The impact of shadows on partitioning of radiometric temperature to canopy and soil temperature based on the contextual two-source energy balance model (TSEB-2T)

Authors: ABOUTALEBI, M. - Utah State University; TORRES-RUA, A. - Utah State University; McKee, Lynn; NIETO, H. - Institute De Recerca I Tecnologia Agroalimentaries (IRTA); Kustas, William - Bill; COOPMANS, C. - Utah State University

Submitted to: Society of Photo-Optical Instrumentation Engineers
Publication Type: Proceedings
Publication Acceptance Date: 8/10/2019
Publication Date: 8/30/2019
Citation: Aboutalebi, M., Torres-Rua, A., Mckee, L.G., Nieto, H., Kustas, W.P., Coopmans, C. 2019. The impact of shadows on partitioning of radiometric temperature to canopy and soil temperature based on the contextual two-source energy balance model (TSEB-2T). Society of Photo-Optical Instrumentation Engineers. https://doi.org/10.1117/12.2519685.
DOI: https://doi.org/10.1117/12.2519685

Interpretive Summary: The use of very high-resolution thermal imagery captured by an unmanned aerial vehicles (UAVs) are being used to model crop water use and stress at the plant level. However, at such high resolutions, shadows will affect the relationships used in modeling crop water use and stress. This study evaluates the impact of shadows on the retrieval of canopy and soil temperature data from four UAV images before and after applying shadow compensation techniques over a commercial vineyard located in California as part of the USDA Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) Program during 2014 to 2016. The results of this study show when it is necessary to employ shadow compensation methods to compute reliable crop water use and stress at the plant level. The shadow compensation algorithm will be important tool to incorporate in the application of high-resolution UAV imagery for assessing crop conditions.

Technical Abstract: Tests of the most recent version of the two-source energy balance model have demonstrated that canopy and soil temperatures can be retrieved from high-resolution thermal imagery captured by an unmanned aerial vehicle (UAV). This work has assumed a linear relationship between vegetation indices (VIs) and radiometric temperature in a square grid (i.e., 3.6 m x 3.6 m) that is coarser than the resolution of the imagery acquired by the UAV. In this method, with visible, near infrared (VNIR), and thermal bands available at the same high-resolution, a linear fit can be obtained over the pixels located in a grid, where the x-axis is a vegetation index (VI) and the y-axis is radiometric temperature. Next, with an accurate VI threshold that separates soil and vegetation pixels from one another, the corresponding soil and vegetation temperatures can be extracted from the linear equation. Although this method is simpler than other approaches, such as TSEB with Priestly-Taylor (TSEB-PT), it could be sensitive to VIs and the parameters that affect VIs, such as shadows. Recent studies have revealed that, on average, the values of VIs, such as normalized difference vegetation index (NDVI) and leaf area index (LAI), that are located in sunlit areas are greater than those in shaded areas. This means that involving or compensating for shadows will affect the linear relationship parameters (slope and bias) between radiometric temperature and VI, as well as thresholds that separate soil and vegetation pixels. This study evaluates the impact of shadows on the retrieval of canopy and soil temperature data from four UAV images before and after applying shadow compensation techniques. The retrieved temperatures, using the TSEB-2T approach, both before and after shadow correction, are compared to the average temperature values for both soil and canopy in each grid. The imagery was acquired by the Utah State University AggieAir UAV system over a commercial vineyard located in California as part of the USDA Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program during 2014 to 2016. The results of this study show when it is necessary to employ shadow compensation methods to retrieve vegetation and soil temperature directly.