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A2: ID.10561 CO2 Assessment in Ecosystem
Remote sensing based monitoring of aquatic carbon dynamics; developments of the Dragon3 CarbMonit project
1University of Siena, Italy; 2State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences; 3Institute for Electromagnetic Sensing of the Environment, National Research Council of Italy (CNR);
Inland freshwater ecosystems play an important role in the global carbon cycle, processing a similar amount of carbon as the world’s oceans. Yet, the carbon dynamics of these ecosystems are poorly understood. This is compounded by the strong spatial and temporal heterogeneity in their transformation, transport and capture of carbon from terrestrial sources (allochthonous carbon). Carbon dynamics are regulated by a combination of biotic and abiotic processes: catchment import and export, detritus dynamics, photosynthetic and respiratory processes in the water column and sediment. Climate change and regional development combine to influence many of these processes, including catchment conditions, hydrology and organic matter degradation. The use of spatially extensive approaches is fundamental to explore the key transformation dynamics between organic and inorganic carbon pools.
In the Dragon3 CarbMonit project, leading research institutions in China and Italy worked together to study aquatic carbon dynamics in the shallow lakes of lower Yangtze basin. Optical and biological measurements collected in situ and in mesocosms were used to develop novel algorithms and modelling tools focused on two major organic carbon pools, particulate (POC) and dissolved organic carbon (DOC). Spectral and biochemical samples from a large number of lower Yangtze basin lakes were used to create algorithms for multispectral and hyperspectral data, especially targeting ESA-Copernicus Sentinel missions, now in orbit. The results of these activities were used to assess the generation and loss of aquatic carbon with respect to the dynamics of potential source and sink mechanisms and the occurrence of black water and algal blooms. This led to a series of studies and resultant publications on the horizontal and vertical distribution of carbon components and primary productivity, expanding the current capabilities of remote sensing for such applications.
Among the main drivers to the variability of POC and DOC concentrations, a focus was on the temporal dynamics influenced by climate and sediment factors using moderate spectral resolution satellite sensors to examine temporal dynamics. A novel algorithm was developed to estimate POC concentrations using absorption at 665 nm in a two step process. The approach was developed for multi-spectral resolution satellite sensors (i.e. Sentinel3-OLCI). For DOC, we investigated the hydrological, optical and biological conditions necessary for the production of black water blooms, where massive production of dissolved organic matter leads to increasing CO2 concentrations in surface waters.
In addition, new methods to improve the temporal and spatial resolution of these analyses were implemented and are now in aa validation phase: a novel sub-pixel algal bloom algorithm (algae pixel-growing algorithm, APA), a new classification and regression tree (CART) to determine vertical phytoplankton biomass profile classes, and a new approach to examine interannual changes in particulate organic carbon in turbid ecosystems with high atmospheric effect disturbance.
Satellite-based Estimation of Column-integrated Algal Biomass in Non-algae Bloom Conditions: A Case Study of Lake Chaohu, China
1Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences , Nanjing 210008, China; 2University of Chinese Academy of Sciences, Beijing 100049, China; 3Dipartimento di Biotecnologie, Chimica e Farmacia,University of Siena, CSGI, Via Aldo Moro 2, Siena53100, Italy;
In shallow lakes, algal biomass has served as a fundamental indicator of eutrophication status and total phytoplankton biomass. However, the vertical movements of phytoplankton occurred over short time periods. This has limits the ability to use remotely sensed data to estimate algal biomass. Here, an algorithm mainly based on remotely sensed imageries was developed to estimate the variability of total algal biomass in shallow eutrophic lake. The Baseline Normalized Difference Bloom Index (BNDBI) algorithm was used to derive surface chlorophyll-a concentration data from satellite data. Local water depth was retrieved from hydrological and bathymetry data. These data were combined to calculate total algal biomass in Lake Chaohu, a large shallow lake in eastern China under the non-algae bloom conditions. A spatial and temporal analysis of total algal biomass over an eleven years’ period (2003-2013) indicated that biomass more than doubled between years (2006, 2007) from 19.95t to 39.50t. A monthly decomposition indicates the highest biomass in June while the lowest in April over this decade. In situ measurements of algal biomass were consistent with estimating using remotely sensed reflectance. In addition, both of the satellite-derived results of two periods in the same day and in consecutive days showed highly consistency. Furthermore, this has also indicated the stability and reliability of the algorithm. The availability of long term satellite-derived algal biomass in the non-algae bloom condition would be helpful in the next study about biomass estimation in algae bloom condition and provide effective information to better understand water ecology development.
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Conference: 2016 Dragon 3 Final Results Symposium
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