Giorgio Dall'Olmo

Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: experimental results

The analytical development and underlying hypothesis of a three-band algorithm for estimating chlorophyll-a concentration ([Chla]) in turbid productive waters are presented. The sensitivity of the algorithm to the spectral location of the bands used is analyzed. A large set of experimental observations ([Chla] varied between 4 and 217 mg m(-3) and turbidity between 2 and 78 nephelometric turbidity units) was used to calibrate and validate the algorithm. It was found that the variability of the chlorophyll-a fluorescence quantum yield and of the chlorophyll-a specific absorption coefficient can reduce considerably the accuracy of remote predictions of [Chla]. Instead of parameterizing these interferences, their effects were minimized by tuning the spectral regions used in the algorithm. This allowed us to predict [Chla] with a relative root-mean-square error of less than 30%.

Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA-advanced very high resolution radiometer data

Retrieval of land surface temperature (LST) from advanced very high resolution radiometer (AVHRR) data is an important methodology in remote sensing. Several split window algorithms have been proposed in last two decades. In this paper we intend to present a better algorithm with less parameters and high accuacry. The algorithm involves only two essential parameters (transmittance and emissivity). The principle and method for the linearization of Plancks radiance equation, the mathematical derivation process of the algorithm, and the method for determining the atmospheric transmittance are discussed with details. Sensitivity analysis of the algorithm has been performed for evaluation of probable LST estimation error due to the possible errors in transmittance and emissivity. Results from the analysis indicate that the proposed algorithm is able to provide an accurate estimation of LST from AVHRR data. Assuming an error of 0.05 in atmospheric transmittance estimate and 0.01 in ground emissivity for the two AVHRR thermal channels, the average LST error with the algorithm is 1.1°C. Two methods have been used to validate the proposed algortihm. Comparison has also been done with the existing 11 algorithms in literature. Results from validation and comparison using the standard atmospheric simulation for various situations and the ground truth data sets demonstrate the applicability of the algorithm. According to the root mean square (RMS) errors of the retrieved LSTs from the measured or assumed LSTs, the proposed algorithm is among the best three. Considering the insignificant RMS error difference among the three, the proposed algorithm is better than the other two because they require more parameters for LST retrieval. Validation with standard atmospheric simulation indicates that this algorithm can achieve the accuacry of 0.25°C in LST retrieval for the case without error in both transmittance and emissivity estimates. The accuary of this algorithm is 1.75°C for the ground truth data set without precise in situ atmospheric water vapor contents. The accuracy increases to 0.24°C for another ground truth data set with precise in situ atmospheric water vapor contents. The much higher accuracy for this data set confirms the appplicability of the proposed algorithm as an alternative for the accurate LST retrieval from AVHRR data.

Effect of bio-optical parameter variability and uncertainties in reflectance measurements on the remote estimation of chlorophyll-a concentration in turbid productive waters : modeling results

Most algorithms for retrieving chlorophyll-a concentration (Chla) from reflectance spectra assume that bio-optical parameters such as the phytoplankton specific absorption coefficient (aPhi*) or the chlorophyll-a fluorescence quantum yield (eta) are constant. Yet there exist experimental data showing large ranges of variability for these quantities. The main objective of this study was to analyze the sensitivity of two Chla algorithms to variations in bio-optical parameters and to uncertainties in reflectance measurements. These algorithms are specifically designed for turbid productive waters and are based on red and near-infrared reflectances. By means of simulated data, it is shown that the spectral regions where the algorithms are maximally sensitive to Chla overlap those of maximal sensitivity to variations in the above bio-optical parameters. Thus, to increase the accuracy of Chla retrieval, we suggest using spectral regions where the algorithms are less sensitive to Chla, but also less sensitive to these interferences. aPhi* appeared to be one of the most important sources of error for retrieving Chla. However, when the phytoplankton backscattering coefficient (bb,Phi) dominates the total backscattering, as is likely during algal blooms, variations in the specific bb,Phi may introduce large systematic uncertainties in Chla estimation. Also, uncertainties in reflectance measurements, which are due to incomplete atmospheric correction or reflected skylight removal, seem to affect considerably the accuracy of Chla estimation. Instead, variations in other bio-optical parameters, such as eta or the specific backscattering coefficient of total suspended particles, appear to have minor importance. Suggestions regarding the optimal band locations to be used in the above algorithms are finally provided.

