Journal
AGRONOMY-BASEL
Volume 13, Issue 5, Pages -Publisher
MDPI
DOI: 10.3390/agronomy13051357
Keywords
Lis Valley Irrigation District; remote sensing; rice monitoring; water management
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Rice is an important crop worldwide, with high consumption rates. Estimating water needs for rice production is essential, and remote sensing techniques have shown effectiveness in this regard. This study derived an approach to estimate actual crop evapotranspiration in rice paddies using satellite images. The findings highlight the potential of remote sensing-assisted methods in monitoring rice growth.
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 (K-c) proposed by FAO and the NDVI evolution in the control rice fields, with R-2 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.
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