Abstract
High temporal and spatial resolution optical image time series have been proven efficient for crop type mapping at the end of the agricultural season. However, due to cloud cover and image availability, crop identification earlier in the season is difficult. The recent availability of high temporal and spatial resolution SAR image time series, opens the possibility of improving early crop type mapping. This paper studies the impact of such SAR image time series when used in complement of optical imagery. The pertinent SAR image features, the optimal working resolution, the effect of speckle filtering and the use of temporal gap-filling of the optical image time series are assessed. SAR image time series as those provided by the Sentinel-1 satellites allow significant improvements in terms of land cover classification, both in terms of accuracy at the end of the season and for early crop identification. Haralik textures (Entropy, Inertia), the polarization ratio and the local mean together with the VV imagery were found to be the most pertinent features. Working at at 10 m resolution and using speckle filtering yield better results than other configurations. Finally it was shown that the use of SAR imagery allows to use optical data without gap-filling yielding results which are equivalent to the use of gap-filling in the case of perfect cloud screening, and better results in the case of cloud screening errors.
Highlights
IntroductionHigher agricultural yields will be required in the future in order to fulfill food supply needs [1]
Higher agricultural yields will be required in the future in order to fulfill food supply needs [1].Added to this, bio-fuel production and urban growth are increasing the pressure on agricultural lands [2,3,4]
Remote sensing imagery in general and, in particular, high temporal and high spatial resolution data as the ones which will be available with recently launched systems such as Sentinel-1 [7] and Sentinel-2 [8] constitute a major asset for this kind of application
Summary
Higher agricultural yields will be required in the future in order to fulfill food supply needs [1]. Bio-fuel production and urban growth are increasing the pressure on agricultural lands [2,3,4]. All these factors will have consequences on natural ecosystems [5,6]. In this context, crop area extent estimates and crop type maps provide crucial information for agricultural monitoring and management. In the case of crop type mapping, this corresponds to at least two images per month
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