Removal of Ionospheric Effects from Sigma Naught Images of the ALOS/PALSAR-2 Satellite

Fábio Furlan Gama,Polyanna Da Conceição Bispo,Natalia Cristina Wiederkehr

doi:10.3390/rs14040962

Fábio Furlan Gama, Polyanna Da Conceição Bispo + Show 1 more

Open Access

https://doi.org/10.3390/rs14040962

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Abstract

The monitoring of forest degradation in the Amazon through radar remote sensing methodologies has increased intensely in recent years. Synthetic aperture radar (SAR) sensors that operate in L-band have an interesting response for land use and land cover (LULC) as well as for aboveground biomass (AGB). Depending on the magnetic and solar activities and seasonality, plasma bubbles in the ionosphere appear in the equatorial and tropical regions; these factors can cause stripes across SAR images, which disturb the interpretation and the classification. Our article shows a methodology to filter these stripes using Fourier fast transform (FFT), in which a stop-band filter removes this noise. In order to make this possible, we used Environment for Visualizing Images (ENVI), Sentinel Application Platform (SNAP), and Interactive Data Language (IDL). The final filtered scenes were classified by random forest (RF), and the results of this classification showed superior performance compared to the original scenes, showing this methodology can help to recover historic series of L-band images.

Highlights

Our classification performance showed that Kappa and the overall accuracy (OA) indexes were improved when noise removal was performed, which presented similar classification results to those obtained using L-band images without scintillation effect by [14] using JERS-1 data, and by [38] using ALOS PALSAR, showing that the methodology used in this study can improve data quality, especially for land use and land cover (LULC) applications
The method proposed here was shown to be effective in removing the scintillation noise from sigma naught ALOS/PALSAR-2 images, improving the classification of different targets of interest
This methodology can be applied to data from other L-band sensors, such as the SAOCOM satellite, over tropical zones, which can be helpful for studies of historic series of L-band images

Summary

Introduction

Commonly used to map and monitor land use and land cover, are ineffective in tropical regions, and especially in the Amazon, due to frequent cloud cover and adverse atmospheric conditions, which often make it difficult to obtain information from these images during rainy season [8]. The greater penetration of microwaves allows us to understand the different forest strata that make up the forest cover at different stages of ecological succession. This makes it possible to improve the level of thematic characterization of the landscape, enabling the discrimination of different land use types [12,13,14,15,16,17,18]

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