Retrieval of Salt Marsh Above-Ground Biomass from High-Spatial Resolution Hyperspectral Imagery Using PROSAIL

Abstract

Salt marsh vegetation density varies considerably on short spatial scales, complicating attempts to evaluate plant characteristics using airborne remote sensing approaches. In this study, we used a mast-mounted hyperspectral imaging system to obtain cm-scale imagery of a salt marsh chronosequence on Hog Island, VA, where the morphology and biomass of the dominant plant species, Spartina alterniflora, varies widely. The high-resolution hyperspectral imagery allowed the detailed delineation of variations in above-ground biomass, which we retrieved from the imagery using the PROSAIL radiative transfer model. The retrieved biomass estimates correlated well with contemporaneously collected in situ biomass ground truth data ( R 2 = 0.73 ). In this study, we also rescaled our hyperspectral imagery and retrieved PROSAIL salt marsh biomass to determine the applicability of the method across spatial scales. Histograms of retrieved biomass changed considerably in characteristic marsh regions as the spatial scale of the imagery was progressively degraded. This rescaling revealed a loss of spatial detail and a shift in the mean retrieved biomass. This shift is indicative of the loss of accuracy that may occur when scaling up through a simple averaging approach that does not account for the detail found in the landscape at the natural scale of variation of the salt marsh system. This illustrated the importance of developing methodologies to appropriately scale results from very fine scale resolution up to the more coarse-scale resolutions commonly obtained in airborne and satellite remote sensing.

Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

“…This study was part of a broader field campaign conducted in summers of 2018 and 2019 to study the beach and intertidal zone sediments 33 as well as to characterize and map the salt marsh ecosystem 69,70 in the southern portion of Hog island. The data collected for this study took place specifically along the shore on the southern tip of the island.…”

Model Sensitivity to Topographic Uncertainty in Meso- and Microtidal Marshes

“…Third, the standing water or wet substrates often present on the marsh surface that absorbs the laser pulse [44]. Fourth, the heterogeneity of above-ground biomass density on small spatial scales [47] can bias the results up to an amount equal to the difference between the synoptic water level and the local ground surface elevation. On hard flat surfaces, often referred to as "open terrain," lidar typically has a root-mean-square error (RMSE) in vertical elevation of less than 10 cm; however, the accepted value for vertical RMSE over all land cover classes is 15 cm.…”

Integration of APSIM and PROSAIL models to develop more precise radiometric estimation of crop traits using deep learning

“…Model inversion methods are generally subdivided into three sub-categories: numerical optimization approach (e.g., Bacour et al, 2002; Eon et al, 2019; Lunagaria and Patel, 2019), look-up table approach (e.g., Weiss et al, 2000; X. Xu et al, 2019; Zhu et al, 2019), machine learning approach including use of neural networks (e.g., Bacour et al, 2006; García-Haro et al, 2018; Upreti et al, 2019).…”