Large Area Aboveground Biomass and Carbon Stock Mapping in Woodlands in Mozambique with L-Band Radar: Improving Accuracy by Accounting for Soil Moisture Effects Using the Water Cloud Model

Yaqing Gou,Johannes Reiche,Casey M Ryan

doi:10.3390/rs14020404

Yaqing Gou, Johannes Reiche + Show 1 more

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https://doi.org/10.3390/rs14020404

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Abstract

Soil moisture effects limit radar-based aboveground biomass carbon (AGBC) prediction accuracy as well as lead to stripes between adjacent paths in regional mosaics due to varying soil moisture conditions on different acquisition dates. In this study, we utilised the semi-empirical water cloud model (WCM) to account for backscattering from soil moisture in AGBC retrieval from L-band radar imagery in central Mozambique, where woodland ecosystems dominate. Cross-validation results suggest that (1) the standard WCM effectively accounts for soil moisture effects, especially for areas with AGBC ≤ 20 tC/ha, and (2) the standard WCM significantly improved the quality of regional AGBC mosaics by reducing the stripes between adjacent paths caused by the difference in soil moisture conditions between different acquisition dates. By applying the standard WCM, the difference in mean predicted AGBC for the tested path with the largest soil moisture difference was reduced by 18.6%. The WCM is a valuable tool for AGBC mapping by reducing prediction uncertainties and striping effects in regional mosaics, especially in low-biomass areas including African woodlands and other woodland and savanna regions. It is repeatable for recent L-band data including ALOS-2 PALSAR-2, and upcoming SAOCOM and NISAR data.

Highlights

Compared to WCM0, which does not account for soil moisture scattering, WCMstd and
WCMk, and 12 tC/ha when WCMstd was applied. This effect from soil moisture decreased as aboveground biomass carbon (AGBC) increased, but still existed within the entire range of the in situ AGBC values
Results from this study suggested that AGBC is a good proxy for vegetation scattering (σ ) in the water cloud model (WCM)

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Radar remote sensing from a space-borne platform provides the possibility of analysing woody aboveground biomass carbon (AGBC) from the local to global scale in a consistent manner and at high resolution [1]. The ability of radar to estimate AGBC follows from the fundamental physical relationship between the number of woody elements of characteristic size and the amount of energy returned to the radar antennae [2,3,4,5,6,7]. Empirical and semi-empirical relationships between radar cross section and forest canopies have been established for the retrieval of AGBC in varying ecosystems, in particular using long-wavelength radar, for example, from the Japanese Phased Array type L-band

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