Crop Calendar Mapping of Bangladesh Rice Paddy Field with ALOS-2 ScanSAR Data

Md Rahedul Islam

doi:10.4236/ars.2021.103008

Abstract

Rice paddy mapping with optical remote sensing is challenging in Bangladesh due to the heterogeneous cropping pattern, fragmented field size and cloud cover during the growing period. The high-resolution Synthetic Aperture Radar (SAR) sensor is the potential alternate to mapping rice area in Bangladesh. The L-band SAR sensor onboard Advanced Land Observing Satellite (ALOS) acquires multi-polarization and multi-temporal images are a very useful tool for rice area mapping. In this study, we used ALOS-2 ScanSAR dual (HH + HV) polarized time series data in the study area. We used orthorectification and slope corrected backscatter (sigma-naught) images and median filtering (3 × 3) window for image processing. The unsupervised classification with the k-means++ algorithm is used for initial clustering (20 categories) of images over the study area. The GPS location of rice paddy field with cropping pattern over study area uses for classifying the different rice-growing season from the k-means clustering data. The result is compared with the moderate resolution imaging spectroradiometer (MODIS) based rice area and national statistical agricultural yearbook statistics. The results show that, based on the MODIS based rice map, the rice fields can be mapped with a conditional Kappa value of 0.68 and at user’s and producer’s accuracies of 86% and 90%, respectively. The large commission error primarily came from confusion between wet season Aus rice and others crop, Aus-Amon and Boro-Aus-Amon cropping pattern because of their similar backscatter amplitudes and temporal similarities in the rice growing season. The relatively high rice mapping accuracy in this study indicates that the ALOS/PALSAR-2 data could provide useful information in rice cropping management in subtropical regions such Bangladesh.

Highlights

Bangladesh is the 4th largest rice producer, importer and consumer country in the world [1]
The results show that, based on the moderate resolution imaging spectroradiometer (MODIS) based rice map, the rice fields can be mapped with a conditional Kappa value of 0.68 and at user’s and producer’s accuracies of 86% and 90%, respectively
The relatively high rice mapping accuracy in this study indicates that the Advanced Land Observing Satellite (ALOS)/PALSAR-2 data could provide useful information in rice cropping management in subtropical regions such Bangladesh

Summary

Introduction

Bangladesh is the 4th largest rice producer, importer and consumer country in the world [1]. The 75% of total agricultural land used for rice cultivation in the country (BBS). The rice cultivated areas of Bangladesh is located low-land, flat and inundate areas and these areas are damaged by natural disaster likes floods, drought and storms. The optical remote single sensor-based Landsat, AVHRR and MODIS data derived rice area mapping is the most common at global and regional scale [8]. The multi-temporal Landsat 8 NDVI and LST data with CNN algorithm used for mapping rice area in China with very high accuracy [10] [11]. The Landsat TM and OLI data from 1993 to 2016 with NDVI and MNDWI index and masking method have been used for rice area mapping in Southern China region [12]. The land surface water index (LSWI), NDSI, and enhanced vegetation index (EVI) have been used for rice area mapping in regional and global scale [15]

Methods

Findings

Discussion

Conclusion