Remote sensing-based green and blue agricultural water footprint estimation at the river basin scale

被引:2
作者
Garrido-Rubio, Jesus [1 ]
Gonzalez-Piqueras, Jose [1 ]
Calera, Alfonso [1 ]
Osann, Anna [2 ]
机构
[1] Univ Castilla La Mancha, Inst Reg Dev, Remote Sensing & GIS Grp, Campus Univ S-N, Albacete 02071, Spain
[2] AgriSat Iberia SL, SL 3a Ave 27, Albacete 02007, Spain
关键词
Crop Water Use; Agricultural Water Accounting; Agricultural Water Footprint; Remote sensing; River Basin; Net Irrigation Requirements; Sentinel-2; CROP COEFFICIENTS; MONITORING EVAPOTRANSPIRATION; TIME-SERIES; IRRIGATION; SOIL; MODEL; EVAPORATION; BALANCE; ENERGY; INDEX;
D O I
10.1016/j.ecolind.2024.112643
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
Since the development of Water Footprint environmental indicator, significant research on blue and green crop water use and the respective water footprint estimations has been published. Such research is commonly approached using different methodologies that leverage tabulated values for crop development characterisation, while studies based on remote sensing data are less abundant, despite crop monitoring using remote sensing- based vegetation indices having demonstrated great capabilities and operability. To help fill this gap, we present a methodology that uses a remote sensing vegetation index time series from Sentinel-2 satellite near infrared and red spectral bands data to derive basal crop coefficient time series to subsequently be used under the Remote Sensing-based Soil Water Balance approach that follows the globally operative FAO56 procedure. It provides pixel-based temporal and spatially distributed estimations of net irrigation requirements and adjusted crop evapotranspiration, with the aim being to divide up the latter and estimate the remote sensing-based green and blue crop water use and the subsequent green and blue water footprint. This is all done under the Agricultural Water Footprint Assessment framework for a growing crop or tree. This methodology was applied over a large, crop-diverse Spanish river basin district (J & uacute;car) and across two different climatological years (humid vs. dry). Its feasibility was demonstrated by the acceptable behaviour of the remote sensing-based blue crop water use estimation for different herbaceous and woody crops, against the official dataset for irrigation water accounting at two water management scales (of a relative mean absolute error of 15.4 % in the case of the largest water user association and of 17.1 % in the case of the river basin water authorities' own estimations). The proposed approach, which we call Remote Sensing-based Agricultural Water Accounting and Footprint, aims to provide reliable and accurate spatially and temporally distributed thematic cartography about the remote sensing-based blue and green crop water use and water footprint. This information is essential for water managers with the goal of generating transparent and complementary information to incorporate into their own working scales.
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页数:20
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