Remote sensing leaf area index (LAI) data assimilation with crop model for yield predictions in rice

Mandapati, R (2024) Remote sensing leaf area index (LAI) data assimilation with crop model for yield predictions in rice. PHD thesis, Centurion University of Technology and Management.

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Supervisors

Supervisors Name	Supervisors ID
Reddy, D M	UNSPECIFIED
Gumma, M K	UNSPECIFIED
Maitra, S	UNSPECIFIED
Roy, S	UNSPECIFIED

Abstract

Crop yield estimation has gained prominent importance due to its vital significance for policymakers and decision-makers in enacting schemes, ensuring food security, and assessing crop insurance losses due to biotic and abiotic stress. Precise and timely crop yield estimates at regional, national and international levels is essential for making policy to overcome food security worldwide and helping farmers for crop insurance through insurance premium pricing by the companies. Rice is considered the major staple food which is having highest area and production in India. Telangana contributes to 4.49 % of rice area (1.9 million ha) and 5.54 % of production (6.25 million tons) with a productivity of 3176 kg ha-1. Several studies revealed that remote sensing technology had resulted in higher accuracy in crop growth monitoring with added advantage of high revisit frequency and precision. On the other hand, crop simulation models were also been recognized to assess the effects of different scenarios like climate change, drought, stress etc., on crop yield under varied climatic conditions. LAI is main criterion for evaluating the grain yield as it shows good correlation with the grain yield. There are lack of studies on comparing the ceptometer LAI to any crop model simulated LAI and also yields estimation at local level though they were done at a broad level like state or district. Hence this research was focused on rice yield estimation at the field level in the Karimnagar district of Telangana during 2021 and 2022 by employing the leaf area index (LAI) as the primary criterion for integrating remote sensing technology and crop simulation models. Optimization of crop cutting experiments were performed based on the criterion encompassing a wide range of potential combinations, further four villages each in Kharif and Rabi were selected for study and 15 fields were selected in each village for study. Ground data visits were planned according to the satellite passing dates and during the visits LAI readings in each field were collected using the LP-80 ceptometer. Supervised classification was performed using the ERDAS imagine. It has been noted that most of the area in the district was occupied by rice in both the seasons. Accuracy showed that overall accuracy of 94.23% and 88.5% was recorded, while kappa coefficient of 0.89 and 0.85 was resulted in kharif and rabi season respectively. On an average, kharif and rabi rice grain yields were 5324 kg ha-1 and 6436 kg ha-1 respectively in selected villages. The average simulated rice grain yield in kharif and rabi were 5339 kg ha-1 and 6858 kg ha-1 respectively with DSSAT model which considered sentinel-2 satellite for estimation of LAI. The R2 values of above 0.72 in kharif and above 0.85 in rabi, D index of 0.70 in both the seasons in all the villages showed the model is accurate for predicting yields. In both the seasons, correlation of above 0.8 was observed between observed rice grain yield with the quantity of nitrogen applied, whereas above 0.77 was noted between ceptometer measured and model simulated LAI. However LAI showed a good R2 of above 0.75 with the grain yield. Due to its strong correlation with LAI of above 0.80, the Normalized Difference Vegetation Index (NDVI) was selected as the critical element for integration with the model. Hence, it can be noted that NDVI is one among the important parameter which can be used to integrate with LAI for grain yield estimation. By utilizing the linear equation generated between the NDVI and model LAI a spatial LAI map was generated for the Karimnagar district. Further the linear equation developed between the model LAI and model grain yield, spatial yield map was generated. From the spatial yield map, it can be concluded that most of the areas fall under the rice grain yield range of 5700 to 6000 kg ha-1 in kharif, while in rabi in the range of 6500 to 7000 kg ha-1. These spatial mean yields for kharif and rabi were 5300 kg ha-1 and 6458 kg ha-1 which were then compared with the Telangana government statistics and it has been noted that a deviation of less than 10 %. Therefore, this study’s findings show that assimilating remote sensing data with crop models enhances the precision of rice yield prediction for insurance companies and policy- and decision-makers.

