Abstract
Accurate yield estimation of paddy crop plays an important role in forecasting paddy productivity for ensuring regional or national food security of the country. Although the crop growth models provide accurate yield forecasting, these models are difficult to implement in developing countries like India due to inhomogeneous or/and lack of required information about crop, soil, weather, etc. On the contrary, remotely sensed imagery available homogeneously provides valuable inputs for this purpose. Particularly, synthetic aperture radar (SAR) data proved to have great potential for paddy growth monitoring and biophysical parameters retrieval over optical data. Moreover, the effective use of artificial neural network (ANN) may enable us to understand the complex relation between parameters as well as improve the forecasting performance than using empirical-/semiempirical-based approaches. Thus, the study aims to analyze multi-temporal dual-polarization C-band Sentinel-1 SAR data for paddy yield forecasting using ANN model. In this study, smart sampling based on the normalized difference vegetation (NDVI) and normalized difference water index has been considered to obtain in situ yield measurements in the study area. The peak stage signature of backscattering coefficients is considered to estimate yield due to the maximum possibility of signal to interact with crop cover characteristics. It is observed that the VH-polarization-based ANN model provides better accuracy with coefficient of determination (R2) and root mean square error (RMSE) of 0.72 and 600.11 kg/ha, respectively, in comparison with VV polarization which has shown 0.26 and 948.46 kg/ha, respectively. Overall, the study demonstrates that the effective use of ANN model may provide reliable yield estimation accuracy from remotely sensed imagery alone.
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Acknowledgements
The study work has been carried out under the project, entitled ‘Gram Panchayat level crop yield estimation using the technology’ with collaboration of MNCFC. The authors would like to express their gratitude to the Mahalanobis National Crop Forecast Center (MNCFC), Delhi-110012, for their enthusiastic support. The satellite data provided by ESA (Sentinel-1/1A & Sentinel-2A) are thankfully acknowledged. The authors are grateful to Dr. V.N Sridhar—former scientists SAC (ISRO) for their advice on conducting this study and analyzing the results. Special thanks to Mr. Thamizh Vendhan, Senior lead Consultant, Amnex Technology, for his kind help and technical suggestion.
Funding
The data for this study come from a central government project called ‘Pilot Study on GP (Gram Panchayat) level Crop Yield Estimation using Advanced Technology’. The Mahalanobis National Crop Forecast Center (MNCFC) provided funding for the project with Reference ID (F.No.: 6/7(2)/PMFBY/2017-MNCFC).
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Sharma, P.K., Kumar, P., Srivastava, H.S. et al. Assessing the Potentials of Multi-temporal Sentinel-1 SAR Data for Paddy Yield Forecasting Using Artificial Neural Network. J Indian Soc Remote Sens 50, 895–907 (2022). https://doi.org/10.1007/s12524-022-01499-7
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DOI: https://doi.org/10.1007/s12524-022-01499-7