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Assessing the Potentials of Multi-temporal Sentinel-1 SAR Data for Paddy Yield Forecasting Using Artificial Neural Network

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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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References

  • Alebele, Y., Zhang, X., Wang, W., Yang, G., Yao, X., Zheng, H., Zhu, Y., Cao, W., & Cheng, T. (2020). Estimation of canopy biomass components in paddy rice from combined optical and SAR data using multi-target Gaussian regressor stacking. Remote Sensing, 12(16), 2564.

    Article  Google Scholar 

  • Antaryami, M., & Jyotsnarani, S. (2013). Soils of Cuttack District. Odisha Review, 69(11), 51–57.

    Google Scholar 

  • Atkinson, P. M., & Tatnall, A. R. (1997). Introduction neural networks in remote sensing. International Journal of Remote Sensing, 18(4), 699–709.

    Article  Google Scholar 

  • Auffhammer, M., Ramanathan, V., & Vincent, J. R. (2012). Climate change, the monsoon, and rice yield in India. Climatic Change, 111(2), 411–424.

    Article  Google Scholar 

  • Brandão, Z. N., Sofiatti, V., Bezerra, J. R., Ferreira, G. B., & Medeiros, J. C. (2015). Spectral reflectance for growth and yield assessment of irrigated cotton. Australian Journal of Crop Science, 9(1), 75–84.

    Google Scholar 

  • Brisco, B., Brown, R. J., Hirose, T., McNairn, H., & Staenz, K. (1998). Precision agriculture and the role of remote sensing: A review. Canadian Journal of Remote Sensing, 24(3), 315–327.

    Article  Google Scholar 

  • Carpenter, G. A., Gjaja, M. N., Gopal, S., & Woodcock, C. E. (1997). ART neural networks for remote sensing: Vegetation classification from Landsat TM and terrain data. IEEE Transactions on Geoscience and Remote Sensing, 35(2), 308–325.

    Article  Google Scholar 

  • Chauhan, S., Srivastava, H. S., & Patel, P. (2018). Wheat crop biophysical parameters retrieval using hybrid-polarized RISAT-1 SAR data. Remote Sensing of Environment, 216, 28–43.

    Article  Google Scholar 

  • Choudhury, I., & Chakraborty, M. (2006). SAR signature investigation of rice crop using RADARSAT data. International Journal of Remote Sensing, 27(3), 519–534.

    Article  Google Scholar 

  • Ferrazzoli, P., Paloscia, S., Pampaloni, P., Schiavon, G., Sigismondi, S., & Solimini, D. (1997). The potential of multifrequency polarimetric SAR in assessing agricultural and arboreous biomass. IEEE Transactions on Geoscience and Remote Sensing, 35(1), 5–17.

    Article  Google Scholar 

  • Gao, B. C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266.

    Article  Google Scholar 

  • Guntukula, R. (2020). Assessing the impact of climate change on Indian agriculture: Evidence from major crop yields. Journal of Public Affairs, 20(1), e2040.

    Article  Google Scholar 

  • Inoue, Y., Sakaiya, E., & Wang, C. (2014a). Capability of C-band backscattering coefficients from high-resolution satellite SAR sensors to assess biophysical variables in paddy rice. Remote Sensing of Environment, 140, 257–266.

    Article  Google Scholar 

  • Inoue, Y., Sakaiya, E., & Wang, C. (2014b). Potential of X-band images from high-resolution satellite SAR sensors to assess growth and yield in paddy rice. Remote Sensing, 6(7), 5995–6019.

    Article  Google Scholar 

  • Jiang, D., Yang, X., Clinton, N., & Wang, N. (2004). An artificial neural network model for estimating crop yields using remotely sensed information. International Journal of Remote Sensing, 25(9), 1723–1732.

    Article  Google Scholar 

  • Jing, Z., Zhang, Y., Wang, K., & Shi, R. (2013). Retrieving rice yield and biomass from Radarsat-2 SAR data with artificial neural network (ANN). In Remote sensing and modeling of ecosystems for sustainability X (Vol. 8869, p. 88690X). International Society for Optics and Photonics.

  • Kriegler, F. J., Malila, W. A., Nalepka, R. F., & Richardson, W. (1969). Preprocessing transformations and their effects on multispectral recognition. Remote Sensing of Environment, VI, 97–132.

