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Monitoring corn nitrogen nutrition index from optical and synthetic aperture radar satellite data and soil available nitrogen
Resumen
Nitrogen (N) nutrition index (NNI) is a reliable indicator of plant N status for field crops, but its determination is both labor- and cost-intensive. The utilization of remote sensing approaches for monitoring N, mainly in relevant crops such as of corn (Zea mays L.), will be critical for enhancing effective use of this nutrient. Therefore, the aim of this study was to assess NNI predicted from optical and C-band Synthetic Aperture Radar (C-SAR)
[ver mas...]
Nitrogen (N) nutrition index (NNI) is a reliable indicator of plant N status for field crops, but its determination is both labor- and cost-intensive. The utilization of remote sensing approaches for monitoring N, mainly in relevant crops such as of corn (Zea mays L.), will be critical for enhancing effective use of this nutrient. Therefore, the aim of this study was to assess NNI predicted from optical and C-band Synthetic Aperture Radar (C-SAR) satellite data and available soil N (Nav) at different vegetative growth stages for corn crop. Eleven field studies were conducted in the Pampas region (Argentina), applying five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1), all at sowing time. Plant samples were collected at sixth-leaf (V6), tenth-leaf (V10), fourteen-leaf (V14), and flowering (R1). Using linear regression models, NNI was best predicted using only optical satellite data from V6 to V14, and integrating optical with C-SAR plus Nav at R1. The best monitoring model integrated vegetation spectral indices, C-SAR and Nav data at V10 with an adjusted R2 of 0.75 achieved during calibration in the northern Pampa. During validation, it predicted NNI with an RMSE of 0.14 and a MAPE of 12% in the southeastern Pampa. The red-edge spectrum and Local Incidence Angle of C-SAR were necessary to monitor the corn N status via prediction of NNI. Thus, this study provided empirical models to remotely sensed corn N status within fields during vegetative period, serving as a foundational data for guiding future N management.
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![Thumbnail](/xmlui/bitstream/handle/20.500.12123/15248/PrecisionAgriculture_cover.jpg?sequence=3&isAllowed=y)
Autor
Lapaz Olveira, Adrián;
Castro Franco, Mauricio;
Sainz Rozas, Hernan Rene;
Carciochi, Walter;
Balzarini, Mónica;
Avila, Oscar;
Ciampitti, Ignacio;
Reussi Calvo, Nahuel Ignacio;
Fuente
Precision Agriculture : 1-15 (Published: 04 August 2023)
Fecha
2023-08
Editorial
Springer
ISSN
1573-1618 (online)
1385-2256 (print)
1385-2256 (print)
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Formato
pdf
Tipo de documento
artículo
Proyectos
(ver más)
INTA/2019-PE-E9-I177-001, Desarrollo y aplicación de tecnologías de mecanización, precisión y digitalización de la Agricultura
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