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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Accurate crop identification and spatial distribution mapping are important for crop production estimation and famine early warning, especially for food-deficit African agricultural countries. By evaluating existing preprocessing methods for classification using satellite image time series (SITS) in Kenya, this study aimed to provide a low-cost method for cultivated land monitoring in sub-Saharan Africa that lacks financial support. SITS were composed of a set of MODIS Vegetation Indices (MOD13Q1) in 2018, and the classification method included the Support Vector Machine (SVM) and Random Forest (RF) classifier. Eight datasets obtained at three levels of preprocessing from MOD13Q1 were used in the classification: (1) raw SITS of vegetation indices (R-NDVI, R-EVI, and R-NDVI + R-EVI); (2) smoothed SITS of vegetation indices (S-NDVI); and (3) vegetation phenological data (P-NDVI, P-EVI, R-NDVI + P-NDVI, and P-NDVI-1). Both SVM and RF classification results showed that the “R-NDVI + R-EVI” dataset achieved the highest performance, while the three pure phenological datasets produced the lowest accuracy. Correlation analysis between variable importance and rainfall time series demonstrated that the vegetation index SITS during rainfall periods showed higher importance in RF classifiers, thus revealing the potential of saving computational costs. Considering the preprocessing cost of SITS and its negative impact on the classification accuracy, we recommend overlaying the original NDVI with the original EVI time series to map the crop distribution in Kenya.

Details

Title
Effectiveness of Common Preprocessing Methods of Time Series for Monitoring Crop Distribution in Kenya
Author
Ni, Rui 1 ; Zhu, Xiaohui 2 ; Lei, Yuping 3 ; Li, Xiaoxin 3 ; Dong, Wenxu 3 ; Zhang, Chuang 1 ; Chen, Tuo 3 ; Mburu, David M 4 ; Hu, Chunsheng 1 

 Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Hebei Laboratory of Agricultural Water-Saving, Key Laboratory of Agricultural Water Resources, Shijiazhuang 050022, China; [email protected] (R.N.); [email protected] (Y.L.); [email protected] (X.L.); [email protected] (W.D.); [email protected] (C.Z.); [email protected] (T.C.); College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100049, China 
 Department of Earth and Environment, Boston University, Boston, MA 02215, USA; [email protected] 
 Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Hebei Laboratory of Agricultural Water-Saving, Key Laboratory of Agricultural Water Resources, Shijiazhuang 050022, China; [email protected] (R.N.); [email protected] (Y.L.); [email protected] (X.L.); [email protected] (W.D.); [email protected] (C.Z.); [email protected] (T.C.) 
 College of Agriculture and Natural Resources, Jomo Kenyatta University of Agriculture and Technology, Nairobi P.O. Box 62000-00200, Kenya; [email protected] 
First page
79
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20770472
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2621247044
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.