Quantifying the Potential Contribution of Submerged Aquatic Vegetation to Coastal Carbon Capture in a Delta System from Field and Landsat 8/9-Operational Land Imager (OLI) Data with Deep Convolutional Neural Network

MLA

Bingqing Liu, et al. “Quantifying the Potential Contribution of Submerged Aquatic Vegetation to Coastal Carbon Capture in a Delta System from Field and Landsat 8/9-Operational Land Imager (OLI) Data with Deep Convolutional Neural Network.” Remote Sensing, vol. 15, no. 15, July 2023, p. 3765. EBSCOhost, https://doi.org/10.3390/rs15153765.

APA

Bingqing Liu, Tom Sevick, Hoonshin Jung, Erin Kiskaddon, & Tim Carruthers. (2023). Quantifying the Potential Contribution of Submerged Aquatic Vegetation to Coastal Carbon Capture in a Delta System from Field and Landsat 8/9-Operational Land Imager (OLI) Data with Deep Convolutional Neural Network. Remote Sensing, 15(15), 3765. https://doi.org/10.3390/rs15153765

Chicago

Bingqing Liu, Tom Sevick, Hoonshin Jung, Erin Kiskaddon, and Tim Carruthers. 2023. “Quantifying the Potential Contribution of Submerged Aquatic Vegetation to Coastal Carbon Capture in a Delta System from Field and Landsat 8/9-Operational Land Imager (OLI) Data with Deep Convolutional Neural Network.” Remote Sensing 15 (15): 3765. doi:10.3390/rs15153765.