Estimation of Crop Water Requirement Based on Planting Structure Extraction from Multi-Temporal MODIS EVI

Changchun Xu (), Xicheng Zhang, Jinxia Zhang, Yapeng Chen, Teshome L. Yami and Yang Hong
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Changchun Xu: Xinjiang University
Xicheng Zhang: Xinjiang University
Jinxia Zhang: Xinjiang University
Yapeng Chen: Chinese Academy of Sciences
Teshome L. Yami: The University of Oklahoma
Yang Hong: The University of Oklahoma

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 7, No 10, 2247 pages

Abstract: Abstract Estimation of crop water requirement (CWR) is key to the rational water use and agricultural water conservation in arid regions. Using remote sensing data to obtain long-term CWR over large areas helps water resources management in water-scarce areas. This study, taking the Kaidu-Kongqi River basin in arid northwest China as the study area, investigated the feasibility of synergistically using phenological characteristics, Savitzky-Golay filter, harmonic analysis and decision tree to extract crop planting structures (CPS) from MODIS EVI, and meanwhile analyzed the spatiotemporal variation in the estimated CWR. The results show that the integrated method for CPS identification and extraction is feasible and reliable with the classification accuracy over 80%. The mid-season stage requires the most water and cash crops need more water than cereal crops. Summer accounts for 69% the total growing season water use. The significant increase in the area of high water demand crops such as cotton raised the total CWR of the basin surging from 14.91× 108m3 in 2000 to 34.92×108m3 in 2017. The spatial distribution of CWR was more related to crop types and area than to climatic conditions. Controlling the expansion of arable land and optimizing the agricultural planting structure remain important tasks for the sustainable management of water resources in the basin.

Keywords: Crop water requirement; Penman-Monteith; Remote sensing; Phenological characteristics; Savitzky-Golay filter; HANTS (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11269-021-02838-y

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