Abstract
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.
Highlights
AND STATE OF THE ARTContinuous regional crop mapping and monitoring is essential especially in countries like India to keep a track on spatio-temporal coverage of various crops
To carry out the regression analysis, we have extracted the data of Normalized Difference Vegetation Index (NDVI), VV, VH, incidence angle and Normalized Ratio Procedure between Bands (NRPB) for all the pixels associated with individual crops
We evaluate the performance of Linear Regression (LR), Support Vector Regression (SVR) and Random Forest Regression (RFR)
Summary
Continuous regional crop mapping and monitoring is essential especially in countries like India to keep a track on spatio-temporal coverage of various crops. Satellite based remote sensing sensors are being effectively used over the years for continuous crop mapping and monitoring Such methods are always preferred over manual surveys due to efficiency in terms of time, accuracy, spatial coverage, etc. Space exploration agencies such as the Indian Space Research Organization and international agencies such as the National Aeronautics and Space Administration (NASA), European Space Agency (ESA) have launched multiple Optical (IRS, Landsat 5,7,8, MODIS Terra, Aqua, Sentinel 2) as well as Synthetic Aperture Radar (RISAT-1, Sentinel 1) satellites. Of optical and SAR sensor observations can generate the con- 2.2.1 Sentinel-2 Data and Preprocessing ESA launched the tinuous stream of NDVI time-series for vegetation monitoring. The GRD product has been generated by pre-processing the scenes for thermal noise re-
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