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Plant Science Today (PST)

Research Articles

Early Access

Comparative analysis of leaf area index and maize yield estimation assimilating remote sensing and DSSAT crop simulation model

DOI
https://doi.org/10.14719/pst.4736
Submitted
20 August 2024
Published
19-09-2024
Versions

Abstract

Maize is a global staple crop, impacting food security, economic development, and agricultural sustainability. This study investigates the integration of Sentinel-1A Synthetic Aperture Radar (SAR) data with the DSSAT CERES-Maize crop simulation model to estimate Leaf Area Index (LAI) and rabi maize yield in Belagavi district, Karnataka. Field data, including LAI, days to anthesis, silking, grain filling, and farmers' field practices, were collected for model calibration and validation, supplemented by crop-cutting experiments (CCE) to determine actual yields. The study revealed strong correlations between LAI values obtained from remote sensing (RS) and field observations, with RS-derived LAI showing an average agreement of approximately 96.07% compared to field measurements. The DSSAT model exhibited slightly better performance, averaging 97.09%. Statistical analysis for LAI showed an R² value of 0.853 for RS and 0.864 for DSSAT, indicating strong correlations with observed LAI values. For maize yield estimation, the DSSAT model demonstrated higher accuracy with an average yield of 8129 kg/ha, compared to RS-derived yield averages of 7533.9 kg/ha and CCE yield averages of 8096.6 kg/ha. The average concordance between DSSAT and CCE yields was 94.19%, while RS and CCE yields had an average concordance of 92.29%. Statistical analyses revealed coefficients of determination of 0.854 for DSSAT-CCE and 0.867 for RS-CCE comparisons. The study underscores the value of combining RS data with DSSAT for comprehensive and accurate crop yield forecasting, highlighting the potential for improved agricultural assessments and decision-making.

