RESUMEN
Understanding the relation between terrestrial microorganisms and edaphic factors in the Antarctic can provide insights into their potential response to environmental changes. Here we examined the composition of bacterial and micro-eukaryotic communities using amplicon sequencing of rRNA genes in 105 soil samples from the Sør Rondane Mountains (East Antarctica), differing in bedrock or substrate type and associated physicochemical conditions. Although the two most widespread taxa (Acidobacteriota and Chlorophyta) were relatively abundant in each sample, multivariate analysis and co-occurrence networks revealed pronounced differences in community structure depending on substrate type. In moraine substrates, Actinomycetota and Cercozoa were the most abundant bacterial and eukaryotic phyla, whereas on gneiss, granite and marble substrates, Cyanobacteriota and Metazoa were the dominant bacterial and eukaryotic taxa. However, at lower taxonomic level, a distinct differentiation was observed within the Cyanobacteriota phylum depending on substrate type, with granite being dominated by the Nostocaceae family and marble by the Chroococcidiopsaceae family. Surprisingly, metazoans were relatively abundant according to the 18S rRNA dataset, even in samples from the most arid sites, such as moraines in Austkampane and Widerøefjellet ("Dry Valley"). Overall, our study shows that different substrate types support distinct microbial communities, and that mineral soil diversity is a major determinant of terrestrial microbial diversity in inland Antarctic nunataks and valleys.
RESUMEN
Planet's SuperDove constellation is evaluated for remote sensing of water targets. SuperDoves are small satellites with on board eight band PlanetScope imagers that add four new bands compared to the previous generations of Doves. The Yellow (612 nm) and Red Edge (707 nm) bands are of particular interest to aquatic applications, for example in aiding the retrieval of pigment absorption. The dark spectrum fitting (DSF) algorithm is implemented in ACOLITE for processing of SuperDove data, and its outputs are compared to matchup data collected using an autonomous pan-and-tilt hyperspectral radiometer (PANTHYR) installed in the turbid waters of the Belgian Coastal Zone (BCZ). Results for 35 matchups from 32 unique SuperDove satellites indicate on average low differences with PANTHYR observations for the first seven bands (443-707 nm), with mean absolute relative differences (MARD) 15-20%. The mean average differences (MAD) are between -0.01 and 0 for the 492-666 nm bands, i.e. DSF results show a negative bias, while the Coastal Blue (444 nm) and Red Edge (707 nm) show a small positive bias (MAD 0.004 and 0.002). The NIR band (866 nm) shows a larger positive bias (MAD 0.01), and larger relative differences (MARD 60%). Root mean squared differences (RMSD) are rather flat at around 0.01 with peaks in the bands with highest water reflectance of around 0.015. The surface reflectance products as provided by Planet (PSR) show a similar average performance to DSF, with slightly larger and mostly positive biases, except in both Green bands, where the MAD is close to 0. MARD in the two Green bands is a bit lower for PSR (9.5-10.6%) compared to DSF (9.9-13.0%). Higher scatter is found for the PSR (RMSD 0.015-0.020), with some matchups showing large, spectrally mostly flat differences, likely due to the external aerosol optical depth (τa) inputs not being representative for these particular images. Chlorophyll a absorption (aChl) is retrieved from PANTHYR measurements, and the PANTHYR data are used to calibrate aChl retrieval algorithms for SuperDove in the BCZ. Various Red band indices (RBI) and two neural networks are evaluated for aChl estimation. The best performing RBI algorithm, i.e. the Red band difference (RBD), showed a MARD of 34% for DSF and 25% for PSR with positive biases of 0.11 and 0.03 m-1 respectively for 24 PANTHYR aChl matchups. The difference in RBD performance between DSF and PSR can be largely explained by their respective average biases in the Red and Red Edge bands, which are opposite signs for DSF (negative bias in the red), and positive for both bands for PSR. Mapping of turbid water aChl and hence chlorophyll a concentration (C) using SuperDove is demonstrated for coastal bloom imagery, showing how SuperDove data can supplement monitoring programmes.
