Drought is one of the most complex and least-understood environmental disasters that can trigger environmental, societal, and economic problems. To accurately assess the drought conditions in the Yellow River Basin, this study reconstructed the Land Surface Temperature (LST) using the Annual Temperature Cycle (ATC) model and the Normalized Difference Vegetation Index (NDVI). The Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature-Vegetation Drought Index (TVDI), which are four typical remote sensing drought indices, were calculated. Then, the air temperature, precipitation, and soil moisture data were used to evaluate the applicability of each drought index to different land types. Finally, this study characterized the spatial and temporal patterns of drought in the Yellow River Basin from 2003 to 2019. The results show that: (1) Using the LST reconstructed by the ATC model to calculate the drought index can effectively improve the accuracy of drought monitoring. In most areas, the reconstructed TCI, VHI, and TVDI are more reliable for monitoring drought conditions than the unreconstructed VCI. (2) The four drought indices (TCI, VCI, VH, TVDI) represent the same temporal and spatial patterns throughout the study area. However, in some small areas, the temporal and spatial patterns represented by different drought indices are different. (3) In the Yellow River Basin, the drought level is highest in the northwest and lowest in the southwest and southeast. The dry conditions in the Yellow River Basin were stable from 2003 to 2019. The results in this paper provide a basis for better understanding and evaluating the drought conditions in the Yellow River Basin and can guide water resources management, agricultural production, and ecological protection of this area.

The dynamics of surface water play a crucial role in the hydrological cycle and are sensitive to climate change and anthropogenic activities, especially for the agricultural zone. As one of the most populous areas in China’s river basins, the surface water in the Huai River Basin has significant impacts on agricultural plants, ecological balance, and socioeconomic development. However, it is unclear how water areas responded to climate change and anthropogenic water exploitation in the past decades. To understand the changes in water surface areas in the Huai River Basin, this study used the available 16,760 scenes Landsat TM, ETM+, and OLI images in this region from 1989 to 2017 and processed the data on the Google Earth Engine (GEE) platform. The vegetation index and water index were used to quantify the spatiotemporal variability of the surface water area changes over the years. The major results include: (1) The maximum area, the average area, and the seasonal variation of surface water in the Huai River Basin showed a downward trend in the past 29 years, and the year-long surface water areas showed a slight upward trend; (2) the surface water area was positively correlated with precipitation (p < 0.05), but was negatively correlated with the temperature and evapotranspiration; (3) the changes of the total area of water bodies were mainly determined by the 216 larger water bodies (>10 km²). Understanding the variations in water body areas and the controlling factors could support the designation and implementation of sustainable water management practices in agricultural, industrial, and domestic usages.

With the increasing population and continuation of climate change, an adequate food supply is vital to economic development and social stability. Winter crops are important crop types in China. Changes in winter crops planting areas not only have a direct impact on China’s production and economy, but also potentially affects China’s food security. Therefore, it is necessary to obtain information on the planting of winter crops. In this study, we use the time series data of individual pixels, calculate the temporal statistics of spectral bands and the vegetation indices of optical data based on the phenological characteristics of specific vegetation or crops and record them in the time series data, and apply decision trees and rule-based algorithms to generate annual maps of winter crops. First, we constructed a dataset combining all the available images from Landsat 7/8 and Sentinel-2A/B. Second, we generated an annual map of land cover types to obtain the cropland mask in 2019. Third, we generated a time series of a single cropland pixel, and calculated the phenological indicators for classification by extracting the differences in phenological characteristics of different crops: these phenological indicators include SOS (start of season), SDP (start date of peak), EOS (end of season), GUS (green-up speed) and GSL (growing-season length). Finally, we identified winter crops in 2019 based on their phenological characteristics. The main advantages of the phenology-based algorithm proposed in this study include: (1) Combining multiple sensor data to construct a high spatiotemporal resolution image collection. (2) By analyzing the whole growth season of winter crops, the planting area of winter crops can be extracted more accurately, and (3) the phenological indicators of different periods are extracted, which is conducive to monitoring winter crop planting information and seasonal dynamics. The results show that the algorithm constructed in this study can accurately extract the planting area of winter crops, with user, producer, overall accuracies and Kappa coefficients of 96.61%, 94.13%, 94.56% and 0.89, respectively, indicating that the phenology-based algorithm is reliable for large area crop classification. This research will provide a point of reference for crop area extraction and monitoring.

The forest stock volume (FSV) is one of the key indicators in forestry resource assessments on local, regional, and national scales. To date, scaling up in situ plot-scale measurements across landscapes is still a great challenge in the estimation of FSVs. In this study, Sentinel-2 imagery, the Google Earth Engine (GEE) cloud computing platform, three base station joint differential positioning technology (TBSJDPT), and three algorithms were used to build an FSV model for forests located in Hunan Province, southern China. The GEE cloud computing platform was used to extract the imagery variables from the Sentinel-2 imagery pixels. The TBSJDPT was put forward and used to provide high-precision positions of the sample plot data. The random forests (RF), support vector regression (SVR), and multiple linear regression (MLR) algorithms were used to estimate the FSV. For each pixel, 24 variables were extracted from the Sentinel-2 images taken in 2017 and 2018. The RF model performed the best in both the training phase (i.e., R² = 0.91, RMSE = 35.13 m³ ha⁻¹, n = 321) and in the test phase (i.e., R² = 0.58, RMSE = 65.03 m³ ha⁻¹, and n = 138). This model was followed by the SVR model (R² = 0.54, RMSE = 65.60 m³ ha⁻¹, n = 321 in training; R² = 0.54, RMSE = 66.00 m³ ha⁻¹, n = 138 in testing), which was slightly better than the MLR model (R² = 0.38, RMSE = 75.74 m³ ha⁻¹, and n = 321 in training; R² = 0.49, RMSE = 70.22 m³ ha⁻¹, and n = 138 in testing) in both the training phase and test phase. The best predictive band was Red-Edge 1 (B5), which performed well both in the machine learning methods and in the MLR method. The Blue band (B2), Green band (B3), Red band (B4), SWIR2 band (B12), and vegetation indices (TCW, NDVI_B5, and TCB) were used in the machine learning models, and only one vegetation index (MSI) was used in the MLR model. We mapped the FSV distribution in Hunan Province (3.50 × 10⁸ m³) based on the RF model; it reached a total accuracy of 63.87% compared with the official forest report in 2017 (5.48 × 10⁸ m³). The results from this study will help develop and improve satellite-based methods to estimate FSVs on local, regional and national scales.