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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Assimilation of LAI and Dry Biomass Data From Optical and SAR Images Into an Agro-Meteorological Model to Estimate Soybean Yield
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Assimilation of LAI and Dry Biomass Data From Optical and SAR Images Into an Agro-Meteorological Model to Estimate Soybean Yield

机译：将来自光学和SAR图像的LAI和干生物量数据同化到农业气象模型中以估算大豆产量

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摘要

Crop monitoring at a fine scale and crop yield estimation are critical from an environmental perspective because they provide essential information to combine increased food production and sustainable management of agricultural landscapes. The aim of this article is to estimate soybean yield using an agro-meteorological model controlled by optical and/or synthetic aperture radar (SAR) multipolarized satellite images. Satellite and ground data were collected over seven working farms. Optical and SAR images were acquired by Formosat-2, Spot-4, Spot-5, and Radarsat-2 satellites during the soybean vegetation cycle. A vegetation index (NDVI) was derived from the optical images, and backscattering coefficients and polarimetric indicators were computed from full quad-pol Radarsat-2 images. An angular normalization of SAR data was performed to minimize the incidence angle effects on SAR signals by using the complementarities provided by SAR and optical data. The best results are obtained when the model is controlled by both the leaf area index (LAI) derived from the optical vegetation index modified triangular vegetation index (MTVI2) or from the SAR backscattering coefficient ${sigma_{{^{circ}}{textsc{vv}}}}$ $({text{LAI}}_{text{MTVI2}} $ or (${text{LAI}}_{sigma^{circ}{textsc{vv}}} $) and the dry biomass (DB) derived from the SAR Pauli matrix T33 $({text{DB}}_{{text{T}}33}) ({text{r}}^{2} > 0.83)$, demonstrating the complementary of optical and SAR data.
机译：从环境的角度来看，精细的作物监测和作物单产的估计至关重要，因为它们提供了必不可少的信息，可以将增加的粮食产量和农业景观的可持续管理结合起来。本文的目的是使用由光学和/或合成孔径雷达（SAR）多极化卫星图像控制的农业气象模型估算大豆产量。收集了七个工作农场的卫星和地面数据。在大豆植被周期中，Formosat-2，Spot-4，Spot-5和Radarsat-2卫星获取了光学和SAR图像。从光学图像中得出植被指数（NDVI），并从完整的四极点Radarsat-2图像中计算出反向散射系数和极化指示剂。通过使用SAR和光学数据提供的互补性，对SAR数据进行了角度归一化，以最小化入射角对SAR信号的影响。当模型既受光学植被指数修改的三角植被指数（MTVI2）或SAR背向散射系数$ {sigma _ {{^^ circ}} {textsc导出的叶面积指数（LAI）的控制时，可获得最佳结果{vv}}}} $ $（{text {LAI}} _ {text {MTVI2}} $或（$ {text {LAI}} _ {sigma ^ {circ} {textsc {vv}}} $）和SAR Pauli矩阵T33 $（{text {DB}} _ {{text {T}} 33}）（{text {r}} ^ {2}> 0.83）$得出的干生物量（DB），证明了互补和SAR数据。

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