Data assimilation method for monitoring soil moisture

Abstract

The invention discloses a data assimilation method for monitoring soil moisture. The method provided by the invention at least comprises an assimilation cycle, and each assimilation cycle comprises the following steps: (1) an ecological process model serves as a dynamic model to stimulate the space distribution state of soil moisture every day, the initial value in the dynamic model is an initial parameter and an initial soil moisture data, and the dynamic model is operated to output simulative soil moisture data; remotely sensed data on the soil surface is performed with inversion to obtain surface layer soil moisture data; the simulative soil moisture data in the remotely sensed data corresponding date is combined with the surface layer soil moisture data obtained by remotely sensed data inversion for data assimilation so as to obtain an optimized ecological process model parameter; (2) the optimized parameter is substituted into the dynamic model in step (1) to operate the dynamic model so as to obtain simulative soil moisture data every day. The method provided by the invention can connect soil moisture variety with vegetable physiological response mechanism by water deficit and stress, which improves the precision of field soil moisture monitoring and drought evaluation and has important application prospect.

Description

technical field [0001] The invention relates to a data assimilation method for soil moisture monitoring. Background technique [0002] Traditional soil moisture monitoring is to obtain soil moisture data through field sampling. This method is time-consuming and laborious, and the monitoring range is small and the data acquisition time is long. In addition, affected by soil properties, land cover conditions, and climatic conditions, soil moisture varies greatly in space and time. Obviously, only using field observation data cannot meet the requirements of large-scale continuous soil moisture monitoring. [0003] Compared with the single-point measurement method, remote sensing has global spatial perception capabilities, and surface soil moisture changes can be expressed through spectral features on remote sensing images. Remote sensing images with various spatial resolutions have become an important technical means for large-scale monitoring of soil moisture. In view of the...

AI Technical Summary

Problems solved by technology

When the ecological process model is extended to a large spatial scale, the variability of model parameters over time and space makes the ecological process model more and more complex and difficult to determine

Especially when some parameters change with time and space, and there is no or only a few discrete field observation data to correct, the error of the model will accumulate over time, and the estimation of soil moisture will gradually deviate from the true value.

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