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Analysis Study Research Paper | Agriculture and Technology | India | Volume 12 Issue 8, August 2023 | Rating: 5.2 / 10
A Semi - Physical Approach using Remote Sensing based Net Primary Productivity (NPP), Spatial, Spectral & Temporal Paddy Yield Model Development for the State of Assam
Upasana Singh [2] | Gargi Gaydhane | Ashutosh Pawar [2]
Abstract: India, renowned as the leading rice exporter and the second - largest rice producer globally, faces the crucial task of ensuring an adequate rice supply to meet the demands of its growing population. Consequently, accurate yield prediction plays a vital role in enabling policymakers and planners to devise effective strategies concerning import - export dynamics to achieve food security objectives. Additionally, such predictions serve as a valuable tool for crop insurance purposes. This research focuses on Assam, a state in India known for its significant cultivation of paddy. In Assam, paddy is cultivated three seasons, namely Ahu (Autumn rice), Sali (Winter rice), and Boro (summer rice). The study primarily focuses on the "Sali" season, given its prominence as the dominant crop, occupying approximately 77.5% of the rice - growing area (dmagri. in) and contributing to nearly 75% of the overall rice production in the state (dmagr. in). The selection of the Sali season is further influencedby its vulnerability to flood - related challenges, rendering it an ideal period for investigation. To achieve cost - effective and efficient crop monitoring, remote sensing technology is employed. This study adopts a semi - physical approach for predicting crop yield, utilizing remote sensing data for crop masking in the study area, coupled with essential physiological parameters including temperature stress, water stress, and insolation. The estimation of Net Primary Productivity (NPP) is accomplished through Monteith's model, leveraging variables such as Photosynthetically Active Radiation (PAR), Fraction of Absorbed Photosynthetically Active Radiation (fAPAR), Radiation Use Efficiency (RUE), water stress, and temperature stress. The NPP and Harvest Index (HI) are then utilized to compute rice/paddy yield. The investigation spans a period of five years (2018 - 2022) and encompasses the entirety of Assam. Comparisons with existing data from the Directorate of Economics and Statistics (DES) demonstrate slight deviations in yield, primarily attributed to the relatively coarse resolution of the remote sensing data (500m or 1km). Nonetheless, this research model exhibits promising potential for semi - operational utilization in forecasting rice crop yield.
Keywords: Remote Sensing, Monteith equation, NPP, Yield Estimation, INSAT 3D
Edition: Volume 12 Issue 8, August 2023,
Pages: 1775 - 1785