National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine

Many challenges prevail in cropland mapping over large areas, including dealing with massive volumes of datasets and computing capabilities. Accordingly, new opportunities have been opened at a breakneck pace with the launch of new satellites, the continuous improvements in data retrieval technology...

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Autores principales:	Abdelaziz Htitiou, Abdelghani Boudhar, Abdelghani Chehbouni, Tarik Benabdelouahab
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Google Earth Engine cropland mapping cloud computing Sentinel-2 phenology random forest Science Q
Acceso en línea:	https://doaj.org/article/4f111b681a4f437f8c7c4c09c449bb43
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spelling	oai:doaj.org-article:4f111b681a4f437f8c7c4c09c449bb432021-11-11T18:54:59ZNational-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine10.3390/rs132143782072-4292https://doaj.org/article/4f111b681a4f437f8c7c4c09c449bb432021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4378https://doaj.org/toc/2072-4292Many challenges prevail in cropland mapping over large areas, including dealing with massive volumes of datasets and computing capabilities. Accordingly, new opportunities have been opened at a breakneck pace with the launch of new satellites, the continuous improvements in data retrieval technology, and the upsurge of cloud computing solutions such as Google Earth Engine (GEE). Therefore, the present work is an attempt to automate the extraction of multi-year (2016–2020) cropland phenological metrics on GEE and use them as inputs with environmental covariates in a trained machine-learning model to generate high-resolution cropland and crop field-probabilities maps in Morocco. The comparison of our phenological retrievals against the MODIS phenology product shows very close agreement, implying that the suggested approach accurately captures crop phenology dynamics, which allows better cropland classification. The entire country is mapped using a large volume of reference samples collected and labelled with a visual interpretation of high-resolution imagery on Collect-Earth-Online, an online platform for systematically collecting geospatial data. The cropland classification product for the nominal year 2019–2020 showed an overall accuracy of 97.86% with a Kappa of 0.95. When compared to Morocco’s utilized agricultural land (SAU) areas, the cropland probabilities maps demonstrated the ability to accurately estimate sub-national SAU areas with an R-value of 0.9. Furthermore, analyzing cropland dynamics reveals a dramatic decrease in the 2019–2020 season by 2% since the 2018–2019 season and by 5% between 2016 and 2020, which is partly driven by climate conditions, but even more so by the novel coronavirus disease 2019 (COVID-19) that impacted the planting and managing of crops due to government measures taken at the national level, like complete lockdown. Such a result proves how much these methods and associated maps are critical for scientific studies and decision-making related to food security and agriculture.Abdelaziz HtitiouAbdelghani BoudharAbdelghani ChehbouniTarik BenabdelouahabMDPI AGarticleGoogle Earth Enginecropland mappingcloud computingSentinel-2phenologyrandom forestScienceQENRemote Sensing, Vol 13, Iss 4378, p 4378 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Google Earth Engine cropland mapping cloud computing Sentinel-2 phenology random forest Science Q
spellingShingle	Google Earth Engine cropland mapping cloud computing Sentinel-2 phenology random forest Science Q Abdelaziz Htitiou Abdelghani Boudhar Abdelghani Chehbouni Tarik Benabdelouahab National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine
description	Many challenges prevail in cropland mapping over large areas, including dealing with massive volumes of datasets and computing capabilities. Accordingly, new opportunities have been opened at a breakneck pace with the launch of new satellites, the continuous improvements in data retrieval technology, and the upsurge of cloud computing solutions such as Google Earth Engine (GEE). Therefore, the present work is an attempt to automate the extraction of multi-year (2016–2020) cropland phenological metrics on GEE and use them as inputs with environmental covariates in a trained machine-learning model to generate high-resolution cropland and crop field-probabilities maps in Morocco. The comparison of our phenological retrievals against the MODIS phenology product shows very close agreement, implying that the suggested approach accurately captures crop phenology dynamics, which allows better cropland classification. The entire country is mapped using a large volume of reference samples collected and labelled with a visual interpretation of high-resolution imagery on Collect-Earth-Online, an online platform for systematically collecting geospatial data. The cropland classification product for the nominal year 2019–2020 showed an overall accuracy of 97.86% with a Kappa of 0.95. When compared to Morocco’s utilized agricultural land (SAU) areas, the cropland probabilities maps demonstrated the ability to accurately estimate sub-national SAU areas with an R-value of 0.9. Furthermore, analyzing cropland dynamics reveals a dramatic decrease in the 2019–2020 season by 2% since the 2018–2019 season and by 5% between 2016 and 2020, which is partly driven by climate conditions, but even more so by the novel coronavirus disease 2019 (COVID-19) that impacted the planting and managing of crops due to government measures taken at the national level, like complete lockdown. Such a result proves how much these methods and associated maps are critical for scientific studies and decision-making related to food security and agriculture.
format	article
author	Abdelaziz Htitiou Abdelghani Boudhar Abdelghani Chehbouni Tarik Benabdelouahab
author_facet	Abdelaziz Htitiou Abdelghani Boudhar Abdelghani Chehbouni Tarik Benabdelouahab
author_sort	Abdelaziz Htitiou
title	National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine
title_short	National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine
title_full	National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine
title_fullStr	National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine
title_full_unstemmed	National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine
title_sort	national-scale cropland mapping based on phenological metrics, environmental covariates, and machine learning on google earth engine
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4f111b681a4f437f8c7c4c09c449bb43
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National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine

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