    Google Scholar 

  • Kumar, P., Prasad, R., Mishra, V. N., Gupta, D. K., & Singh, S. K. (2016). Artificial neural network for crop classification using C-band RISAT-1 satellite datasets. Russian Agricultural Sciences, 42(3), 281–284.

    Article  Google Scholar 

  • Lek, S., & Guégan, J. F. (1999). Artificial neural networks as a tool in ecological modelling, an introduction. Ecological Modelling, 120(2–3), 65–73.

    Article  Google Scholar 

  • Li, Y., Liao, Q., Li, X., Liao, S., Chi, G., & Peng, S. (2003). Towards an operational system for regional-scale rice yield estimation using a time-series of Radarsat ScanSAR images. International Journal of Remote Sensing, 24(21), 4207–4220.

    Article  Google Scholar 

  • Liao, C., Wang, J., Shang, J., Huang, X., Liu, J., & Huffman, T. (2018). Sensitivity study of Radarsat-2 polarimetric SAR to crop height and fractional vegetation cover of corn and wheat. International Journal of Remote Sensing, 39(5), 1475–1490.

    Article  Google Scholar 

  • Liu, J., Shang, J., Qian, B., Huffman, T., Zhang, Y., Dong, T., Jing, Q., & Martin, T. (2019). Crop yield estimation using time-series MODIS data and the effects of cropland masks in Ontario, Canada. Remote Sensing, 11(20), 2419.

    Article  Google Scholar 

  • Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870.

    Article  Google Scholar 

  • Macelloni, G., Paloscia, S., Pampaloni, P., Marliani, F., & Gai, M. (2001). The relationship between the backscattering coefficient and the biomass of narrow and broad leaf crops. IEEE Transactions on Geoscience and Remote Sensing, 39(4), 873–884.

    Article  Google Scholar 

  • Mahajan, G., Kumar, V., & Chauhan, B. S. (2017). Rice production in India. In B. S. Chauhan, K. Jabran, & G. Mahajan (Eds.), Rice production worldwide (pp. 53–91). Springer.

    Chapter  Google Scholar 

  • Maki, M., Sekiguchi, K., Homma, K., Hirooka, Y., & Oki, K. (2017). Estimation of rice yield by SIMRIW-RS, a model that integrates remote sensing data into a crop growth model. Journal of Agricultural Meteorology, 73(1), 2–8.

    Article  Google Scholar 

  • Mandal, D., & Rao, Y. S. (2020). SASYA: An integrated framework for crop biophysical parameter retrieval and within-season crop yield prediction with SAR remote sensing data. Remote Sensing Applications: Society and Environment, 20, https://doi.org/10.1016/j.rsase.2020.100366.

    Article  Google Scholar 

  • Mas, J. F., & Flores, J. J. (2008). The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing, 29(3), 617–663.

    Article  Google Scholar 

  • Murthy, C. S., Raju, P. V., & Badrinath, K. V. S. (2003). Classification of wheat crop with multi-temporal images: Performance of maximum likelihood and artificial neural networks. International Journal of Remote Sensing, 24(23), 4871–4890.

    Article  Google Scholar 

  • Parida, B. R., & Singh, S. (2021). Spatial mapping of winter wheat using C-band SAR (Sentinel-1A) data and yield prediction in Gorakhpur district, Uttar Pradesh (India). Journal of Spatial Science. https://doi.org/10.1080/14498596.2021.1896393

    Article  Google Scholar 

  • Patel, N. K., Ravi, N., Navalgund, R. R., Dash, R. N., Das, K. C., & Patnaik, S. (1991). Estimation of rice yield using IRS-1A digital data in coastal tract of Orissa. International Journal of Remote Sensing, 12(11), 2259–2266.

    Article  Google Scholar 

  • Patel, P., & Srivastava, H. S. (2013). RADARSAT-2 announcement of opportunity project on soil moisture, surface roughness and vegetation parameter retrieval using SAR polarimetry. In SAC/EPSA/MPSG/CVD/TDP R& D/01/13, SOAR International Closing and Reporting–2013.

  • Patel, P., Srivastava, H. S., & Navalgund, R. R. (2006). Estimating wheat yield: an approach for estimating number of grains using cross-polarised ENVISAT-1 ASAR data. In Microwave remote sensing of the atmosphere and environment V (Vol. 6410, p. 641009). International Society for Optics and Photonics.

