Estimating leaf index and aboveground biomass of grazing pastures using sentinel-1, sentinel-2 and landsat images

Authors: WANG, JIE - UNIVERSITY OF OKLAHOMA; XIAO, XIANGMING - UNIVERSITY OF OKLAHOMA; BAJGAIN, RAJEN - UNIVERSITY OF OKLAHOMA; STARKS, PATRICK; STEINER, JEAN; DOUGHTY, RUSSELL - UNIVERSITY OF OKLAHOMA; CHANG, QING - UNIVERSITY OF OKLAHOMA

Submitted to: Journal of Photogrammetry and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/16/2019
Publication Date: 6/21/2019
Citation: Wang, J., Xiao, X., Bajgain, R., Starks, P.J., Steiner, J.L., Doughty, R.B., Chang, Q. 2019. Estimating leaf index and aboveground biomass of grazing pastures using sentinel-1, sentinel-2 and landsat images. Journal of Photogrammetry and Remote Sensing. 154:189-201. https://doi.org/10.1016/j.isprsjprs.2019.06.007.
DOI: https://doi.org/10.1016/j.isprsjprs.2019.06.007

Interpretive Summary: In recent years, grassland degradation has accelerated in response to climate extremes such as drought and to increasing human activity. Rangeland and grassland conditions directly affect forage and livestock production and regional grassland resources. In this study, we examined the potential of using synthetic aperture radar (SAR) from the Sentinel-1 satellite alone and in conjunction with optical remote sensing data from the Landsat-8 and Sentinel-2 satellites to monitor the conditions of a native warm-season grass and an improved (Old World bluestem) pasture in Oklahoma, USA. Leaf area index (LAI) and aboveground biomass (AGB) were used to represent the pasture conditions as affected by variations in climate and management (e.g., grazing, fertilizing, etc.). LAI and AGB seasonal dynamics were estimated based on Sentinel-1 (S1), Landsat-8 (LC8) and Sentinel-2 (S2) data, both individually and integrally, using multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) techniques. Results indicated that integration of S1, LC8 and S2 data captured the seasonal dynamics of grasslands at a 10-30-m spatial resolution. The satellite-based LAI and AGB models developed from the ground measurements in 2015 (a wet year) reasonably predicted the grassland dynamics in LAI and AGB in 2016 (a dry year). In comparison, SAR data from S1 was preferable to predict AGB while the optical data from LC8 and S2 better predicted LAI. These results demonstrate the potential of combining S1, LC8 and S2 monitoring tallgrass prairie and pasture to provide timely and accurate data for grassland management.

Technical Abstract: Grassland degradation has accelerated in recent decades in response to changing climate and increasing human activity. Rangeland and grassland conditions directly affect forage and livestock production and regional grassland resources. In this study, we examined the potential of integrating synthetic aperture radar (SAR, Sentinel-1) and optical remote sensing (Landsat-8 and Sentinel-2) data to monitor the conditions of a native warm-season grass and an improved (Old World bluestem) pasture in Oklahoma, USA. Leaf area index (LAI) and aboveground biomass (AGB) were used to represent the pasture conditions as affected by variations in climate and management (e.g., grazing, fertilizing, etc.). LAI and AGB seasonal dynamics were estimated based on Sentinel-1 (S1), Landsat-8 (LC8) and Sentinel-2 (S2) data, both individually and integrally, using multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) techniques. Results indicated that integration of S1, LC8 and S2 data captured the seasonal dynamics of grasslands at a 10-30-m spatial resolution. The satellite-based LAI and AGB models developed from the ground measurements in 2015 (a wet year) reasonably predicted the grassland dynamics in LAI and AGB in 2016 (a dry year). In comparison, SAR data from S1 was preferable to predict AGB while the optical data from LC8 and S2 better predicted LAI. These results demonstrate the potential of combining S1, LC8 and S2 monitoring tallgrass prairie and pasture to provide timely and accurate data for grassland management.