Assessment of the SASI Spectral Shape Index Time Series for Mapping Rice Ecosystems in the Mediterranean Region

Abstract

There is a growing need to map rice ecosystems and to develop methods for monitoring rice distribution in order to account for rapid land use changes worldwide. In this study, we evaluated a methodology based on Vegetation Indices time series derived from an 8-day MODIS composite to identify rice fields and develop rice maps that can be timely updated in the long term. We have assessed the potential of the Spectral Shape Index time series and compared its performance with the Normalized Difference Vegetation Index in two coastal locations and in an inland location in the Mediterranean Region for 2012. A profile similarity comparison method, the Spectral Angle Mapper, was accomplished between the reference rice annual profile and the annual profiles of both indices in a pixel basis in order to determine rice pixels. The resultant maps were validated with rice masks, where available, or ortophotos and crop surface statistics where not. The results obtained demonstrated the potential of both indices to provide accurate rice maps when applied together with spectral matching techniques. The overall accuracy was 92.8%, 98.1% and 90.1% for the Spectral Shape Index and 92.4%, 77.24% and 82.8% for the Normalized Difference Vegetation Index in each location. The excellent performance of the Spectral Shape Index in the three locations highlighted the importance of exploring angular indices to improve the identification of land cover dynamics.

Rice Crop Yield Prediction from Sentinel-2 Imagery Using Phenological Metric

“…Therefore, and in line with similar studies [3,4,7], this study aims to quantify rice yield using phenological metrics from a Normalized Difference Vegetation Index (NDVI) time series derived from Sentinel-2 images.…”

“…The importance of yield knowledge has been a challenge for future food production due to climatic factors and water scarcity. Remote sensing tools allow yield prediction using satellite imagery (MODIS, LandSat, Sentinel) [3]. However, being Sentinel-2 optical imagery, with higher spatial and temporal resolution it allows to decrease cloud-induced noise.…”

Paddy Rice Mapping Using a Dual-Path Spatio-Temporal Network Based on Annual Time-Series Sentinel-2 Images

“…Spectral matching methods identify rice by measuring the similarity with spectral features or secondorder features (NDVI, etc.) [28,29]. This requires the preextracted rice features to be accurate enough, which is hard especially when there is more than one rice ripe pattern.…”