Waterlogging crop disasters are caused by continuous and excessive soil water in the upper layer of soil. In order to enable waterlogging monitoring, it is important to collect continuous and accurate soil moisture data. The distributed hydrology soil vegetation model (DHSVM) is selected as the basic hydrological model for soil moisture estimation and winter-wheat waterlogging monitoring. To handle the error accumulation of the DHSVM and the poor continuity of remote sensing (RS) inversion data, an agro-hydrological model that assimilates RS inversion data into the DHSVM is used for winter-wheat waterlogging monitoring. The soil moisture content maps retrieved from satellite images are assimilated into the DHSVM by the successive correction method. Moreover, in order to reduce the modeling error accumulation, monthly and real-time RS inversion maps that truly reflect local soil moisture distributions are regularly assimilated into the agro-hydrological modeling process each month. The results show that the root mean square errors (RMSEs) of the simulated soil moisture value at two in situ experiment points were 0.02077 and 0.02383, respectively, which were 9.96% and 12.02% of the measured value. From the accurate and continuous soil moisture results based on the agro-hydrological assimilation model, the waterlogging-damaged ratio and grade distribution information for winter-wheat waterlogging were extracted. The results indicate that there were almost no high-damaged-ratio and severe waterlogging damage areas in Lixin County, which was consistent with the local field investigation.

Crop type data are an important piece of information for many applications in agriculture. Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions. In this research, we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device (CCD) data. We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a long-term time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution. To increase accuracy, four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images. These phenological metrics were used to further identify each of the crop types with less, but easier to access, ancillary field survey data. We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment. The results show that our classification accuracy was 92% when compared with the highly accurate but limited ZY-3 images and matched up to 80% to the statistical crop areas.

The assessment of crop water productivity (CWP) is of practical significance for improving regional agricultural water use efficiency and water conservation levels. The remote sensing method is a common method for estimating large scale CWP, and the assessment errors in CWP by remote sensing originate mainly from remote sensing inversion errors in crop yield and evapotranspiration (ET). The phenological period is the important factor in crop ET and yield estimation. The crop coefficient (Kc) and harvest index (HI), which are closely related to different phenological periods, are considered during the processes of crop ET and yield estimation. The crop phenological period is detected from enhanced vegetation index (EVI) curves using Moderate Resolution Imaging Spectroradiometer (MODIS) data and Sentinel-2 data. The crop ET is estimated using the surface–energy balance algorithm for land (SEBAL) model and Penman‒Monteith (P-M) equation, and the crop yield is estimated using the dry matter mass–harvest index method. The CWP is calculated as the ratio of the crop yield to ET during the growing season. The results show that the daily ET and crop yield estimated from remote sensing images are consistent with the measured values. It is found from the variation in daily ET that the peaks appear at the heading period of wheat and maize, which are in good agreement with the rainfall and growth characteristics of the crop. The relationship between crop yield and ET shows a negative parabolic correlation, and that between CWP and crop yield shows a linear correlation. The average CWPs of wheat and maize are 1.60 kg/m³ and 1.39 kg/m³, respectively. The results indicate that the phenology-based remote sensing inversion method has a good effect on the assessment of CWP in Lixin County.

The demand for freshwater is increasing with population growth and rapid socio-economic development. It is more and more important for refined irrigation water management to conduct research on crop evapotranspiration (ET) data with a high spatiotemporal resolution in agricultural regions. We propose the unmixing–weight ET image fusion model (UWET), which integrates the advantages of the unmixing method in spatial downscaling and the weight-based method in temporal prediction to produce daily ET maps with a high spatial resolution. The Landsat-ET and MODIS-ET datasets for the UWET fusion data are retrieved from Landsat and MODIS images based on the surface energy balance model. The UWET model considers the effects of crop phenology, precipitation, and land cover in the process of the ET image fusion. The precision evaluation is conducted on the UWET results, and the measured ET values are monitored by eddy covariance at the Luancheng station, with average MAE values of 0.57 mm/day. The image results of UWET show fine spatial details and capture the dynamic ET changes. The seasonal ET values of winter wheat from the ET map mainly range from 350 to 660 mm in 2019–2020 and from 300 to 620 mm in 2020–2021. The average seasonal ET in 2019–2020 is 499.89 mm, and in 2020–2021, it is 459.44 mm. The performance of UWET is compared with two other fusion models: the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Spatial and Temporal Reflectance Unmixing Model (STRUM). UWET performs better in the spatial details than the STARFM and is better in the temporal characteristics than the STRUM. The results indicate that UWET is suitable for generating ET products with a high spatial–temporal resolution in agricultural regions.

High-spatiotemporal-resolution evapotranspiration (ET) is critical for precision irrigation management and water resource regulation. Regarding the existing spatiotemporal fusion methods suffering from sparse high-resolution observations and coarse land surface temperature (LST), this study took winter wheat in Luancheng District, Hebei Province, as the research object, and proposed a remote sensing ET retrieval method based on the LST sharpening model. The Data Mining Sharpener (DMS) algorithm combined with Sentinel-2 multispectral data was used to downscale MODIS LST from 1000 m to 10 m, with auxiliary variables (DEM, albedo, NDVI, land cover) integrated into the Cubist regression tree to improve the physical rationality and spatial details of MODIS LST. The 10 m resolution ET was estimated from 10 m sharpened LST and Sentinel-2 multispectral data using the surface energy balance model, and the unmixing–weight ET image fusion model (UWET) was adopted to fuse the 10 m resolution ET with MODIS low-resolution ET to generate a daily 10 m ET dataset covering the entire winter wheat growing season. Validation with eddy covariance flux measurements showed that the correlation coefficient R = 0.921, RMSE = 0.779 mm/day during 2019–2020, and R = 0.900, RMSE = 0.831 mm/day during 2020–2021. The results demonstrate that auxiliary variables significantly enhance the spatial reality of LST, LST sharpening effectively improves the spatial heterogeneity of ET, and Sentinel-2 data compensates for the temporal deficiency of Landsat, thereby greatly promoting the accuracy of spatiotemporal fusion. This method can provide reliable high-spatiotemporal-resolution data support for refined farmland irrigation management and water resources regulation.