Abstract
Mapping of agricultural crop types and practices is important for setting up agricultural production plans and environmental conservation measures. Sugarcane is a major tropical and subtropical crop; in general, it is grown in small fields with large spatio-temporal variations due to various crop management practices, and satellite observations of sugarcane cultivation areas are often obscured by clouds. Surface information with high spatio-temporal resolution obtained through the use of emerging satellite constellation technology can be used to track crop growth patterns with high resolution. In this study, we used Planet Dove imagery to reveal crop growth patterns and to map crop types and practices on subtropical Kumejima Island, Japan (lat. 26°21′01.1″ N, long. 126°46′16.0″ E). We eliminated misregistration between the red-green-blue (RGB) and near-infrared band imagery, and generated a time series of seven vegetation indices to track crop growth patterns. Using the Random Forest algorithm, we classified eight crop types and practices in the sugarcane. All the vegetation indices tested showed high classification accuracy, and the normalized difference vegetation index (NDVI) had an overall accuracy of 0.93 and Kappa of 0.92 range of accuracy for different crop types and practices in the study area. The results for the user’s and producer’s accuracy of each class were good. Analysis of the importance of variables indicated that five image sets are most important for achieving high classification accuracy: Two image sets of the spring and summer sugarcane plantings in each year of a two-year observation period, and one just before harvesting in the second year. We conclude that high-temporal-resolution time series images obtained by a satellite constellation are very effective in small-scale agricultural mapping with large spatio-temporal variations.
Highlights
Agricultural land is a key component of land use and land cover
Using a time series of Planet Dove images with high spatio-temporal resolution, we investigated the performance of crop type and practice classification in subtropical small-scale agricultural areas with a focus on sugarcane crop practices, which have large spatio-temporal variations
Cloud contamination remained in the vegetation index time series created from Planet Dove images, so it was necessary to reduce its effect by using a noise removal algorithm such as the integrated best index slope extraction (BISE) and maximum value interpolated (MVI) algorithm
Summary
Agricultural land is a key component of land use and land cover. Nearly 40% of the Earth’s land surface is being used for agriculture [1]. Mapping and monitoring of agricultural land use is an important topic of high public interest. The use of satellite remote sensing to monitor the use and management of agricultural land over a large area is an efficient approach (e.g., [6,7,8,9,10,11,12,13,14,15,16,17]). Attention has been paid to high-frequency observation satellites (e.g., MODerate resolution Imaging Spectroradiometer (MODIS); spatial resolution = 250 m), and an agricultural monitoring method that uses high-temporal-resolution data has been developed [7,14,24,25,26,27]. Many regions with frequent cloud cover have small agricultural areas (
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