Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery

This work has been accepted by IEEE TGRS for publication. The majority of optical observations acquired via spaceborne earth imagery are affected by clouds. While there is numerous prior work on reconstructing cloud-covered information, previous studies are oftentimes confined to narrowly-defined regions of interest, raising the question of whether an approach can generalize to a diverse set of observations acquired at variable cloud coverage or in different regions and seasons. We target the challenge of generalization by curating a large novel data set for training new cloud removal approaches and evaluate on two recently proposed performance metrics of image quality and diversity. Our data set is the first publically available to contain a global sample of co-registered radar and optical observations, cloudy as well as cloud-free. Based on the observation that cloud coverage varies widely between clear skies and absolute coverage, we propose a novel model that can deal with either extremes and evaluate its performance on our proposed data set. Finally, we demonstrate the superiority of training models on real over synthetic data, underlining the need for a carefully curated data set of real observations. To facilitate future research, our data set is made available onlineComment: This work has been accepted by IEEE TGRS for publicatio

Cloud Removal in Sentinel-2 Imagery using a Deep Residual Neural Network and SAR-Optical Data Fusion

Optical remote sensing imagery is at the core of many Earth observation activities. The regular, consistent and global-scale nature of the satellite data is exploited in many applications, such as cropland monitoring, climate change assessment, land-cover and land-use classification, and disaster assessment. However, one main problem severely affects the temporal and spatial availability of surface observations, namely cloud cover. The task of removing clouds from optical images has been subject of studies since decades. The advent of the Big Data era in satellite remote sensing opens new possibilities for tackling the problem using powerful data-driven deep learning methods. In this paper, a deep residual neural network architecture is designed to remove clouds from multispectral Sentinel-2 imagery. SAR-optical data fusion is used to exploit the synergistic properties of the two imaging systems to guide the image reconstruction. Additionally, a novel cloud-adaptive loss is proposed to maximize the retainment of original information. The network is trained and tested on a globally sampled dataset comprising real cloudy and cloud-free images. The proposed setup allows to remove even optically thick clouds by reconstructing an optical representation of the underlying land surface structure

mec13390_genotypes

Microsatellite data set (n=692) follows the Structure input file format (Unix format txt) with individual information on a single line (two columns per marker). The file contains loci names (1st row), individual names (1st column), codes for sampling localities (2nd col.) and 3 additional columns (GenBank Acc. n., haplogroup and haplotype for cyt-b mtDNA sequence)

Data from: Defining conservation units in a stocking-induced genetic melting pot: unravelling native and multiple exotic genetic imprints of recent and historical secondary contact in Adriatic grayling

The definition of conservation units is crucial for the sustainable management of endangered species, though particularly challenging when recent and past anthropogenic and natural gene flow might have played a role. The conservation of the European grayling, Thymallus thymallus, is particularly complex in its southern distribution area, where the Adriatic evolutionary lineage is endangered by a long history of anthropogenic disturbance, intensive stocking and potentially widespread genetic introgression. We provide mtDNA sequence and microsatellite data of 683 grayling from 30 sites of Adriatic as well as Danubian and Atlantic origin. We apply Bayesian clustering and Approximate Bayesian Computation (ABC) to detect microgeographic population structure and to infer the demographic history of the Adriatic populations, to define appropriate conservation units. Varying frequencies of indigenous genetic signatures of the Adriatic grayling were revealed, spanning from marginal genetic introgression to the collapse of native gene pools. Genetic introgression involved multiple exotic source populations of Danubian and Atlantic origin, thus evidencing the negative impact of few decades of stocking. Within the Adige River system, a contact zone of western Adriatic and eastern Danubian populations was detected, with ABC analyses suggesting a historical anthropogenic origin of eastern Adige populations, most likely founded by medieval translocations. Substantial river-specific population substructure within the Adriatic grayling Evolutionary Significant Unit points to the definition of different conservation units. We finally propose a catalog of management measures, including the legal prohibition of stocking exotic grayling and the use of molecular markers in supportive- and captive-breeding programs

nABCtemolo_4pops_SSRS_I

Microsatellite data set analysed in AB

Data from: Massive invasion of exotic Barbus barbus and introgressive hybridisation with endemic B. plebejus in Northern Italy: where, how and why?

Biological invasions and introgressive hybridisation are major drivers for the decline of native freshwater fish. However, the magnitude of the problem across a native species range, the mechanisms shaping introgression as well as invader’s dispersal and the relative role of biological invasions in the light of multiple environmental stressors are rarely described. Here, we report extensive (N=665) mtDNA sequence and (N=692) microsatellite genotypic data of 32 Northern Adriatic sites aimed to unravel the invasion of the European Barbus barbus in Italy, and the hybridisation and decline of the endemic B. plebejus. We highlight an exceptionally fast breakthrough of B. barbus within the Po River Basin, leading to widespread introgressive hybridisation with the endemic B. plebejus within few generations. In contrast, adjacent drainage systems are still unaffected from B. barbus invasion. We show that barriers to migration are inefficient to halt the invasion process and that propagule pressure, and not environmental quality, is the major driver responsible for B. barbus success. Both introgressive hybridisation and invader’s dispersal are facilitated by ongoing fisheries management practices. Therefore, immediate changes in fisheries management (i.e. stocking and translocation measures) and a detailed conservation plan, focussed on remnant purebred B. plebejus populations, are urgently needed

SSR_dataset

Microsatellite data set, with individual information about sampling location and GenBank reference for mtDNA control regio

final-ALL_Thymallus_aligned-ins.phy

mtDNA control region sequence alignment used to generate the phylogenetic tree presented in Fig. S1. Nexus format

mec13390_SimDataset6000inds

Microsatellite data set (n=6,000) simulated with HYBRIDLAB 1.0 (Nielsen et al. 2001). The file is in Genepop format, with 6 populations representing B. plebejus purebreds, B. barbus purebreds, F1, F2, Backcross F1 X B. plebejus and Backcross F1 X B. barbus