Tariq, A, Yan, J, Gagnon, A, Khan, MR and Mumtaz, F (2022) Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-spatial Information Science. pp. 1-19. ISSN 1001-4993

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Abstract

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 similar 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 statistical 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. Hence, this methodology has benefits for the assessment and monitoring of food security in Pakistan, adding to the evidence base of studies on the use of remote sensing to identify crop types and cropping patterns from other countries.

Item Type:	Article
Uncontrolled Keywords:	cropland; cropping patterns; Decision Tree Classifier; Random Forest; Sentinel-2; Geological & Geomatics Engineering; 0909 Geomatic Engineering
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Divisions:	Biological & Environmental Sciences (from Sep 19)
Publisher:	Wuhan University
SWORD Depositor:	A Symplectic
Date Deposited:	02 Aug 2022 11:36
Last Modified:	02 Aug 2022 11:45
DOI or ID number:	10.1080/10095020.2022.2100287
URI:	https://researchonline.ljmu.ac.uk/id/eprint/17241

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