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  Integration of Sentinel optical and radar data for mapping smallholder coffee production systems in Vietnam

Maskell, G. M., Chemura, A., Nguyen, H., Gornott, C., Mondal, P. (2021): Integration of Sentinel optical and radar data for mapping smallholder coffee production systems in Vietnam. - Remote Sensing of Environment, 266, 112709.
https://doi.org/10.1016/j.rse.2021.112709

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Item Permalink: https://publications.pik-potsdam.de/pubman/item/item_25995 Version Permalink: https://publications.pik-potsdam.de/pubman/item/item_25995_1
Genre: Journal Article

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 Creators:
Maskell, Gina Marie1, Author              
Chemura, Abel1, Author              
Nguyen, Huong2, Author
Gornott, Christoph1, Author              
Mondal, Pinki2, Author
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1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: Perennial commodity crops, such as coffee, often play a large role globally in agricultural markets and supply chains and locally in livelihoods, poverty reduction, and biodiversity. Yet, the production of spatial information on these crops are often overlooked in favor of annual food crops. Remote sensing detection of coffee faces a particular set of challenges due to persistent cloud cover in the tropical “coffee belt,” hilly topography in coffee growing regions, diversity of coffee growing systems, and spectral similarity to other tree crops and agricultural land. Looking at the major coffee growing region in Dak Lak, Vietnam, we integrate multi-temporal 10 m optical Sentinel-2 and Sentinel-1 SAR data in order to map three coffee production systems: i) open-canopy sun coffee, ii) intercropped and other shaded coffee and iii) newly planted or young coffee. Leveraging Google Earth Engine (GEE), we compute five sets of features in order to best enhance separability between coffee and other land cover and within coffee production systems. The features include Sentinel-2 dry and wet season composites, Sentinel-1 texture features, Sentinel-1 spatiotemporal metrics, and topographic features. Using a random forest classification algorithm, we produce a 9-class land cover map including our three coffee production classes and a binary coffee/non-coffee map. The binary map has an overall accuracy of 89% and the three coffee production systems have user accuracies of 65, 56, 71% for sun coffee, intercropped coffee and newly planted coffee, respectively. This is a first effort at large-scale distinction of within-crop production styles and has implications across many applications. The binary coffee map can be used as a high-resolution crop mask, whereas the detailed land cover map can inform monitoring of deforestation dynamics, biodiversity, sustainability certification and implementation of climate adaptation strategies. This work offers a scalable approach to integrating optical and radar Sentinel data for production of spatially explicit agricultural information and contributes particularly to tree crop and agroforestry mapping, which often is overlooked in between agricultural and forestry sciences.

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Language(s): eng - English
 Dates: 2021-09-162021-09-252021-10-06
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: MDB-ID: yes - 3125
DOI: 10.1016/j.rse.2021.112709
PIKDOMAIN: RD2 - Climate Resilience
Organisational keyword: RD2 - Climate Resilience
Working Group: Adaptation in Agricultural Systems
Research topic keyword: Adaptation
Research topic keyword: Food & Agriculture
Regional keyword: Asia
Model / method: Machine Learning
 Degree: -

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Title: Remote Sensing of Environment
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 266 Sequence Number: 112709 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals427
Publisher: Elsevier