Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation-猫眼课题宝

将 Sentinel-1 和 Sentinel-2 数据获取的土壤水分同化到 WOFOST 模型中以改进冬小麦产量估算

DOI：

10.3390/rs11131618

发表时间：

2019-07

期刊：

Remote Sensing

影响因子：

通讯作者：

中科院分区：

工程技术2区

文献类型：

作者：

Zhuo Wen;Huang Jianxi;Li Li;Zhang Xiaodong;Ma Hongyuan;Gao Xinran;Huang Hai;Xu Baodong;Xiao Xiangming

区域尺度作物估产对粮食安全具有重要意义。在过去的几十年里，遥感观测和作物生长模型的集成已被公认为是一种有前途的方法，用于作物生长监测和产量估计。光学遥感数据易受云和雨的影响，而合成孔径雷达（SAR）可以穿透云层，具有全天候能力。这就允许在雷达传感器数据方面进行更可靠和一致的作物监测和产量估计。为提高冬小麦估产精度，将SAR和光学数据结合，利用水云模型反演的土壤水分时间序列图像同化到作物模型中。在这项研究中，SAR图像获得的C波段SAR传感器搭载在哨兵1号卫星和光学图像获得的哨兵2多光谱仪器（MSI）在中国河北省衡水市。遥感数据和地面数据均采集于冬小麦主生长季。这两个归一化差异植被指数（NDVI），来自哨兵2，后向散射系数和极化指标，计算哨兵1，在水云模型中使用，以获得时间序列土壤湿度（SM）图像。为了提高作物产量的预测，在田间尺度上，我们将遥感土壤水分纳入世界粮食研究（WOFOST）模型使用Enhancement卡尔曼滤波（EnKF）算法。总的来说，土壤水分反演的趋势与地面测量一致，决定系数（R2）分别为0.45，0.53和0.49，RMSE分别为9.16%，7.43%和8.53%，为三个观测日期。冬小麦估产结果表明，同化遥感土壤水分改善了观测和模拟产量的相关性（R2 = 0.35; RMSE =934 kg/ha）相比，没有数据同化的情况下（R2 = 0.21; RMSE = 1330 kg/ha）。研究结果表明，将Sentinel-1 C波段SAR和Sentinel-2 MSI光学遥感数据同化到WOFOST模型中进行冬小麦估产具有一定的潜力和实用性，也可为其他作物类型的作物估产提供参考。

Crop yield estimation at a regional scale over a long period of time is of great significance to food security. In past decades, the integration of remote sensing observations and crop growth models has been recognized as a promising approach for crop growth monitoring and yield estimation. Optical remote sensing data are susceptible to cloud and rain, while synthetic aperture radar (SAR) can penetrate through clouds and has all-weather capabilities. This allows for more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images, which are retrieved by a water cloud model using SAR and optical data as input, into the crop model. In this study, SAR images were acquired by C-band SAR sensors boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi-spectral instrument (MSI) for Hengshui city of Hebei province in China. Remote sensing data and ground data were all collected during the main growing season of winter wheat. Both the normalized difference vegetation index (NDVI), derived from Sentinel-2, and backscattering coefficients and polarimetric indicators, computed from Sentinel-1, were used in the water cloud model to derive time series soil moisture (SM) images. To improve the prediction of crop yields at the field scale, we incorporated remotely sensed soil moisture into the World Food Studies (WOFOST) model using the Ensemble Kalman Filter (EnKF) algorithm. In general, the trend of soil moisture inversion was consistent with the ground measurements, with the coefficient of determination (R2) equal to 0.45, 0.53, and 0.49, respectively, and RMSE was 9.16%, 7.43%, and 8.53%, respectively, for three observation dates. The winter wheat yield estimation results showed that the assimilation of remotely sensed soil moisture improved the correlation of observed and simulated yields (R2 = 0.35; RMSE =934 kg/ha) compared to the situation without data assimilation (R2 = 0.21; RMSE = 1330 kg/ha). Consequently, the results of this study demonstrated the potential and usefulness of assimilating SM retrieved from both Sentinel-1 C-band SAR and Sentinel-2 MSI optical remote sensing data into WOFOST model for winter wheat yield estimation and could also provide a reference for crop yield estimation with data assimilation for other crop types.