Abstract
Time series data capture crop growth dynamics and are some of the most effective data sources for crop mapping. However, a drawback of precise crop classification at medium resolution (30 m) using multi-temporal data is that some images at crucial time periods are absent from a single sensor. In this research, a medium-resolution, 15-day time series was obtained by merging Landsat-5 TM and HJ-1 CCD data (with similar radiometric performances in multi-spectral bands). Subsequently, optimal temporal windows for accurate crop mapping were evaluated using an extension of the Jeffries–Matusita (JM) distance from the merged time series. A support vector machine (SVM) was then used to compare the classification accuracy of the optimal temporal windows and the entire time series. In addition, different training sample sizes (10% to 90% of the entire training sample in 10% increments; five repetitions for each sample size) were used to investigate the stability of optimal temporal windows. The results showed that time series in optimal temporal windows can achieve high classification accuracies. The optimal temporal windows were robust when the training sample size was sufficiently large. However, they were not stable when the sample size was too small (i.e., less than 300) and may shift in different agro-ecosystems, because of different classes. In addition, merged time series had higher temporal resolution and were more likely to comprise the optimal temporal periods than time series from single-sensor data. Therefore, the use of merged time series increased the possibility of precise crop classification.
Highlights
Multi-temporal remote sensing data can be used to describe changes in vegetation characteristics over time [1,2,3], and cropland distribution maps can be produced by classifying multi-temporal remote sensing images throughout the growing season [4,5,6]
The objectives of this research are as follows: (1) to compare the radiometric performance of Landsat-5 Thematic Mapper (TM) and Huan Jing (HJ)-1 CCD data; (2) to select the optimal temporal windows for accurate crop classification from a time series obtained by merging Landsat-5 and HJ-1 CCD data; (3) to evaluate the potential of time series data for an entire growing season and from optimal temporal windows for crop mapping using a support vector machine (SVM); and (4) to detect the stability of optimal temporal windows when different training sample sets are used
We evaluated the radiometric similarity between Landsat-5 TM and HJ-1 CCD data by comparing the land-surface reflectance and Normalized Difference Vegetation Index (NDVI) of the two sensors for similar dates
Summary
Multi-temporal remote sensing data can be used to describe changes in vegetation characteristics over time [1,2,3], and cropland distribution maps can be produced by classifying multi-temporal remote sensing images throughout the growing season [4,5,6]. At medium spatial resolution (10–100 m), multi-temporal Landsat-5 Thematic Mapper (TM) images, with 30-m spatial resolution and a 16-day revisit frequency, have a proven potential for crop classification [7,8]. Images of key time-periods are sufficient for accurate crop mapping [9,10,11], misclassification may still occur, because cloud-free images that cover all critical periods are difficult to obtain from large areas using a single sensor, such as the Landsat-5 TM. The Huan Jing Constellation satellite system, launched in 2008, was expected to overcome these limitations, because HJ-1 CCD data have high temporal resolution (four days) and similar wavebands (near-infrared, red, green and blue), medium spatial resolution (30 m) and radiometric calibration performance to Landsat-5 TM [12]. There are only a few studies in which Landsat 5
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