Crop Production Estimation Using Remote Sensing
The ever-increasing global population demands a steep increase in food grain production. To cope up with this demand and maintain a steady supply, proper crop monitoring and production forecasting systems are some of the major requirements. Advance estima
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Crop Production Estimation Using Remote Sensing Dibyendu Deb, Subhadeep Mandal, Shovik Deb, Ashok Choudhury, and Satyajit Hembram
Contents 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Traditional Ways for Crop Yield Estimation: Global and Indian Perspective . . . . . . . . . . . 6.3 Crop Yield Modeling and Use of Remotely Sensed Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Use of Vegetation Indices as Input Variable for Model . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Traditional Statistical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Simulation Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.4 Use of Machine Learning and Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Present Operational Programs and Its Success . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Conclusion and Future Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Abstract The ever-increasing global population demands a steep increase in food grain production. To cope up with this demand and maintain a steady supply, proper crop monitoring and production forecasting systems are some of the major requirements. Advance estimation of crop yield is useful for different stakeholders to plan standard agronomical practices, procurement, determine storage availability, transportation, price fixation, and marketing of agricultural products. This estimation can be done by statistical analysis using traditional ground-based study or by using remotely sensed data. The developments in the field of satellite and sensor technologies in the last few decades have established the second method as the most trusted and efficient tool to forecast crop production. Its time and cost-effectiveness with precise estimation capacity ascertain its competence. This chapter presents an exhaustive discussion on the role of these methods (particularly satellite remote sensing) in crop yield estimation. Analysis and transformation of space data to process different vegetation indices and their use in crop production estimation
D. Deb Indian Grassland and Fodder Research Institute, Jhansi, India S. Mandal · S. Deb (*) · A. Choudhury · S. Hembram Uttar Banga Krishi Viswavidyalaya, Cooch Behar, West Bengal, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 T. Mitran et al. (eds.), Geospatial Technologies for Crops and Soils, https://doi.org/10.1007/9
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