Proxy For Biomass Research Articles

Tillage erosion as an important driver of in‐field biomass patterns in an intensively used hummocky landscape

AbstractTillage erosion causes substantial soil redistribution that can exceed water erosion especially in hummocky landscapes under highly mechanized large field agriculture. Consequently, truncated soil profiles can be found on hill shoulders and top slopes, whereas colluvial material is accumulated at footslopes, in depressions, and along downslope field borders. We tested the hypothesis that soil erosion substantially affects in‐field patterns of the enhanced vegetation index (EVI) of different crop types on landscape scale. The interrelation between the EVI (RAPIDEYE satellite data; 5 m spatial resolution) as a proxy for crop biomass and modeled total soil erosion (tillage and water erosion modeled using SPEROS‐C) was analyzed for the Quillow catchment (size: 196 km2) in Northeast Germany in a wet versus normal year for four crop types (winter wheat, maize, winter rapeseed, winter barley). Our findings clearly indicate that eroded areas had the lowest EVI values, while the highest EVI values were found in depositional areas. The differences in the EVI between erosional and depositional sites are more pronounced in the analyzed normal year. The net effect of total erosion on the EVI compared to areas without pronounced erosion or deposition ranged from −10.2% for maize in the normal year to +3.7% for winter barley in the wet year. Tillage erosion has been identified as an important driver of soil degradation affecting in‐field crop biomass patterns in a hummocky ground moraine landscape. While soil erosion estimates are to be made, more attention should be given toward tillage erosion.

Performance of Ocean Colour Chlorophyll a algorithms for Sentinel-3 OLCI, MODIS-Aqua and Suomi-VIIRS in open-ocean waters of the Atlantic

The proxy for phytoplankton biomass, Chlorophyll a (Chl a), is an important variable to assess the health and state of the oceans which are under increasing anthropogenic pressures. Prior to the operational use of satellite ocean-colour Chl a to monitor the oceans, rigorous assessments of algorithm performance are necessary to select the most suitable products. Due to their inaccessibility, the oligotrophic open-ocean gyres are under-sampled and therefore under-represented in global in situ data sets. The Atlantic Meridional Transect (AMT) campaigns fill the sampling gap in Atlantic oligotrophic waters. In-water underway spectrophotometric data were collected on three AMT field campaigns in 2016, 2017 and 2018 to assess the performance of Sentinel-3A (S3-A) and Sentinel-3B (S3-B) Ocean and Land Colour Instrument (OLCI) products. Three Chl a algorithms for OLCI were compared: Processing baseline (pb) 2, which uses the ocean colour 4 band ratio algorithm (OC4Me); pb 3 (OL_L2M.003.00) which uses OC4Me and a colour index (CI); and POLYMER v4.8 which models atmosphere and water reflectance and retrieves Chl a as a part of its spectral matching inversion. The POLYMER Chl a for S-3A OLCI performed best. The S-3A OLCI pb 2 tended to under-estimate Chl a especially at low concentrations, while the updated OL_L2M.003.00 provided significant improvements at low concentrations. OLCI data were also compared to MODIS-Aqua (R2018 processing) and Suomi-NPP VIIRS standard products. MODIS-Aqua exhibited good performance similar to OLCI POLYMER whereas Suomi-NPP VIIRS exhibited a slight under-estimate at higher Chl a values. The reasons for the differences were that S-3A OLCI pb 2 Rrs were over-estimated at blue bands which caused the under-estimate in Chl a. There were also some artefacts in the Rrs spectral shape of VIIRS which caused Chl a to be under-estimated at values >0.1 mg m-3. In addition, using in situ Rrs to compute Chl a with OC4Me we found a bias of 25% for these waters, related to the implementation of the OC4ME algorithm for S-3A OLCI. By comparison, the updated OLCI processor OL_L2M.003.00 significantly improved the Chl a retrievals at lower concentrations corresponding to the AMT measurements. S-3A and S-3B OLCI Chl a products were also compared during the Sentinel-3 mission tandem phase (the period when S-3A and S-3B were flying 30 sec apart along the same orbit). Both S-3A and S-3B OLCI pb 2 under-estimated Chl a especially at low values and the trend was greater for S-3A compared to S-3B. The performance of OLCI was improved by using either OL_L2M.003.00 or POLYMER Chl a. Analysis of coincident satellite images for S-3A OLCI, MODIS-Aqua and VIIRS as composites and over large areas illustrated that OLCI POLYMER gave the highest Chl a concentrations and percentage (%) coverage over the north and south Atlantic gyres, and OLCI pb 2 produced the lowest Chl a and % coverage.

