Estimating South African maize biomass using integrated high-resolution UAV and sentinel 1 and 2 datasets

Naidoo, LavenMain, Russell SCho, Moses AMadonsela, SabeloMajozi, Nobuhle, P2022-05-042022-05-042021-07Naidoo, L., Main, R., Cho, M.A., Madonsela, S. & Majozi, N. 2021. Estimating South African maize biomass using integrated high-resolution UAV and sentinel 1 and 2 datasets. http://hdl.handle.net/10204/12390 .978-1-6654-0369-6978-1-6654-0368-92153-70032153-6996DOI: 10.1109/IGARSS47720.2021.9554261http://hdl.handle.net/10204/12390Sentinel-1 and Sentinel-2 have provided consistent hyper-temporal information (5–7 days or earlier) at high spatial resolutions (10m) on biophysical composition, structural and physiological conditions of crops in a variety of environments. Unmanned aerial vehicles (UAVs) can provide sufficient calibration and validation data for model upscaling and regional extrapolation. Of the numerous maize crop parameters which require regular and accurate modelling, maize above ground biomass (AGB) is important for yield estimates. The aim of this study was to evaluate the Random Forest modelling performance of Sentinel 1 SAR C-band and Sentinel 2 multispectral imagery for maize AGB estimation whilst utilising UAV-derived maize AGB for model upscaling. Results illustrated that Sentinel 2 reflectance bands predicted more accurate estimates of maize AGB than the VV and VH polarisation bands of Sentinel 1 (R2 = 0.91; RMSE = 355.11g/m 2 ; rRMSE = 21.28% versus R2 = 0.31; RMSE = 974.72g/m 2 ; rRMSE = 59.04%).AbstractenBiomassCropsBiological system modellingReflectivityGeoscience and remote sensingUnmanned Aerial VehiclesEstimating South African maize biomass using integrated high-resolution UAV and sentinel 1 and 2 datasetsConference PresentationNaidoo, L., Main, R., Cho, M. A., Madonsela, S., & Majozi, N. (2021). Estimating South African maize biomass using integrated high-resolution UAV and sentinel 1 and 2 datasets. http://hdl.handle.net/10204/12390Naidoo, Laven, Russell Main, Moses A Cho, Sabelo Madonsela, and Nobuhle Majozi. "Estimating South African maize biomass using integrated high-resolution UAV and sentinel 1 and 2 datasets." <i>2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 11-16 July 2021</i> (2021): http://hdl.handle.net/10204/12390Naidoo L, Main R, Cho MA, Madonsela S, Majozi N, Estimating South African maize biomass using integrated high-resolution UAV and sentinel 1 and 2 datasets; 2021. http://hdl.handle.net/10204/12390 .TY - Conference Presentation AU - Naidoo, Laven AU - Main, Russell, S AU - Cho, Moses A AU - Madonsela, Sabelo AU - Majozi, Nobuhle, P AB - Sentinel-1 and Sentinel-2 have provided consistent hyper-temporal information (5–7 days or earlier) at high spatial resolutions (10m) on biophysical composition, structural and physiological conditions of crops in a variety of environments. Unmanned aerial vehicles (UAVs) can provide sufficient calibration and validation data for model upscaling and regional extrapolation. Of the numerous maize crop parameters which require regular and accurate modelling, maize above ground biomass (AGB) is important for yield estimates. The aim of this study was to evaluate the Random Forest modelling performance of Sentinel 1 SAR C-band and Sentinel 2 multispectral imagery for maize AGB estimation whilst utilising UAV-derived maize AGB for model upscaling. Results illustrated that Sentinel 2 reflectance bands predicted more accurate estimates of maize AGB than the VV and VH polarisation bands of Sentinel 1 (R2 = 0.91; RMSE = 355.11g/m 2 ; rRMSE = 21.28% versus R2 = 0.31; RMSE = 974.72g/m 2 ; rRMSE = 59.04%). DA - 2021-07 DB - ResearchSpace DP - CSIR J1 - 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 11-16 July 2021 KW - Biomass KW - Crops KW - Biological system modelling KW - Reflectivity KW - Geoscience and remote sensing KW - Unmanned Aerial Vehicles LK - https://researchspace.csir.co.za PY - 2021 SM - 978-1-6654-0369-6 SM - 978-1-6654-0368-9 SM - 2153-7003 SM - 2153-6996 T1 - Estimating South African maize biomass using integrated high-resolution UAV and sentinel 1 and 2 datasets TI - Estimating South African maize biomass using integrated high-resolution UAV and sentinel 1 and 2 datasets UR - http://hdl.handle.net/10204/12390 ER -25535