Spatially distributed crop model based on remote sensing

Congying Han, Baozhong Zhang, He Chen, Zheng Wei and Yu Liu

Agricultural Water Management, 2019, vol. 218, issue C, 165-173

Abstract: Crop models are useful tools in investigating crop yield and water consumption and thus far, they have been well-developed in field applications. Moreover, their regional applications have evolved from lumped models to distributed models by considering the spatial variation in soil, climate, crop, and management measures to some extent. With the advent of modernized and refined farming and water management, the spatial variation of environment-related initial and crop parameters of models should be appropriately reflected. On this basis, this study performs spatial inversion of sowing date, maximum canopy coverage CCx, and relative biomass Brel based on the AquaCrop-GIS model and by using remote sensing data. These parameters are incorporated as independent spatial variables into the distributed AquaCrop-GIS model to produce a remote sensing-based distributed AquaCrop-RS crop model, which will be validated with the corn data in the middle oasis of Heihe River. Findings suggest that the initial condition-sowing date and crop parameters CCx and Brel exhibit considerable spatial variation, the introduction of sowing date has a considerable influence on the simulation accuracy of evapotranspiration (ET), and crop parameters CCx and Brel have a considerable influence on yield simulation accuracy. In comparison with the traditional AquaCrop-GIS model, the simulation accuracy of AquaCrop-RS model is generally improved by 5%–26% in regional ET and by 35%–72% in regional yield.

Keywords: AquaCrop-RS; Maximum canopy coverage; Relative biomass; Sowing date (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:218:y:2019:i:c:p:165-173

DOI: 10.1016/j.agwat.2019.03.035

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