Incorporating crop phenological trajectory and texture for paddy rice detection with time series MODIS, HJ-1A and ALOS PALSAR imagery

Mrinal Singha,Sangeeta Sarmah

doi:10.1080/22797254.2018.1556568

Abstract

ABSTRACT Repeated monitoring of paddy rice is essential for government agencies and policy makers to maintain the balance of supply and demand for rice. Recent studies have mostly concentrated on the mapping of paddy rice with temporal satellite imagery during growing seasons. Given the phenological variation within paddy rice fields and spectral confusion between paddies and other vegetation classes, our ability to identify paddy rice fields with temporal imagery remains limited. The objective of this study is to develop new phenology and textural-based strategies to detect paddies with HJ-1A, MODIS and PALSAR FNF imagery. Two phenology-based strategies that track the seasonal trajectory of crops and one textural-based strategy that contains image surface characteristics are presented. With the proposed strategies, temporal, spectral and textural features were investigated for paddy rice detection. The results indicate that the phenology-based strategies could reveal the phenological variation within paddy rice and significantly improved the detection accuracy. Seasonal amplitude, grey level co-occurrence matrix entropy and spectral features of the heading stage were proven to be important in identifying paddy rice. It was concluded that the combination of HJ-1A, MODIS and PALSAR FNF imagery are promising in facilitating the rapid mapping of paddy rice at a regional scale.

Highlights

Food security is one of the basic factors of our wellbeing and happiness (Wu et al, 2015)
Four competing detection strategies were explored in this study to characterize the intra-annual phenological trajectory and texture variation in paddy rice classification
The classification results were considered from four perspectives: (1) the effect of spectral features from a single-date image; (2) the effect of phenological trajectory; (3) the effect of textural features and (4) the optimal features for paddy rice detection

Summary

Introduction

Food security is one of the basic factors of our wellbeing and happiness (Wu et al, 2015). Accurate and timely mapping of paddy rice is essential to enhance our knowledge of food security, water resource management, infectious disease transmission and environmental sustainability (Boschetti, Nutini, Manfron, Brivio, & Nelson, 2014; Zhang et al, 2015). Mapping of paddy rice is conducted using either optical or microwave sensors (Kuenzer & Knauer, 2013). Microwave sensors can penetrate through clouds and are more useful for paddy rice mapping in regions dominated by frequent cloud cover Over the last several years, due to long-term archiving and free data distribution, MODIS and Landsat have been the most frequently used data sources for paddy rice mapping using conventional pixel-based image analysis techniques (Dong et al, 2016)

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Conclusion