Effectiveness of Common Preprocessing Methods of Time Series for Monitoring Crop Distribution in Kenya

Agriculture

Chunsheng Hu¹  Tuo Chen¹  Rui Ni¹  Wenxu Dong¹  Xiaoxin Li¹  Yuping Lei¹  Chuang Zhang¹  David M. Mburu²  Xiaohui Zhu³ 

[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;College of Agriculture and Natural Resources, Jomo Kenyatta University of Agriculture and Technology, Nairobi P.O. Box 62000-00200, Kenya;Department of Earth and Environment, Boston University, Boston, MA 02215, USA;

关键词: Kenya;    satellite image time series;    MODIS;    random forest;    support vector machine;    cropland;   

DOI  :  10.3390/agriculture12010079

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown