Study of integrated optical and synthetic aperture radar-based temporal indices database for specific crop mapping using fuzzy machine learning model

Abstract

For specific crop mapping, temporal data is used to handle the spectral overlap between classes. But a number of times, availability of temporal optical remote sensing data is not consistent with the requirements. In this study, a medicinal crop, psyllium husk, was mapped in Jalore district of Rajasthan, India while trying to integrate optical temporal data with synthetic aperture radar (SAR) data. Vegetation indices derived from optical and SAR data, i.e., modified soil adjusted vegetation index 2 (MSAVI2) and radar vegetation index (RVI), respectively, were used to prepare temporal indices databases. Three different temporal layer combinations of MSAVI2 and RVI layers, corresponding to different phenological stages of the target crop, were studied to determine the efficacy of different sensors for crop mapping. The outputs were assessed on the basis of classification efficiency with mean membership difference and variance for determining the effect of heterogeneity in the mapped fields. The results were compared with those obtained using only optical temporal data. From the results obtained, it was observed that the introduction of SAR-based vegetation index as a temporal layer gave good results for fully vegetated and harvest stages of the target crop. The variance within mapped fields was observed as low as 0.0003.

Use of synthetic aperture radar data for the determination of normalized difference vegetation index and normalized difference water index

“…Recently, several studies investigated the relationship between vegetation and soil moisture metrics with backscatter data and other secondary variables obtained from optical sensor data in the microwave spectral region. The results indicate that horizontal-horizontal (HH) and vertical-vertical (VV) cross-polarization can contribute significantly to the determination of these attributes 3 , 8 . Moreover, promising results were observed recently by Filgueiras et al., 9 Mandal et al., 6 Bai et al., 10 Bahrami et al., 1 Jesus et al., 11 Lu et al., 12 and Chen et al 2 .…”

“…The results indicate that horizontal-horizontal (HH) and verticalvertical (VV) cross-polarization can contribute significantly to the determination of these attributes. 3,8 Moreover, promising results were observed recently by Filgueiras et al, 9 Mandal et al, 6 Bai et al, 10 Bahrami et al, 1 Jesus et al, 11 Lu et al, 12 and Chen et al 2 when using Sentinel-1 SAR data for the determination of vegetation indices.…”

Crop-Net: A Novel Deep Learning Framework for Crop Classification using Time-series Sentinel-1 Imagery by Google Earth Engine

“…From 2013 to 2016, a large number of optical and synthetic aperture radar (SAR) RS satellites with a high spatial resolution (10-30 m) were launched, in particular Sentinel-1A/B and Sentinel-2A during the same period [14,15]. In addition, multi-temporal, multi-source satellite imagery, including multispectral [16], hyperspectral [4], and synthetic aperture radar (SAR) [17,18], is also used to identify the speci c growth stages of the crop.…”