Estimating multi-scale irrigation amounts using multi-resolution soil moisture data: A data-driven approach using PrISM

Abstract

Irrigated agriculture is the primary driver of freshwater use and is continuously expanding. Precise knowledge of irrigation amounts is critical for optimizing water management, especially in semi-arid regions where water is a limited resource. This study proposed to adapt the PrISM (Precipitation inferred from Soil Moisture) methodology to detect and estimate irrigation events from soil moisture remotely sensed data. PrISM was originally conceived to correct precipitation products, assimilating Soil Moisture (SM) observations into an antecedent precipitation index (API) formula, using a particle filter scheme. This novel application of PrISM uses initial precipitation and SM observations to detect instances of water excess in the soil (not caused by precipitation) and estimates the amount of irrigation, along with its uncertainty. This newly proposed approach does not require extensive calibration and is adaptable to different spatial and temporal scales. The objective of this study was to analyze the performance of PrISM for irrigation amount estimation and compare it with current state-of-the-art approaches. To develop and test this methodology, a synthetic study was conducted using SM observations with various noise levels to simulate uncertainties and different spatial and temporal resolutions. The results indicated that a high temporal resolution (less than 3 days) is crucial to avoid underestimating irrigation amounts due to missing events. However, including a constraint on the frequency of irrigation events, deduced from the system of irrigation used at the field level, could overcome the limitation of low temporal resolution and significantly reduce underestimation of irrigation amounts. Subsequently, the developed methodology was applied to actual satellite SM products at different spatial scales (1 km and 100 m) over the same area. Validation was performed using in situ data at the district level of Algerri-Balaguer in Catalunya, Spain, where in situ irrigation amounts were available for various years. The validation resulted in a total Pearson’s correlation coefficient (r) of 0.80 and a total root mean square error (rmse) of 7.19 mm∕week for the years from 2017 to 2021. Additional validation was conducted at the field level in the Segarra-Garrigues irrigation district using in situ data from a field where SM profiles and irrigation amounts were continuously monitored. This validation yielded a total bi-weekly r of 0.81 and a total rmse of −9.34 mm∕14-days for the years from 2017 to 2021. Overall, the results suggested that PrISM can effectively estimate irrigation from SM remote sensing data, and the methodology has the potential to be applied on a large scale without requiring extensive calibration or site-specific knowledge.