Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Research Articles

Early Access

Spatial yield estimation in rice using spectral indices derived from satellite datasets and DSSAT crop simulation model

DOI
https://doi.org/10.14719/pst.6162
Submitted
20 November 2024
Published
13-02-2025
Versions

Abstract

An inter-comparison of 2 rice yield estimation procedures namely, the Semi-Physical model and the Crop Simulation model, Decision Support System for Agrotechnology Transfer (DSSAT), was conducted for the Samba Season (August 2023 to January 2024). This study focused on the Mayiladuthurai and Nagapattinam districts of Tamil Nadu. The Semi-Physical model utilized
satellite-derived remote sensing data, including Photosynthetically Active Radiation (PAR), Fraction of Photosynthetically Active Radiation (FPAR), Water stress, Temperature stress and crop physiological parameters such as Radiation Use Efficiency (RUE) and Harvest Index (HI), to estimate crop yield. The DSSAT crop simulation model estimated rice yield using inputs such as soil properties, weather data, genetic coefficients and crop management practices. The simulated yield was further integrated with Synthetic Aperture Radar (SAR) data to generate spatial yield maps. The generated yields were validated with Crop Cutting Experiment (CCE) based yield estimates. The DSSAT model demonstrated superior performance, achieving an agreement of 88.2% in the Mayiladuthurai district and 86.8% in the Nagapattinam district.