References

  1. Agriculture and Food, World Bank. 2022. https://www.worldbank.org/en/topic/agriculture/overview
  2. World Employment and Social Outlook: Trends 2024, International Labour Organisation. https://www.ilo.org/publications/flagship-reports/world-employment-and-social-outlook-trends-2024
  3. Population Reference Bureau, World population data sheet. 2023. https://2023-wpds.prb.org/
  4. Lobell DB, Asseng S. Comparing estimates of climate change impacts from process-based and statistical crop models. Environ Res Lett. 2017;12(1):015001. https://iopscience.iop.org/article/10.1088/1748-9326/aa518a
  5. Ahmad I, Saeed U, Fahad M, Ullah A, Rahman MH, Ahmad A, Judge J. Yield forecasting of spring maize using remote sensing and crop modeling in Faisalabad-Punjab Pakistan. J Indian Soc Remote Sens. 2018;46:1701-1711. https://doi.org/10.1007/s12524-018-0825-8
  6. Fermont AM, Benson T. Estimating yield of food crops grown by smallholder farmers. Int. Food Policy Res. Inst. 2011;Working Paper 27. https://www.researchgate.net/publication/254416399_Estimating_yield_of_food_crops_grown_by_smallholder_farmers_A_review_in_the_Uganda_context
  7. Setiyono TD, Quicho ED, Holecz FH, Khan NI, Romuga G, Maunahan A, Garcia C, et al. 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. Int J Remote Sens. 2019;40(21):8093-8124. https://doi.org/10.1080/01431161.2018.1547457
  8. Huang J, Gómez-Dans JL, Huang H, Ma H, Wu Q, Lewis PE, Liang S, et al. Assimilation of remote sensing into crop growth models: Current status and perspectives. Agric For Meteorol. 2019;276:107609. https://doi.org/10.1016/j.agrformet.2019.06.008
  9. Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, et al. The DSSAT cropping system model. Eur J Agron. 2003;18(3-4):235-265. https://doi.org/10.1016/S1161-0301(02)00107-7
  10. Corbeels M, Chirat G, Messad S, Thierfelder C. Performance and sensitivity of the DSSAT crop growth model in simulating maize yield under conservation agriculture. Eur. J. Agron. 2016;76:41-53. https://doi.org/10.1016/j.eja.2016.02.001
  11. Dorigo W, Zurita-Milla R, de Wit A, Brazile J, Singh R, Schaepman ME. A review on reflective remote sensing and data assimilation techniques for enhanced agricultural monitoring. Int J Appl Earth Obs. Geoinf. 2015;18:191-204. https://doi.org/10.1016/j.jag.2006.05.003
  12. Basso B, Cammarano D, Carfagna E. Review of crop yield forecasting methods and early warning systems. In: First Meeting of the Scientific Advisory Committee of the Global Strategy to Improve Agricultural and Rural Statistics. 2013. https://www.fao.org/fileadmin/templates/ess/documents/meetings_and_workshops/GS_SAC_2013/Improving_methods_for_crops_estimates/Crop_Yield_Forecasting_Methods_and_Early_Warning_Systems_Lit_review.pdf
  13. Directorate of Economics and Statistics, Ministry of Agriculture and Farmers Welfare, Govt. of India Accessed on August 01, 2024. https://data.desagri.gov.in/website/crops-apy-report-web
  14. Ramachandra TV, Setturu B. Ecologically sensitive regions in Belgaum district, Karnataka, Central Western Ghats. J Environ Biol. 2023;44(1):11-26. https://doi.org/10.22438/jeb/44/1/MRN-5041
  15. Amrutha Rani HR, Shreedhar R. Study of rainfall trends and variability for Belgaum. Int J Res Eng Technol. 2014;3(6):148-155.
  16. Carlotto MJ. Effect of errors in ground truth on classification accuracy. Int J Remote Sens. 2009;30(18):4831-4849. https://doi.org/10.1080/01431160802672864
  17. Ali U, Esau TJ, Farooque AA, Zaman QU, Abbas F, Bilodeau MF. Limiting the collection of ground truth data for land use and land cover maps with machine learning algorithms. ISPRS Int J Geo-Inf. 2022;11(6):333. https://doi.org/10.3390/ijgi11060333
  18. Dasari K, Anjaneyulu L, Nadimikeri J. Application of C-band sentinel-1A SAR data as proxies for detecting oil spills of Chennai, East Coast of India. Mar Pollut Bull. 2022;174:113182. https://doi.org/10.1016/j.marpolbul.2021.113182
  19. Alaska Satellite Facility DAAC (ASF DAAC). Accessed on April 15, 2024. https://asf.alaska.edu/
  20. Castriotta AG, Volpi F. Copernicus sentinel data access annual report 2022. Annu. Rep. 2022. https://sentiwiki.copernicus.eu/web/document-library
  21. Wu B, Zhang M, Zeng HW, Tian FY, Potgieter AB, Qin X, Yan N, et al. Challenges and opportunities in remote sensing-based crop monitoring: A review. Nat Sci Rev. 2023;10(4). https://doi.org/10.1093/nsr/nwac290
  22. Corbeels M, Chirat G, Messad S, Thierfelder C. Performance and sensitivity of the DSSAT crop growth model in simulating maize yield under conservation agriculture. Eur J Agron. 2016;76:41-53. https://doi.org/10.1016/j.eja.2016.02.001
  23. Gumma MK, Nukala RM, Panjala P, Bellam PK, Gajjala S, Dubey SK, Sehgal VK, Mohammed I, Deevi KC. 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
  24. Pazhanivelan S, Geethalakshmi V, Tamilmounika R, Sudarmanian NS, Kaliaperumal R, Ramalingam K, Sivamurugan AP, Mrunalini K, Yadav MK, Quicho ED. 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
  25. Hoogenboom G, Porter CH, Boote KJ, Shelia V, Wilkens PW. Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.7. Gainesville: DSSAT Foundation. 2019. https://link.springer.com/chapter/10.1007/978-94-017-3624-4_8
  26. Dong J, Xiao X, Menarguez MA, Zhang G, Qin Y, Luo P, Zhang Y. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sens. Environ. 2016;185:142-154. https://doi.org/10.1016/j.rse.2016.02.016
  27. Cui Y, Liu S, Li X, Geng H, Xie Y, He Y. Estimating maize yield in the black soil region of Northeast China using land surface data assimilation: integrating a crop model and remote sensing. Front Plant Sci. 2022;13:915109. https://doi.org/10.3389/fpls.2022.915109
  28. NASA Power. Accessed on June 21, 2024. https://power.larc.nasa.gov/
  29. Harvard Dataverse. Accessed on June 22, 2024. https://doi.org/10.7910/DVN/1PEEY0

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