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

Abstract

ABSTRACT:Timely availability of crop acreage estimation is crucial for maintaining economic and ecological sustainability or modelling purposes. Remote sensing data has proven to be a reliable source for crop mapping and acreage estimation on parcel-level. However, when relying on a single source of remote sensing data, e.g. multispectral sensors like RapidEye or Landsat, several obstacles can hamper the desired outcome, for example cloud cover or haze. Another limitation may be a similarity in optical reflectance patterns of crops, especially in an early season approach by the end of March, early April. Usually, a reliable crop type map for winter-crops (winter wheat/rye, winter barley and rapeseed) in Central Europe can be obtained by using optical remote sensing data from late April to early May, given a full coverage of the study area and cloudless conditions. These prerequisites can often not be met. By integrating dual-polarimetric SAR-sensors with high temporal and spatial resolution, these limitations can be overcome. SARsensors are not influenced by clouds or haze and provide an additional source of information due to the signal-interaction with plantarchitecture. The overall goal of this study is to investigate the contribution of Sentinel-1 SAR-data to regional crop type mapping for an early season map of disaggregated winter-crops for a subset of the Rur-Catchment in North Rhine-Westphalia (Germany). For this reason, RapidEye data and Sentinel-1 data are combined and the performance of Support Vector Machine and Maximum Likelihood classifiers are compared. Our results show that a combination of Sentinel-1 and RapidEye is a promising approach for most crops, but consideration of phenology for data selection can improve results. Thus the combination of optical and radar remote sensing data indicates advances for crop-type classification, especially when optical data availability is limited.

Remote Sensing for Management of Invasive Plants in Great Lakes Coastal Wetlands

“…1.7 The full-input random forest model (RF4) was superior in overall classification accuracy to the MLE with identical full inputs (Table 1.5). This consistent with similar studies comparing MLE to machine learning techniques such as random forest and Support Vector Machines in crop delineations (Lussem et al, 2016;Nitze et al, 2012).…”

“…However, RapidEye has been seldom integrated with complementary SAR imagery for fine-scale wetland mapping. A hybrid approach that incorporates both X-band SAR and optical products has previously been used to map wetland communities in great detail (Van Beijma et al, 2014), and RapidEye and Sentinel-1 SAR have been used to classify (categorize) crop types (Lussem et al, 2016). However, RapidEye has never been used with Sentinel-1 Cband SAR to classify wetland plant communities.…”

Multi-data approach for crop classification using multitemporal, dual-polarimetric TerraSAR-X data, and official geodata

“…One such mission is Sentinel-1 with a higher temporal resolution and wider coverage (Bargiel, 2017;Lussem et al, 2016).…”

“…recently, Bargiel, 2017;Hütt, Koppe, Miao, & Bareth, 2016;Lussem, Hütt, & Waldhoff, 2016;Sonobe et al, 2014), or they have been combined with optical acquisitions (Blaes et al, 2005;Forkuor et al, 2014;McNairn et al, 2009). Radar-based crop identification goes back to Simonett (1967).…”

Maize crop monitoring with sentinel-1 radar satellite- case study of Endebess,Kenya

“…Satellite images supply data continuously with frequent revisit periods, are operationally sustainable, timely and have a wide geographical coverage (Attema et al, 2009) and thus have been applied in the mapping and monitoring of various crops. Satellites with optical and radar sensors have been used for this purpose since the reflectance from the crops' surface can be related to the different crops' structure and their phenological stage (Dusseux et al, 2014;Lussem et al, 2016;Navarro et al, 2016;Sabour et al, 2008). In tropical areas like Kenya however, optical sensors are affected by cloud cover and haze, which are predominant during the cropping season (Castillejo-González et al, 2009;Devadas et al, 2012).…”