Enhancing Plant Area Index Retrieval Using Gaussian Process Regression from Dual-Polarimetric SAR Data

In this paper, a Gaussian Process Regression (GPR) model is implemented to retrieve the Plant Area Index (PAI) of wheat and canola. Backscatter information from Sentinel-l dualpol GRD SAR data and in-situ measurements collected during the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) Manitoba campaign were used to calibrate and validate the proposed GPR model. A recently proposed pseudo scattering entropy, Hc derived from dual-pol GRD SAR data has been used along with backscatter information to investigate the improvement in retrieval accuracy. Including the pseudo entropy parameter in the feature, space showed an improvement of 4.28% and 3.66% in the correlation coefficient ($\rho$) for wheat and canola respectively. Similarly, a decrease in nRMSE by 4% for wheat and 4.76% for canola was observed during PAI retrieval.

• Publication year

  2023

• Venue

  2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS)

• Publication date

  2023-01-27

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/MIGARS57353.2023.10064591
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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