Soil Moisture Estimation for Winter-Wheat Waterlogging Monitoring by Assimilating Remote Sensing Inversion Data into the Distributed Hydrology Soil Vegetation Model

Abstract

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.

Roles of Hydrology and Transport Processes in Denitrification at Watershed Scale

“…Zhang et al. (2022) used DHSVM for soil moisture modeling to monitor impact of waterlogging for crops.…”

“…Yearsley et al (2019) modified DHSVM with a stream temperature model included to study impact of hydrology and land use on water temperature. Zhang et al (2022) used DHSVM for soil moisture modeling to monitor impact of waterlogging for crops.…”

Irrigation Schedule Optimization for Wheat and Sunflower Intercropping under Water Supply Restrictions in Inner Mongolia, China

“…The frequency of irrigation was insufficient, and the amounts of water allocated for the second and third irrigations were excessive. Consequently, the water use efficiency was poor and there was a decline in output [41].…”

Monitoring Waterlogging Damage of Winter Wheat Based on HYDRUS-1D and WOFOST Coupled Model and Assimilated Soil Moisture Data of Remote Sensing

“…Based on the above waterlogging damage criteria, the formula for calculating the waterlogging damage ratio R is as follows: R = n/N (26) where n is the sum of days of waterlogging and N is the total number of days in each growth period. The study area is divided into four waterlogging levels, which refers to Zhang et al [13]: no waterlogging area, mild waterlogging area (R greater than 0.1 and less than 0.3), moderate waterlogging area (R greater than 0.3 and less than 0.6) and severe waterlogging area (R greater than 0.6).…”

“…For example, Synthetic Aperture Radar (SAR) can detect surface soil moisture under vegetation [12]. Since waterlogging is a situation where crops are affected by excessive soil moisture over several consecutive days, high temporal-resolution soil-moisture data is required to observe waterlogging [13]. However, due to the long re-entry period of SAR, continuous soil moisture data cannot be obtained.…”