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

• Published in: Agriculture (Basel) 2024-10, Vol.14 (10), p.1753
• Main Authors: Fita, David, Bautista, Alberto San, Castiñeira-Ibáñez, Sergio, Franch, Belén, Domingo, Concha, Rubio, Constanza
• Format: Article
• Language: English
• Subjects: Agricultural production; Cereal crops; Climatic conditions; Correlation; Crop management; Crop production; Crop yield; Cultivars; Cultivation; Data acquisition; Environmental management; Evaluation; Fertilizers; Food security; Grain cultivation; Humidity; Impact analysis; Leaf area; Leaf area index; modelling; Nitrogen; Parameter identification; Performance evaluation; Plant layout; Plants (botany); Radiation; Remote monitoring; Remote sensing; Rice; Satellite observation; Satellites; Sentinel-2; Sustainability; Temperature; Variables; Vegetation; yield
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture14101753

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 m2, 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.