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In-Season Major Crop-Type Identification for US Cropland from Landsat Images Using Crop-Rotation Pattern and Progressive Data Classification

1
Center for Spatial Information Science and Systems (CSISS), George Mason University, Fairfax, VA 22030, USA
2
School of Urban and Regional Planning, The University of Iowa, Iowa City, IA 52242, USA
*
Author to whom correspondence should be addressed.
Agriculture 2019, 9(1), 17; https://doi.org/10.3390/agriculture9010017
Received: 19 November 2018 / Revised: 2 January 2019 / Accepted: 4 January 2019 / Published: 9 January 2019
Crop type information at the field level is vital for many types of research and applications. The United States Department of Agriculture (USDA) provides information on crop types for US cropland as a Cropland Data Layer (CDL). However, CDL is only available at the end of the year after the crop growing season. Therefore, CDL is unable to support in-season research and decision-making regarding crop loss estimation, yield estimation, and grain pricing. The USDA mostly relies on field survey and farmers’ reports for the ground truth to train image classification models, which is one of the major reasons for the delayed release of CDL. This research aims to use trusted pixels as ground truth to train classification models. Trusted pixels are pixels which follow a specific crop rotation pattern. These trusted pixels are used to train image classification models for the classification of in-season Landsat images to identify major crop types. Six different classification algorithms are investigated and tested to select the best algorithm for this study. The Random Forest algorithm stands out among selected algorithms. This study classified Landsat scenes between May and mid-August for Iowa. The overall agreements of classification results with CDL in 2017 are 84%, 94%, and 96% for May, June, and July, respectively. The classification accuracies have been assessed through 683 ground truth data points collected from the fields. The overall accuracies of single date multi-band image classification are 84%, 89% and 92% for May, June, and July, respectively. The result also shows higher accuracy (94–95%) can be achieved through multi-date image classification compared to single date image classification. View Full-Text
Keywords: CDL; CROP DATA LAYER; Landsat; major crop; USA CDL; CROP DATA LAYER; Landsat; major crop; USA
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MDPI and ACS Style

Rahman, M.S.; Di, L.; Yu, E.; Zhang, C.; Mohiuddin, H. In-Season Major Crop-Type Identification for US Cropland from Landsat Images Using Crop-Rotation Pattern and Progressive Data Classification. Agriculture 2019, 9, 17. https://doi.org/10.3390/agriculture9010017

AMA Style

Rahman MS, Di L, Yu E, Zhang C, Mohiuddin H. In-Season Major Crop-Type Identification for US Cropland from Landsat Images Using Crop-Rotation Pattern and Progressive Data Classification. Agriculture. 2019; 9(1):17. https://doi.org/10.3390/agriculture9010017

Chicago/Turabian Style

Rahman, Md. S., Liping Di, Eugene Yu, Chen Zhang, and Hossain Mohiuddin. 2019. "In-Season Major Crop-Type Identification for US Cropland from Landsat Images Using Crop-Rotation Pattern and Progressive Data Classification" Agriculture 9, no. 1: 17. https://doi.org/10.3390/agriculture9010017

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