A Remote-Sensing-Assisted Estimation of Water Use in Rice Paddy Fields: A Study on Lis Valley, Portugal

• Published in: Agronomy (Basel) Vol. 13; no. 5; p. 1357
• Main Authors: Ferreira, Susana, Sánchez, Juan Manuel, Gonçalves, José Manuel
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-05-2023
• Subjects: Algorithms; Climate change; Crop fields; Crop yield; Crops; Energy; Energy balance; Estimation; Evapotranspiration; Growing season; Irrigation; Lis Valley Irrigation District; Monitoring; Monitoring methods; Portugal; Productivity; Remote sensing; Rice; Rice fields; rice monitoring; Satellite imagery; Seasonal variations; Spain; Surface energy; Surface properties; Valleys; Vegetation; Water demand; water management; Water monitoring; Water use; Weeds; Portugal; Spain
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy13051357

Rice culture is one of the most important crops in the world, being the most consumed cereal grain (755 million tons in 2020). Since rice is usually produced under flooding conditions and water performs several essential functions for the crop, estimating its water needs is essential. Remote sensing techniques have shown effectiveness in estimating and monitoring the water use in crop fields. An estimation from satellite data is a challenge, but could be very useful, in order to spatialize local estimates and operationalize production models. This study intended to derive an approach to estimate the actual crop evapotranspiration (ETa) in rice paddies from a temporal series of satellite images. The experimental data were obtained in the Lis Valley Irrigation District (central coast of Portugal), during the 2019 to 2021 rice growing seasons. The average seasonal ETa (FAO56) resulted 586 ± 23 mm and the water productivity (WP) was 0.47 ± 0.03 kg m−3. Good correlations were found between the crop coefficients (Kc) proposed by FAO and the NDVI evolution in the control rice fields, with R2 ranging between 0.71 and 0.82 for stages II+III (development + middle) and between 0.76 and 0.82 for stage IV (late). The results from the derived RS-assisted method were compared to the ETa values obtained from the surface energy balance model METRIC, showing an average estimation error of ±0.8 mm d−1, with a negligible bias. The findings in this work are promising and show the potential of the RS-assisted method for monitoring ETa and water productivity, capturing the local and seasonal variability in rice growing, and then predicting the rice yield, being a useful and free tool available to farmers.