Abstract
Accurate crop-type maps are urgently needed as input data for various applications, leading to improved planning and more sustainable use of resources. Satellite remote sensing is the optimal tool to provide such data. Images from Synthetic Aperture Radar (SAR) satellite sensors are preferably used as they work regardless of cloud coverage during image acquisition. However, processing of SAR is more complicated and the sensors have development potential. Dealing with such a complexity, current studies should aim to be reproducible, open, and built upon free and open-source software (FOSS). Thereby, the data can be reused to develop and validate new algorithms or improve the ones already in use. This paper presents a case study of crop classification from microwave remote sensing, relying on open data and open software only. We used 70 multitemporal microwave remote sensing images from the Sentinel-1 satellite. A high-resolution, high-precision digital elevation model (DEM) assisted the preprocessing. The multi-data approach (MDA) was used as a framework enabling to demonstrate the benefits of including external cadastral data. It was used to identify the agricultural area prior to the classification and to create land use/land cover (LULC) maps which also include the annually changing crop types that are usually missing in official geodata. All the software used in this study is open-source, such as the Sentinel Application Toolbox (SNAP), Orfeo Toolbox, R, and QGIS. The produced geodata, all input data, and several intermediate data are openly shared in a research database. Validation using an independent validation dataset showed a high overall accuracy of 96.7% with differentiation into 11 different crop-classes.
Highlights
Global food insecurity is on the rise again [1]
It covers the entire 2500 km2 of the area of interest (AOI) at a spatial resolution of 10 m. It is available for download in the TR32 project database (TR32DB) [70]
This paper presents an open-data and open-source remote sensing workflow to derive crop type for a region in west Germany, the area of the Rur Catchment
Summary
Global food insecurity is on the rise again [1]. Current and future challenges evolve from a growing world population with an increasing nutrition demand under climate change conditions [2]. [3] demand a higher crop yield from agricultural production To achieve this efficiency increase, the decision makers in this domain can use information from agricultural monitoring systems based on satellite remote sensing data [4]. While on a local scale, crop-type information is needed and available for agricultural management decisions (e.g., [7]), on regional, national, or continental scales, such crop-type data are missing [8], especially on an annual basis. This data gap lowers the capabilities of agroecosystem models [9] and results in less information about the current state of the agricultural production for decision makers
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