Multi-Temporal Land-Cover Classification of Agricultural Areas in Two European Regions with High Resolution Spotlight TerraSAR-X Data

Abstract

Functioning ecosystems offer multiple services for human well-being (e.g., food, freshwater, fiber). Agriculture provides several of these services but also can cause negative impacts. Thus, it is essential to derive up-to-date information about agricultural land use and its change. This paper describes the multi-temporal classification of agricultural land use based on high resolution spotlight TerraSAR-X images. A stack of l4 dual-polarized radar images taken during the vegetation season have been used for two different study areas (North of Germany and Southeast Poland). They represent extremely diverse regions with regard to their population density, agricultural management, as well as geological and geomorphological conditions. Thereby, the transferability of the classification method for different regions is tested. The Maximum Likelihood classification is based on a high amount of ground truth samples. Classification accuracies differ in both regions. Overall accuracy for all classes for the German area is 61.78% and 39.25% for the Polish region. Accuracies improved notably for both regions (about 90%) when single vegetation classes were merged into groups of classes. Such regular land use classifications, applicable for different European agricultural sites, can serve as basis for monitoring systems for agricultural land use and its related ecosystems.

Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa

“…Thus, backscatter intensities are expected to be higher for yam fields than rice. Additionally, previous studies that used SAR data for crop mapping have distinguished between "broad leafed" and "fine/narrow leaf" crops and noted the usefulness of radar data in differentiating them based on their canopy architecture [24,25]. Broad-leaved crops have higher backscatter intensity than fine-leaved crops, due to a high absorption of the radar signal in the latter [75].…”

“…Thus, in vegetation studies, radar backscatter intensities will differ based on the size, shape and orientation of the canopy components (e.g., leaves, stalks, fruit, etc.). Crops with different canopy architecture and cropping characteristics (e.g., planting in mounds) can be distinguished based on their backscatter intensities [23][24][25]. The recent introduction of dual and quad-polarization acquisition modes in many radar satellites (e.g., Radarsat-2, PALSAR, TerraSAR-X) further increases the information content in radar data.…”

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

“…Remote sensing plays an important role in generating land use/cover information from regional to global scales, not only due to its spatially-explicit representation of the earth surface, but also due to its frequent temporal coverage and relatively low observation costs [3,4]. The improved data availability from new sensors and improved computing resources and data analysis tools have resulted in a number of studies conducted to unravel the geographic distribution of land use/cover and its dynamic changes over time, space and scale [5,6].…”

Simulated multipolarized MAPSAR images to distinguish agricultural crops

“…A more detailed field work would be necessary to confirm these possibilities. Bardiel and Herrmann (2011) utilized multitemporal images of high resolution from Terra SAR-X to classify land use in two agricultural regions from Europe. They tested the grouping of the crop classes in order to improve the classification results and observed the sensitivity of the SAR images in relation to different agricultural management practices adopted in each studied region.…”