Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring

Abstract

The aim of this study was to assess the ability of optical images, SAR (Synthetic Aperture Radar) images and the combination of both types of data to discriminate between grasslands and crops in agricultural areas where cloud cover is very high most of the time, which restricts the use of visible and near-infrared satellite data. We compared the performances of variables extracted from four optical and five SAR satellite images with high/very high spatial resolutions acquired during the growing season. A vegetation index, namely the NDVI (Normalized Difference Vegetation Index), and two biophysical variables, the LAI (Leaf Area Index) and the fCOVER (fraction of Vegetation Cover) were computed using optical time series and polarization (HH, VV, HV, VH). The polarization ratio and polarimetric decomposition (Freeman-Durden and Cloude-Pottier) were calculated using SAR time series. Then, variables derived from optical, SAR and both types of remotely-sensed data were successively classified using the Support Vector Machine (SVM) technique. The results show that the classification accuracy of SAR variables is higher than those using optical data (0.98 compared to 0.81). They also highlight that the combination of optical and SAR time series data is of prime interest to discriminate grasslands from crops, allowing an improved classification accuracy.

Combined Analysis of Sentinel-1 and Rapideye Data for Improved Crop Type Classification: An Early Season Approach for Rapeseed and Cereals

“…This may be due to later acquisition dates on which the investigation was performed (Lussem, 2016), because in mid-March to early April grassland and cereals bear a very similar structure and size. Until the heading stage of cereals a good discrimination of grassland and cereals cannot be obtained with a C-band Sensor (Dusseux et al, 2014). Particularly interesting is the good performance of MLC for grassland, since the SVM approach shows higher errors of omission and commission.…”

Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series

“…However, if the temporal density of the time series improves jointly with its consistency, finer vegetation changes could be exploited to improve the classification. One could also consider to increase the density of the time series by combining optical data with radar (such as in [80] and [4]), which would be particularly useful in cloud-prone areas.…”

A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring

“…While the complementarity of data from both optical and radar sensors for the characterization of LUCC has been put to use in many very recent studies, e.g., [42][43][44][45][46][47], the development of adequate data fusion techniques is an important ongoing field of research [48]. In general, fusion refers to a formal concept for combining data from different sources [49,50], with the aim of generating information of "greater quality" than the individual input datasets.…”

“…Notably, data from a number of past and current spaceborne SAR systems-Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR), European Remote Sensing (ERS-1 and -2), Advanced Synthetic Aperture Radar (ASAR), Japanese Earth Resources Satellite (JERS-1), RADARSAT-1 and -2, Advanced Land Observation Satellite (ALOS-1)-are commonly in use and applied at regional-scales, with very few studies addressing global-scale mapping, e.g., [73]. Studies have covered a variety of themes related to land cover, including improved land cover classifications [35,74], forest cover classifications [75], grassland monitoring [47], identification of degraded woodlands [27,76,77] and mapping deforestation [78] and successional forest dynamics [11]. Similarly, land use-specific studies have focussed on various themes, including urban land use analysis [79,80], classification of agricultural areas [81], mapping and monitoring specific crop types (e.g., rice [82][83][84]), etc.…”