Using zooplankton metabarcoding to assess the efficacy of different techniques to clean-up an oil-spill in a boreal lake.

Regulators require adequate information to select best practices with less ecosystem impacts for remediation of freshwater ecosystems after oil spills. Zooplankton are valuable indicators of aquatic ecosystem health as they play pivotal roles in biochemical cycles while stabilizing food webs. Compared with morphological identification, metabarcoding holds promise for cost-effective, high-throughput, and benchmarkable biomonitoring of zooplankton communities. The objective of this study was to apply DNA and RNA metabarcoding of zooplankton for ecotoxicological assessment and compare it with traditional morphological identification in experimental shoreline enclosures in a boreal lake. These identification methods were also applied in context of assessing response of the zooplankton community exposed to simulated spills of diluted bitumen (dilbit), with experimental remediation practices (enhanced monitored natural recovery and shoreline cleaner application). Metabarcoding detected boreal zooplankton taxa up to the genus level, with a total of 24 shared genera, and while metabarcoding-based relative abundance served as an acceptable proxy for biomass inferred by morphological identification (ρ ≥ 0.52). Morphological identification determined zooplankton community composition changes due to treatments at 11 days post-spill (PERMANOVA, p=0.0143) while metabarcoding methods indicated changes in zooplankton richness and communities at 38 days post-spill (T-test, p < 0.05; PERMANOVA, p≤0.0429). Shoreline cleaner application overall seemed to have the largest impact on zooplankton communities relative to enhanced monitored natural recovery, regardless of zooplankton identification method. Both metabarcoding and morphological identification were able to discern the differences between the two experimental remediation practices. Metabarcoding of zooplankton could provide informative results for ecotoxicological assessment of the remediation practices of dilbit, advancing our knowledge of best practices for remediating oil-impacted aquatic ecosystems while serving to accelerate the assessment of at-risk freshwater ecosystems.

Multi-trophic communities re-establish with canopy cover and microclimate in a subtropical forest biodiversity experiment

Plant diversity affects multi-trophic communities, but in young regrowth forests, where forest insects are in the process of re-establishment, other biotic and also abiotic factors might be more important. We studied cavity-nesting bees, wasps and their natural enemies along an experimental tree diversity gradient in subtropical South-East China. We compared insect communities of experimental young forests with communities of established natural forests nearby the experiment and tested for direct and indirect effects of tree diversity, tree basal area (a proxy of tree biomass), canopy cover and microclimate on bee and wasp community composition, abundance and species richness. Finally, we tested if the trophic levels of bees, herbivore-hunting wasps, spider-hunting wasps and their natural enemies respond similarly. Forest bee and wasp community composition re-established towards communities of the natural forest with increasing tree biomass and canopy cover. These factors directly and indirectly, via microclimatic conditions, increased the abundance of bees, wasps and their natural enemies. While bee and wasp species richness increased with abundance and both were not related to tree diversity, abundance increased directly with canopy cover, mediated by tree biomass. Abundance of natural enemies increased with host (bee and wasp) abundance irrespective of their trophic position. In conclusion, although maximizing tree diversity is an important goal of reforestation and forest conservation, rapid closure of canopies is also important for re-establishing communities of forest bees, wasps and their natural enemies.