  • Prasad, R., Pandey, A., Singh, K. P., Singh, V. P., Mishra, R. K., & Singh, D. (2012). Retrieval of spinach crop parameters by microwave remote sensing with back propagation artificial neural networks: A comparison of different transfer functions. Advances in Space Research, 50(3), 363–370.

    Article  Google Scholar 

  • Ranjan, A. K., & Parida, B. R. (2021). Predicting paddy yield at spatial scale using optical and Synthetic Aperture Radar (SAR) based satellite data in conjunction with field-based Crop Cutting Experiment (CCE) data. International Journal of Remote Sensing, 42(6), 2046–2071.

    Article  Google Scholar 

  • Ranson, K. J., & Sun, G. (1994). Mapping biomass of a northern forest using multifrequency SAR data. IEEE Transactions on Geoscience and Remote Sensing, 32, 388–396.

    Article  Google Scholar 

  • Setiyono, T. D., Holecz, F., Khan, N. I., Barbieri, M., Quicho, E., Collivignarelli, F., Maunahan, A., Gatti, L., & Romuga, G. C. (2017). Synthetic Aperture Radar (SAR)-based paddy rice monitoring system: Development and application in key rice producing areas in Tropical Asia. In IOP conference series: Earth and environmental science (Vol. 54, No. 1, p. 012015). IOP Publishing.

  • Setiyono, T. D., Quicho, E. D., Holecz, F. H., Khan, N. I., Romuga, G., Maunahan, A., Garcia, C., Rala, A., Raviz, J., Collivignarelli, F., Gatti, L., Barbieri, M., Phuong, D. M., Minh, V. Q., Vo, Q. T., Intrman, A., Rakwatin, P., Sothy, M., Veasna, T., … Mabalay, M. R. O. (2019). Rice yield estimation using synthetic aperture radar (SAR) and the ORYZA crop growth model: development and application of the system in South and South-east Asian countries. International Journal of Remote Sensing, 40(21), 8093–8124.

    Article  Google Scholar 

  • Sharifi, A., & Hosseingholizadeh, M. (2020). Application of Sentinel-1 data to estimate height and biomass of rice crop in Astaneh-ye Ashrafiyeh, Iran. Journal of the Indian Society of Remote Sensing, 48(1), 11–19.

    Article  Google Scholar 

  • Sivasankar, T., Kumar, D., Shanker Srivastava, H., & Patel, P. (2020). Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data. Geocarto International, 35(8), 905–915.

    Article  Google Scholar 

  • Sivasankar, T., Kumar, D., Srivastava, H. S., & Patel, P. (2018). Advances in radar remote sensing of agricultural crops: A review. International Journal on Advanced Science, Engineering and Information Technology, 8, 1126.

    Article  Google Scholar 

  • Sivasankar, T., Sharma, P. K., Ramya, M. N. S., Venkatesh, P., & Bairagi, G. D. (2020b). Evaluation of multi-temporal Sentinel-1 dual polarization SAR data for crop type classification. In Spatial Information Science for Natural Resource Management (pp. 44–61). IGI Global.

  • Wang, J., Dai, Q., Shang, J., Jin, X., Sun, Q., Zhou, G., & Dai, Q. (2019). Field-scale rice yield estimation using Sentinel-1A Synthetic Aperture Radar (SAR) data in coastal saline region of Jiangsu Province, China. Remote Sensing, 11, 2274. https://doi.org/10.3390/rs11192274

    Article  Google Scholar 

  • Yang, Z., Li, K., Liu, L., Shao, Y., Brisco, B., & Li, W. (2014). Rice growth monitoring using simulated compact polarimetric C band SAR. Radio Science, 49(12), 1300–1315.

    Article  Google Scholar 

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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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Authors and Affiliations

  1. GIS Department, Amnex Infotechnologies Pvt. Ltd, Ahmedabad, Gujarat, 380054, India

    Pavan Kumar Sharma & Pratyush Kumar

  2. Agriculture and Soils Department, Indian Institute of Remote Sensing, ISRO, Dehradun, India

    Hari Shanker Srivastava

  3. Geographic Information Systems (GIS) Area, NIIT University, Neemrana, 301705, India

    Thota Sivasankar

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  1. Pavan Kumar Sharma
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Correspondence to Pavan Kumar Sharma.

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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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