Item Type:	Thesis (PHD)
Divisions:	Global Research Program - Resilient Farm and Food Systems
CRP:	UNSPECIFIED
Uncontrolled Keywords:	yield predictions, crop model, leaf area index (LAI), remote sensing, rice
Subjects:	Others > Crop Modelling Others > Remote Sensing Others > Rice Others > Crop Yield
Depositing User:	Mr Nagaraju T
Date Deposited:	23 Sep 2024 05:58
Last Modified:	23 Sep 2024 05:58
URI:	http://oar.icrisat.org/id/eprint/12820
Acknowledgement:	I wish to start by expressing my profound gratitude to the Almighty for bestowing upon me the strength and blessings to complete this thesis. The depth of my appreciation for those who have significantly influenced my research journey is beyond words. I am genuinely thankful to all of them. This project is the result of years of dedicated effort, and I am deeply indebted to the numerous individuals who have directly and indirectly supported me in its successful completion. My deepest gratitude goes to Dr. M. Devender Reddy , Professor and Dean, M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Paralakhemundi, Odisha for serving as my primary advisor and for your invaluable guidance, encouragement, and unwavering support throughout this project. Your expertise and mentorship have been instrumental in shaping my research and keeping me motivated. It is a remarkable chance to convey my profound gratitude for this momentous occasion to my major advisory person Dr. Gumma Murali Krishna, Cluster Leader and Principal Scientist, Geo-spatial and Big Data Science, ICRISAT. I am incredibly grateful to you for providing me with the exceptional opportunity to conduct research at ICRISAT. Your leadership, vision, and ongoing support have been a source of immense inspiration. I would like to thank Dr. Sagar Maitra, Professor and Head, Department of Agronomy and Dr. Soumik Roy, Assistant Professor and Head, Department of Statistics, M.S. Swaminathan School of Agriculture for your valuable feedback, suggestions, and insights on my research. Your contributions significantly enhanced the quality and refinement of my work. My sincere thanks to Mr. Pranay, Mr. B. Pavan Kumar, and Md. Rafi Ismail each of you for your invaluable support during field data collection. Your dedication and hard work were essential to this crucial aspect of my research. I would like to express my appreciation to the entire team of Geo-spatial and Big Data Science, ICRISAT Remote Sensing, especially Mr. Ishrad, Snigdha, Rajani, and Vineetha for your assistance and collaboration throughout the research process. I would like to thank S.P Nanda (CUTM) and Dr.K. Dhakshina Murthy (ANGRAU) your support and guidance during my studies and Research. My heartful thanks to Learning System Unit - Pratap and Nalini for your assistance with the intake process at ICRISAT. Words cannot express my heartfelt gratitude to my Parents M. Prasada Rao and M. Padmavathi, In-Laws P. Siva Prasad and Annapurna, Husband P. Chaitanya Kumar and Son Manvik for their unwavering love, support, and encouragement throughout my academic journey. Your sacrifices and understanding allowed me to focus on my research and complete this project. VI As members of my maternal and extended family P. Krishna Kishore, M. Venkatesh, and M. Sravanthi, your unwavering support has been greatly appreciated. Your encouragement and belief in me have been a source of strength throughout this journey. I appreciate Ramalaxmi, Deepayan, Sagar, and Sairam for the camaraderie and support we shared during our studies. Thank you for Karimnagar Agricultural Extension Officers by providing timely input data, which was crucial for my research. My thanks to the entire department of Agronomy, MSSSOA, CUTM for creating a supportive learning environment. Thank you Deepu, Raghuveer, Parvathi, VM Rao, VB Rao, Preethi, Priya, Rahul, Bujji, Srinu, Praneeth, Sai, Chandu, Suneetha, Eswari and Shivansh for your friendship and for believing in me. In addition, I would like to acknowledge the many supporters and critics who have accompanied me throughout this journey. While I may not be able to name each one of you, please know that your contributions, whether they were words of encouragement or constructive criticism, have been invaluable to my growth and the successful completion of this thesis. Thank you. In conclusion, I am incredibly fortunate to have such a wonderful network of support. This thesis would not have been possible without your contributions.
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