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.

中文翻译：

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集成作物生长和辐射测量建模，以支持 Sentinel 合成孔径雷达对农田的观测

摘要。使用合成孔径雷达进行作物监测需要了解动态作物特征如何影响雷达响应。我们使用来自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）的作物参数作为密歇根微波冠层散射 (MIMICS) 模型（一种辐射测量模型）的输入，以模拟选定小麦、水稻、 2015 年加利福尼亚州约洛县的玉米田。我们将 DSSAT-MIMICS 建模的反向散射与 Sentinel-1A 反向散射进行了比较，并进行了敏感性分析以检查影响反向散射的作物特征。对于每种作物，DSSAT-MIMICS 建模的 VV（垂直发射和接收）反向散射与 Sentinel-1A σVV0（平均 R 值 = 0.76，p < 0.05）、均方根误差 <2 dB 和模型偏差相关介于 -0.23 和 0 之间。99 分贝。然而，没有足够的 Sentinel-1A VH（垂直传输和水平接收）反向散射观测来可靠地评估 DSSAT-MIMICS 建模的 VH 性能。敏感性分析表明，建模的反向散射对小麦和水稻茎以及玉米叶的响应最为灵敏。使用这些分析，我们开发了一个作物生长指数，将 Sentinel-1A 反向散射标准化为建模反向散射并绘制玉米、水稻和小麦变异性图，识别田间作物的高低生长。这项研究有助于 Sentinel-1A 在作物监测中的潜在应用。敏感性分析表明，模拟的反向散射对小麦和水稻茎以及玉米叶的响应最为灵敏。使用这些分析，我们开发了一个作物生长指数，将 Sentinel-1A 反向散射标准化为建模反向散射并绘制玉米、水稻和小麦变异性图，识别田间作物的高低生长。这项研究有助于 Sentinel-1A 在作物监测中的潜在应用。敏感性分析表明，模拟的反向散射对小麦和水稻茎以及玉米叶的响应最为灵敏。使用这些分析，我们开发了一个作物生长指数，将 Sentinel-1A 反向散射标准化为建模反向散射并绘制玉米、水稻和小麦变异性图，识别田间作物的高低生长。这项研究有助于 Sentinel-1A 在作物监测中的潜在应用。