Contents lists available at ScienceDirect Int J Appl Earth Obs Geoinformation journal homepage: www.elsevier.com/locate/jag Assessing SAR C-band data to effectively distinguish modified land uses in a heavily disturbed Amazon forest Andrea Puzzi Nicolau a, *, Africa Flores-Anderson b,c , Robert Griffin a,c , Kelsey Herndon b,c , Franz J. Meyer d,e a Department of Atmospheric and Earth Science, University of Alabama in Huntsville, 320 Sparkman Drive, Huntsville, AL 35805, USA b Earth System Science Center, University of Alabama in Huntsville, 320 Sparkman Drive, Huntsville, AL 35805, USA c NASA-SERVIR Science Coordination Ofce, 320 Sparkman Drive, Huntsville, AL 35805, USA d Geophysical Institute, University of Alaska Fairbanks, 2156 Koyukuk Drive, Fairbanks, AK 99775, USA e Alaska Satellite Facility, University of Alaska Fairbanks, 2156 Koyukuk Drive, Fairbanks, AK 99775, USA ARTICLEINFO Keywords: Synthetic aperture radar Sentinel-1 Time series Amazon LULC Land use Deforestation drivers Urban ABSTRACT The Amazon is the largest expanse of tropical rainforest globally and deforestation resulting from land use changes poses a major concern for sustainable resource management. Synthetic aperture radar (SAR) data have all-weather and all-day capability, and thus are well-suited for mapping land use land cover (LULC) in tropical regions, which are seasonally influenced by cloud cover. Understanding modified land uses and drivers of de- forestation is fundamental for the development of policies and measures to reduce emissions and for developing forest reference levels. Sentinel-1 C-band SAR data present unprecedented potential since the observations are free and openly available, providing for the first regular and standardize SAR data. This study analyzes the applicability of Sentinel-1 data for LULC classification as an effort to differentiate modified land uses, which is a current need for early-warning deforestation systems. The study area covers a deforestation frontier in the Peruvian Amazon where the landscape is characterized by a mosaic of LULC types. Collect Earth Online is used for reference LULC data collection, and seven classes are defined for this study: forest, secondary vegetation, agriculture, pasture, urban, mining, water. Amplitude γ o time-series spanning 2017–2019 are analyzed along with statistical metrics for each class, and a classification decision tree is developed in Google Earth Engine. Overall accuracy obtained is considered low (52%). Results show high user's accuracy for forest and water classification, a lot of confusion between agriculture, secondary vegetation, and forest, and the use of the po- larization ratio VV/VH is suggested to be useful for pasture classification. The orientation of streets in a urban environment is confirmed to have high influence on backscattering response. This study provides information for future research on LULC and the identification of drivers in deforestation monitoring systems that could result in additional actionable information for decision-making. 1. Introduction Tropical deforestation and degradation resulting from land use changes pose a major concern in the international community, since tropical forests are critical in mitigating climate change (Stern, 2007; Canadell and Raupach, 2008; Le Quéré et al., 2009) and support the richest biodiversity on our planet (Laurance et al., 2012; Lewis et al., 2015). The Amazon is the largest expanse of tropical rainforest globally and the reduction in tropical forest on a global scale is well-documented by the research community (Hansen et al., 2013; Achard et al., 2014; Austin et al., 2017; Song et al., 2018). As part of an effort to monitor and control Amazon deforestation, countries such as Peru, Brazil, and Colombia, have been using satellite data to annually map deforestation and/or for early warning detection of deforestation (MINAM, 2017; INPE, 2018b,a, 2008; IDEAM, 2017; INPE and EMBRAPA, 2018). Understanding processes of land use land cover (LULC) change that drive deforestation is relevant towards more sustainable land man- agement and will aid global initiatives (e.g. REDD+) (UNFCCC, 2014). Therefore, understanding drivers of deforestation is fundamental for the development of policies and measures to reduce emissions and for de- veloping forest reference levels (Hosonuma et al., 2012; Kissinger et al., 2012). Moreover, identifying drivers of deforestation is a need in cur- rent remote sensing technology, as cited, for example, during the In- ternational Forum on Forest Early Warning Systems held in Lima, Peru https://doi.org/10.1016/j.jag.2020.102214 Received 16 March 2020; Received in revised form 18 July 2020; Accepted 5 August 2020 ⁎ Corresponding author. E-mail address: an0052@uah.edu (A.P. Nicolau). Int J Appl Earth Obs Geoinformation 94 (2021) 102214 0303-2434/ © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). T