In Thailand, rice yield is normally derived from Crop Cutting Experiments (CCEs) and found lately for agricultural planning policy. The potential of remote sensing is widely adopted for crop monitoring and yield estimation; however, there are few research using satellite data and rice biophysical parameters in Thailand. Thus, the objective of study is investigating the potential of Optical (Sentinel-2) and Synthetic Aperture Radar (SAR- Sentinel-1) for estimating rice biophysical and rice yield by developing a linear regression model in the three representative’s provinces located in the Chao Phraya River delta, Thailand.
Rice biophysical is relevant with rice yield and able to analyses the correlation with satellite data. Thus, the study aims to investigate which parameters significant with rice yield. The study conducts in three provinces located in the Chao Phraya River delta in Thailand, which is main rice cultivated area of country. The primary data use based on the field experiments in the wet season rice of 2017 and separated different 5 growth stages (e.g., seeding, tillering, panicle, flowering, and harvesting). Several of rice biophysical are collected such as agricultural practices, stem density, water depth, height, Leaf Area Index (LAI), chlorophyll contents, wet-dry biomass, and rice yield. Then, the average of rice biophysical are demonstrated with the different rice variety and irrigation. The dynamics of rice biophysical are distinguish from others; then, the study analyses with the Pearson correlation at P-value 0.05 and two-tailed significance. The result suggests the appropriate rice biophysical based on rice yield.
Rice is the world’s major staple food crop occupying over 12% of global cropland area which produces around 800 million tons. Nearly 90% of the world’s rice is produced and consumed in Asian countries. Therefore, information on agricultural plantation area, yield, and production are essential to ensure food security of nearly 3 billion people. At the moment this information is either lacking in many countries or only available post-harvest, this is too late to input into any effecting policy in a specific year. Therefore, there is a pressing need to provide accurate and reliable yield estimation well ahead of harvest. In this project we explore potential of multi source remote sensing data coupled with crop model to provide country scale yield estimation in Thailand. For optical sensor, the study utilised Landsat8 OLI/TIRS satellite data to develop common vegetation indexes (VIs) approach to derive essential crop biophysical variables such as Leaf Area Index. This is supplemented with information from microwave sensor such as Sentinel 1 to overcome issues with cloud. At the end, we produced a regular time series of crop biophysical variable across the growing season. These satellite-based estimates were validated with dedicated field campaign in three provinces covering the entire growing season. Initial results suggest a good agreement between the optical/microwave derived crop biophysical variables and ground data. Finally, these will be used as an input to the ORYZA 2000 crop model to adjust the model parameters and develop a high resolution yield prediction.
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