+ Site Statistics
References:
52,654,530
Abstracts:
29,560,856
PMIDs:
28,072,755
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach



A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach



Remote Sensing of Environment 210: 35-47



Accurate and timely spatial classification of crop types based on remote sensing data is important for both scientific and practical purposes. Spatially explicit crop-type information can be used to estimate crop areas for a variety of monitoring and decision-making applications such as crop insurance, land rental, supply-chain logistics, and financial market forecasting. However, there is no publically available spatially explicit in-season crop-type classification information for the U.S. Corn Belt (a landscape predominated by corn and soybean). Instead, researchers and decision-makers have to wait until four to six months after harvest to have such information from the previous year. The state-of-the-art research on crop-type classification has been shifted from relying on only spectral features of single static images to combining together spectral and time-series information. While Landsat data have a desirable spatial resolution for field-level crop-type classification, the ability to extract temporal phenology information based on Landsat data remains a challenge due to low temporal revisiting frequency and inevitable cloud contamination. To address this challenge and generate accurate, cost-effective, and in-season crop-type classification, this research uses the USDA's Common Land Units (CLUs) to aggregate spectral information for each field based on a time-series Landsat image data stack to largely overcome the cloud contamination issue while exploiting a machine learning model based on Deep Neural Network (DNN) and high-performance computing for intelligent and scalable computation of classification processes. Experiments were designed to evaluate what information is most useful for training the machine learning model for crop-type classification, and how various spatial and temporal factors affect the crop-type classification performance in order to derive timely crop type information. All experiments were conducted over Champaign County located in central Illinois, and a total of 1322 Landsat multi-temporal scenes including all the six optical spectral bands spanning from 2000 to 2015 were used. Computational experiments show the inclusion of temporal phenology information and evenly distributed spatial training samples in the study domain improves classification performance. The shortwave infrared bands show notably better performance than the widely used visible and near-infrared bands for classifying corn and soybean. In comparison with USDA's Crop Data Layer (CDL), this study found a relatively high Overall Accuracy (i.e. the number of the corrected classified fields divided by the number of the total fields) of 96% for classifying corn and soybean across all CLU fields in the Champaign County from 2000 to 2015. Furthermore, our approach achieved 95% Overall Accuracy by late July of the concurrent year for classifying corn and soybean. The findings suggest the methodology presented in this paper is promising for accurate, cost-effective, and in-season classification of field-level crop types, which may be scaled up to large geographic extents such as the U.S. Corn Belt.

(PDF emailed within 0-6 h: $19.90)

Accession: 066360753

Download citation: RISBibTeXText

DOI: 10.1016/j.rse.2018.02.045


Related references

A support vector machine to identify irrigated crop types using time-series Landsat NDVI data. International Journal of Applied Earth Observation and Geoinformation 34: 103-112, 2015

Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data. Computers and Electronics in Agriculture 115: 171-179, 2015

Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data. Remote Sensing 7(12): 16091-16107, 2015

In-Season Major Crop-Type Identification for US Cropland from Landsat Images Using Crop-Rotation Pattern and Progressive Data Classification. Agriculture-Basel 9(1): 17, 2019

A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data. Remote Sensing 7(4): 3633-3650, 2015

Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time-Series MODIS Reflectance Data. Sensors 17(1), 2017

Automatic corn-soybean classification using Landsat MSS data. II. Early season crop proportion estimation. Remote Sensing of Environment 14(1-3): 31-37, 1984

Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data. International Journal of Applied Earth Observation and Geoinformation 33: 32-38, 2014

Use of Vegetation Change Tracker and Support Vector Machine to Map Disturbance Types in Greater Yellowstone Ecosystems in a 1984–2010 Landsat Time Series. IEEE Geoscience and Remote Sensing Letters 12(8): 1650-1654, 2015

The Potential of Time Series Merged from Landsat-5 TM and HJ-1 CCD for Crop Classification: A Case Study for Bole and Manas Counties in Xinjiang, China. Remote Sensing 6(8): 7610-7631, 2014

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing. Sensors 18(9), 2018

A machine learning approach to multi-level ECG signal quality classification. Computer Methods and Programs in Biomedicine 117(3): 435-447, 2015

Validation of synthetic daily Landsat NDVI time series data generated by the improved spatial and temporal data fusion approach. Information Fusion 40: 34-44, 2018

Classification of usual interstitial pneumonia in patients with interstitial lung disease: assessment of a machine learning approach using high-dimensional transcriptional data. Lancet. Respiratory Medicine 3(6): 473-482, 2016

Geographic Field Data Collection: Using machine learning techniques to verify minimum data requirements for the classification task. Journal of Geography 105(5): 636-648, 1996