RESUMEN
The performance of the dark spectrum fitting (DSF) atmospheric correction algorithm is evaluated using matchups between metre- and decametre-scale satellite imagery as processed with ACOLITE and measurements from autonomous PANTHYR hyperspectral radiometer systems deployed in the Adriatic and North Sea. Imagery from the operational land imager (OLI) on Landsat 8, the multispectral instrument (MSI) on Sentinel-2 A and B, and the PlanetScope CubeSat constellation was processed for both sites using a fixed atmospheric path reflectance in a small region of interest around the system's deployment location, using a number of processing settings, including a new sky reflectance correction. The mean absolute relative differences (MARD) between in situ and satellite measured reflectances reach <20% in the Blue and 11% in the Green bands around 490 and 560 nm for the best performing configuration for MSI and OLI. Higher relative errors are found for the shortest Blue bands around 440 nm (30-100% MARD), and in the Red-Edge and near-infrared bands (35-100% MARD), largely influenced by the lower absolute data range in the observations. Root mean squared differences (RMSD) increase from 0.005 in the NIR to about 0.015-0.020 in the Blue band, consistent with increasing atmospheric path reflectance. Validation of the Red-Edge and NIR bands on Sentinel-2 is presented, as well as for the first time, the Panchromatic band (17-26% MARD) on Landsat 8, and the derived Orange contra-band (8-33% MARD for waters in the algorithm domain, and around 40-80% MARD overall). For Sentinel-2, excluding the SWIR bands from the DSF gave better performances, likely due to calibration issues of MSI at longer wavelengths. Excluding the SWIR on Landsat 8 gave good performance as well, indicating robustness of the DSF to the available band set. The DSF performance was found to be rather insensitive to (1) the wavelength spacing in the lookup tables used for the atmospheric correction, (2) the use of default or ancillary information on gas concentration and atmospheric pressure, and (3) the size of the ROI over which the path reflectance is estimated. The performance of the PlanetScope constellation is found to be similar to previously published results, with the standard DSF giving the best results in the visible bands in terms of MARD (24-40% overall, and 18-29% for the turbid site). The new sky reflectance correction gave mixed results, although it reduced the mean biases for certain configurations and improved results for the processing excluding the SWIR bands, giving lower RMSD and MARD especially at longer wavelengths (>600 nm). The results presented in this article should serve as guidelines for general use of ACOLITE and the DSF.
RESUMEN
The potential for mapping of turbidity in inland and coastal waters using imagery from the PlanetScope (PS) and RapidEye (RE) constellations is evaluated. With >120 PS and 5 RE satellites in orbit these constellations are able to provide metre scale imagery on a daily basis and could significantly enhance high spatial resolution monitoring of turbidity worldwide. The Dark Spectrum Fitting (DSF) atmospheric correction is adapted to the PS and RE imaging systems to retrieve surface reflectances. Due to the large amount of imagery and the limited band sets on these sensors, automated pixel classification is required. This is here performed using a neural network approach, which is able to classify water pixels for clear to moderately turbid waters. Due to the limited band set and sensor performance, some issues remain with classifying extremely turbid waters and cloud shadows based on a spectral approach. Surface reflectance data compares well with in situ measurements from the AERONET-OC network. Turbidity is estimated from the Red, RedEdge (RE only) and NIR bands and is compared with measurements from autonomous stations in the San Francisco Bay area and the coastal waters around the United Kingdom. Good performance is found for Red band derived turbidity from PS data, while the NIR band performance is mediocre, likely due to calibration issues. For RE, all three turbidity products give reasonable results. A high revisit density allows for the mapping of temporal variability in water turbidity using these satellite constellations. Thanks to the RedEdge band on RE, chlorophyll a absorption can be avoided, and perhaps even estimated.