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

Jalilvand, Ehsan; Tajrishy, Masoud; Hashemi, Sedigheh Alsadat Ghazi Zadeh; Brocca, Luca

doi:10.1016/j.rse.2019.111226

Cited by 168 publications

(128 citation statements)

References 46 publications

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“…In an attempt to estimate the irrigation volumes from remote sensing data, some recent studies have explored the utility of surface soil moisture estimates from micro-wave sensors (Brocca et al, 2018(Brocca et al, , 2017Escorihuela and Quintana-Seguí, 2016;Jalilvand et al, 2019;Kumar et al, 2015;Lawston et al, 2017;Malbéteau et al, 2018;Zhang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…This work was applied over various semi-arid and semi-humid regions worldwide but could not be quantitatively assessed due to the unavailability of reliable in situ observations of irrigation over corresponding irrigated perimeters. However, this approach was quantitatively assessed at ~50 km resolution over a semi-arid region (Jalilvand et al, 2019). Some deficiencies were obtained over periods with sustained rainfalls and the method was not implemented in winter because the method fails in correctly separating irrigation from precipitation (Brocca et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Irrigation retrieval from Landsat optical/thermal data integrated into a crop water balance model: A case study over winter wheat fields in a semi-arid region

Olivera-Guerra

Merlin

Er-Raki

2020

Remote Sensing of Environment

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Monitoring irrigation is essential for an efficient management of water resources in arid and semi-arid regions. We propose to estimate the timing and the amount of irrigation throughout the agricultural season using optical and thermal Landsat-7/8 data. The approach is implemented in four steps: i) partitioning the Landsat land surface temperature (LST) to derive the crop water stress coefficient (Ks), ii) estimating the daily root zone soil moisture (RZSM) from the integration of Landsat-derived Ks into a crop water balance model, iii) retrieving irrigation at the Landsat pixel scale and iv) aggregating pixel-scale irrigation estimates at the crop field scale. The new irrigation retrieval method is tested over three agricultural areas during four seasons and is evaluated over five winter wheat fields under different irrigation techniques (drip, flood and no-irrigation). The model is very accurate for the seasonal accumulated amounts (R ~ 0.95 and RMSE ~ 44 mm). However, lower agreements with observed irrigations are obtained at the daily scale. To assess the performance of the irrigation retrieval method over a range of time periods, the daily predicted and observed irrigations are cumulated from 1 to 90 days. Generally, acceptable errors (R = 0.52 and RMSE = 27 mm) are obtained for irrigations cumulated over 15 days and the performance gradually improves by increasing the accumulation period, depicting a strong link to the frequency of Landsat overpasses (16 days or 8 days by combining Landsat-7 and-8). Despite the uncertainties in retrieved irrigations at daily to weekly scales, the daily RZSM and evapotranspiration simulated from the retrieved daily irrigations are estimated accurately and are very close to those estimated from actual irrigations. This research demonstrates the utility of high spatial resolution optical and thermal data for estimating irrigation and consequently for better closing the water budget over agricultural areas. We also show that significant improvements can be expected at daily to weekly time scales by reducing the revisit time of high-spatial resolution thermal data, as included in the TRISHNA future mission requirements.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Irrigation retrieval from Landsat optical/thermal data integrated into a crop water balance model: A case study over winter wheat fields in a semi-arid region

Olivera-Guerra

Merlin

Er-Raki

2020

Remote Sensing of Environment

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“…An interesting aspect of this approach is that the model accounts for underground water losses, which could otherwise be estimated only through the use of an irrigation efficiency coefficient. The same approach was recently applied in Iran [22], and this method has demonstrated its strong potential for the quantification of irrigation, providing good quality of SSM data where there are prolonged periods of low rainfall. Zaussinger et al [23] also used different coarse resolution datasets and compared them to a water balance, forced by MERRA-2 reanalysis, to determine the use of irrigation water.…”