Light-induced fluorescence quenching leads to errors in sensor measurements of phytoplankton chlorophyll and phycocyanin

Optical sensors for fluorescence of chlorophyll a (f-Chl a) and phycocyanin (f-PC) are increasingly used as a proxy for biomass of algae and cyanobacteria, respectively. They provide measurements at high-frequency and modest cost. These sensors require site-specific calibration due to a range of interferences. Light intensity affects the fluorescence yield of cyanobacteria and algae through light harvesting regulation mechanisms, but is often neglected as a potential source of error for in-situ f-Chl a and f-PC measurements. We hypothesised that diel light variations would induce significant f-Chl a and f-PC suppression when compared to dark periods. We tested this hypothesis in a controlled experiment using three commercial fluorescence probes which continuously measured f-Chl a and f-PC from a culture of the cyanobacterium Dolichospermum variabilis as well as f-Chl a from a culture of the green alga Ankistrodesmus gracilis in a simulated natural light regime. Under light, all devices showed a significant (p<0.01) suppression of f-Chl a and f-PC compared to measurements in the dark. f-Chl a decreased by up to 79% and f-PC by up to 59% at maximum irradiance compared to dark-adapted periods. Suppression levels were higher during the second phase of the diel cycle (declining light), indicating that quenching is dependent on previous light exposure. Diel variations in light intensity must be considered as a significant source of bias for fluorescence probes used for algal monitoring. This is of high relevance as most monitoring activities take place during daytime and hence f-Chl a and f-PC are likely to be systematically underestimated under bright conditions. Compensation models, design modifications to fluorometers and sampling design are discussed as suitable alternatives to overcome light-induced fluorescence quenching.

Area from image analyses accurately estimates dry-weight biomass of juvenile tissue from the moss Ceratodon purpureus.

PREMISEMosses have long served as models for studying many areas of plant biology. Investigators have used two‐dimensional measurements of juvenile growth from photographs as a surrogate for dry‐weight biomass. The relationship between area and biomass, however, has not been critically evaluated.METHODSHere we grew axenic tissue cultures of 10 Ceratodon purpureus isolates to study the relationship between these parameters. We measured area and biomass on replicate cultures with two distinct starting inoculum sizes each week for three weeks. We then examined the correlation between area and biomass as well as the influence of variation in inoculum size on both parameters.RESULTSWe found a strong correlation between area and biomass after two weeks of growth. Furthermore, we found inoculum size affected biomass during the first week of growth but not in subsequent weeks and inoculum size had no detectable effect on area.DISCUSSIONThese analyses provide experimental confirmation that area is a suitable proxy for biomass and provide clear guidelines for when inoculum size variation may affect downstream growth estimates.

Fine-scale oceanographic drivers of reef manta ray (Mobula alfredi) visitation patterns at a feeding aggregation site.

Globally, reef manta rays (Mobula alfredi) are in decline and are particularly vulnerable to exploitation and disturbance at aggregation sites. Here, passive acoustic telemetry and a suite of advanced oceanographic technologies were used for the first time to investigate the fine‐scale (5‐min) influence of oceanographic drivers on the visitation patterns of 19 tagged M. alfredi to a feeding aggregation site at Egmont Atoll in the Chagos Archipelago. Boosted regression trees indicate that tag detection probability increased with the intrusion of cold‐water bores propagating up the atoll slope through the narrow lagoon inlet during flood tide, potentially transporting zooplankton from the thermocline. Tag detection probability also increased with warmer near‐surface temperature close to low tide, with near‐surface currents flowing offshore, and with high levels of backscatter (a proxy of zooplankton biomass). These combinations of processes support the proposition that zooplankton carried from the thermocline into the lagoon during the flood may be pumped back out through the narrow inlet during an ebb tide. These conditions provide temporally limited feeding opportunities for M. alfredi, which are tied on the tides. Results also provide some evidence of the presence of Langmuir Circulation, which transports and concentrates zooplankton, and may partly explain why M. alfredi occasionally remained at the feeding location for longer than that two hours. Identification of these correlations provides unique insight into the dynamic synthesis of fine‐scale oceanographic processes which are likely to influence the foraging ecology of M. alfredi at Egmont Atoll, and elsewhere throughout their range.

