Abstract
As a by-product of Interferometric Synthetic Aperture Radar (SAR, InSAR) technique, interferometric coherence is a measure of the decorrelation noise for InSAR observation, where the lower the coherence value, the more serious the decorrelation noise. In the densely vegetated area, the coherence value could be too low to obtain any valuable signals, leading to the degradation of InSAR performance and the possible waste of expensive SAR data. Normalized Difference Vegetation Index (NDVI) value is a measure of the vegetation coverage and can be estimated from the freely available optical satellite images. In this paper, a multi-stage model is established to quantitatively estimate the decorrelation noise for vegetable areas based on Landsat-derived NDVI prior to the acquisition of SAR data. The modeling process is being investigated with the L-band ALOS-1/PALSAR-1 data and the Landsat-5 optical data acquired in the Meitanba area of Hunan Province, China. Furthermore, the reliability of the established model is verified in the Longhui area, which is situated near the Meitanba area. The results demonstrate that the established model can quantitatively estimate InSAR decorrelation associated with the vegetation coverage.
Highlights
Interferometric Synthetic Aperture Radar (InSAR) has proved to be an effective tool for monitoring surface deformation with its advantages of all-weather, all-day, large-scale, and satisfactory precision [1,2,3,4,5]
The results demonstrate that the established model can quantitatively estimate InSAR
If the performance of InSAR deformation measurements can be accurately estimated before acquiring SAR data, it can help to select the most suitable SAR data configuration, and provide a way to access the accuracy of SAR data
Summary
Interferometric Synthetic Aperture Radar (InSAR) has proved to be an effective tool for monitoring surface deformation with its advantages of all-weather, all-day, large-scale, and satisfactory precision [1,2,3,4,5]. It is difficult to accurately judge the monitoring performance of the SAR data used in the area of interest prior to the acquisition of SAR data, which means that there is certain blindness in data selection. This will result in InSAR technology failing to perform its best in subsequent deformation monitoring and interpretation, and even lead to the waste of expensive data resources. If the performance of InSAR deformation measurements can be accurately estimated before acquiring SAR data, it can help to select the most suitable SAR data configuration, and provide a way to access the accuracy of SAR data
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