Section: Introductionmentioning

confidence: 99%

Potential for the Detection of Irrigation Events on Maize Plots Using Sentinel-1 Soil Moisture Products

et al. 2020

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Although the real timing and flow rates used for crop irrigation are controlled at the scale of individual plots by the irrigator, they are not generally known by the farm upper management. This information is nevertheless essential, not only to compute the water balance of irrigated plots and to schedule irrigation, but also for the management of water resources at regional scales. The aim of the present study was to detect irrigation timing using time series of surface soil moisture (SSM) derived from Sentinel-1 radar observations. The method consisted of assessing the direction of change of surface soil moisture (SSM) between observations and a water balance model, and to use thresholds to be calibrated. The performance of the approach was assessed on the F-score quantifying the accuracy of the irrigation event detections and ranging from 0 (none of the irrigation timing is correct) to 100 (perfect irrigation detection). The study focused on five irrigated and one rainfed plot of maize in South-West France, where the approach was tested using in situ measurements and surface soil moisture (SSM) maps derived from Sentinel-1 radar data. The use of in situ data showed that (1) irrigation timing was detected with a good accuracy (F-score in the range (80–83) for all plots) and (2) the optimal revisit time between two SSM observations was 2–4 days. The higher uncertainties of microwave SSM products, especially when the crop is well developed (normalized difference of vegetation index (NDVI) > 0.7), degraded the score (F-score = 69), but various possibilities of improvement were discussed. This paper opens perspectives for the irrigation detection at the plot scale over large areas and thus for the improvement of irrigation water management.

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“…Specifically, the latter applies for the study area, because no information respective to water extraction for agricultural proposes is registered [96]. Although irrigated agriculture is the sector that consumes the highest amounts of the local hydrological resources in semiarid regions [97,98], a quantification is complex, due to the poorly managed irrigation canals and the uncontrolled irrigation practices in the study area.…”

Section: Discussionmentioning

confidence: 99%

Water Balance and Soil Moisture Deficit of Different Vegetation Units under Semiarid Conditions in the Andes of Southern Ecuador

Fries

Silva

Pucha-Cofrep

et al. 2020

Climate

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Water availability in semiarid regions is endangered, which is not only due to changing climate conditions, but also to anthropogenic land use changes. The present study analyzed the annual and monthly water balance (WBc) and the soil moisture deficit (Ds) for different vegetation units under semiarid conditions in the Andes of southern Ecuador, based on limited meteorological station data and field measurements (soil samples). To calculate crop evapotranspiration (ETc) the Blaney-Criddle method was applied, and the specific crop factor (Kc) included, because only temperature (T) and precipitation (P) data were available. By means of the soil samples the water retention capacity (RC) of the different soil types present in the study area were estimated, which, in combination with WBc, provided reliable results respective to water surpluses or deficits for the different vegetation units. The results indicated highest Ds for cultivated areas, particularly for corn and sugarcane plantations, where annual deficits up to −1377.5 mm ha −1 and monthly deficits up to −181.1 mm ha −1 were calculated. Natural vegetation cover (scrubland, forest and paramo), especially at higher elevations, did not show any deficit throughout the year (annual surpluses up to 1279.6 mm ha −1 ; monthly surpluses up to 280.1 mm ha −1 ). Hence, it could be concluded that the prevailing climate conditions in semiarid regions cannot provide the necessary water for agricultural practices, for which reason irrigation is required. The necessary water can be supplied by areas coved by natural vegetation, but these areas are endangered due to population growth and the associated land use changes.

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

Cited by 168 publications

References 46 publications

Irrigation retrieval from Landsat optical/thermal data integrated into a crop water balance model: A case study over winter wheat fields in a semi-arid region

Irrigation retrieval from Landsat optical/thermal data integrated into a crop water balance model: A case study over winter wheat fields in a semi-arid region

Potential for the Detection of Irrigation Events on Maize Plots Using Sentinel-1 Soil Moisture Products

Water Balance and Soil Moisture Deficit of Different Vegetation Units under Semiarid Conditions in the Andes of Southern Ecuador

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