Abstract A field study was conducted to estimate the regional wheat yield by integration of remotely sensed soil moisture index into CERES-Wheat model. The calibration and evaluation of model was performed using experimental data and then applied on the area of Faisalabad district for yield estimation. Area of Faisalabad district was divided into 7929 cells for independent simulations. The weather data of the wheat season were used uniformly to all cells, while site-specific soil data were used for each cell. Recommended crop management practices were used in the model for all cells. Median normalized difference water index (NDWI) were used to estimate the irrigation amount for each cell. The estimated yield was validated with observed yield of 25 random farms. Model calibration results showed a good agreement between observed and simulated values of grain yield (RMSE = 284.8 kg ha−1). The validation of model at regional scale showed a close association with simulated and observed yield of 25 farms (R2 = 0.71). The regional yield estimation results indicated that grain yield varies from 1500 to 3593 kg ha−1 in Faisalabad district. The estimated mean yield was 2979 kg ha−1, which was 5.2% higher than the yield reported by Crop Reporting Service (CRS), Punjab.

中文翻译：

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通过将遥感土壤水分整合到作物模型中来估计区域小麦产量

摘要 通过将遥感土壤水分指数整合到CERES-Wheat模型中，进行了田间研究，估算区域小麦产量。模型的校准和评估是使用实验数据进行的，然后应用于费萨拉巴德地区的产量估算。Faisalabad 区被划分为 7929 个单元以进行独立模拟。小麦季节的天气数据统一用于所有单元格，而特定地点的土壤数据用于每个单元格。模型中所有细胞都使用了推荐的作物管理实践。中值归一化差异水指数（NDWI）用于估计每个细胞的灌溉量。估计产量通过 25 个随机农场的观察产量进行验证。模型校准结果显示谷物产量的观测值和模拟值之间具有良好的一致性（RMSE = 284.8 kg ha-1）。模型在区域范围内的验证显示与 25 个农场的模拟和观察到的产量密切相关 (R2 = 0.71)。区域产量估算结果表明，费萨拉巴德地区的粮食产量从 1500 到 3593 kg ha-1 不等。估计的平均产量为 2979 kg ha-1，比旁遮普省作物报告服务 (CRS) 报告的产量高 5.2%。