Monitoring Corn Nitrogen Concentration from Radar (C-SAR), Optical, and Sensor Satellite Data Fusion

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 3; p. 824
• Main Authors: Lapaz Olveira, Adrián, Saínz Rozas, Hernán, Castro-Franco, Mauricio, Carciochi, Walter, Nieto, Luciana, Balzarini, Mónica, Ciampitti, Ignacio, Reussi Calvo, Nahuel
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-02-2023
• Subjects: Backscattering; C-SAR backscatter; Corn; Crops; Data integration; Economics; Fertilizers; Monitoring; Multisensor fusion; Nitrogen; Precipitation; Prediction models; Profitability; Radiation; Remote sensing; Remote sensors; Root-mean-square errors; Satellites; Seasons; Sensors; Sentinel-1; Sentinel-2; Short wave radiation; Spectral bands; Synthetic aperture radar; Vegetables; Vegetation; Vegetation index; Zea mays
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15030824

Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods. Vegetation indices were calculated with new combinations of spectral bands, C-SAR backscatters, and sensor data fusion derived from Sentinel-1 and Sentinel-2. Predictive models of Nc with the best fit (R2 = 0.91) were calibrated with spectral band combinations and sensor data fusion in six experiments. During validation of the models in five experiments, sensor data fusion predicted corn Nc with lower error (MAPE: 14%, RMSE: 0.31 %Nc) than spectral band combination (MAPE: 20%, RMSE: 0.44 %Nc). The red-edge (704, 740, 740 nm), short-wave infrared (1375 nm) bands, and VV backscatter were all necessary to monitor corn Nc. Thus, satellite remote sensing via sensor data fusion is a critical data source for predicting changes in plant N status.