreased with the increase in burn severity, but the values were obviously higher than those in artificially restored areas. This indicates that both the burn severity level and restoration methods have important effects on forest restoration, but the results may also have been affected by other factors.

Forest disturbances in central Europe caused by fungal pests may result in widespread tree mortality. To assess the state of health and to detect disturbances of entire forest ecosystems, up-to-date knowledge of the tree species diversity is essential. The German state Mecklenburg–Vorpommern is severely affected by ash (Fraxinus excelsior) dieback caused by the fungal pathogen Hymenoscyphus pseudoalbidus. In this study, species diversity and the magnitude of ash mortality was assessed by classifying seven different tree species and multiple levels of damaged ash. The study is based on a multispectral WorldView-2 (WV-2) scene and uses object-based supervised classification methods based on multinomial logistic regressions. Besides the original multispectral image, a set of remote sensing indices (RSI) was derived, which significantly improved the accuracies of classifying different levels of damaged ash but only slightly improved tree species classification. The large number of features was reduced by three approaches, of which the linear discriminant analysis (LDA) clearly outperformed the more commonly used principal component analysis (PCA) and a stepwise selection method. Promising overall accuracies (83%) for classifying seven tree species and (73%) for classifying four different levels of damaged ash were obtained. Detailed tree damage and tree species maps were visually inspected using aerial images. The results are of high relevance for forest managers to plan appropriate cutting and reforestation measures to decrease ash dieback over entire regions.

As the global ecosystem has been severely disturbed by an increasing number of human activities at different scales, remote sensing technology, as an effective quantitative measure of environmental quality, has been widely used. The remote sensing ecological index (RSEI) is one of the most popular and comprehensive ecological quality assessment indices based on the remote sensing data. However, the RSEI model exhibits that the ecological environment under natural conditions is not limited by the spatial scales. In addition, the model has major shortcomings in index selection and eigenvector, which greatly limit the application of RSEI. In this paper, the RSEI model is improved and a remote sensing ecological index optimized by the regional scale (RO-RSEI) is proposed. The result of the study, conducted in Shuangyang District, Changchun City, Jilin Province, shows that the RO-RSEI model has regional ecological significance after the introduction of the scale theory of landscape ecology; the index is preferred to solve problems like the RSEI model applied mechanization and baseless index selection. Meanwhile, due to the optimization of the eigenvector contribution of the optimal index, it solves the problems like non-unique model calculation result caused by principal component analysis or even antipodal calculation result. Compared with the RSEI model, the monitoring result of RO-RSEI model can better reflect the regional ecological changes. The improved model offers the possibility of monitoring ecological environment quality with remote sensing big data and provides a scientific basis for future scholars’ batch computing.