Abstract
Most methods used for crop classification rely on the ground-reference data of the same year, which leads to considerable financial and labor cost. In this study, we presented a method that can avoid the requirements of a large number of ground-reference data in the classification year. Firstly, we extracted the Normalized Difference Vegetation Index (NDVI) time series profiles of the dominant crops from MODIS data using the historical ground-reference data in multiple years (2006, 2007, 2009 and 2010). Artificial Antibody Network (ABNet) was then employed to build reference NDVI time series for each crop based on the historical NDVI profiles. Afterwards, images of Landsat and HJ were combined to obtain 30 m image time series with 15-day acquisition frequency in 2011. Next, the reference NDVI time series were transformed to Landsat/HJ NDVI time series using their linear model. Finally, the transformed reference NDVI profiles were used to identify the crop types in 2011 at 30 m spatial resolution. The result showed that the dominant crops could be identified with overall accuracy of 87.13% and 83.48% in Bole and Manas, respectively. In addition, the reference NDVI profiles generated from multiple years could achieve better classification accuracy than that from single year (such as only 2007). This is mainly because the reference knowledge from multiple years contains more growing conditions of the same crop. Generally, this approach showed potential to identify crops without using large number of ground-reference data at 30 m resolution.
Highlights
Multi-temporal remote sensing data can be used to describe changes in vegetation characteristics over time [1,2,3], and these data have been employed to produce crop distributions from regional to national scales using both, supervised and unsupervised classifiers [4,5,6,7,8,9,10]
Coarse spatial resolution data, which are characterized by density temporal resolution, have shown the potential to identify crop types using the classification model built from the adjacent years [21], whereas a drawback is the relatively coarse spatial resolution cannot discriminate crop types in heterogeneous landscape [26,27,28,29]
This study was composed of six main parts: (1) extracting Normalized Difference Vegetation Index (NDVI) time series profiles from MODIS data using ground reference data in 2006, 2007, 2009 and 2010; (2) building historical reference NDVI time series for each crop using Antibody Network (ABNet) based on the historical NDVI time series profiles, and measuring the separability of reference NDVI for crop classification; (3) using Landsat-5 Thematic Mapper (TM) and HJ data to build multi-source
Summary
Multi-temporal remote sensing data can be used to describe changes in vegetation characteristics over time [1,2,3], and these data have been employed to produce crop distributions from regional to national scales using both, supervised and unsupervised classifiers [4,5,6,7,8,9,10]. When the cropland map need to be provided on yearly basis, the ground-reference data will be collected at annual frequency, which leads to considerable financial, time and labor costs [18]. Some pixel unmixing attempts, which are based on both linear and non-linear regression principles, have been proposed to solve this situation [30,31,32,33], but there are still some limitations: (1) the unmixing approaches can solely derive sub-pixel fractions of crop area in a pixel, and hardly provide the crop distribution within the pixel; (2) the VI profiles of some crops maybe too similar to be reliably separated; and (3) if the endmembers were extracted across multiple years, the inter-annual differences will result in the variable temporal signatures of endmembers of the same crop [30]
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