Remote Sensing Dynamics for Analyzing Nitrogen Impact on Rice Yield in Limited Environments

David Fita, Alberto San Bautista, Sergio Castiñeira-Ibáñez (), Belén Franch, Concha Domingo and Constanza Rubio
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David Fita: Department de Producción Vegetal, Universitat Politècnica de València, 46022 Valencia, Spain
Alberto San Bautista: Department de Producción Vegetal, Universitat Politècnica de València, 46022 Valencia, Spain
Sergio Castiñeira-Ibáñez: Centro de Tecnologías Físicas, Universitat Politècnica de València, 46022 Valencia, Spain
Belén Franch: Global Change Unit, Image Processing Laboratory, Universitat de València, 46980 Valencia, Spain
Concha Domingo: Instituto Valenciano de Investigaciones Agrarias, 46113 Valencia, Spain
Constanza Rubio: Centro de Tecnologías Físicas, Universitat Politècnica de València, 46022 Valencia, Spain

Agriculture, 2024, vol. 14, issue 10, 1-24

Abstract: Rice production remains highly dependent on nitrogen (N). There is no positive linear correlation between N concentration and yield in rice cultivation because an excess of N can unbalance the distribution of photo-assimilates in the plant and consequently produce a lower yield. We intended to study these imbalances. Remote sensing is a useful tool for monitoring rice crops. The purpose of this study was to evaluate the effectiveness of using remote sensing to assess the impact of N applications on rice crop behavior. An experiment with three different doses (120, 170 and 220 kg N·ha −1 ) was carried out over two years (2021 and 2022) in Valencia, Spain. Biomass, Leaf Area Index (LAI), plants per m 2 , yield, N concentration and N uptake were determined. Moreover, reflectance values in the green, red, and NIR bands of the Sentinel-2 satellite were acquired. The two data matrices were merged in a correlation study and the resulting interpretation ended in a protocol for the evaluation of the N effect during the main phenological stages. The positive effect of N on the measured parameters was observed in both years; however, in the second year, the correlations with the yield were low, being attributed to a complex interaction with climatic conditions. Yield dependence on N was optimally evaluated and monitored with Sentinel-2 data. Two separate relationships between NIR–red and NDVI–NIR were identified, suggesting that using remote sensing data can help enhance rice crop management by adjusting nitrogen input based on plant nitrogen concentration and yield estimates. This method has the potential to decrease nitrogen use and environmental pollution, promoting more sustainable rice cultivation practices.

Keywords: rice; nitrogen; remote sensing; yield; modelling; Sentinel-2 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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