References

  1. FAO. Triannual Global Report. Rome, Italy: FAO; 2024. https://doi.org/10.4060/cd1503en
  2. All India: Crop-wise Area, Production and Yield [Internet]. Department of Agriculture and Farmers Welfare, Ministry of Agriculture, New Delhi; 2024 [cited 2024-11-05].
  3. Population Projections for India And States 2011-2036. National Commission on Population, Ministry Of Health and Family Welfare, Nirman Bhawan ND; 2019.
  4. Casley DJ, Kumar K. The collection, analysis and use of monitoring and evaluation data. 1988.
  5. Singh D, Jha M, editors. Statistical problems in crop forecasting. A Celebration of Statistics: The ISI Centenary Volume A Volume to Celebrate the Founding of the International Statistical Institute in 1885; 1985: Springer. https://doi.org/10.1007/978-1-4613-8560-8_25
  6. Sud U, Ahmad T, Gupta V, Chandra H, Sahoo P, Aditya K, et al. Methodology for estimation of crop area and crop yield under mixed and continuous cropping. ICAR-Indian Agricultural Statistics Research Institute: New Delhi, India. 2017.
  7. Kulpanich N, Worachairungreung M, Thanakunwutthirot K, Chaiboonrueang P. The application of Unmanned Aerial Vehicles (UAVs) and Extreme Gradient Boosting (XGBoost) to crop yield estimation: A case study of Don Tum district, Nakhon Pathom, Thailand. International Journal of Geoinformatics. 2023;19(2). https://doi.org/10.52939/ijg.v19i2.2569
  8. Peng X, Han W, Ao J, Wang Y. Assimilation of LAI derived from UAV multispectral data into the SAFY model to estimate maize yield. Remote Sensing. 2021;13(6):1094. https://doi.org/10.3390/rs13061094
  9. Yang K, Gong Y, Fang S, Duan B, Yuan N, Peng Y, et al. Combining spectral and texture features of UAV images for the remote estimation of rice LAI throughout the entire growing season. Remote Sensing. 2021;13(15):3001. https://doi.org/10.3390/rs13153001
  10. Duan B, Fang S, Zhu R, Wu X, Wang S, Gong Y, et al. Remote estimation of rice yield with Unmanned Aerial Vehicle (UAV) data and spectral mixture analysis. Frontiers in Plant Science. 2019;10. https://doi.org/10.3389/fpls.2019.00204
  11. Darra N, Espejo-Garcia B, Kasimati A, Kriezi O, Psomiadis E, Fountas S. Can satellites predict yield? Ensemble machine learning and statistical analysis of Sentinel-2 imagery for processing tomato yield prediction. Sensors. 2023;23(5):2586. https://doi.org/10.3390/s23052586
  12. Dietterich TG, editor. Ensemble methods in machine learning. International Workshop on Multiple Classifier Systems; 2000: Springer. https://doi.org/10.1007/3-540-45014-9_1 https://doi.org/10.1007/3-540-45014-9_1
  13. Wang J, Wang P, Tian H, Tansey K, Liu J, Quan W. A deep learning framework combining CNN and GRU for improving wheat yield estimates using time series remotely sensed multi-variables. Computers and Electronics in Agriculture. 2023;206:107705. https://doi.org/10.1016/j.compag.2023.107705
  14. Liu D, Mishra AK, Yu Z. Evaluation of hydroclimatic variables for maize yield estimation using crop model and remotely sensed data assimilation. Stochastic Environmental Research and Risk Assessment. 2019;33:1283?95. https://doi.org/10.1007/s00477-019-01700-3
  15. Chaves MED, de Carvalho Alves M, De Oliveira MS, Sáfadi T. A geostatistical approach for modeling soybean crop area and yield based on census and remote sensing data. Remote Sensing. 2018;10(5):680. https://doi.org/10.3390/rs10050680
  16. Gumma MK, Kadiyala M, Panjala P, Ray SS, Akuraju VR, Dubey S, et al. Assimilation of remote sensing data into crop growth model for yield estimation: A case study from India. Journal of the Indian Society of Remote Sensing. 2022;50(2):257?70. https://doi.org/10.1007/s12524-021-01341-6
  17. Official Website of Mayiladuthurai District, Tamil Nadu: National Informatics Centre,
  18. Ministry of Electronics and Information Technology, Government of India; 2024 [updated 04-11-2024. Available from: https://mayiladuthurai.nic.in/
  19. Official Website of Nagapattinam District, Tamil Nadu: National Informatics Centre,
  20. Ministry of Electronics and Information Technology, Government of India; 2024 [updated 02-11-2024. Available from: https://www.nagapattinam.nic.in/
  21. Agency ES. Overview of Sentinel-1 Mission: European Space Agency; 2024 [cited 2024 12/12/2024]. Available from: https://sentiwiki.copernicus.eu/web/s1-mission
  22. MMDRPS Incremental ATBD, INSAT-3D, Algorithm therotical basis document. In: Centre SA, editor. Ahmedabad; 2018. p. 228
  23. Myneni R, Knyazikhin Y, Park T. MODIS/Terra+Aqua Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V061. In: Center. NELPDAA, editor. V061 ed2021.
  24. Vermote E. MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V061. In: Center. NELPDAA, editor. V061 ed2021.
  25. (LaRC) NAaSANLRC. NASA POWER. In: (LaRC) NAaSANLRC, editor. 2024.
  26. Amiri E, Rezaei M, Bannayan M, Soufizadeh S. Calibration and evaluation of CERES rice model under different nitrogen-and water-management options in semi-mediterranean climate condition. Communications in Soil Science and Plant Analysis. 2013;44(12):1814?30. https://doi.org/10.1080/00103624.2013.769565
  27. Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt L, et al. The DSSAT cropping system model. European Journal of Agronomy. 2003;18(3-4):235?65. https://doi.org/10.1016/S1161-0301(02)00107-7
  28. Zheng G, Moskal LM. Retrieving Leaf Area Index (LAI) using remote sensing: Theories, methods and sensors. Sensors. 2009;9(4):2719?45. https://doi.org/10.3390/s90402719
  29. Mandapati R, Gumma MK, Metuku DR, Maitra S. Field-level rice yield estimations under different farm practices using the crop simulation model for better yield. Plant Science Today. 2024;11(1):234?40. https://doi.org/10.14719/pst.2690
  30. Gumma MK, Nukala RM, Panjala P, Bellam PK, Gajjala S, Dubey SK, et al. Optimizing crop yield estimation through geospatial technology: A comparative analysis of a semi-physical model, crop simulation and machine learning algorithms. AgriEngineering. 2024;6(1):786?802. https://doi.org/10.3390/agriengineering6010045
  31. Pazhanivelan S, Geethalakshmi V, Tamilmounika R, Sudarmanian N, Kaliaperumal R, Ramalingam K, et al. Spatial rice yield estimation using multiple linear regression analysis, semi-physical approach and assimilating SAR satellite derived products with DSSAT crop simulation model. Agronomy. 2022;12(9):2008. https://doi.org/10.3390/agronomy12092008
  32. Ahmad S, Ahmad A, Soler CMT, Ali H, Zia-Ul-Haq M, Anothai J, et al. Application of the CSM-CERES-Rice model for evaluation of plant density and nitrogen management of fine transplanted rice for an irrigated semiarid environment. Precision Agriculture. 2012;13(2):200?18. https://doi.org/10.1007/s11119-011-9238-1
  33. Fageria NK. Yield physiology of rice. Journal of Plant Nutrition. 2007;30(6):843?79. https://doi.org/10.1080/15226510701374831
  34. Kaur K, Kaur P. Simulation of yield of rice cultivars under variable agronomic management options using CERES-Rice and InfoCrop-rice models in irrigated plains of Punjab. Agricultural Research Journal. 2022;59(3). https://doi.org/10.5958/2395-146X.2022.00066.7
  35. Dwivedi M, Saxena S, Ray S. Assessment of rice biomass production and yield using semi-physical approach and remotely sensed data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019;42:217?22. https://doi.org/10.5194/isprs-archives-XLII-3-W6-217-2019
  36. Tripathy R, Chaudhary KN, Nigam R, Manjunath KR, Chauhan P, Ray SS, et al. Operational semi-physical spectral-spatial wheat yield model development. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2014;XL-8:977?82. https://doi.org/10.5194/isprsarchives-XL-8-977-2014
  37. Patel N, Bhattacharjee B, Mohammed A, Tanupriya B, Saha S. Remote sensing of regional yield assessment of wheat in Haryana, India. International Journal of Remote Sensing. 2006;27(19):4071?90. https://doi.org/10.1080/01431160500377188
  38. Jha MN, Kumar A, Dubey S, Pandey A. Yield estimation of rice crop using semi-physical approach and remotely sensed data. In: Jha CS, Pandey A, Chowdary VM, Singh V, editors. Geospatial technologies for resources planning and management. Cham: Springer International Publishing; 2022. p. 331?49. https://doi.org/10.1007/978-3-030-98981-1
  39. Kshetrimayum A, Goyal A, H R, Bhadra BK. Semi physical and machine learning approach for yield estimation of pearl millet crop using SAR and optical data products. Journal of Spatial Science. 2024;69(2):573?92. https://doi.org/10.1080/14498596.2023.2259857

Downloads

Download data is not yet available.