Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing,Agricultural Systems

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Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing
Agricultural Systems ( IF 6.1 ) Pub Date : 2021-10-25 , DOI: 10.1016/j.agsy.2021.103299
Rajkumar Dhakar ₁ , Vinay Kumar Sehgal ₁ , Debasish Chakraborty _{1,

2} , Rabi Narayan Sahoo ₁ , Joydeep Mukherjee ₁ , Amor V.M. Ines ₃ , Soora Naresh Kumar ₄ , Paresh B. Shirsath ₅ , Somnath Baidya Roy ₆

Affiliation

CONTEXT

An accurate crop yield forecast with sufficient lead time is critical for various applications, such as crop management, resources mobilization, agri-commodity trading, crop insurance, etc. Accurate yield forecasting well ahead of harvest at field scale with minimal field input data remains a challenge.

OBJECTIVE

This study aimed to develop a novel prototype wheat yield forecasting system by assimilating remote sensing (RS) derived crop parameters and weather forecast into InfoCrop-Wheat crop simulation model (CSM), using minimum field measurements.

METHODS

The CSM was calibrated and validated at both research farm and farmers' fields. The crop LAI was retrieved through inversion of the PROSAIL radiative transfer model from Sentinel-2A and Landsat-8 images and validated using in-situ LAI measurements. The CSM was modified to test assimilation of RS derived LAI through “Ensemble Kalman Filtering” (EnKF) and “Forcing” strategies at multiple time-steps. The RS derived LAI was not only used to correct/replace model simulated LAI but other model state variables were also adjusted accordingly. A major challenge of adjusting crop phenology based on RS derived LAI was also attempted. The WRF weather forecast was bias-corrected and incorporated into the modified model-LAI assimilation framework. Generic crop management inputs were specified to the model. Finally, the study demonstrated a workable prototype of a field scale wheat growth and yield forecasting system under limited field data availability.

RESULTS AND CONCLUSIONS

The inversion of PROSAIL showed an RMSE of 0.56 m²/m² in LAI retrievals. Model validation with measured inputs showed normalized error (NE) of 6‐–8% in grain yield. The proposed framework showed only 2%, 5%, 3% and 1% higher NE in simulating days to anthesis, days to physiological maturity, dry matter and grain yield, respectively, than with measured inputs. The “EnkF” outperformed “Forcing” for predicting crop yield as well as phenology and growth of wheat using generic management inputs. The system showed acceptable accuracy in forecasting phenology, dry matter and yield of wheat at field scale when weighted adaptive bias-correction of weather forecast was incorporated with a 15 days lead time.

SIGNIFICANCE

The prototype can be scaled-up for wheat and other crops for predicting real-time crop condition and yield losses at farmers' field for a range of applications, notably, crop-insurance, resources allocation, targeted agro-advisories and triggering contingency plans. It offers considerable potential for objective assessment of crops in the marginal and smallholder systems supporting the smart farming paradigm.

中文翻译：

结合作物模拟模型与天气预报和卫星遥感在有限的田间数据可用性下的田间尺度空间小麦产量预测系统

语境

具有足够提前期的准确作物产量预测对于各种应用至关重要，例如作物管理、资源调动、农业商品交易、作物保险等。在田间规模的收获之前以最少的田间输入数据进行准确的产量预测仍然是一个挑战。

客观的

本研究旨在通过使用最少的田间测量将遥感 (RS) 派生的作物参数和天气预报同化到 InfoCrop-Wheat 作物模拟模型 (CSM) 中，开发一种新型的原型小麦产量预测系统。

方法

CSM 已在研究农场和农民田间进行了校准和验证。作物 LAI 是通过从 Sentinel-2A 和 Landsat-8 图像中反演 PROSAIL 辐射传输模型来检索的，并使用原位 LAI 测量进行验证。CSM 被修改为通过“集成卡尔曼滤波”（EnKF）和“强制”策略在多个时间步长测试 RS 衍生的 LAI 的同化。RS 导出的 LAI 不仅用于校正/替换模型模拟的 LAI，还相应地调整其他模型状态变量。还尝试了基于 RS 衍生的 LAI 调整作物物候的主要挑战。WRF 天气预报进行了偏差校正，并纳入了修改后的模型-LAI 同化框架。对模型指定了通用作物管理输入。最后，

结果和结论

在 LAI反演中，PROSAIL 的反演显示 RMSE 为 0.56 m ² /m ²。使用测量输入进行模型验证显示，谷物产量的归一化误差 (NE) 为 6–8%。所提出的框架显示，在模拟开花天数、生理成熟天数、干物质和谷物产量方面，NE 仅比测量输入高 2%、5%、3% 和 1%。“EnkF”在使用通用管理投入预测作物产量以及小麦的物候和生长方面优于“Forcing”。当天气预报的加权自适应偏差校正与 15 天的提前期相结合时，该系统在预测田间小麦的物候、干物质和产量方面表现出可接受的准确性。