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

Abstract

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.

Satellite‐Based Monitoring of Irrigation Water Use: Assessing Measurement Errors and Their Implications for Agricultural Water Management Policy

“…Lower, but still significant, errors (RMSE of 44 mm relative to actual mean water use of 245 mm) are reported by Olivera‐Guerra et al (2020) for five plots over two to four seasons using a thermal‐infrared‐based model. Olivera‐Guerra et al (2020) focus on wheat production in Morocco where precipitation and cloud cover are minimal and use validation data from experimental plots, showing that uncertainty in water use estimation remains significant even if it is possible to reduce errors introduced by input data uncertainty or unobserved heterogeneity in farmer irrigation management practices.…”

“…Garrido-Rubio et al ( 2020) report plot-level root-mean-square error (RMSE) in irrigation water use by crop type ranging from 44-176 mm (6-77% of actual water use), while Battude et al ( 2017) report RMSE's of 32-60 mm (19-35% of actual water use) at field levels even when averaging estimates over several years. Lower, but still significant, errors (RMSE of 44 mm relative to actual mean water use of 245 mm) are reported by Olivera-Guerra et al (2020) for five plots over two to four seasons using a thermal-infrared-based model. Olivera-Guerra et al (2020) focus on wheat production in Morocco where precipitation and cloud cover are minimal and use validation data from experimental plots, showing that uncertainty in water use estimation remains significant even if it is possible to reduce errors introduced by input data uncertainty or unobserved heterogeneity in farmer irrigation management practices.…”

“…While Sentinel‐1 may improve the underlying spatial resolution of soil moisture data, observations still only capture moisture signals in the near‐surface (top 5–10 cm). Thus, where rapid drainage occurs to deeper soil layers—for example, as would be expected under large irrigation events—resulting depths of irrigation may be underestimated (Olivera‐Guerra et al, 2020), with potential for uncertainty to be further compounded by imperfect knowledge of local soil hydraulic properties governing water fluxes and storage.…”

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

“…In another study of Ras Ghareb city in Egypt, Sentinel-2 data and fuzzy analytic hierarchy process approaches were also used for monitoring and assessing urban flash flood impacts [208]. In their case study of winter wheat fields in a semi-arid region, Olivera-Guerra et al [209] showed irrigation retrieval from Landsat optical and thermal data integrated into a crop water balance model.…”

Spatiotemporal Assessment of Agricultural Drought Using a Cell-Based Daily Soil Water Analysis Model

“…For this reason, many studies have been carried out to evaluate agricultural drought severity using remote sensing data or soil moisture analysis models [8][9][10][11][12][13][14][15][16][17]. In the case of study using remote sensing data, the drought was evaluated using soil moisture, vegetation activity, and land surface temperature data, which are used to monitor agricultural droughts [8][9][10][11][12]. These studies have the advantage of overcoming the spatial limitation of ground observation data, but there are limitations such as image acquisition frequency, sensor-specific errors, and uncertainty of specific algorithms.…”