Investigating the Performance of Random Forest and Support Vector Regression for Estimation of Cloud-Free Ndvi Using Sentinel-1 Sar Data

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.

Recurrent-based regression of Sentinel time series for continuous vegetation monitoring

“…For instance, the work in (Wang et al, 2019) has proposed to apply SVM and RF algorithms on Sentinel-1, Sentinel-2 and Landsat 8 data to predict frequent Leaf Area Index (LAI) estimations. RF and Support Vector Regression have been used in (Mohite et al, 2020) to generate dense NDVI time series. A six-month time period has been investigated over five different crop types.…”

Sentinel-1 to NDVI for Agricultural Fields Using Hyperlocal Dynamic Machine Learning Approach

“…The overall RMSE of the SNAF method, based on 6880 paired SAR-NDVI images, was 0.06, which is better than the 0.08-0.11 value achieved by [36] (for rice, cotton, turmeric, and banana in India) and the 0.07 value achieved by [29] (for soybean and maize in Brazil), which did not use a large dataset for testing. Specifically, Ref.…”

“…The SNAF method was tested on 548 commercial fields in 18 countries with 28 different crops through 2021, and the high performance is outlined in this study. The machine learning model used by the SNAF is the random forest, which was also found to be more useful than other models used in previous studies [29,36,46,47].…”

“…Specifically, Ref. [36] reached an RMSE of 0.09 and an R 2 of 0.76 for cotton, whereas the SNAF method, for cotton, had an RMSE of 0.05 and an R 2 of 0.95 (Table 5). Ref.…”

A Convolutional Neural Network Algorithm for Soil Moisture Prediction from Sentinel-1 SAR Images

“…Many indices were used extensively with Sentinel-1 data. The most common are: cross-ratio (CR = σ 0 V H /σ 0 VV ) [61], Ratio (Ratio = σ 0 VV /σ 0 V H ) [62][63][64], radar vegetation index (RV [65,66], dual polarization SAR vegetation index (DPSV I = σ 0 VV + σ 0 V H /σ 0 VV ) [67][68][69], and normalized ratio procedure between bands [70][71][72]. We therefore need to know the impact of the use of these indices as input data to the CNN model on the soil moisture retrieval.…”