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

Xiaochun Zhang,Xu Yuan,Hongsi Gao,Xiugui Wang,Hairuo Liu

doi:10.3390/rs14030792

Xiaochun Zhang, Xu Yuan + Show 3 more

Open Access

https://doi.org/10.3390/rs14030792

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Journal: Remote sensing	Publication Date: Feb 8, 2022
Citations: 8	License type: CC BY 4.0

Affiliation: Wuhan University

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.

Highlights

Crop waterlogging disasters, which affect crop growth and reduce grain yield [1,2], are caused by excessive water in the crop root zone that disturbs the equilibrium between water and air
Since the model inversion error gradually accumulated as the simulation time increased, we used a shorter simulation time to determine the sensitivity of the model parameters
The daily soil moisture results by the agrohydrological assimilation model were input into the Interactive Data Language (IDL)

Summary

Introduction

Crop waterlogging disasters, which affect crop growth and reduce grain yield [1,2], are caused by excessive water in the crop root zone that disturbs the equilibrium between water and air. The main factors that influence the occurrence of waterlogging are precipitation, topographic conditions, soil physical properties, farming methods, and drainage and irrigation conditions [3]. According to statistics, waterlogging reduces winter-wheat production by more than 50–70%, and long-term waterlogging of farmland aggravates soil salinization [4]. Waterlogging has become an important limiting factor of agricultural development and jeopardizes national food security. Accurate assessment and real-time analysis of waterlogging are critical for regional waterlogging prevention, which can lay a solid foundation for improving agricultural production capacity. The existing monitoring methods of waterlogging either consider redundant factors, resulting in excessive investment in human and material resources, or adopt insufficient influencing factors, resulting in low accuracy of waterlogging monitoring

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