COMBINED ANALYSIS OF SENTINEL-1 AND RAPIDEYE DATA FOR IMPROVED CROP TYPE CLASSIFICATION: AN EARLY SEASON APPROACH FOR RAPESEED AND CEREALS

被引:14
|
作者
Lussem, U. [1 ]
Huett, C. [1 ]
Waldhoff, G. [1 ]
机构
[1] Univ Cologne, Inst Geog, Albertus Magnus Pl, D-50923 Cologne, Germany
来源
XXIII ISPRS CONGRESS, COMMISSION VIII | 2016年 / 41卷 / B8期
关键词
crop type mapping; Sentinel-1; RapidEye; agriculture; Support Vector Machine; Maximum Likelihood; SUPPORT VECTOR MACHINES; C-BAND; IMAGES; COVER; SAR;
D O I
10.5194/isprsarchives-XLI-B8-959-2016
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
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. SAR-sensors are not influenced by clouds or haze and provide an additional source of information due to the signal-interaction with plant-architecture. 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.
引用
收藏
页码:959 / 963
页数:5
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