Coherence of particulate beam attenuation and backscattering coefficients in diverse open ocean environments

We present an extensive data set of particle attenuation (c(p)), backscattering (b(bp)), and chlorophyll concentration (Chl) from a diverse set of open ocean environments. A consistent observation in the data set is the strong coherence between c(p) and b(bp) and the resulting constancy of the backscattering ratio (0.010 +/- 0.002). The strong covariability between c(p) and b(bp) must be rooted in one or both of two explanations, 1) the size distribution of particles in the ocean is remarkably conserved and particle types responsible for c(p) and b(bp) covary, 2) the same particle types exert influence on both quantities. Therefore, existing relationships between c(p) or Chl:c(p) and phytoplankton biomass and physiological indices can be conceptually extended to the use of b(bp). This finding lends support to use of satellite-derived Chl and b(bp) for investigation of phytoplankton biomass and physiology and broadens the applications of existing ocean color retrievals.

Monitoring phenological cycles of desert ecosystems using NDVI and LST data derived from NOAA-AVHRR imagery

The potential of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) for monitoring phenological cycles in semi-arid lands has been demonstrated in this study. Attention was focused on two areas located only a few kilometres apart but across the political border between the Negev (Israel) and Sinai (Egypt). Although the areas are identical from the pedological, geomorphological, and climatic points of view, due to different land management, the Negev is under a continuous rehabilitation process while Sinai is under a desertification process. Four years of digital data were used to compute the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperatures (LST) over two sampling polygons. The NDVI was used to monitor the vegetation reaction to rainfall, while LST proved to be a good indicator of seasonal climatic fluctuations. Using these biological and physical variables, the potential for following the vegetation dynamics throughout the year was demonstrated. Through cluster analysis, it was shown that the movements of the Sinai desertified side in the LST-NDVI space are only due to seasonal climatic fluctuations. On the Israeli recovered side, on the other hand, three different parts of the annual ecological cycle of the indigenous vegetation are evident: the dry season in which plants reduce their activity, the rainy season, and a growing season characterised by relatively intense biological activity. Within the LST-NDVI space it was also shown that Sinai is positioned similarly to the Sahara biome and the Negev similarly to the Sahel biome. Finally, LST-NDVI data were used to estimate phenological parameters that can be exploited for defining protection policies or, on the long term, for climate change studies.

Remote‐sensing monitoring of desertification, phenology, and droughts

Year‐to‐year fluctuations of rainfall in the northern Negev desert provide an opportunity to characterize and assess the temporal dynamics of desertification, phenology, and drought processes. Such information was retrieved and analyzed by combined use of satellite imageries in the reflectivity and thermal spectral bands. Data covering four years of coarse spatial resolution and images from a high revisit time satellite, namely the NOAA‐14, were used. The images were processed to produce the normalized difference vegetation index (NDVI) and the land surface temperature (LST). These measures were applied to the sand field in the northwestern Negev (Israel), which is almost totally covered by biological soil crusts, and to an adjacent region in Sinai (Egypt), consisting mainly of bare dune sands. Various manipulations of the data were applied. Time series presentation of the NDVI and LST reveals that the NDVI values correspond to the reaction of the vegetation to rainfall and that LST values represent seasonal climatic fluctuation. Scatterplot analysis of LST vs NDVI demonstrates the following: the two different biomes (Sinai and the Negev) exhibit different yearly variation of the phenological patterns (two seasons in Sinai moving along the LST axis, and three seasons in the Negev, where the NDVI axis represents the growing season); the Sinai has an ecosystem similar to that found in the Sahara, while the Negev, only a few kilometers away, has an ecosystem similar to the one found in the Sahel; and drought indicators were derived by using several geometrical expressions based on the two extreme points of the LST‐NDVI scatterplot. The later analysis led to a discrimination function that aims to distinguish between the drought years and the wet years in both biomes. Results from the current study show that a great deal of information on dryland ecosystems can be derived from four, out of five, NOAA/AVHRR spectral bands. The NDVI is derived from the red and the near‐infrared bands and the LST from the two thermal bands. Combined use of these two products provides more information than any product alone.

