Predicting Sugarcane Harvest Date and Productivity with a Drone-Borne Tri-Band SAR

Autor: Gian Oré, Marlon S. Alcântara, Juliana A. Góes, Bárbara Teruel, Luciano P. Oliveira, Jhonnatan Yepes, Valquíria Castro, Leonardo S. Bins, Felicio Castro, Dieter Luebeck, Laila F. Moreira, Rodrigo Cintra, Lucas H. Gabrielli, Hugo E. Hernandez-Figueroa
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Remote Sensing, Vol 14, Iss 7, p 1734 (2022)
Druh dokumentu: article
ISSN: 2072-4292
94083800
DOI: 10.3390/rs14071734
Popis: This article presents a novel method for predicting the sugarcane harvesting date and productivity using a three-band imaging radar. Taking advantage of working with a multi-band radar, this system was employed to estimate the above-ground biomass (AGB), achieving a root-mean-square error (RMSE) of 2 kg m−2 in sugarcane crops, which is an unprecedented result compared with other works based on the Synthetic Aperture Radar (SAR) system. By correlating the field measurements of the ripening index (RI) with the AGB measurements by radar, an indirect estimate of the RI by the radar is obtained. It is observed that the AGB reaches its maximum approximately 280 days after planting and the maximum RI, which defines the harvesting date, approximately 360 days after planting for the species IACSP97-4039. Starting from an AGB map collected by the radar, it is then possible to predict the harvesting date and the corresponding productivity with competitive average errors of 8 days and 10.7%, respectively, with three months in advance, whereas typical methods employed on a test site achieve an average error of 30 days with three months in advance. To the best of our knowledge, it is the first time that a multi-band radar is employed for productivity prediction in sugarcane crops.
Databáze: Directory of Open Access Journals
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