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Estimation of rice yield from a C-band radar remote sensing image by integrating a physical scattering model and an optimization algorithm
Precision Agriculture ( IF 5.4 ) Pub Date : 2019-05-07 , DOI: 10.1007/s11119-019-09664-8 Yuan Zhang , Wenjia Yan , Bin Yang , Tianpeng Yang , Xiaohui Liu
Precision Agriculture ( IF 5.4 ) Pub Date : 2019-05-07 , DOI: 10.1007/s11119-019-09664-8 Yuan Zhang , Wenjia Yan , Bin Yang , Tianpeng Yang , Xiaohui Liu
Accurate estimation of rice yield with remotely sensed data plays a role in ensuring food security at local or national scales. In this study, an estimation scheme integrating a rice canopy scattering model (RCSM) and a genetic algorithm optimization tool (GAOT) was proposed on a basis of radar remote sensing technology. A C-band Radarsat-2 synthetic aperture radar (SAR) image acquired at rice maturity stage in Northeast, China was tested to simulate three yield-related rice parameters via an integrated RCSM-GAOT scheme, with a parallel computing environment. Rice yield was then estimated via a regression analysis by linking the simulated parameters to rice ear weights. Results showed that three parameters, ear length, ear diameter and ear density, can be simulated by the RCSM-GAOT scheme with a simulation error of 1.2 cm, 0.11 cm and 29 ears/m 2 , respectively. Rice yield is estimated with an average error of 0.28 kg/m 2 and 0.22 kg/m 2 for fresh ear weight and dry ear weight, respectively. In comparison with the need of multiple SAR data acquisitions in past studies, this study demonstrates the capability of one time Radarsat-2 quad-polarized SAR image in regional rice yield estimation. At the same time, it also indicates that the proposed RCSM-GAOT scheme has a potential in future precision agriculture applications.
更新日期：2019-05-07
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