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Detecting global irrigated areas by using satellite and reanalysis products

Author:
Zohaib, Muhammad, Kim, Hyunglok, Choi, Minha
Source:
The Science of the total environment 2019 v.677 pp. 679-691
ISSN:
0048-9697
Subject:
data collection, energy balance, irrigated farming, irrigation, models, remote sensing, satellites, semiarid zones, soil water, surface temperature, uncertainty
Abstract:
Despite the importance of irrigation in meeting the world's food demand and as an essential human modification to water and energy cycles, the reliable extent and distribution of the global irrigated areas remain undefined. In this study, an intuitive method is proposed, based on the aftereffects of irrigation, to detect global irrigated areas by combining satellite and reanalysis datasets. The proposed methodology assumes that irrigation is an unmodeled land surface process, while satellite observations can effectively detect irrigation signals in near real-time. The spatial extents of irrigation were derived by calculating the difference between the remotely sensed and reanalysis datasets. To detect the irrigated areas, three irrigation-dependent variables, soil moisture (SM), land surface temperature (LST), and surface albedo (AL), were used. In the absence of reliable ground truths, the proposed irrigation map was compared to the commonly used global irrigation maps, namely Global Map of Irrigated areas, Global Irrigated Area Map, and recently developed Global Irrigated Areas by Meier et al. (2018). Individual detection by SM, LST, and AL has discrepancies in detecting irrigation signals in highly irrigated, urbanized, and semi-arid regions. However, by combining the individual detection maps, the proposed method showed reasonable agreement with the reference irrigated maps overlapping with approximately 70% of the irrigated areas. We believe that the proposed method, as stand-alone or in combination with the existing irrigation maps, will benefit the studies regarding water and energy balance closure in near-real time for large-scale land surface models by minimizing the uncertainties in model parameterization.
Agid:
6393060