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.
This paper presents a high-accuracy single-pass drone-borne Interferometric Synthetic Aperture Radar System in the P-band (P-InSAR) for forest inventory, where ground and canopy heights are accurately determined. Full penetration is proven for a eucalyptus forest with a tree spacing of 2.5 m by 3.0 m, and the measured digital terrain accuracy is compared with well-known statistical models. Combining a C-band single-pass InSAR with P-InSAR, forest height is estimated with 5 % accuracy for forest inventory. Both surface and digital ground models are presented and compared with ground truth measurements.
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.
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