Exploring the Potential to Improve the Estimation of Boreal Tree Structural Attributes with Simple Height- and Distance-Based Competition Index

In many cases, the traditional ground-based estimates of competition between trees are not directly applicable with modern aerial inventories, due to incompatible measurements. Moreover, many former studies of competition consider extreme stand densities, hence the effect of competition under the density range in managed stands remains less explored. Here we explored the utility of a simple tree height- and distance-based competition index that provides compatibility with data produced by modern inventory methods. The index was used for the prediction of structural tree attributes in three boreal tree species growing in low to moderate densities within mixed stands. In silver birch, allometric models predicting tree diameter, crown height, and branch length all showed improvement when the effect of between-tree competition was included. A similar but non-significant trend was also present in a proxy for branch biomass. In Siberian larch, only the prediction of branch length was affected. In Scots pine, there was no improvement. The results suggest that quantification of competitive interactions based on individual tree heights and locations alone has potential to improve the prediction of tree attributes, although the outcomes can be species-specific.

Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central Italy

In this paper, multi-frequency synthetic aperture radar (SAR) data at L- and C-bands (ALOS PALSAR and Envisat/ASAR) were used to estimate forest biomass in Tuscany, in Central Italy. The ground measurements of woody volume (WV, in m3/ha), which can be considered as a proxy of forest biomass, were retrieved from the Italian National Forest Inventory (NFI). After a preliminary investigation to assess the sensitivity of backscatter at C- and L-bands to forest biomass, an approach based on an artificial neural network (ANN) was implemented. The ANN was trained using the backscattering coefficient at L-band (ALOS PALSAR, HH and HV polarization) and C-band (Envisat ASAR in HH polarization) as inputs. Spatially distributed WV values for the entire test area were derived by the integration (fusion) of a canopy height map derived from the Ice, Cloud, and Land Elevation Geoscience Laser Altimeter System (ICESat GLAS) and the NFI data, in order to build a significant ground truth dataset for the training stage. The analysis of the backscattering sensitivity to WV showed a moderate correlation at L-band and was almost negligible at C-band. Despite this, the ANN algorithm was able to exploit the synergy of SAR frequencies and polarizations, estimating WV with average Pearson’s correlation coefficient (R) = 0.96 and root mean square error (RMSE) ≃ 39 m3/ha when applied to the test dataset and average R = 0.86 and RMSE ≃ 75 m3/ha when validated on the direct measurements from the NFI. Considering the heterogeneity of the scenario (Mediterranean mixed forests in hilly landscape) and the small amount of available ground measurements with respect to the spatial variability of different plots, the obtained results can be considered satisfactory. Moreover, the successful use of WV from global maps for implementing the algorithm suggests the possibility to apply the algorithm to wider areas or even to global scales.

Quantifying the uncertainties in multi-wavelength PAM fluorometry due to innate and irradiance-induced variability of fluorescence spectra

Multi-wavelength Chl a fluorometers are increasingly applied to assess phytoplankton photosynthetic capacity and composition, but their usefulness is limited by uncertainties in fluorescence excitation spectra (FES). We investigated this issue using the Phyto-PAM fluorometer to evaluate the effects of innate and irradiance-dependent variations in background (F) and variable (Fv) FES on analysis of three pigment groups (cyanobacteria, chlorophytes and chromophytes). The effects on group-specific estimates of minimum fluorescence (F0), a proxy for biomass, and Fv/Fm, the quantum yield of photochemistry, presented some challenges to the interpretation of group-specific results. F0 estimates usually had a 5–15% margin of error, even when measuring highly uneven mixtures, and applying imperfectly matched calibration FES or stressing samples with photosynthetically active and ultraviolet radiation; errors in Fv/Fm were commonly < 15%. Despite such relatively good accuracy, estimates for F0 and, especially, Fv/Fm are unreliable for groups at low relative abundance, and results can sometimes be reported for groups not actually present. We report margins of error for different levels of relative abundance to inform interpretation of measurements from natural communities and show that F and Fv spectra for some taxa can differ in ways that produce severe errors in F0 and Fv/Fm estimates if used uncritically.

