The temperature vegetation dryness index (TVDI) is widely used for the monitoring of global or regional drought because of its strong drought-monitoring capabilities and ease of implementation. However, the temporal errors in the land surface temperature (LST) and normalized difference vegetation index (NDVI) can affect warm and cold edges, thus determining the quality of the TVDI, especially in regions affected by climate change, such as Shandong Province. This paper explores this issue in the region in 2011, using daily MODIS MOD09GA and MOD11A1 data products. For each image acquisition time, the warm and cold edges of the NDVI–LST were extracted based on the NDVI, derived from red and near-infrared reflectance data, and the LST, derived from the MOD11A1 dataset. Then, the variations in the warm and cold edges with the LST and NDVI were analyzed. Subsequently, the influence of warm and cold edges, based on the daily values of the temperature, NDVI and precipitation during the observed period, was assessed using a linear regression. The soil moisture (SM) data obtained from the Global Land Data Assimilation System (GLDAS) datasets and the crop water stress index (CWSI) obtained from the MOD16A2 products were used for the assessment. The spatial and temporal variations in drought in Shandong Province from 2011 to 2020 were measured based on Theil–Sen median trend analysis and the Mann–Kendall test. The results show that apparently random variations were evident in the temporal evolution of the slope of the warm edge, indicating that daily data were appropriate to determine the boundary of the warm edge. Daily data were also appropriate to determine the boundary of the cold edge in a similar way. Additionally, the temperature, NDVI and precipitation in this region affected by climate change had a negative correlation with the slope and a positive correlation with the intercept. The validation results show that there was a significant negative correlation between the observed TVDI and GLDAS soil moisture values (R2 > 0.62) in 12 scatter plots. Therefore, we deduced that the monthly or yearly TVDI product produced by the daily MODIS data has a higher precision than that produced by 8-day or monthly data in regions affected by climate change. The spatial and temporal variations show that the trend of slight and moderate droughts first increased and then decreased, and, in particular, some areas presented severe drought from 2011 to 2015. The results obtained in this study are important for the scheduling of irrigation and drought warnings.
Building change detection is an important task in the remote sensing field, and the powerful feature extraction ability of the deep neural network model shows strong advantages in this task. However, the datasets used for this study are mostly three-band high-resolution remote sensing images from a single data source, and few spectral features limit the development of building change detection from multisource remote sensing images. To investigate the influence of spectral and texture features on the effect of building change detection based on deep learning, a multisource building change detection dataset (MS-HS BCD dataset) is produced in this paper using GF-1 high-resolution remote sensing images and Sentinel-2B multispectral remote sensing images. According to the different resolutions of each Sentinel-2B band, eight different multisource spectral data combinations are designed, and six advanced network models are selected for the experiments. After adding multisource spectral and texture feature data, the results show that the detection effects of the six networks improve to different degrees. Taking the MSF-Net network as an example, the F1-score and IOU improved by 0.67% and 1.09%, respectively, compared with high-resolution images, and by 7.57% and 6.21% compared with multispectral images.
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