Crop Growth Monitoring with Drone-Borne DInSAR

Oré, Gian; Alcântara, Marlon S.; Góes, Juliana A.; Oliveira, Luciano P.; Yepes, Jhonnatan; Teruel, Bárbara; Castro, Valquíria; Bins, Leonardo S.; Castro, Felicio; Luebeck, Dieter; Moreira, Laila F.; Gabrielli, Lucas H.; Hernandez-Figueroa, Hugo E.

doi:10.3390/rs12040615

Open AccessArticle

Crop Growth Monitoring with Drone-Borne DInSAR

by

Gian Oré

¹

,

Marlon S. Alcântara

¹,

Juliana A. Góes

¹

,

Luciano P. Oliveira

^1,*

,

Jhonnatan Yepes

²

,

Bárbara Teruel

²,

Valquíria Castro

¹,

Leonardo S. Bins

³,

Felicio Castro

¹

,

Dieter Luebeck

⁴,

Laila F. Moreira

⁴,

Lucas H. Gabrielli

¹

and

Hugo E. Hernandez-Figueroa

¹

School of Electrical and Computer Engineering, University of Campinas—UNICAMP, Campinas 13083-852, Brazil

²

School of Agricultural Engineering, University of Campinas—UNICAMP, Campinas 13083-875, Brazil

³

National Institute for Space Research—INPE, São José dos Campos 12227-010, Brazil

⁴

Radaz Indústria e Comércio de Produtos Eletrônicos Ltda., São José dos Campos 12244-000, Brazil

^*

Author to whom correspondence should be addressed.

Remote Sens. 2020, 12(4), 615; https://doi.org/10.3390/rs12040615

Submission received: 31 December 2019 / Revised: 30 January 2020 / Accepted: 3 February 2020 / Published: 12 February 2020

(This article belongs to the Special Issue Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms)

Download

Browse Figures

Versions Notes

Abstract

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.

Keywords: differential interferometry; DInSAR; precision agriculture; drone-borne radar; crop growth deficit map

Graphical Abstract

Share and Cite

MDPI and ACS Style

Oré, G.; Alcântara, M.S.; Góes, J.A.; Oliveira, L.P.; Yepes, J.; Teruel, B.; Castro, V.; Bins, L.S.; Castro, F.; Luebeck, D.; et al. Crop Growth Monitoring with Drone-Borne DInSAR. Remote Sens. 2020, 12, 615. https://doi.org/10.3390/rs12040615

AMA Style

Oré G, Alcântara MS, Góes JA, Oliveira LP, Yepes J, Teruel B, Castro V, Bins LS, Castro F, Luebeck D, et al. Crop Growth Monitoring with Drone-Borne DInSAR. Remote Sensing. 2020; 12(4):615. https://doi.org/10.3390/rs12040615

Chicago/Turabian Style

Oré, Gian, Marlon S. Alcântara, Juliana A. Góes, Luciano P. Oliveira, Jhonnatan Yepes, Bárbara Teruel, Valquíria Castro, Leonardo S. Bins, Felicio Castro, Dieter Luebeck, and et al. 2020. "Crop Growth Monitoring with Drone-Borne DInSAR" Remote Sensing 12, no. 4: 615. https://doi.org/10.3390/rs12040615

APA Style

Oré, G., Alcântara, M. S., Góes, J. A., Oliveira, L. P., Yepes, J., Teruel, B., Castro, V., Bins, L. S., Castro, F., Luebeck, D., Moreira, L. F., Gabrielli, L. H., & Hernandez-Figueroa, H. E. (2020). Crop Growth Monitoring with Drone-Borne DInSAR. Remote Sensing, 12(4), 615. https://doi.org/10.3390/rs12040615

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Crop Growth Monitoring with Drone-Borne DInSAR

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI