Next Article in Journal
Changes in Leaf Structure and Chemical Compositions Investigated by FTIR Are Correlated with Different Low Potassium Adaptation of Two Cotton Genotypes
Next Article in Special Issue
Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring
Previous Article in Journal
Establishment of Acid Hydrolysis by Box–Behnken Methodology as Pretreatment to Obtain Reducing Sugars from Tiger Nut Byproducts
Previous Article in Special Issue
Combined Use of Multi-Temporal Landsat-8 and Sentinel-2 Images for Wheat Yield Estimates at the Intra-Plot Spatial Scale
Open AccessArticle

Mapping Maize Cropping Patterns in Dak Lak, Vietnam Through MODIS EVI Time Series

1
Center for Agricultural Research and Ecological Studies (CARES), Faculty of Environment, Vietnam National University of Agriculture, Trau Quy, Gia Lam, Hanoi 100000, Vietnam
2
Faculty of Agronomy, Vietnam National University of Agriculture, Trau Quy, Gia Lam, Hanoi 100000, Vietnam
3
Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
4
Department of Agronomy, Kansas State University, 2004 Throckmorton PSC, 1712 Claflin Road, Manhattan, KS 66506-0110, USA
5
Faculty of Agriculture and Forestry, Tay Nguyen University, 567 Le Duan, Buon Ma Thuot 630000, Đak Lak, Vietnam
*
Authors to whom correspondence should be addressed.
Agronomy 2020, 10(4), 478; https://doi.org/10.3390/agronomy10040478
Received: 19 February 2020 / Revised: 19 March 2020 / Accepted: 20 March 2020 / Published: 1 April 2020
(This article belongs to the Special Issue Remote Sensing of Agricultural Monitoring)
Land use maps specifying up-to-date acreage information on maize (Zea mays L.) cropping patterns are required by many stakeholders in Vietnam. Government statistics, however, lag behind by one year, and the official land use maps are only updated at 5-year intervals. The aim of this study was to apply the Savitzky–Golay algorithm to reconstruct noisy Enhanced Vegetation Index (EVI) time series (2003–2018) from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) to allow timely detection of changes in maize crop phenology, and then to employ a linear kernel Support Vector Machine (SVM) classifier on the reconstructed EVI time series to prepare the present-day maize cropping pattern map of Dak Lak province of Vietnam. The method was able to specify the spatial extent of areas cropped to maize with an overall map accuracy of 79% and could also differentiate the areas cropped to maize just once versus twice annually. The by-district mapped maize acreage shows a good agreement with the official governmental data, with a 0.93 correlation coefficient (r) and a root mean square deviation (RMSD) of 1624 ha. View Full-Text
Keywords: maize; cropping pattern; MODIS MOD13Q1; EVI; Savitzky-Golay; SVM classifier maize; cropping pattern; MODIS MOD13Q1; EVI; Savitzky-Golay; SVM classifier
Show Figures

Figure 1

MDPI and ACS Style

Nguyen, H.T.T.; Nguyen, L.V.; de Bie, C.K.; Ciampitti, I.A.; Nguyen, D.A.; Nguyen, M.V.; Nieto, L.; Schwalbert, R.; Nguyen, L.V. Mapping Maize Cropping Patterns in Dak Lak, Vietnam Through MODIS EVI Time Series. Agronomy 2020, 10, 478.

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.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop