Abstract
ABSTRACTAlong the season crop classification maps based on satellite data is a challenging task for countries with large diversity of agricultural crops with different phenology (crop calendars). In this paper, we investigate feasibility of delivering early and along the season crop specific maps using available free satellite data over multiple years, including Landsat-8, Sentinel-1 and Sentinel-2. For this study, a test site in Kyiv region (Ukraine) is selected, for which we have been collecting ground data on crop types every year since 2011. Crop type maps are generated through a supervised classification of multi-temporal multi-source satellite data using previously developed artificial neural network algorithms. It is shown, how multi-year crop classification maps are used for crop rotation violation detection. The study shows that in case of considerable cloud cover, synthetic aperture radar (SAR) data, for example acquired by Sentinel-1 satellite, can be interchangeably used with optical imagery to achieve the target 85% accuracy for crop classification.
Highlights
Crop mapping with remote sensing dataAvailability of reliable and accurate crop maps at regional and national scale is a prerequisite for efficient monitoring of agricultural land use
For 2016, when producing in season crop maps, we investigated availability of optical and synthetic aperture radar (SAR) imagery to discriminate different crop type early in the season at acceptable target accuracy of 85%
In a situation of persistent cloud cover early in spring, can optical data be substituted with SAR imagery and whether the same level of performance can be achieved? In 2016, the test region experienced a lot of clouds during spring, so we considered the difference between using optical data from Sentinel-2 and SAR data from Sentinel1A
Summary
Availability of reliable and accurate crop maps at regional and national scale is a prerequisite for efficient monitoring of agricultural land use. A wide range of agricultural applications, including crop area estimation, crop yield forecasting, crop state assessment, land use intensity rely heavily on the use of crop maps. Information on crop frequency derived from historical maps can be effectively used for stratification purposes in crop area estimation (Boryan, Yang, Di, & Hunt, 2014; Gallego et al, 2012). Availability of multi-year crop maps can be used to estimate land use intensity, which includes crop planting frequency and crop rotation (Kuemmerle et al, 2013). Time-series of such maps is essential for detection of crop rotation violations, which usually lead to soil degradation and decrease of crop production
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