Details:

Journal Title:
Remote Sensing
Personal Author:
Wei, Shiqi ; Xu, Tianfang ; Niu, Guo-Yue ; Zeng, Ruijie
Wei, Shiqi ; Xu, Tianfang ; Niu, Guo-Yue ; Zeng, Ruijie Less -
NOAA Program & Office:
OAR (Oceanic and Atmospheric Research)
Description:
Groundwater-based irrigation has dramatically expanded over the past decades. It has important implications for terrestrial water, energy fluxes, and food production, as well as local to regional climates. However, irrigation water use is hard to monitor at large scales due to various constraints, including the high cost of metering equipment installation and maintenance, privacy issues, and the presence of illegal or unregistered wells. This study estimates irrigation water amounts using machine learning to integrate in situ pumping records, remote sensing products, and climate data in the Kansas High Plains. We use a random forest regression to estimate the annual irrigation water amount at a reprojected spatial resolution of 6 km based on various data, including remotely sensed vegetation indices and evapotranspiration (ET), land cover, near-surface meteorological forcing, and a satellite-derived irrigation map. In addition, we assess the value of ECOSTRESS ET products for irrigation water use estimation and compare with the baseline results by using MODIS ET. The random forest regression model can capture the temporal and spatial variability of irrigation amounts with a satisfactory accuracy (R2 = 0.82). It performs reasonably well when it is calibrated on the western portion of the study area and tested on the eastern portion that receives more rain than the western one, suggesting its potential transferability to other regions. ECSOTRESS ET and MODIS ET yield a similar irrigation estimation accuracy.
Keywords:
[+]
General Earth and Planetary Sciences
Source:
Remote Sensing, 14(13), 3004
DOI:
https://doi.org/10.3390/rs14133004
ISSN:
2072-4292
Format:
PDF
Publisher:
MDPI AG
Document Type:
Journal Article
Funding:
Grant no. NA20OAR4310341
License:
https://creativecommons.org/licenses/by/4.0/
Rights Information:
CC BY
Compliance:
Library
Main Document Checksum:
[+]
urn:sha256:7e5e9a7b002ff881470f7ae6953224476b610843953015e10f966e317c5b0a44
Download URL:
https://repository.library.noaa.gov/view/noaa/58794/noaa_58794_DS1.pdf
File Type:

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