Using radiative transfer models for mapping soil moisture content under grassland with UAS-borne hyperspectral data

Veronika U. Döpper; Alby Duarte Rocha; Tobias Gränzig; Birgit Kleinschmit; Michael Förster

doi:10.1117/12.2600296

12 September 2021 Using radiative transfer models for mapping soil moisture content under grassland with UAS-borne hyperspectral data

Veronika U. Döpper, Alby Duarte Rocha, Tobias Gränzig, Birgit Kleinschmit, Michael Förster

Author Affiliations +

Proceedings Volume 11856, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII; 1185610 (2021) https://doi.org/10.1117/12.2600296
Event: SPIE Remote Sensing, 2021, Online Only

Abstract

Soil moisture content (SMC) is a key parameter of environmental processes. Remote sensing provides effective methods for mapping SMC at different spatial resolutions. Using UAS-borne hyperspectral observations enables a SMC retrieval at sub-meter scales. Radiative transfer models (RTMs) such as ProSAIL or Scope include a SMC specific input variable and are thus a potential tool to derive SMC and avoiding extensive reference SMC measurements. The inverse application of RTMs supplies information on SMC and plant traits. Scope and ProSAIL involve SMC data of the root zone and at the surface, respectively. The combined use of both models offers the possibility to derive SMC at two vertical depths. Moreover, SMC relevant vegetation proxies such as leaf water content can be retrieved and alternatively used as indicator for SMC. Such plant traits are highest correlated to SMC at depths of major water uptake. However, their response can have a significant time-lag. We analyze the derivation of SMC at the soil surface and at the root zone using the SMC parameters within existing RTMs. As a first step, we investigate on the sensitivity of ProSAIL and Scope to their soil moisture parameters. We apply these findings on UAS-borne hyperspectral and TIR imagery acquired over a pre-alpine TERENO grassland area. The site is equipped with a SoilNet that measures SMC at different depths. Using this data, we assess the vertical extent of both soil moisture content parameters. By inverse modelling of the vegetation parameters and the use of the temporally continuous SoilNet data at root zone level, we analyze the time-lag between changes in SMC and the corresponding plant trait response to optimize the retrieval of SMC.

Conference Presentation

Citation Download Citation

Veronika U. Döpper, Alby Duarte Rocha, Tobias Gränzig, Birgit Kleinschmit, and Michael Förster "Using radiative transfer models for mapping soil moisture content under grassland with UAS-borne hyperspectral data", Proc. SPIE 11856, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII, 1185610 (12 September 2021); https://doi.org/10.1117/12.2600296

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available