The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1379–1383, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1379-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1379–1383, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1379-2020

  22 Aug 2020

22 Aug 2020

INVESTIGATING THE PERFORMANCE OF RANDOM FOREST AND SUPPORT VECTOR REGRESSION FOR ESTIMATION OF CLOUD-FREE NDVI USING SENTINEL-1 SAR DATA

J. D. Mohite1, S. A. Sawant1, A. Pandit1, and S. Pappula2 J. D. Mohite et al.
  • 1TCS Research and Innovation, Tata Consultancy Services, Mumbai, India
  • 2TCS Research and Innovation, Tata Consultancy Services, Hyderabad, India

Keywords: Cloud-Free NDVI, SAR, Random Forest Regression, Sentinel 1, Sentinel 2

Abstract. The current study focuses on the estimation of cloud-free Normalized Difference Vegetation Index (NDVI) using the Synthetic Aperture Radar (SAR) observations obtained from Sentinel-1 (A and B) sensor. South-West Summer Monsoon over the Indian sub-continent lasts for four months (mid-June to mid-October). During this time, optical remote sensing observations are affected by dense cloud cover. Therefore, there is a need for methodology to estimate state of vegetation during the cloud cover. The crops considered in this study are Paddy (Rice) from Punjab and Haryana, whereas Cotton, Turmeric, and Banana from Andhra Pradesh, India. We have considered, observations of Sentinel-1 and Sentinel-2 sensors with the same overpass day and non-cloudy pixels for each crop. We used Google Earth Engine to extract surface reflectance for the Sentinel-2 and Ground Range Detected (GRD) backscatter for Sentinel-1. The Red and NIR bands of Sentinel 2 were used to estimate NDVI. Sentinel-1 based VV, and VH backscatter was used for estimation of Normalized Ratio Procedure between Bands (NRPB). Regression analysis was performed by using NDVI as an independent variable, and VV, VH, NRPB, and radar incidence angle as dependant variables. We evaluated the performance of Linear regression with tuned Support Vector Regression (SVR) as well as tuned Random Forest Regression (RFR) using the independent data. Results showed that the RFR produced the lowest RMSE for all the crops in the study. The average RMSE using the RFR was 0.08, 0.09, 0.11, and 0.10 for Rice, Cotton, Banana, and Turmeric, respectively. Similarly, we have obtained R2 values of 0.79, 0.76, 0.69, and 0.71 for the same crops using the RFR. A model with 80 trees produced the best results for Rice and Cotton, whereas the model with 90 trees produced the best results for Banana and Turmeric. Analysis with NDVI threshold of 0.25 showed improved R2 and RMSE. We found that for grown crop canopy, SAR based NDVI estimates are reasonably matching with the optical NDVI. A good agreement was observed between the actual and estimated NDVI using the tuned RFR model.