Abstract
This paper proposes an open-boundary locally weighted dynamic time warping (OLWDTW) method using MODIS Normalized Difference Vegetation Index (NDVI) time-series data for cropland recognition. The method solves the problem of flexible planting times for crops in Southeast Asia, which has sufficient thermal and water conditions. For NDVI time series starting at the beginning of the year and terminating at the end of the year, the method can separate the non-growing season cycle and growing season cycle for crops. The non-growing season cycle may provide some useful information for crop recognition, such as soil conditions. However, the shape of the growing season’s NDVI time series for crops is the key to separating cropland from other land cover types because the shape contains all of the crop growth information. The principle of the OLWDTW method is to enhance the effects of the growing season cycle on the NDVI time series by adding a local weight to the growing season when comparing the similarity of time series based on the open-boundary dynamic time warping (DTW) method. Experiments with two satellite datasets located near the Khorat Plateau in the Lower Mekong Basin validate that OLWDTW effectively improves the precision of cropland recognition compared to a non-weighted open-boundary DTW method in terms of overall accuracy. The method’s classification accuracy on cropland exceeds the non-weighted open-boundary DTW by 5–7%. In future studies, an open-boundary self-adaption locally weighted DTW and a more effective combination rule for different crop types should be explored for the method’s best performance and highest extraction accuracy for cropland.
Highlights
Global agricultural production will likely need to increase in the future due to population growth, changing diets, and the rising importance of bioenergy [1]
Our research proves that properly enhancing the weighting factor of growth season sections in the annual Normalized Difference Vegetation Index (NDVI) time series can help improve the classification accuracy of identifying cropland
When the original dynamic time warping (DTW) distance method was applied to the extraction of crops, the annual remote sensing time series were weighted for the similarity measure
Summary
Global agricultural production will likely need to increase in the future due to population growth, changing diets, and the rising importance of bioenergy [1]. Cropland mapping is an important part of land-use/land cover (LULC) mapping. Mapping cropland distribution [2,3] and monitoring crop growth status [4] as well as forecasting crop yield [5,6] using remote sensing data have been popular remote sensing applications. And effective observation of the distribution of cropland in Southeast Asia is important for the food security of people living in the region. There have been recent studies focused on land cover mapping or crop mapping in Southeast Asia [3,7]. To obtain seasonal information on crops and separate rice paddy fields from dryland fields, time-series remote sensing data are usually used to extract cropland distribution information [8,9,10]
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