Статья

Crop Growth Monitoring with Drone-Borne DInSAR

Gian OréSchool of Electrical and Computer Engineering, University of Campinas—UNICAMP, Campinas 13083-852, BrazilMarlon S. AlcântaraSchool of Electrical and Computer Engineering, University of Campinas—UNICAMP, Campinas 13083-852, BrazilJuliana A. GóesSchool of Electrical and Computer Engineering, University of Campinas—UNICAMP, Campinas 13083-852, BrazilLuciano P. OliveiraSchool of Electrical and Computer Engineering, University of Campinas—UNICAMP, Campinas 13083-852, BrazilJhonnatan YepesSchool of Agricultural Engineering, University of Campinas—UNICAMP, Campinas 13083-875, BrazilBárbara TeruelSchool of Agricultural Engineering, University of Campinas—UNICAMP, Campinas 13083-875, BrazilValquíria CastroSchool of Electrical and Computer Engineering, University of Campinas—UNICAMP, Campinas 13083-852, BrazilLeonardo S. BinsNational Institute for Space Research—INPE, São José dos Campos 12227-010, BrazilFelicio CastroSchool of Electrical and Computer Engineering, University of Campinas—UNICAMP, Campinas 13083-852, BrazilDieter LuebeckLaila F. MoreiraLucas H. GabrielliSchool of Electrical and Computer Engineering, University of Campinas—UNICAMP, Campinas 13083-852, BrazilHugo E. Hernández‐FigueroaSchool of Electrical and Computer Engineering, University of Campinas—UNICAMP, Campinas 13083-852, Brazil

2020en

ABI

Аннотация

Accurate, high-resolution maps of for crop growth monitoring are strongly needed by precision agriculture. The information source for such maps has been supplied by satellite-borne radars and optical sensors, and airborne and drone-borne optical sensors. This article presents a novel methodology for obtaining growth deficit maps with an accuracy down to 5 cm and a spatial resolution of 1 m, using differential synthetic aperture radar interferometry (DInSAR). Results are presented with measurements of a drone-borne DInSAR operating in three bands—P, L and C. The decorrelation time of L-band for coffee, sugar cane and corn, and the feasibility for growth deficit maps generation are discussed. A model is presented for evaluating the growth deficit of a corn crop in L-band, starting with 50 cm height. This work shows that the drone-borne DInSAR has potential as a complementary tool for precision agriculture.

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Идентификаторы

DOI: 10.3390/rs12040615

Цитирования и источники

Цитирований: 2Использованных источников: 0

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