Quantification of irrigation water using remote sensing of soil moisture in a semi-arid region

Irrigated agriculture is the principal consumer of fresh water resources. Most countries do not have a precise measurement of water consumption for irrigation. In this study, an innovative approach is proposed that allows for estimation of irrigation water use at the catchment scale based on satelli...

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Veröffentlicht in:Remote sensing of environment 2019-09, Vol.231, p.111226, Article 111226
Hauptverfasser: Jalilvand, Ehsan, Tajrishy, Masoud, Ghazi Zadeh Hashemi, Sedigheh Alsadat, Brocca, Luca
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container_start_page 111226
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creator Jalilvand, Ehsan
Tajrishy, Masoud
Ghazi Zadeh Hashemi, Sedigheh Alsadat
Brocca, Luca
description Irrigated agriculture is the principal consumer of fresh water resources. Most countries do not have a precise measurement of water consumption for irrigation. In this study, an innovative approach is proposed that allows for estimation of irrigation water use at the catchment scale based on satellite soil moisture data. To this end, the SM2RAIN algorithm, which had been originally developed for estimation of rainfall from the soil moisture observations, is adopted. The satellite soil moisture observations obtained from Advanced Microwave Scanning Radiometer 2 (AMSR2) along with different rainfall and evapotranspiration (ET) products in the period 2012–2015 are used as the input to the model. The methodology is tested in the agricultural plains of southern Urmia Lake, which is one of the main agricultural plains in Iran for which actual irrigation data is available. The results reveal that the proposed approach can capture the overall irrigation pattern, although; it is systematically overestimating irrigation volume compared to observed irrigation data. Thus the bias is calculated over largely non-irrigated pixels and used to modify the model estimates. The bias-corrected results show good agreement with the in situ irrigation data. In particular, the average model performance in the irrigated pixels in terms of R and RMSE (mm/month) are (0.86 and 12.895) respectively. Accuracy varied depending on the inputs, with improvement in order of 11% and 42% in R and RMSE depending on the inputs chosen. The method is also applied to less irrigated areas that result in obtaining significantly lower irrigation rates. The low spatial resolution of soil moisture products (i.e. ~50 km) makes it difficult to capture the irrigation water of small irrigated croplands. Unreliable rainfall and ET data can also lead to the over/underestimation of irrigation. In spite of the above limitations (particularly lack of reliable ET dataset), the proposed model can still capture the irrigation pattern, given that strong soil moisture signal from irrigation is detected by the satellite. •AMSR2 satellite soil moisture data is used to quantify irrigation amount.•SM2RAIN is able to capture irrigation pattern consistent with observations.•Nearly zero irrigation amount is estimated at the non-irrigated pixels.•Weekly signal are observed in soil moisture fluctuation at irrigated pixels.•Coarse resolution of microwave data hampers the usage of the model in small parcels.
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Thus the bias is calculated over largely non-irrigated pixels and used to modify the model estimates. The bias-corrected results show good agreement with the in situ irrigation data. In particular, the average model performance in the irrigated pixels in terms of R and RMSE (mm/month) are (0.86 and 12.895) respectively. Accuracy varied depending on the inputs, with improvement in order of 11% and 42% in R and RMSE depending on the inputs chosen. The method is also applied to less irrigated areas that result in obtaining significantly lower irrigation rates. The low spatial resolution of soil moisture products (i.e. ~50 km) makes it difficult to capture the irrigation water of small irrigated croplands. Unreliable rainfall and ET data can also lead to the over/underestimation of irrigation. 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Most countries do not have a precise measurement of water consumption for irrigation. In this study, an innovative approach is proposed that allows for estimation of irrigation water use at the catchment scale based on satellite soil moisture data. To this end, the SM2RAIN algorithm, which had been originally developed for estimation of rainfall from the soil moisture observations, is adopted. The satellite soil moisture observations obtained from Advanced Microwave Scanning Radiometer 2 (AMSR2) along with different rainfall and evapotranspiration (ET) products in the period 2012–2015 are used as the input to the model. The methodology is tested in the agricultural plains of southern Urmia Lake, which is one of the main agricultural plains in Iran for which actual irrigation data is available. The results reveal that the proposed approach can capture the overall irrigation pattern, although; it is systematically overestimating irrigation volume compared to observed irrigation data. Thus the bias is calculated over largely non-irrigated pixels and used to modify the model estimates. The bias-corrected results show good agreement with the in situ irrigation data. In particular, the average model performance in the irrigated pixels in terms of R and RMSE (mm/month) are (0.86 and 12.895) respectively. Accuracy varied depending on the inputs, with improvement in order of 11% and 42% in R and RMSE depending on the inputs chosen. The method is also applied to less irrigated areas that result in obtaining significantly lower irrigation rates. The low spatial resolution of soil moisture products (i.e. ~50 km) makes it difficult to capture the irrigation water of small irrigated croplands. Unreliable rainfall and ET data can also lead to the over/underestimation of irrigation. In spite of the above limitations (particularly lack of reliable ET dataset), the proposed model can still capture the irrigation pattern, given that strong soil moisture signal from irrigation is detected by the satellite. •AMSR2 satellite soil moisture data is used to quantify irrigation amount.•SM2RAIN is able to capture irrigation pattern consistent with observations.•Nearly zero irrigation amount is estimated at the non-irrigated pixels.•Weekly signal are observed in soil moisture fluctuation at irrigated pixels.•Coarse resolution of microwave data hampers the usage of the model in small parcels.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2019.111226</doi><orcidid>https://orcid.org/0000-0003-4704-9920</orcidid><orcidid>https://orcid.org/0000-0002-9080-260X</orcidid></addata></record>
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identifier ISSN: 0034-4257
ispartof Remote sensing of environment, 2019-09, Vol.231, p.111226, Article 111226
issn 0034-4257
1879-0704
language eng
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source Elsevier ScienceDirect Journals Complete
subjects Agricultural land
Agricultural practices
Agriculture
Algorithms
AMSR2
Arid regions
Arid zones
Bias
Catchment scale
Evapotranspiration
Fresh water
Freshwater resources
Irrigated areas
Irrigation
Irrigation systems
Irrigation water
Microwave radiometers
Pixels
Rainfall
Remote sensing
Satellite observation
Satellite soil moisture estimates
Satellites
Semi arid areas
Semi-arid region
Semiarid lands
Semiarid zones
SM2RAIN
Soil moisture
Soils
Spatial discrimination
Spatial resolution
Water consumption
Water resources
Water use
title Quantification of irrigation water using remote sensing of soil moisture in a semi-arid region
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