Particle backscattering as a function of chlorophyll and phytoplankton size structure in the open-ocean

Using an extensive database of in situ observations we present a model that estimates the particle backscattering coefficient as a function of the total chlorophyll concentration in the open-ocean (Case-1 waters). The parameters of the model include a constant background component and the chlorophyll-specific backscattering coefficients associated with small (<20 μm) and large (>20 μm) phytoplankton. The new model performed with similar accuracy when compared with a traditional power-law function, with the additional benefit of providing information on the role of phytoplankton size. The observed spectral-dependency (γ) of model parameters was consistent with past observations, such that γ associated with the small phytoplankton population was higher than that of large phytoplankton. Furthermore, γ associated with the constant background component suggests this component is likely attributed to submicron particles. We envisage that the model would be useful for improving Case-1 ocean-colour models, assimilating light into multi-phytoplankton ecosystem models and improving estimates of phytoplankton size structure from remote sensing.

Carbon export by small particles in the Norwegian Sea

Despite its fundamental role in controlling the Earths climate, present estimates of global organic carbon export to the deep sea are affected by relatively large uncertainties. These uncertainties are due to lack of observations as well as disagreement among methods and assumptions used to estimate carbon export. Complementary observations are thus needed to reduce these uncertainties. Here we show that optical backscattering measured by Bio-Argo floats can detect a seasonal carbon export flux in the Norwegian Sea. This export was most likely due to small particles (i.e., 0.2–20 μm), was comparable to published export values, and contributed to long-term carbon sequestration. Our findings highlight the importance of small particles and of physical mixing in the biological carbon pump and support the use of autonomous platforms as tools to improve our mechanistic understanding of the ocean carbon cycle.

Substantial energy input to the mesopelagic ecosystem from the seasonal mixed-layer pump

The “mesopelagic” is the region of the ocean between about 100 and 1000 m that harbours one of the largest ecosystems and fish stocks on the planet1,2. This vastly unexplored ecosystem is believed to be mostly sustained by chemical energy, in the form of fast-sinking particulate organic carbon, supplied by the biological carbon pump3. Yet, this supply appears insufficient to match mesopelagic metabolic demands4–6. The mixed-layer pump is a physically-driven biogeochemical process7–11 that could further contribute to meet these energetic requirements. However, little is known about the magnitude and spatial distribution of this process at the global scale. Here we show that the mixed-layer pump supplies an important seasonal flux of organic carbon to the mesopelagic. By combining mixed-layer depths from Argo floats with satellite retrievals of particulate organic carbon, we estimate that this pump exports a global flux of about 0.3 Pg C yr−1 (range 0.1 – 0.5 Pg C yr−1). In high-latitude regions where mixed-layers are deep, this flux is on average 23%, but can be greater than 100% of the carbon supplied by fast sinking particles. Our results imply that a relatively large flux of organic carbon is missing from current energy budgets of the mesopelagic.

Uncertainty in Ocean-Color Estimates of Chlorophyll for Phytoplankton Groups

Over the past decade, techniques have been presented to derive the community structure of phytoplankton at synoptic scales using satellite ocean-colour data. There is a growing demand from the ecosystem modelling community to use these products for model evaluation and data assimilation. Yet, from the perspective of an ecosystem modeller these products are of limited use unless: (i) the phytoplankton products provided by the remote-sensing community match those required by the ecosystem modellers; and (ii) information on per-pixel uncertainty is provided to evaluate data quality. Using a large dataset collected in the North Atlantic, we re-tune a method to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size (pico- (20μm)). The method is modified to account for the influence of sea surface temperature, also available from satellite data, on model parameters and on the partitioning of microphytoplankton into diatoms and dinoflagellates, such that the phytoplankton groups provided match those simulated in a state of the art marine ecosystem model (the European Regional Seas Ecosystem Model, ERSEM). The method is validated using another dataset, independent of the data used to parameterise the method, of more than 800 satellite and in situ match-ups. Using fuzzy-logic techniques for deriving per-pixel uncertainty, developed within the ESA Ocean Colour Climate Change Initiative (OC-CCI), the match-up dataset is used to derive the root mean square error and the bias between in situ and satellite estimates of the chlorophyll for each phytoplankton group, for 14 different optical water types (OWT). These values are then used with satellite estimates of OWTs to map uncertainty in chlorophyll on a per pixel basis for each phytoplankton group. It is envisaged these satellite products will be useful for those working on the validation of, and assimilation of data into, marine ecosystem models that simulate different phytoplankton groups.