Abstract
Advanced technologies can improve the operational implementation of the Indian national crop insurance scheme, the Pradhan Mantri Fasal Bima Yojana (PMFBY), particularly in terms of accuracy and timeliness of the crop yield estimates that are used to determine yield losses at the Gram Panchayat (GP) level. In this study, conducted as a pilot test for PMFBY during the kharif season of 2018, technologies based on the Terrestrial Observation and Prediction System (TOPS) were tested and implemented for estimating GP-level crop yields of bajra (pearl millet, Pennisetum glaucum) in Firozabad District of Uttar Pradesh and rice (Oryza sativa) in Kendujhar District of Odisha. A combination of Synthetic Aperture Radar and optical data was used to map crop extent. Daily 2-km grids of input weather conditions were generated using a machine learning algorithm that incorporated station observations, satellite data, and reanalysis model outputs. Required crop biophysical estimates of leaf area index (LAI) and the fraction of intercepted photosynthetically active radiation (FPAR) were derived using daily cloud-screened MODIS 250-m data from the Terra and Aqua satellites and a modified MOD15 LAI/FPAR backup algorithm. A light-use-efficiency (LUE) model adapted from the MODIS (Moderate Resolution Imaging Spectroradiometer) algorithm (MOD17-GPP/NPP) was then used to spatially estimate crop yields. Crop extent maps, daily climate and gap-filled FPAR and the LUE model were used to estimate above-ground biomass, which was accumulated over the growing season and converted to crop yields using a crop-specific harvest index. The estimated yields at 250 m were aggregated within each GP and compared with crop yield data from crop cutting experiments (CCEs) conducted in 142 GPs for rice and 42 GPs for bajra. Crop extent mapping was 96% accurate in rice and 80% in bajra when validated with field surveys. A comparison of modeled yields with CCE yields showed a promising performance by the model in both crops (rice: r = 0.80, root-mean-square error (RMSE) = 411 kg/ha, mean absolute error (MAE) = 359 kg/ha, percent error (PE) = 7, Observed mean = 1500 kg/ha; Bajra: r = 0.84, RMSE = 309 kg/ha, MAE = 262 kg/ha, PE = −12.8, Observed mean = 1859 kg/ha). Although the approach showed promising results for both crops, further progress is needed to ensure consistent and reliable results. Some of the needed improvements include incorporating a dynamic crop calendar, improved maximum LUE estimates, and harvest index values that represent crop varietals grown in India. Routinely conducted CCEs in different crops and seasons around the country could provide a valuable resource for improving these parameters and, ultimately, crop yield estimates.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aggarwal, P. K., Chand, R., Bhutani, A., Kumar, V., Goel, S. K., Rao, K. N., et al. (2016). Report of the Task Force on Enhancing technology use in agriculture insurance (Technical Report) (p. 26). Aayog, New Delhi: National Institute for Transforming India. http://eprints.cmfri.org.in/12312/
Begue, A., Desprat, J. F., Imbernon, J., & Baret, F. (1991). Radiation use efficiency of pearl millet in the Sahelian zone. Agricultural and Forest Meteorology, 56(1), 93–110. https://doi.org/10.1016/0168-1923(91)90106-Z
Boschetti, M., Stroppiana, D., Confalonieri, R., Brivio, P. A., Crema, A., & Bocchi, S. (2011). Estimation of rice production at regional scale with a light use efficiency model and MODIS time series. Italian Journal of Remote Sensing, 43(3), 63–81. https://doi.org/10.5721/ItJRS20114335
Campos-Taberner, M., García-Haro, F. J., Camps-Valls, G., Grau-Muedra, G., Nutini, F., Busetto, L., et al. (2017). Exploitation of SAR and optical sentinel data to detect rice crop and estimate seasonal dynamics of leaf area index. Remote Sensing, 9(3), 248. https://doi.org/10.3390/rs9030248
Chang, K.-W., Shen, Y., & Lo, J.-C. (2005). Predicting Rice Yield Using Canopy Reflectance Measured at Booting Stage. Agronomy Journal, 97(3), 872–878. https://doi.org/10.2134/agronj2004.0162.
Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C., et al. (2018). The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, 219, 145–161. https://doi.org/10.1016/j.rse.2018.09.002
Dadhwal, V. K., & Ray, S. S. (2000). Crop assessment using remote sensing—part II: Crop condition and yield assessment. Indian Journal of Agricultural Economics, 55(2-Supplement). Retrieved October 16, 2020, from https://econpapers.repec.org/article/agsinijae/297744.htm
Dadhwal, V. K., Singh, R. P., Dutta, S., & Parihar, J. S. (2002). Remote sensing based crop inventory: A review of Indian experience. Tropical Ecology, 43(1), 107–122
Dubey, S. K., Gavli, A. S., Yadav, S. K., Sehgal, S., & Ray, S. S. (2018). Remote sensing-based yield forecasting for sugarcane (Saccharum officinarum L.) crop in India. Journal of the Indian Society of Remote Sensing, 46(11), 1823–1833. https://doi.org/10.1007/s12524-018-0839-2
Eklundh, L., & Jönsson, P. (2015). TIMESAT: A software package for time-series processing and assessment of vegetation dynamics. In C. Kuenzer, S. Dech, & W. Wagner (Eds.), Remote sensing time series: revealing land surface dynamics (pp. 141–158). Springer. https://doi.org/10.1007/978-3-319-15967-6_7
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Guan, K., Hien, N. T., & Rao, L. N. (2018). Measuring rice yield from space: The Case of Thai Binh Province, Viet Nam (No. 541). Asian Development Bank. Retrieved October 16, 2020, from https://ideas.repec.org/p/ris/adbewp/0541.html
Gulati, A., Terway, P., & Hussain, S. (2018). Crop insurance in India: Key issues and way forward (Working Paper No. 352). Working Paper. Retrieved October 16, 2020, from https://www.econstor.eu/handle/10419/176379
Gupta, R. K., Prasad, S., Rao, G. H., & Nadham, T. S. V. (1993). District level wheat yield estimation using NOAA/AVHRR NDVI temporal profile. Advances in Space Research, 13(5), 253–256. https://doi.org/10.1016/0273-1177(93)90553-N
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter, 11(1), 10–18. https://doi.org/10.1145/1656274.1656278
Hashimoto, H., Wang, W., Melton, F. S., Moreno, A. L., Ganguly, S., Michaelis, A. R., & Nemani, R. R. (2019). High-resolution mapping of daily climate variables by aggregating multiple spatial data sets with the random forest algorithm over the conterminous United States. International Journal of Climatology, 39(6), 2964–2983. https://doi.org/10.1002/joc.5995
Hashimoto, H., Wang, W., Milesi, C., Xiong, J., Ganguly, S., Zhu, Z., & Nemani, R. R. (2013). Structural uncertainty in model-simulated trends of global gross primary production. Remote Sensing, 5(3), 1258–1273. https://doi.org/10.3390/rs5031258
Hatfield, J. L., & Dold, C. (2019). Chapter 1—Photosynthesis in the solar corridor system. In C. L. Deichman & R. J. Kremer (Eds.), The solar corridor crop system (pp. 1–33). Academic Press. https://doi.org/10.1016/B978-0-12-814792-4.00001-2
Hengl, T., de Jesus, J. M., MacMillan, R. A., Batjes, N. H., Heuvelink, G. B. M., Ribeiro, E., et al. (2014). SoilGrids1 km—global soil information based on automated mapping. PLoS ONE, 9(8), e105992. https://doi.org/10.1371/journal.pone.0105992
Holzworth, D. P., Huth, N. I., deVoil, P. G., Zurcher, E. J., Herrmann, N. I., McLean, G., et al. (2014). APSIM—evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software, 62, 327–350. https://doi.org/10.1016/j.envsoft.2014.07.009
Hufkens, K., Melaas, E. K., Mann, M. L., Foster, T., Ceballos, F., Robles, M., & Kramer, B. (2019). Monitoring crop phenology using a smartphone based near-surface remote sensing approach. Agricultural and Forest Meteorology, 265, 327–337. https://doi.org/10.1016/j.agrformet.2018.11.002
Jain, V., Saxena, S., Dubey, S., Choudhary, K., Sehgal, S., Neetu, & Ray, S. S. (2019). Rice (kharif) production estimation using SAR data of different satellites and yield models: a comparative analysis of the estimates generated under FASAL project. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W6, pp. 99–107. https://doi.org/10.5194/isprs-archives-XLII-3-W6-99-2019
Jin, X., Kumar, L., Li, Z., Feng, H., Xu, X., Yang, G., & Wang, J. (2018). A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, 92, 141–152. https://doi.org/10.1016/j.eja.2017.11.002
Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., et al. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18(3), 235–265. https://doi.org/10.1016/S1161-0301(02)00107-7
Katsura, K., Okami, M., Mizunuma, H., & Kato, Y. (2010). Radiation use efficiency, N accumulation and biomass production of high-yielding rice in aerobic culture. Field Crops Research, 117(1), 81–89. https://doi.org/10.1016/j.fcr.2010.02.006
Kiniry, J. R., Williams, J. R., Vanderlip, R. L., Atwood, J. D., Reicosky, D. C., Mulliken, J., et al. (1997). Evaluation of two maize models for nine U.S. locations. Agronomy Journal, 89(3), 421–426. https://doi.org/10.2134/agronj1997.00021962008900030009x
Kumar, M., & Monteith, J. L. (1981). Remote sensing of crop growth. In H. Smith (Ed.), Plants and the daylight spectrum. (pp. 133–144). Academic Press. Retrieved October 16, 2020, from https://www.cabdirect.org/cabdirect/abstract/19830747586
Lee, J. S., Jurkevich, L., Dewaele, P., Wambacq, P., & Oosterlinck, A. (1994). Speckle filtering of synthetic aperture radar images: A review. Remote Sensing Reviews, 8(4), 313–340. https://doi.org/10.1080/02757259409532206
Li, T., Angeles, O., Marcaida, M., Manalo, E., Manalili, M. P., Radanielson, A., & Mohanty, S. (2017). From ORYZA2000 to ORYZA (v3): An improved simulation model for rice in drought and nitrogen-deficient environments. Agricultural and Forest Meteorology, 237–238, 246–256. https://doi.org/10.1016/j.agrformet.2017.02.025
Lobell, D. B., Thau, D., Seifert, C., Engle, E., & Little, B. (2015). A scalable satellite-based crop yield mapper. Remote Sensing of Environment, 164, 324–333. https://doi.org/10.1016/j.rse.2015.04.021
Murthy, C. S., Thiruvengadachari, S., Raju, P. V., & Jonna, S. (1996). Improved ground sampling and crop yield estimation using satellite data. International Journal of Remote Sensing, 17(5), 945–956. https://doi.org/10.1080/01431169608949057
Myneni, R., Knyazikhin, Y., & Park, T. (2015). MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MCD15A3H.006
Myneni, R. B., Nemani, R., & Running, S. W. (1997). Estimation of global leaf area index and absorbed par using radiative transfer models. IEEE Transactions on Geoscience and Remote Sensing, 35(6), 1380–1393. https://doi.org/10.1109/36.649788
Nemani, R., Hashimoto, H., Votava, P., Melton, F., Wang, W., Michaelis, A., et al. (2009). Monitoring and forecasting ecosystem dynamics using the terrestrial observation and prediction system (TOPS). Remote Sensing of Environment, 113(7), 1497–1509. https://doi.org/10.1016/j.rse.2008.06.017
Nguyen, D. B., Gruber, A., & Wagner, W. (2016). Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data. Remote Sensing Letters, 7(12), 1209–1218. https://doi.org/10.1080/2150704X.2016.1225172
Nguyen, D. B., & Wagner, W. (2017). European rice cropland mapping with Sentinel-1 data: The Mediterranean region case study. Water, 9(6), 392. https://doi.org/10.3390/w9060392
Oteng-Darko, P., Yeboah, S., Addy, S., Amponsah, S., & Owusu Danquah, E. (2013). Crop modeling: A tool for agricultural research—a review. E3 Journal of Agricultural Research and Development, 2, 1–6.
Peng, D., Huang, J., Li, C., Liu, L., Huang, W., Wang, F., & Yang, X. (2014). Modelling paddy rice yield using MODIS data. Agricultural and Forest Meteorology, 184, 107–116. https://doi.org/10.1016/j.agrformet.2013.09.006
PMFBY. (2020). Pradhan Mantri Fasal Bima Yojana - Crop Insurance | PMFBY - Crop Insurance. Retrieved October 18, 2020, from https://www.pmfby.gov.in/guidelines
Ray, S. S. (2018). Use of satellite remote sensing for Crop Insurance - SmartAgriPost | Smart Agri Post | Worldwide Agriculture News | Smart Agriculture Forum. SmartAgriPost | Smart Agri Post | Worldwide Agriculture News | Smart Agriculture Forum, 4(1), 28–31.
Ray, S. S., & Dubey, S. (2018). Space technology use in crop insurance. In G. Sylvester (Ed.), E-agriculture in action: Drones for agriculture. (pp. 65–70). Food and Agriculture Organization of the United Nations and International Telecommunication Union.
Ray, S. S., & Neetu, S. (2017). Crop area estimation with remote sensing. In Handbook on remote sensing for agricultural statistics (pp. 131–183). Global Strategy Improving Agricultural and Rural Statistics (GSARS), FAO Statistics Division (ESS), FAO.
