Next Article in Journal
An Efficient Method for Water Treatment of Artificial Ponds in Jordan Valley Based on Photovoltaic Pumping System
Previous Article in Journal
Morphological and Agronomic Characterization of Spanish Landraces of Phaseolus vulgaris L.
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessCase Report

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

Land Resources and Surveying Institute, Guangxi Teachers Education University, Nanning 530001, China
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
College of Geography and Planning, Guangxi Teachers Education University, Nanning 530001, China
Nanning City Planning Geographic Information Technology Center, Nanning 530001, China
Author to whom correspondence should be addressed.
Agriculture 2019, 9(7), 150;
Received: 29 April 2019 / Revised: 4 July 2019 / Accepted: 5 July 2019 / Published: 9 July 2019
PDF [4097 KB, uploaded 9 July 2019]


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%. View Full-Text
Keywords: crop-type mapping; phenology; topographic effect; MODIS time series; remote sensing crop-type mapping; phenology; topographic effect; MODIS time series; remote sensing

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Wei, Y.; Tong, X.; Chen, G.; Liu, D.; Han, Z. Remote Detection of Large-Area Crop Types: The Role of Plant Phenology and Topography. Agriculture 2019, 9, 150.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Agriculture EISSN 2077-0472 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top