Improved food-insecurity prediction in smallholder-dominated landscapes using MODIS Enhanced Vegetation Index and Google Earth Engine: a case study in South Central Ethiopia

ABSTRACT Recent droughts and food insecurity underline the need for objective, timely, spatially explicit food aid prediction in Ethiopia. We developed a generic user-friendly method to detect greening of agricultural areas and derive predictions of agricultural production for potentially food-insecure areas. We used the Enhanced Vegetation Index (EVI) from combined Terra/Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) images to generate EVI time series over multiple growing seasons. Maximum seasonal greening (EVImax), as proxy for biomass and expected crop yield, was related to rainfall variability and to indicate areas of risk for crop failure due to drought within the necessary reaction time for emergency aid. Four agroecological zones were covered from 2003 to 2019. Vegetation periods per 250m pixel were calculated back from EVImax. EVImax was validated against measured yields on large-scale farms. Interannual means and variability of EVImax served to assess production and drought risk. Yield predictions corresponded well with wheat production (r2≅0.5 p≤0.05). High temporal variability and low absolute EVI indicated drought-prone areas. EVI was positively correlated with rainfall data in cropped drought-prone areas (r2≅0.4, p≤0.05), but negatively in temporally water-logged highlands (r2≅0.3, p≤0.05). Our user-friendly approach on Google Earth Engine can accurately detect imminent food insecurity and facilitate timely interventions.

Climatic, vegetative, and disturbance predictors of lichen species’ height in Arctic Alaska, USA

Lichens cover approximately 8% of land surface, primarily in alpine and polar habitats. Lichen biomass is often correlated with cover and thallus height, but quick and effective methods of lichen biomass estimation have not been tested across large areas. We utilize a dataset of over 7800 height measurements made throughout the Arctic Network of National Parks in the US to model mat-forming macrolichen height by species as a function of morphological, climatic, and habitat factors. Our goal was to understand how lichen height, as proxy for biomass, varied by species in different areas, and determine which factors best predict lichen height. We found that the single best predictor of lichen height was surface downwelling shortwave radiation. Climatic, vegetative, and other physiographic variables were moderate predictors of lichen height. We determined that differences in height observed between species were not explained by the degree of branching nor thallus width. Our findings highlight that the key factors predicting lichen height may not be easy to mitigate through management (e.g., radiation, vapor pressure deficit, length of growing season), yet may be altered in future climate change scenarios. Further, rapid height field measurements, like those used in our study, could improve our understanding of the drivers of lichen biomass, underscore its variability across species, and could be used for other applications. Accurate biomass measurements are necessary for landscape-scale monitoring and predictions of future lichen biomass in the face of climate perturbations.

Performance of dry and wet spells combined with remote sensing indicators for crop yield prediction in Senegal

Studying the relationship between potential high-impact precipitation and crop yields can help us understand the impact of the intensification of the hydrological cycle on agricultural production. The objective of this study is to analyse the contribution of intra seasonal rainfall indicators, namely dry and wet spells, for predicting millet yields at regional scale in Senegal using multiple linear regression. Using dry and wet spells with traditional indicators i.e. proxies of crop biomass and cumulated rainfall, hereafter called remote sensing indicators (NDVI, SPI3, WSI and RG), we analysed the ability of dry and wet spells alone or combined with these remote sensing indicators to provide intraseasonal forecasts covering the period 1991–2010. We analysed all 12 regions producing millet and found that results vary strongly between regions and also during the season, as a function of the dekad of prediction. At the spatial scale, the strongest performing combinations include the dry spell indicators DSC20 and DSxl in the peanut basin. While in the south of the country, the combination of wet period indicators WS1 or WSC5 with the RG is fairly reliable. Focussing on Thies, our best region in the groundnut basin, we showed that dry and wet spells indicators can explain up to 80% of yield variations, alone or in combination with remote sensing indicators. Regarding the timing of prediction, millet yield can be forecast as early as July with an accuracy of 40% of the mean yield but the best forecast is obtained in early September, at the peak of crop development (accuracy of 100 kg/ha i.e. 20% of the mean yield). Although, the estimated yields show biases over some years identified as extremely deficient or in oversupply in terms of agricultural yields.

