Abstract
Currently, accurate information on crop area coverage is vital for food security and industry, and there is strong demand for timely crop mapping. In this study, we used MODIS time series data to investigate the effect of the time series length on crop mapping. Eight time series with different lengths (ranging from one month to eight months) were tested. For each time series, we first used the Random Forest (RF) algorithm to calculate the importance score for all features (including multi-spectral data, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and phenological metrics). Subsequently, an extension of the Jeffries–Matusita (JM) distance was used to measure class separability for each time series. Finally, the RF algorithm was used to classify crop types, and the classification accuracy and certainty were used to analyze the influence of the time series length and the number of features on classification performance; the features were added one by one based on their importance scores. Results indicated that when the time series was longer than five months, the top ten features remained stable. These features were mainly in July and August. In addition, the NDVI features contributed the majority of the most significant features for crop mapping. The NDWI and data from multi-spectral bands also contributed to improving crop mapping. On the other hand, separability, classification accuracy, and certainty increased with the number of features used and the time series length, although these values quickly reached saturation. Five months was the optimal time series length, as longer time series provided no further improvement in the classification performance. This result shows that relatively short time series have the potential to identify crops accurately, which allows for early crop mapping over large areas.
Highlights
Crop-type information is important for food security, and the demand for accurate crop maps is increasing in society and in the plant industry [1,2,3]
During April and May (Figure 3a,b), the most important features were mainly multi-spectral bands data. The selection of these features may not have a phenological component because most summer crops were immature, and the standard deviations of the importance scores from 20 model runs were relatively high
NDVI and Normalized Difference Water Index (NDWI) were selected when the time series length was longer than three months (Figure 3c)
Summary
Crop-type information is important for food security, and the demand for accurate crop maps is increasing in society and in the plant industry [1,2,3]. Remote sensing data have shown potential for mapping crop distributions at both regional and local scales [4,6,7], and substantial efforts have been made toward monitoring agricultural land and accurately assessing crop acreage [8,9]. Multi-temporal remote sensing data can be used to describe the vegetation conditions over different periods, and have been widely employed to produce crop distribution maps [10,11,12,13]. Hao et al [17] merged Landsat and Huan Jing (HJ) data, which have similar spatial resolution to Landsat and higher temporal resolution, to obtain an image time series with relatively high temporal resolution, and increase the possibility of acquiring images in the optimal periods for crop identification. The timeline is an important consideration for crop classification because obtaining an early classification result benefits both decision makers and the private sector [18]
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