Reeves, M. C., Zhao, M., & Running, S. W. (2005). Usefulness and limits of MODIS GPP for estimating wheat yield. International Journal of Remote Sensing, 26(7), 1403–1421. https://doi.org/10.1080/01431160512331326567
Roy, P. S., Roy, A., Joshi, P. K., Kale, M. P., Srivastava, V. K., Srivastava, S. K., et al. (2015). Development of Decadal (1985–1995–2005) land use and land cover database for India. Remote Sensing, 7(3), 2401–2430. https://doi.org/10.3390/rs70302401
Running, S. W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., & Hashimoto, H. (2004). A continuous satellite-derived measure of global terrestrial primary production. BioScience, 54(6), 547–560. https://doi.org/10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2
Running, S. W., & Zhao, M. (2015). User’s guide: Daily GPP and annual NPP (MOD17A2/A3) products NASA earth observing system MODIS land algorithm (User’s Guide) (p. 28). Retrieved February 28, 2021, from https://www.ntsg.umt.edu/files/modis/MOD17UsersGuide2015_v3.pdf
Shanahan, J. F., Schepers, J. S., Francis, D. D., Varvel, G. E., Wilhelm, W. W., Tringe, J. M., et al. (2001). Use of remote-sensing imagery to estimate corn grain yield. Agronomy Journal, 93(3), 583–589. https://doi.org/10.2134/agronj2001.933583x
Singh, R., Semwal, D. P., Rai, A., & Chhikara, R. S. (2002). Small area estimation of crop yield using remote sensing satellite data. International Journal of Remote Sensing, 23(1), 49–56. https://doi.org/10.1080/01431160010014756
Tomar, V., Mandal, V. P., Srivastava, P., Patairiya, S., Singh, K., Ravisankar, N., et al. (2014). Rice equivalent crop yield assessment using MODIS sensors’ based MOD13A1-NDVI data. IEEE Sensors Journal, 14(10), 3599–3605. https://doi.org/10.1109/JSEN.2014.2329185
van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 177, 105709. https://doi.org/10.1016/j.compag.2020.105709
Wang, D. (2017). MODIS/Terra+Aqua surface radiation Daily/3-Hour L3 global 5 km SIN Grid V006. (Dataset). https://doi.org/10.5067/MODIS/MCD18A1.006
Weiss, M., & Baret, F. (2016). S2ToolBox Level 2 products: LAI, FAPAR, FCOVER, Version 1.1. (ESA Contract nr 4000110612/14/I-BG) (p. 52). Avignon, France: INRA. Retrieved February 28, 2021, from https://step.esa.int/docs/extra/ATBD_S2ToolBox_L2B_V1.1.pdf
White, M. A., Thornton, P. E., Running, S. W., & Nemani, R. R. (2000). Parameterization and sensitivity analysis of the BIOME–BGC terrestrial ecosystem model: Net primary production controls. Earth Interactions, 4(3), 1–85. https://doi.org/10.1175/1087-3562(2000)004%3c0003:PASAOT%3e2.0.CO;2
Willmott, C. J. (1981). On the validation of models. Physical Geography, 2(2), 184–194. https://doi.org/10.1080/02723646.1981.10642213
Xu, B., Park, T., Yan, K., Chen, C., Zeng, Y., Song, W., et al. (2018). Analysis of global LAI/FPAR products from VIIRS and MODIS sensors for spatio-temporal consistency and uncertainty from 2012–2016. Forests, 9(2), 73. https://doi.org/10.3390/f9020073
Yan, K., Park, T., Yan, G., Chen, C., Yang, B., Liu, Z., et al. (2016). Evaluation of MODIS LAI/FPAR product collection 6. Part 1: Consistency and Improvements. Remote Sensing, 8(5), 359. https://doi.org/10.3390/rs8050359
Acknowledgments
The authors are thankful to Dr. Ashish Bhutani, CEO, PMFBY, Dr. Shibendu S. Ray, Director MNCFC, Dr. Sunil Dubey, Assistant Director MNCFC and Dr Vinay K. Dadhwal, Chair, Expert Committee of PMFBY Pilot studies and Dr. Ramakrishna Nemani, NASA and Dr. Hirofumi Hashimoto for their technical guidance.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Field data collection and preliminary analysis were performed under the direction of Mallikarjun Kukunuri. Satellite data analysis and model development were performed by Cristina Milesi. The first draft of the manuscript was written by Cristina Milesi, and both authors commented on previous versions of the manuscript. Both authors read and approved the final manuscript.
Corresponding author
Ethics declarations
The study was conducted for the Pradhan Mantri Fasal Bima Yojana (PMFBY), funded by the Mahalanobis National Crop Forecast Centre (MNCFC) of the Ministry of Agriculture & Farmers’ Welfare, Government of India.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Submitted to the special issue of “Journal of the Indian Society of Remote Sensing” focusing on “Use of Advanced Technologies in Agriculture”.
About this article
Cite this article
Milesi, C., Kukunuri, M. Crop Yield Estimation at Gram Panchayat Scale by Integrating Field, Weather and Satellite Data with Crop Simulation Models. J Indian Soc Remote Sens 50, 239–255 (2022). https://doi.org/10.1007/s12524-021-01372-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-021-01372-z