Drought is one of the most destructive natural disasters in China and can cause serious environmental and socio-economic impacts. Based on monthly precipitation and temperature data from 571 meteorological stations, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated on a 12-month scale (SPEI-12) from 1985 to 2018 in mainland China, and it was compared with the records of the Bulletin of Flood and Drought Disasters in China to verify its drought monitoring accuracy. Then, run theory was used to explore in depth the spatiotemporal distribution of drought characteristics and trends in various climatic sub-regions. The results showed that: (1) the comparison with the Bulletin of Flood and Drought Disasters in China indicated that the results of SPEI-12 monitoring drought had high accuracy and could identify drought events in mainland China. (2) About 70% of the regions in mainland China experienced droughts more than 50 times, and about 43% of the regions had drought durations of between 100 and 110 months. Nearly 11% of the regions had drought severities of more than 130, which were mainly located in the Northwest Desert (NWC), the southern part of Northeast China (NEC), and the western part of North China (NC). (3) In the past 34 years, the droughts in the Northwest Desert (NWC), the western part of Inner Mongolia (IM), and the Qinghai-Tibet Plateau (TP) showed an aridity trend, while the droughts in South China (SC) and eastern Central and South China (CSC) presented a mitigation tendency. (4) Specifically, the droughts in South China (SC) were more influenced by precipitation, while those in the Northwest Desert (NWC) and Northeast China (NEC) were influenced by temperature and potential evapotranspiration, and the evolution of drought in North China (NC) and the Qinghai-Tibet Plateau (TP) was mainly influenced by soil moisture. The study could provide scientific guidance and a reference for drought response and sustainable development in China.

The frequency and intensity of droughts are increasing in many parts of the world as a result of global climate change and human activity, posing a serious threat to regional ecological security. The climate in the middle reaches of the Yellow River Basin (MRYRB) has been warm and dry in recent years, with frequent droughts. In order to investigate the temporal trend of drought, and reveal the resistance of vegetation to drought in the MRYRB, this study used remotely-sensed vegetation index products (MODIS-NDVI and SPOT-NDVI) and the standardized precipitation evapotranspiration index (SPEI). The results indicated that: (1) drought intensity showed a weak upward trend in the study area from 2000 to 2018, with linear growth rates of SPEI at temporal scales of 1, 3, 6, 9 and 12 months of −0.002, 0.0034, 0.0198, 0.0234, and 0.0249, respectively; (2) drought was positively correlated with vegetation in most areas (97.6%), and vegetation was most affected by drought on long-term time scales (9 and 12 months); (3) with the extension of drought, vegetation resistance index decreased, then gradually recovered after the end of the drought. Forest had the longest resistance duration of 260 days, while grassland and cultivated land had resistance durations of only 170 days. This study adds to the understanding of vegetation’s ability to withstand drought, and these findings provide evidence to support drought response in the MRYRB.

Under the background of climate warming, the increase in the frequency and severity of drought leads to vegetation facing severe challenges. A comprehensive and systematic assessment of the stability of vegetation under drought stress in the middle reaches of Yellow River basin (MRYRB) will help to grasp the characteristics of vegetation response to drought. In this study, the normalized difference vegetation index (NDVI) was used to achieve quantitative and qualitative assessments of vegetation stability to drought, and the smoothed monthly standardized precipitation evapotranspiration index (SPEI) was used to describe the characteristics of drought events in 2005/2006 and identified vegetation stability parameters using a standardized anomaly of NDVI across space, which included the resistance duration, resilience duration, drought threshold, and lag time. Vegetation was dominated by less resistance and less resilience. The 2005/2006 drought event affected most of the study area, and vegetation growth was inhibited. The duration of vegetation resistance over 100 days accounted for 65.7%, and vegetation in 89.4% of the regions could return to normal within 100 days. The drought threshold of vegetation gradually decreased from northwest to southeast, and the lag time was mainly concentrated from 1 to 3 months. These findings contribute to a better understanding of the effects of drought on the environment, as well as scientific references for reducing ecological, economic, and social losses in future droughts, and promoting ecological environmental governance and high-quality development in the MRYRB.

Accurate identification of sandy land plays an important role in sandy land prevention and control. It is difficult to identify the nature of sandy land due to vegetation covering the soil in the sandy area. Therefore, HJ-2A hyperspectral data and GF-3 Synthetic Aperture Radar (SAR) data were used as the main data sources in this article. The advantages of the spectral characteristics of a hyperspectral image and the penetration characteristics of SAR data were used synthetically to carry out mixed-pixel decomposition in the “horizontal” direction and polarization decomposition in the “vertical” direction. The results showed that in the study area of the Otingdag Sandy Land, in China, the accuracy of sandy land detection based on feature-level fusion and single GF-3 data was verified to be 92% in both cases by field data; the accuracy of sandy land detection based on feature-level fusion was verified to be 88.74% by the data collected from Google high-resolution imagery, which was higher than that based on single HJ-2A (74.17%) and single GF-3 data (88.08%). To further verify the universality of the feature-level fusion method for sandy land detection, Alxa sandy land was also used as a verification area and the accuracy of sandy land detection was verified to be as high as 88.74%. The method proposed in this paper made full use of the horizontal and vertical structural information of remote sensing data. The problem of mixed pixels in sparse-vegetation scenes in the horizontal direction and the problem of vegetation covering sandy soil in the vertical direction were both well solved. Accurate identification of sandy land can be realized effectively, which can provide technical support for sandy land prevention and control.

Accurate and timely extraction and evaluation of sandy land are essential for ecological environmental protection; it is urgent to do the research to support the sustainable development goals (SDGs) of Land Degradation Neutrality. This study used Sentinel-1 Synthetic Aperture Radar (SAR) data and Landsat 8 OLI multispectral data as the main data sources. Combining the rich spectral information from optical data and the penetrating advantages of radar data, a feature-level fusion method was employed to unveil the intrinsic nature of vegetative cover and accurately identify sandy land. Simultaneously, leveraging the results obtained from training with measured data, a comprehensive desertification assessment model was proposed, which combines multiple indicators to achieve a thorough evaluation of sandy land. The results showed that the method based on feature-level fusion achieved an overall accuracy of 86.31% in sandy land detection in Gansu Province, China. The integrated multi-indicator model C22_C/FVC is the ratio of correlation texture features of VH to vegetation cover based on which sandy land can be classified into three categories. When C22_C/FVC is less than 2.2, the pixel is classified as fixed sandy land. Pixels of semi-fixed sandy land have an indicator value between 2.2 and 5.2. Shifting sandy land has values greater than 5.2. Results showed that shifting sandy land and semi-fixed sandy land are the predominant types in Gansu Province, with 85,100 square kilometers and 87,100 square kilometers, respectively. The acreage of fixed sandy land was the least, 51,800 square kilometers. The method presented in this paper is robust for the detection and evaluation of sandy land from satellite imageries, which can potentially be applied for conducting high-resolution and large-scale detection and evaluation of sandy land.