Abstract
Sustainable agricultural practices necessitate accurate baseline data of crop types and their detailed spatial distribution. Compared with field surveys, remote sensing has demonstrated superior performance, offering spatially explicit crop distribution in a timely manner. Recent studies have taken advantage of remote sensing time series to capture the variation in plant phenology, inferring major crop types. However, such an approach was rarely used to extract detailed, multiple crop types spanning a large area, and the impact of topography has yet to be well analyzed in mountainous regions. This study aims to answer two questions in crop type extraction: (i) Is it feasible to accurately map multiple crop types over a large mountainous area with phenology-based modeling? (ii) What are the effects of topography in such modeling? To answer the questions, phenological metrics were extracted from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite time series, and the random forests classifier was used to map 12 crop types in South China (236,700 km2), featuring a subtropical monsoon climate and high topographic variation. Our study revealed promising results using MODIS EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index) time series, although EVI outperformed NDVI (overall accuracy: 85% versus 81%). The spectral and temporal metrics of plant phenology significantly contributed to crop identification, where the spectral information exhibited greater importance. The increase of slope led to a decrease in model accuracy in general. However, uniformly distributed tree plantations (e.g., tea-oil camellia, gum, and tea trees) being cultivated on large slopes (>15 degrees) achieved accuracies greater than 80%.
Highlights
Estimates of crop types and extent can provide essential baseline data for managing agricultural lands, determining food pricing, designing trade policies, and supporting carbon balance research [1,2,3].Crop mapping is important for the tropical or subtropical mountainous regions
For most of the crops, the classification results were comparable using EVI versus NDVI, EVI led to better performance for estimating tomatoes, tea-oil camellia, and gum trees
Normalized phenological metrics were derived from the the Normalized variable variableimportance importancefor forallallthe thetested tested phenological metrics were derived from classification
Summary
Estimates of crop types and extent can provide essential baseline data for managing agricultural lands, determining food pricing, designing trade policies, and supporting carbon balance research [1,2,3]. Crop mapping is important for the tropical or subtropical mountainous regions. Crop types are highly diverse throughout the regions, mainly due to climate and significant agricultural expansion at the expense of intact forests [4,5]. High topographic complexity in mountains can cause significant logistical challenges for land surveying. The Guangxi Zhuang Autonomous Region (hereafter Guangxi) in South China has a subtropical monsoon climate with cool, dry winters and long, hot, wet summers [6]. Accurately estimating crop types and extent remains a challenging task, Agriculture 2019, 9, 150; doi:10.3390/agriculture9070150 www.mdpi.com/journal/agriculture
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