Coarse spatial resolution remote sensing data with AVHRR and MODIS miss the greening area compared with the Landsat data in Chinese drylands - Zhang, Jianshuang; Zhang, Yangjian; Cong, Nan; Tian, Li; Zhao, Guang; Zheng, Zhoutao; Gao, Jie; Zhu, Yixuan; Zhang, Yu (Frontiers in Plant Science, 2023)
The warming-wetting climates in Chinese drylands, together with the series of ecological engineering projects, have caused apparent changes to vegetation therein. About the vegetation greening trend and magnitude, different remote sensing data have yielded distinct findings. It is critical to evaluate vegetation dynamics in Chinese drylands using the series of remote sensing data. By comparing the three most commonly used remote sensing datasets (i.e. MODIS, AVHRR, and Landsat), this study comprehensively investigates vegetation dynamics for Chinse drylands. All the three remote sensing datasets exhibites evident vegetation greening trend from 2000 to 2020 in Chinese drylands, especially in the Loess Plateau and Northeast China. But Landsat identifies the largest greening areas (89.8%), while AVHRR identifies the smallest greening area (58%). The vegetation greening areas identified by Landsat comprise more small patches than those of MODIS and AVHRR. The consistent vegetation greening area detected by both AVHRR and MODIS is 1523174.27 km2, and the consistent areas with significant vegetation greening are mainly distributed in Loess Plateau and Northeast China, with an area of 480836.24 km2. The area with consistent browning between the two AVHRR and MODIS datasets is 290,645.1 km2 and the consistent areas with significantly browning areas were 21573.17 km2. MODIS data exhibits a higher consistency with Landsat than with AVHRR in terms of detecting vegetation greening areas. The three datasets exhibit high consistence in identifying vegetation greening in Northeast China, Loess Plateau, and Xinjiang. The percentage of inconsistent areas among the three datasets is 39.56%. The higher the spatial resolution is the data, the finer and more accurate the spatial distribution of greening was detected. Increasing the accuracy of mapping vegetation greening is the basis for adapting management on drylands ecosystems.