Carbon Inventory
Carbon inventory/Organic and phytoplankton carbon stocks in upper ocean
Marine organic carbon can be divided into DOC and POC. Changes in DOC and POC reserves are closely related to marine carbon cycle processes such as land-source input, marine biological activities, POC sedimentation, and DOC transport to deep oceans. Changes in carbon storage can also cause changes in carbon sink flux. There is a close relationship between POC sink flux at certain depths in many sea areas and the POC reserves or primary productivity in the upper layer. Therefore, studies on organic carbon reserves in the ocean are important.
Remote sensing-based estimates of organic carbon storage can be obtained by integrating the surface organic carbon concentration with the vertical distribution of water column organic carbon at a certain depth (generally 100 m or the euphotic layer). The general ideas behind the algorithm are to: 1) use spectral fitting or intrinsic optical quantity semi-analytical methods to obtain the surface organic carbon concentration; 2) construct vertical profile models of POC or DOC in the study area; 3) identify different water body types based on remote sensing surface information; and, 4) use the water column integral calculation to obtain remote sensing estimates of organic carbon storage at certain depths.
Regarding POC reserves, Liu (2013) used sea surface temperature (SST), sea surface salinity (SSS), and total suspended matter (TSM) to determine the vertical distribution type of POC (exponential decay model, Gaussian distribution model, and mixed model) and, combined with sea surface POC concentration inversion, to estimate the East China Sea POC reserves. Cui (2017) used the water depth to mixed-layer depth (depth/Zmld) ratio to identify three types of POC in the northern South China Sea and estimate the POC reserves of the euphotic layer.
Research on remote sensing estimates of DOC reserves remains rare, but some studies on the vertical distributions of DOC in marginal seas have been reported. Liu et al. (2014) proposed five different DOC vertical distribution models, including vertical mixing distribution, Gaussian distribution, and exponential decay distribution, based on measured data in four seasons in the East China Sea, and established a judging method of different water types based on SST and SSS data. Finally, a remote sensing model of DOC reserves in the East China Sea was established for the first time, in combination with surface DOC concentration and a vertical profile model.
Representative articles：
Liu, Q., Pan, D., Bai, Y., Wu, K., Chen, C. T. A., Liu, Z., & Zhang, L. (2015). Estimating dissolved organic carbon inventories in the East China Sea using remote‐sensing data. Journal of Geophysical Research Oceans, 119(10), 6557-6574.
Liu, Q., Pan, D., Bai, Y., Wu, K., Chen, C. A., Sun, J., & Zhang, L. (2013). The satellite reversion of dissolved organic carbon （DOC） based on the analysis of the mixing behavior of DOC and colored dissolved organic matter： the East China Sea as an example. Acta Oceanologica Sinica, 32(2), 1-11.
Li, T., Bai, Y., Li, G., He, X., Chen, C. A., Gao, K., & Liu, D. (2015). Effects of ultraviolet radiation on marine primary production with reference to satellite remote sensing. Frontiers of Earth Science, 9(2), 237-247.
Pan, D., Guan, W., Yan, B., & Huang, H. (2005). Ocean primary productivity estimation of China Sea by remote sensing. Progress In Natural Science, 15(7), 627-632.
Remote sensing-based estimates of organic carbon storage can be obtained by integrating the surface organic carbon concentration with the vertical distribution of water column organic carbon at a certain depth (generally 100 m or the euphotic layer). The general ideas behind the algorithm are to: 1) use spectral fitting or intrinsic optical quantity semi-analytical methods to obtain the surface organic carbon concentration; 2) construct vertical profile models of POC or DOC in the study area; 3) identify different water body types based on remote sensing surface information; and, 4) use the water column integral calculation to obtain remote sensing estimates of organic carbon storage at certain depths.
Regarding POC reserves, Liu (2013) used sea surface temperature (SST), sea surface salinity (SSS), and total suspended matter (TSM) to determine the vertical distribution type of POC (exponential decay model, Gaussian distribution model, and mixed model) and, combined with sea surface POC concentration inversion, to estimate the East China Sea POC reserves. Cui (2017) used the water depth to mixed-layer depth (depth/Zmld) ratio to identify three types of POC in the northern South China Sea and estimate the POC reserves of the euphotic layer.
Research on remote sensing estimates of DOC reserves remains rare, but some studies on the vertical distributions of DOC in marginal seas have been reported. Liu et al. (2014) proposed five different DOC vertical distribution models, including vertical mixing distribution, Gaussian distribution, and exponential decay distribution, based on measured data in four seasons in the East China Sea, and established a judging method of different water types based on SST and SSS data. Finally, a remote sensing model of DOC reserves in the East China Sea was established for the first time, in combination with surface DOC concentration and a vertical profile model.
Representative articles：
Liu, Q., Pan, D., Bai, Y., Wu, K., Chen, C. T. A., Liu, Z., & Zhang, L. (2015). Estimating dissolved organic carbon inventories in the East China Sea using remote‐sensing data. Journal of Geophysical Research Oceans, 119(10), 6557-6574.
Liu, Q., Pan, D., Bai, Y., Wu, K., Chen, C. A., Sun, J., & Zhang, L. (2013). The satellite reversion of dissolved organic carbon （DOC） based on the analysis of the mixing behavior of DOC and colored dissolved organic matter： the East China Sea as an example. Acta Oceanologica Sinica, 32(2), 1-11.
Li, T., Bai, Y., Li, G., He, X., Chen, C. A., Gao, K., & Liu, D. (2015). Effects of ultraviolet radiation on marine primary production with reference to satellite remote sensing. Frontiers of Earth Science, 9(2), 237-247.
Pan, D., Guan, W., Yan, B., & Huang, H. (2005). Ocean primary productivity estimation of China Sea by remote sensing. Progress In Natural Science, 15(7), 627-632.
