Retrieval of canopy component temperatures through Bayesian inversion of directional thermal measurements

Abstract

Abstract. Evapotranspiration is usually estimated in remote sensing from single temperature value representing both soil and vegetation. This surface temperature is an aggregate over multiple canopy components. The temperature of the individual components can differ significantly, introducing errors in the evapotranspiration estimations. The temperature aggregate has a high level of directionality. An inversion method is presented in this paper to retrieve four canopy component temperatures from directional brightness temperatures. The Bayesian method uses both a priori information and sensor characteristics to solve the ill-posed inversion problem. The method is tested using two case studies: 1) a sensitivity analysis, using a large forward simulated dataset, and 2) in a reality study, using two datasets of two field campaigns. The results of the sensitivity analysis show that the Bayesian approach is able to retrieve the four component temperatures from directional brightness temperatures with good success rates using multi-directional sensors (S spectra r ≈0.3, S gonio r ≈0.3, and S AATSR r ≈0.5), and no improvement using monoangular sensors (S r ≈1). The results of the experimental study show that the approach gives good results for high LAI values (RMSE grass =0.50 K, RMSE wheat =0.29 K, RMSE sugar beet =0.75 K, RMSE barley =0.67 K); but for low LAI values the results were unsatisfactory (RMSE young maize =2.85 K). This discrepancy was found to originate from the presence of the metallic construction of the setup. As these disturbances, were only present for two Correspondence to: J. Timmermans (timmermans@itc.nl) crops and were not present in the sensitivity analysis, which had a low LAI, it is concluded that using masked thermal images will eliminate this discrepancy.

Temporal patterns of thermal emission directionality of crop canopies

“…Chehbouni et al [2001a] found that accurate dual angles of measured directional radiative temperatures could be used to estimate sensible heat flux of sparse vegetation canopies. Timmermans et al [2009] applied an inversion method to retrieve four canopy component temperatures from directional brightness temperatures.…”

A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index

“…Many optimized algorithms, such as least-square, Bayes, and genetic algorithms have been proposed to improve the inversion results [16,[29][30][31]. Since only two observed angles are used and direct results can be clear, we do not use an optimization algorithm in this paper.…”

“…Using these RTMs, the temperatures of surface sunlit and shaded areas and their vertical distributions can be inverted. For instance, Timmermans et al [16] retrieved four-component (sunlit and shaded leaves and sunlit and shaded soil) temperatures using the four-stream scattering by arbitrarily inclined leaves (4SAIL) model. Colaizzi et al [22] proposed a radiometer footprint model to estimate sunlit and shaded components for row crops.…”

“…Previous efforts have been directed toward inverting component temperatures using both experimental and satellite-borne multi-angle datasets [16][17][18][19]. Many directional algorithms have been proposed based on radiative transfer models (RTMs), such as the geometric model proposed by Kimes et al [20] for row-planted crops and the analytical RTM known as FR97 proposed by Francois et al [21] for homogenous canopies.…”

“…Multi-angle remote sensing has been identified as a useful tool for the separation of leaf and soil temperatures [16,17]. Previous efforts have been directed toward inverting component temperatures using both experimental and satellite-borne multi-angle datasets [16][17][18][19].…”