Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Authors

Jie Wang, University of Oklahoma
Xiangming Xiao, University of OklahomaFollow
Rajen Bajgain, University of Oklahoma
Patrick Starks, USDA-ARS Grazinglands Research Laboratory
Jean Steiner, USDA-ARS Grazinglands Research Laboratory
Russell B. Doughty, University of Oklahoma
Qing Chang, University of Oklahoma

Date of this Version

6-21-2019

Citation

U.S. Government Work

Comments

https://doi.org/10.1016/j.isprsjprs.2019.06.007 ISPRS Journal of Photogrammetry and Remote Sensing 154 (2019) 189–201

Abstract

Grassland degradation has accelerated in recent decades in response to increased climate variability and human activity. Rangeland and grassland conditions directly affect forage quality, 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 pasture and an introduced pasture in Oklahoma, USA. Leaf area index (LAI) and aboveground biomass (AGB) were used as indicators of pasture conditions under varying climate and human activities. We estimated the seasonal dynamics of LAI and AGB using Sentinel-1 (S1), Landsat-8 (LC8), and Sentinel-2 (S2) data, both individually and integrally, applying three widely used algorithms: Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). Results indicated that integration of LC8 and S2 data provided sufficient data to capture the seasonal dynamics of grasslands at a 10–30-m spatial resolution and improved assessments of critical phenology stages in both pluvial and dry years. The satellite-based LAI and AGB models developed from ground measurements in 2015 reasonably predicted the seasonal dynamics and spatial heterogeneity of LAI and AGB in 2016. By comparison, the integration of S1, LC8, and S2 has the potential to improve the estimation of LAI and AGB more than 30% relative to the performance of S1 at low vegetation cover (LAI < 2m2/m2, AGB < 500 g/m2) and optical data of LC8 and S2 at high vegetation cover (LAI > 2m2/m2, AGB > 500 g/m2). These results demonstrate the potential of combining S1, LC8, and S2 monitoring grazing tallgrass prairie to provide timely and accurate data for grassland management.