Integrated crop growth and radiometric modeling to support Sentinel synthetic aperture radar observations of agricultural fields
Integrated crop growth and radiometric modeling to support Sentinel synthetic aperture radar observations of agricultural fields
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DOI：
10.1117/1.jrs.14.044508
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发表时间：
2020-10
影响因子：
1.7
通讯作者：
A. Davitt;J. Winter;K. McDonald
中科院分区：
文献类型：
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作者：
A. Davitt;J. Winter;K. McDonald
抽象的。利用合成孔径雷达进行作物监测需要了解作物动态特征如何影响雷达响应。我们使用农业技术转移决策支持系统(DSSAT)模型(一种动态作物生长模型)中的作物参数作为密歇根微波冠层散射(MIMICS)模型的输入，该模型是一种辐射模型，以模拟2015年期间来自加利福尼亚州约洛县选定小麦、水稻和玉米田的雷达散射。我们比较了模拟的后向散射和哨兵-1A后向散射，并进行了敏感性分析，以检查影响后向散射的作物特征。对于每一种作物，模拟的DSSAT-MIMICS后向散射VV(垂直发射和接收)与哨兵-1AσVV0(平均R值=0.76，p<；0.05)、均方根误差和2分贝以及在−0.23和0.99分贝之间的模型偏差相关。然而，没有足够的Sentinel-1AVH(垂直发射和水平接收)后向散射观测来稳健地评估模拟的DSSAT VH的性能。敏感性分析表明，模拟的后向散射对小麦和水稻茎以及玉米叶片的响应最快。利用这些分析，我们开发了一个作物生长指数，该指数将Sentinel-1A后向散射归一化，以模拟后向散射，并绘制玉米、水稻和小麦的变异性图，识别田间作物生长的高低。本研究为Sentinel-1A在作物监测中的潜在应用奠定了基础。
Abstract. Crop monitoring using synthetic aperture radar requires an understanding of how dynamic crop features influence radar response. We use crop parameters from the decision support system for agrotechnology transfer (DSSAT) model, a dynamic crop growth model, as inputs to the Michigan microwave canopy scattering (MIMICS) model, a radiometric model, to simulate radar scattering from selected wheat, rice, and corn fields in Yolo County, California, during 2015. We compared DSSAT-MIMICS modeled backscatter to Sentinel-1A backscatter and conducted sensitivity analyses to examine crop features that influence backscatter. For each crop, DSSAT-MIMICS modeled VV (vertically transmitted and received) backscatter was correlated to Sentinel-1A σVV0 (mean R-value = 0.76, p < 0.05), root-mean-square error <2 dB, and a model bias between −0.23 and 0.99 dB. However, there were not sufficient Sentinel-1A VH (vertically transmitted and horizontally received) backscatter observations to robustly evaluate DSSAT-MIMICS modeled VH performance. The sensitivity analyses revealed modeled backscatter was most responsive to wheat and rice stems, and corn leaves. Using the analyses, we developed a crop growth index that normalizes Sentinel-1A backscatter to modeled backscatter and mapped corn, rice, and wheat variability, identifying high and low crop growth in fields. This research contributes to the potential application of Sentinel-1A for crop monitoring.