Phenological Monitoring of Paddy Crop Using Time Series MODIS Data

Abstract

Rice is an important staple food crop worldwide, especially in India. Accurate and timely prediction of rice phenology plays a significant role in the management of water resources, administrative planning, and food security. In addition to conventional methods, 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 the classification of paddy fields, local maxima, and local minima present in each pixel of time series, the EVI product was used to determine the paddy growing stages in the study area. The results show 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 were cross-validated using the field data.

Anthropogenic Factors Affecting the Vegetation Dynamics in the Arid Middle East

“…The vegetation index (VI) is used as an indicator of vegetation variability, and is a sensitive spectral signature of vegetation phenology. It combines different spectral bands of RS data, which can be used to quantify the vegetation conditions [27,28], growth status, biophysical variables (e.g., Leaf Area Index LAI, productivity, and vegetation cover types) [29][30][31]. Among all VIs, the Normalized Difference Vegetation Index (NDVI) is the most well-known and widely used.…”

Fuzzy clustering for the within-season estimation of cotton phenology

“…In the past two decades, there has been a number of related studies that focus on the estimation of vegetation phenology using both Earth Observation (EO) and weather data, under a wide variety of methodological frameworks. Initial approaches to the problem, many of which continue to develop to this day, offered after-season phenology estimations and were usually applied at large geographic scales using medium resolution imagery [11][12][13][14][15]. The term afterseason indicates that phenology is estimated after the crop is harvested and thus leverages the entire data time-series.…”

Anthropogenic Factors Affecting the Vegetation Dynamics in the Arid Middle East

“…The vegetation index (VI) is used as an indicator of vegetation variability and is a sensitive spectral signature of vegetation phenology. It combines different spectral bands of RS data, which can be used to quantify the vegetation conditions [36], [37], growth status, or biophysical variables (e.g., Leaf Area Index LAI, productivity, and vegetation cover types) [38]- [41]. Among all VIs, Normalized Difference Vegetation Index (NDVI) is the most well-known and widely used.…”