首页|Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest

Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest

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Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security, notably from climate change and, for that purpose, remote sensing is routinely used. However, identifying specific crop types, cropland, and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures. This study applied a methodology to identify cropland and specific crop types, including tobacco, wheat, barley, and gram, as well as the following cropping patterns: wheat-tobacco, wheat-gram, wheat-barley, and wheat-maize, which are common in Gujranwala District, Pakistan, the study region. The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning (ML) methods, namely a Decision Tree Classifier (DTC) and a Random Forest (RF) algorithm. The best time-periods for differentiating cropland from other land cover types were identified, and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms. The methodology was subsequently evaluated using Landsat images, crop statis-tical data for 2020 and 2021, and field data on cropping patterns. The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images, together with ML techniques, for mapping not only the distribution of cropland, but also crop types and cropping patterns when validated at the county level. These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan, adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.

Sentinel-2Random Forestcroplandcrop typescropping patternsDecision Tree Classifier

Aqil Tariq、Jianguo Yan、Alexandre S. Gagnon、Mobushir Riaz Khan、Faisal Mumtaz

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State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

School of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool, UK

School of Environmental Science, Charles Sturt University, Albury, Australia

State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

University of Chinese Academy of Sciences (UCAS), Beijing, China

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2023

10.1080/10095020.2022.2100287

地球空间信息科学学报(英文版)
武汉大学（原武汉测绘科技大学）

地球空间信息科学学报(英文版)

CSCD
影响因子：0.207
ISSN：1009-5020
年,卷(期)：2023.26(3)
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