To investigate the performance of extreme gradient boosting (XGBoost) in remote sensing image classification tasks, XGBoost was first introduced and comparatively investigated for the spectral-spatial classification of hyperspectral imagery using the extended maximally stable extreme-region-guided morphological profiles (EMSER_MPs) proposed in this study. To overcome the potential issues of XGBoost, meta-XGBoost was proposed as an ensemble XGBoost method with classification and regression tree (CART), dropout-introduced multiple additive regression tree (DART), elastic net regression and parallel coordinate descent-based linear regression (linear) and random forest (RaF) boosters. Moreover, to evaluate the performance of the introduced XGBoost approach with different boosters, meta-XGBoost and EMSER_MPs, well-known and widely accepted classifiers, including support vector machine (SVM), bagging, adaptive boosting (AdaBoost), multi class AdaBoost (MultiBoost), extremely randomized decision trees (ExtraTrees), RaF, classification via random forest regression (CVRFR) and ensemble of nested dichotomies with extremely randomized decision tree (END-ERDT) methods, were considered in terms of the classification accuracy and computational efficiency. The experimental results based on two benchmark hyperspectral data sets confirm the superior performance of EMSER_MPs and EMSER_MPs with mean pixel values within region (EMSER_MPsM) compared to that for morphological profiles (MPs), morphological profile with partial reconstruction (MPPR), extended MPs (EMPs), extended MPPR (EMPPR), maximally stable extreme-region-guided morphological profiles (MSER_MPs) and MSER_MPs with mean pixel values within region (MSER_MPsM) features. The proposed meta-XGBoost algorithm is capable of obtaining better results than XGBoost with the CART, DART, linear and RaF boosters, and it could be an alternative to the other considered classifiers in terms of the classification of hyperspectral images using advanced spectral-spatial features, especially from generalized classification accuracy and model training efficiency perspectives.

A lack of long-term soil wind erosion data impedes sustainable land management in developing regions, especially in Central Asia (CA). Compared with large-scale field measurements, wind erosion modeling based on geospatial data is an efficient and effective method for quantitative soil wind erosion mapping. However, conventional local-based wind erosion modeling is time-consuming and labor-intensive, especially when processing large amounts of geospatial data. To address this issue, we developed a Google Earth Engine-based Revised Wind Erosion Equation (RWEQ) model, named GEE-RWEQ, to delineate the Soil Wind Erosion Potential (SWEP). Based on the GEE-RWEQ model, terabytes of Remote Sensing (RS) data, climate assimilation data, and some other geospatial data were applied to produce monthly SWEP with a high spatial resolution (500 m) across CA between 2000 and 2019. The results show that the mean SWEP is in good agreement with the ground observation-based dust storm index (DSI), satellite-based Aerosol Optical Depth (AOD), and Absorbing Aerosol Index (AAI), confirming that GEE-RWEQ is a robust wind erosion prediction model. Wind speed factors primarily determined the wind erosion in CA (r = 0.7, p < 0.001), and the SWEP has significantly increased since 2011 because of the reversal of global terrestrial stilling in recent years. The Aral Sea Dry Lakebed (ASDLB), formed by shrinkage of the Aral Sea, is the most severe wind erosion area in CA (47.29 kg/m²/y). Temporally, the wind erosion dominated by wind speed has the largest spatial extent of wind erosion in Spring (MAM). Meanwhile, affected by the spatial difference of the snowmelt period in CA, the wind erosion hazard center moved from the southwest (Karakum Desert) to the middle of CA (Kyzylkum Desert and Muyunkum Desert) during spring. According to the impacts of land cover change on the spatial dynamic of wind erosion, the SWEP of bareland was the highest, while that of forestland was the lowest.

The ecological environment of the Yellow River Basin in China is characterized by drought, which has been exacerbated by global warming. It is critical to keep accurate track of the region’s agricultural drought conditions. To enhance the vegetation health index (VHI), the optimal time scale for the standardized precipitation evapotranspiration index (SPEI) was determined by using the maximum correlation coefficient method, and the calculation method for VHI was optimized. The contributions of the vegetation condition index (VCI) and the temperature condition index (TCI) to the VHI were scientifically optimized, leading to the development of the optimal VHI (VHIopt). Soil moisture anomaly (SMA) and the SPEI were employed for assessing the performance of VHIopt. Furthermore, the temporal and spatial evolution of agricultural drought in the Yellow River Basin (YRB) was analyzed using VHIopt. The results indicate the following: (1) In the YRB, the optimal contribution of the VCI to the VHI is lower than that of the TCI. (2) The drought monitoring accuracy of VHIopt in forests, grasslands, croplands, and other vegetation types exceeds that of the original VHI (VHIori). Additionally, it demonstrates a high level of consistency with the SMA and the SPEI03 regarding spatial and temporal characteristics. (3) Agricultural drought in the YRB is gradually diminishing; however, significant regional differences remain. Generally, the findings of this study highlight that VHIopt is better suited to the specific climate and vegetation conditions of the Yellow River Basin, enhancing its effectiveness for agricultural drought monitoring in this region.

With the aggravation of air pollution in recent years, a great deal of research on haze episodes is mainly concentrated on the east-central China. However, fine particulate matter (PM_2.5) pollution in northwest China has rarely been discussed. To fill this gap, based on the standard deviational ellipse analysis and spatial autocorrelation statistics method, we explored the spatio-temporal variation and aggregation characteristics of PM_2.5 concentrations in Xinjiang from 2001 to 2016. The result showed that annual average PM_2.5 concentration was high both in the north slope of Tianshan Mountain and the western Tarim Basin. Furthermore, PM_2.5 concentrations on the northern slope of the Tianshan Mountain increased significantly, while showing an obviously decrease in the western Tarim Basin during the period of 2001–2016. Based on the result of the geographical detector method (GDM), population density was the most dominant factor of the spatial distribution of PM_2.5 concentrations (q = 0.550), followed by road network density (q = 0.423) and GDP density (q = 0.413). During the study period (2001–2016), the driving force of population density on the distribution of PM_2.5 concentrations showed a gradual downward trend. However, other determinants, like DEM (Digital elevation model), NSL (Nighttime stable light), LCT (Land cover type), and NDVI (Normalized Difference Vegetation Index), show significant increased trends. Therefore, further effort is required to reveal the role of landform and vegetation in the spatio-temporal variations of PM_2.5 concentrations. Moreover, the local government should take effective measures to control urban sprawl while accelerating economic development.