Remote Detection of Large-Area Crop Types: The Role of Plant Phenology and Topography

Yanfei Wei, Xinhua Tong, Gang Chen, Deqiang Liu and Zhenfeng Han
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Yanfei Wei: Land Resources and Surveying Institute, Guangxi Teachers Education University, Nanning 530001, China
Xinhua Tong: College of Geography and Planning, Guangxi Teachers Education University, Nanning 530001, China
Gang Chen: Laboratory for Remote Sensing and Environmental Change (LRSEC), Department of Geography and Earth Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA
Deqiang Liu: Nanning City Planning Geographic Information Technology Center, Nanning 530001, China
Zhenfeng Han: College of Geography and Planning, Guangxi Teachers Education University, Nanning 530001, China

Agriculture, 2019, vol. 9, issue 7, 1-14

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 km 2 ), 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%.

Keywords: crop-type mapping; phenology; topographic effect; MODIS time series; remote sensing (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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