Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing
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...
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2022
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/129178 |
| _version_ | 1855532397964034048 |
|---|---|
| author | Dhakar, Rajkumar Sehgal, Vinay Kumar Chakraborty, Debasish Sahoo, Rabi Narayan Mukherjee, Joydeep Ines, Amor V.M. Soora Naresh Kumar Shirsath, Paresh B. Roy, Somnath Baidya |
| author_browse | Chakraborty, Debasish Dhakar, Rajkumar Ines, Amor V.M. Mukherjee, Joydeep Roy, Somnath Baidya Sahoo, Rabi Narayan Sehgal, Vinay Kumar Shirsath, Paresh B. Soora Naresh Kumar |
| author_facet | Dhakar, Rajkumar Sehgal, Vinay Kumar Chakraborty, Debasish Sahoo, Rabi Narayan Mukherjee, Joydeep Ines, Amor V.M. Soora Naresh Kumar Shirsath, Paresh B. Roy, Somnath Baidya |
| author_sort | Dhakar, Rajkumar |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace129178 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1291782025-10-26T13:01:22Z Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing Dhakar, Rajkumar Sehgal, Vinay Kumar Chakraborty, Debasish Sahoo, Rabi Narayan Mukherjee, Joydeep Ines, Amor V.M. Soora Naresh Kumar Shirsath, Paresh B. Roy, Somnath Baidya forecasting crops phenology data remote sensing 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. 2022-01 2023-03-03T16:17:45Z 2023-03-03T16:17:45Z Journal Article https://hdl.handle.net/10568/129178 en Limited Access Elsevier Dhakar, R., Sehgal, V.K., Chakraborty, D., Sahoo, R.N., Mukherjee, J., Ines, A.V.M., Kumar, S.N., Shirsath, P.B. and Roy, S.B. 2022. 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, 195, 103299. |
| spellingShingle | forecasting crops phenology data remote sensing Dhakar, Rajkumar Sehgal, Vinay Kumar Chakraborty, Debasish Sahoo, Rabi Narayan Mukherjee, Joydeep Ines, Amor V.M. Soora Naresh Kumar Shirsath, Paresh B. Roy, Somnath Baidya Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing |
| title | Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing |
| title_full | Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing |
| title_fullStr | Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing |
| title_full_unstemmed | Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing |
| title_short | Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing |
| title_sort | field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing |
| topic | forecasting crops phenology data remote sensing |
| url | https://hdl.handle.net/10568/129178 |
| work_keys_str_mv | AT dhakarrajkumar fieldscalespatialwheatyieldforecastingsystemunderlimitedfielddataavailabilitybyintegratingcropsimulationmodelwithweatherforecastandsatelliteremotesensing AT sehgalvinaykumar fieldscalespatialwheatyieldforecastingsystemunderlimitedfielddataavailabilitybyintegratingcropsimulationmodelwithweatherforecastandsatelliteremotesensing AT chakrabortydebasish fieldscalespatialwheatyieldforecastingsystemunderlimitedfielddataavailabilitybyintegratingcropsimulationmodelwithweatherforecastandsatelliteremotesensing AT sahoorabinarayan fieldscalespatialwheatyieldforecastingsystemunderlimitedfielddataavailabilitybyintegratingcropsimulationmodelwithweatherforecastandsatelliteremotesensing AT mukherjeejoydeep fieldscalespatialwheatyieldforecastingsystemunderlimitedfielddataavailabilitybyintegratingcropsimulationmodelwithweatherforecastandsatelliteremotesensing AT inesamorvm fieldscalespatialwheatyieldforecastingsystemunderlimitedfielddataavailabilitybyintegratingcropsimulationmodelwithweatherforecastandsatelliteremotesensing AT sooranareshkumar fieldscalespatialwheatyieldforecastingsystemunderlimitedfielddataavailabilitybyintegratingcropsimulationmodelwithweatherforecastandsatelliteremotesensing AT shirsathpareshb fieldscalespatialwheatyieldforecastingsystemunderlimitedfielddataavailabilitybyintegratingcropsimulationmodelwithweatherforecastandsatelliteremotesensing AT roysomnathbaidya fieldscalespatialwheatyieldforecastingsystemunderlimitedfielddataavailabilitybyintegratingcropsimulationmodelwithweatherforecastandsatelliteremotesensing |