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Free keywords:
PROSAIL
Gaussian Processes
Unmanned Aerial Systems
Anthropogenic Influence
Time Lag
Leaf Area Index
LAI
Irrigation
Grazing
Mowing
Abstract:
The monitoring of soil moisture content (SMC) at very high spatial resolution (<10 m) using unmanned aerial
systems (UAS) is of high interest for precision agriculture and the validation of large scale SMC products. Data-
driven approaches are the most common method to retrieve SMC with UAS-borne data at water limited sites over
non-disturbed agricultural crops. A major disadvantage of data-driven algorithms is the limited transferability in
space and time and the need of a high number of ground reference samples. Physically-based approaches are less
dependent on the amount of samples and are transferable in space and time. This study explores the potential of
(1) a hybrid method targeting the soil brightness factor of the PROSAIL model using a variational heteroscedastic
Gaussian Processes regression (VHGPR) algorithm, and (2) a data-driven method employing VHGPR for the
retrieval of SMC over three grassland sites based on UAS-borne VIS–NIR (399–1001 nm) hyperspectral data. The
sites were managed by mowing (Fendt), grazing (Grosses Bruch) and irrigation (Marquardt). With these distinct
local pre-conditions we aimed to identify factors that favor and limit the retrieval of SMC.
The hybrid approach presented encouraging results in Marquardt (RMSE = 1.5 Vol_%, R2 = 0.2). At the
permanent grassland sites (Fendt, Grosses Bruch) the thatch layer jeopardized the application of the hybrid
model. We identified the complex canopy structure of grassland as the main factor impacting the hybrid SMC
retrieval. The data-driven approach showed high accuracy for Fendt (R2 = 0.84, RMSE = 8.66) and Marquardt
(R2 = 0.4, RMSE = 10.52). All data-driven models build on the LAI-SMC relationship. However, this relationship
was hampered by mowing (Fendt), leading to a lack of transferability in time. The alteration of plant traits by
grazing prevents finding a relationship with SMC in Grosses Bruch. In Marquardt, we identified the timelag
between changes in SMC and plant response as the main reason of decrease in model accuracy. Yet, the model
performance is accurate in undisturbed and water-limited areas (Marquardt). The analysis points to challenges
that need to be tackled in future research and opens the discussion for the development of robust models to
retrieve high resolution SMC from UAS-borne remote sensing observations.