Design of an Unmanned Ground Vehicle and LiDAR Pipeline for the High-Throughput Phenotyping of Biomass in Perennial Ryegrass

Perennial ryegrass biomass yield is an important driver of profitability for Australian dairy farmers, making it a primary goal for plant breeders. However, measuring and selecting cultivars for higher biomass yield is a major bottleneck in breeding, requiring conventional methods that may be imprecise, laborious, and/or destructive. For forage breeding programs to adopt phenomic technologies for biomass estimation, there exists the need to develop, integrate, and validate sensor-based data collection that is aligned with the growth characteristics of plants, plot design and size, and repeated measurements across the growing season to reduce the time and cost associated with the labor involved in data collection. A fully automated phenotyping platform (DairyBioBot) utilizing an unmanned ground vehicle (UGV) equipped with a ground-based Light Detection and Ranging (LiDAR) sensor and Real-Time Kinematic (RTK) positioning system was developed for the accurate and efficient measurement of plant volume as a proxy for biomass in large-scale perennial ryegrass field trials. The field data were collected from a perennial ryegrass row trial of 18 experimental varieties in 160 plots (three rows per plot). DairyBioBot utilized mission planning software to autonomously capture high-resolution LiDAR data and Global Positioning System (GPS) recordings. A custom developed data processing pipeline was used to generate a plant volume estimate from LiDAR data connected to GPS coordinates. A high correlation between LiDAR plant volume and biomass on a Fresh Mass (FM) basis was observed with the coefficient of determination of R2 = 0.71 at the row level and R2 = 0.73 at the plot level. This indicated that LiDAR plant volume is strongly correlated with biomass and therefore the DairyBioBot demonstrates the utility of an autonomous platform to estimate in-field biomass for perennial ryegrass. It is likely that no single platform will be optimal to measure plant biomass from landscape to plant scales; the development and application of autonomous ground-based platforms is of greatest benefit to forage breeding programs.

Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean

Phytoplankton plays a key role in the carbon cycle and supports the oceanic food web. While its seasonal and interannual cycles are rather well characterized owing to the modern satellite ocean color era, its longer time variability remains largely unknown due to the short time-period covered by observations on a global scale. With the aim of reconstructing this longer-term phytoplankton variability, a support vector regression (SVR) approach was recently considered to derive surface Chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) from physical oceanic model outputs and atmospheric reanalysis. However, those early efforts relied on one particular algorithm, putting aside the question of whether different algorithms may have specific behaviors. Here, we show that this approach can also be applied on satellite observations and can even be further improved by testing performances of different machine learning algorithms, the SVR and a neural network with dense layers (a multi-layer perceptron, MLP). The MLP, thanks to its ability to capture complex non-linear relationships, outperforms the SVR to capture satellite Chl spatial patterns (correlation of 0.75 vs. 0.65 on a global scale, respectively) along with its interannual variability and trend, despite an underestimated amplitude. Among deep learning algorithms, neural network such as MLP models appear to be promising tools to investigate phytoplankton long-term time-series.

Stochastic simulation of phytoplankton biomass using eighteen years of daily data - predictability of phytoplankton growth in a large, shallow lake

