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Phenological Monitoring of Paddy crop using Time Series MODIS Data
* 1 , 2 , 3 , 4
1  Department of Civil Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
2  Department of Geography, School of Earth Science, Bharathidasan University, Tiruchirappalli - 620 024, Tamil Nadu, India
3  Photogrammetry and Remote Sensing Department, Indian Institute of Remote Sensing ISRO, Dehradun- 248001, India
4  Department of Geography, School of Earth Science, Central University of Tamil Nadu, Thiruvarur - 610 005, Tamil Nadu, India


Rice is one of the important staple food crop worldwide, especially in India. Accurate and timely prediction of rice phenology plays a significant role in the management of water resource, administrative planning, and food security. Apart from the conventional method of rice yield estimate, remotely sensed time series data can provide the necessary estimation of rice phenological stages over a large region. Thus, the present study utilizes the 16-day composite Enhanced Vegetation Index (EVI) product with a spatial resolution of 250 m from the moderate resolution imaging spectroradiometer (MODIS) to monitor the rice phenological stages over Karur district of Tamil Nadu, India using the Google Earth Engine (GEE) platform. The rice fields in the study area were classified using the machine learning algorithm in GEE. The ground truth was obtained from the paddy fields during crop production which was used for classifying the paddy grown area. After classification of paddy fields, local maxima, and local minima present in each pixel of time series EVI product was used to determine the paddy growing stages in the study area. The results shows that in the initial stage, the pixel value of EVI in the paddy field shows local minima (0.23) whereas local maxima (0.41) were obtained during the peak vegetative stage. The results derived from the present study using MODIS data was cross-validated using the field data.

Keywords: Rice Phenology; MODIS; Enhanced Vegetation Index; Google Earth Engine; Machine Learning Algorithm