Rice-planted area extraction by time series analysis of ENVISAT ASAR WS data using a phenology-based classification approach: A case study for Red River Delta, Vietnam

Abstract

ABSTRACT:Recent studies have shown the potential of Synthetic Aperture Radars (SAR) for mapping of rice fields and some other vegetation types. For rice field classification, conventional classification techniques have been mostly used including manual threshold-based and supervised classification approaches. The challenge of the threshold-based approach is to find acceptable thresholds to be used for each individual SAR scene. Furthermore, the influence of local incidence angle on backscatter hinders using a single threshold for the entire scene. Similarly, the supervised classification approach requires different training samples for different output classes. In case of rice crop, supervised classification using temporal data requires different training datasets to perform classification procedure which might lead to inconsistent mapping results. In this study we present an automatic method to identify rice crop areas by extracting phonological parameters after performing an empirical regression-based normalization of the backscatter to a reference incidence angle. The method is evaluated in the Red River Delta (RRD), Vietnam using the time series of ENVISAT Advanced SAR (ASAR) Wide Swath (WS) mode data. The results of rice mapping algorithm compared to the reference data indicate the Completeness (User accuracy), Correctness (Producer accuracy) and Quality (Overall accuracies) of 88.8%, 92.5 % and 83.9 % respectively. The total area of the classified rice fields corresponds to the total rice cultivation areas given by the official statistics in Vietnam (R²  0.96). The results indicates that applying a phenology-based classification approach using backscatter time series in optimal incidence angle normalization can achieve high classification accuracies. In addition, the method is not only useful for large scale early mapping of rice fields in the Red River Delta using the current and future C-band Sentinal-1A&B backscatter data but also might be applied for other rice cultivation areas.

Integrating Global Satellite-Derived Data Products as a Pre-Analysis for Hydrological Modelling Studies: A Case Study for the Red River Basin

“…The main Remote Sens. 2016, 8, 279 5 of 28 distinctive feature between locations with a single, exclusively rainfed crop and multi-cropped areas with at least one irrigated cycle is therefore the occurrence of a winter and/or spring crop [58].…”

“…Literature sources report irrigated acreages varying from 670,000 to 850,000 ha, although the exact spatial and temporal scope of these figures is not always specified in these studies [43,45,[61][62][63]. These values are all somewhat lower than the acreage found in a recent Advanced Synthetic Aperture Radar (ASAR)-based study, reporting 1,180,000 ha of doublecropped rice for 2007-2011 [58], so some uncertainty persists. Overall, the new LULC map corresponds well with the majority of available information from other sources and suffices for the current purpose.…”

“…cropped rice for 2007-2011[58], so some uncertainty persists. Overall, the new LULC map corresponds well with the majority of available information from other sources and suffices for the current purpose.…”

Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data

“…The goal of our study is therefore to classify rice areas and determine the seasonality of rice crops in the Mekong Delta by using phenological techniques applied on multi-year Envisat ASAR WSM time series from 2007 to 2011. This study builds upon methods initially developed by Nguyen et al [60] over the Red River Delta, Vietnam.…”

Rapid Assessment of Flood Inundation and Damaged Rice Area in Red River Delta from Sentinel 1A Imagery

“…Due to the high frequent cloud cover in this area, Chuc et al proposed a compositing method for Landsat-8 images, which is used for rice mapping with XGBoost classifier later [29]. Other studies utilized time-series SAR imagery for rice classification using ML techniques including SVM [30,31], Simple Linear Iterative Clustering super-pixel algorithm [32], decision tree based on phenology analysis and the thresholding approach [33]. We found that the rice map of all 11 provinces in the RRD area has not been noticed due to the confusion in available administrative maps in previous studies.…”