During the past decades, on-line monitoring of freshwater lakes has developed rapidly. To use high frequency time-series in lake management, novel models are needed that are simple and provide insight into the complexity of phytoplankton dynamics. Chlorophyll a (Chl), a proxy for phytoplankton biomass and environmental drivers were monitored on-line in large, shallow Lake Balaton during the vegetation periods between 2001 and 2018. Growth and non-growth (G and non-G) states of algae were deduced from daily change in Chl. Random forests (RF) were used to find stochastic response rules of phytoplankton to growth-supporting environmental habitat templates. The stochastic G/non-G state was translated into long-term daily biomass dynamics by a deterministic biomass model to assess uncertainty and to distinguish between inevitable and unpredictable blooms. A biomass peak was qualified as inevitable or unpredictable if the lower 95% confidence limit of simulations exceeded or remained at the baseline Chl level, respectively. Compared to a stochastic null model based on monthly Markovian transition probabilities, RF-based models captured wax and wane of biomass realistically. Timing of peaks could be better simulated than their magnitude, likely because habitat templates were primarily determined by light whereas peak sizes might depend on unmeasured processes, such as phosphorus availability. In general, algal growth was favored by wind-induced sediment resuspension that decreased light availability but simultaneously enhanced the P supply. Seasonal temperature and an integral of departures from the “normal” seasonal temperature over 2 to 3 generations were important drivers of phytoplankton growth, whereas short-term (diel and day to day) changes in water temperature appeared to be irrelevant. Four types of years could be distinguished during the study period with respect to algal growth conditions. The present modeling approach can reasonably be used even in highly variable aquatic environments when 3 to 4 years of daily data are available.

Spectral range within global aCDOM(440) algorithms for oceanic, coastal, and inland waters with application to airborne measurements

The optically active component of dissolved organic material in aquatic ecosystems, or colored dissolved organic matter (CDOM), is represented by the coefficient of absorption due to the dissolved aquatic constituents at 440 nm, aCDOM(440). Remote sensing of aCDOM(440) enables characterization of ecosystem processes and aids in retrieval of chlorophyll a, a proxy for phytoplankton biomass. Spectrally adjacent band-ratio domains, e.g., blue to green, have previously been applied for remote sensing of aCDOM(440) in coastal and oceanic waters with similar results compared to more complex semi-analytical algorithms. Estimation of aCDOM(440) from ratios of the most spectrally separated ocean color wavebands (end members), e.g., ultraviolet (UV) to near-infrared (NIR), termed end-member analysis (EMA), has previously been shown to increase the accuracy of global aCDOM(440) retrievals from in-water observations of diffuse attenuation and to enable a unified algorithmic perspective without requiring regional adjustment of internal bio-optical parameters. EMA of above-water observations is evaluated herein, with a focus on coastal and inland waters in which increasing optical complexity and likelihood of bottom reflectance challenge the oceanic algorithms developed for deep and optically simple (case-1) waters. Analysis herein of three independent, in situ, bio-optical datasets indicates significant correlation between aCDOM(440) and end-member band ratios (next-generation 320 and 780 nm or legacy 412 and 670 nm ratios) with a coefficient of determination, R2, of 0.87 (log-scale) or higher based on a dataset spanning the dynamic range of global, conservative water bodies. For applicable wavelengths, EMA algorithms are shown to agree with case-1 relationships and to produce consistent log-scale uncertainties across more than three orders of magnitude in aCDOM(440) values (0.001–2.305 m−1). EMA using UV and NIR wavelengths (320 and 780 nm) is applied to low-altitude airborne observations and satisfies 25% uncertainty based on unbiased percent differences (UPDs) within each of three dissimilar match-up sites ranging in aCDOM(440) from 0.02–0.57 m−1. Results demonstrate that EMA is a useful and robust approach for the remote sensing of aCDOM(440) in coastal and inland waters, which are generally shallower, contain more optically complex environments, and span a greater range in aCDOM(440) than oceanic waters.

Using sentinel-2 satellite imagery to develop microphytobenthos-based water quality indices in estuaries

Microphytobenthos (MPB) is composed of unicellular photosynthetic organisms that colonize intertidal sediments within the first millimeters of the photic zone and form biofilms at low tide. In estuaries, this benthic group can represent the main primary producer and deliver several ecosystem services. However, it is not currently used as a bioindicator of water quality, contrary to the widespread use of phytobenthos in freshwater settings. This study assesses the potential of developing MBP metrics to assess water quality in transitional waters using Sentinel-2 (S2) satellite imagery. A Random Forest machine learning classification was used to distinguish different types of intertidal vegetation in 26 French estuaries and bays, in particular MPB and green macroalgae (chlorophytes), using multispectral indices. High accuracy was generally achieved for the identification of MPB when compared with field validation data, for both User’s and Producer’s accuracies, which corresponded to 94% and 84% respectively. Two Earth observation variables were retrieved: the Normalized Difference Vegetation Index (NDVI), a proxy of MPB biomass, and MPB percent cover integrated over the entire intertidal area of the water body. From a total of 918 S2 images from over a full year, 28% were exploitable due to the combined requirements of cloud-free pixels collected during low tide. With its 10 m spatial resolution, S2 was able to map all estuaries. MPB percent cover showed a stronger gradient between estuaries than MPB NDVI. MPB percent cover was also significantly correlated with green macroalgae percent cover, and a group of estuaries characterized by the highest MPB and green macroalgae coverage corresponded to eutrophic sites impacted by intensive farming activities. A multivariate analysis confirmed that MPB percent cover was indeed related to nutrients. It was also related to sediment type which was one of the main factors underlying differences between estuaries. This work is a first step toward a water quality metric using MPB, and several recommendations are proposed to refine this approach. Sentinel-2 imagery, which is publicly available, presents an interesting compromise to map estuarine microphytobenthos in order to assess the ecological status of transitional waters.

Allometric equations, wood density and partitioning of aboveground biomass in the arboretum of Ruhande, Rwanda

There is growing interest in plantation forests throughout Africa because of their role in environment, economy and people's livelihoods. However, the contribution of planted forests to climate mitigation is poorly understood, partly due to lack of allometric equations for biomass estimation. This study aimed to determine wood density and biomass fractions in aboveground components, and to develop biomass estimation equations for multispecies plantation forests in the arboretum of Ruhande in Rwanda. Allometric equations were developed by regressing diameter at breast height (DBH) alone or in combination with height or wood density or age of trees against the biomass of 45 trees harvested from a 200-ha site. Biomass estimates obtained from destructively sampled trees were up-scaled to estimate the amount of carbon stocked in the arboretum of Ruhande, assuming a stem density of 250 stems per ha. Wood density varied among the species but not tree size. The greatest fraction of aboveground biomass was allocated to stems (71–77%) compared to branches (19–27%) and leaves (1–8%) and varied by species. Equations developed fit the data well with DBH explaining over 90% of the observed variation in aboveground and stem biomass. Including height or wood density as supporting parameters reduced the relative error for aboveground biomass by 6.4 and 8.0% and improved model fit by 2.1 and 2.9%, respectively. Akaike information criterion (AIC) showed that wood density (AIC=63.6) and height (AIC=48.2) were the most suitable parameters to support DBH as a proxy for aboveground and stem biomass, respectively. Allometric equations developed in this study are useful tool for estimating carbon stocks of plantation forests in Rwanda and can enhance the accuracy of biomass predictions where site-specific equations rather than generalized models are recommended. Further studies focusing on development of allometric equations on belowground biomass in such systems are recommended.

Improving the Retrieval of Carbon-Based Phytoplankton Biomass from Satellite Ocean Colour Observations

Phytoplankton is at the base of the marine food web and plays a fundamental role in the global carbon cycle. Ongoing climate change significantly impacts phytoplankton distribution in the ocean. Monitoring phytoplankton is crucial for a full understanding of changes in the marine ecosystem. To observe phytoplankton from space, chlorophyll-a concentration (Chl) has been widely used as a proxy of algal biomass, although it can be impacted by physiology. Therefore, there has been an increasing focus towards estimating phytoplankton biomass in units of carbon (Cphyto). Here, we developed an algorithm to quantify Cphyto from space-based observations that accounts for the spatio-temporal variations of the backscattering coefficient associated with the fraction of detrital particles that do not covary with Chl. The main findings are: (i) a spatial and temporal variation of the detritus component must be accounted for in the Cphyto algorithm; (ii) the refined Cphyto algorithm performs better (relative bias of 23.7%) than any previously existing model; and (iii) our algorithm shows the lowest error in Cphyto across areas where picophytoplankton dominates (relative bias of 14%). In other areas, it is currently not possible to accurately assess the performance of the refined algorithm due to the paucity of in situ carbon data associated with nano- and micro-phytoplankton size classes.