Advances in the Two Source Energy Balance (TSEB) model using very high resolution remote sensing data in vineyards

Wednesday, 16 December 2015: 12:05
3022 (Moscone West)
Hector Nieto Solana1,2, William P Kustas3, Alfonso F Torres-Rua4, Manal ELarab4, Lisheng Song5, Joseph G Alfieri6, John H Prueger7, Lynn McKee7, Martha C. Anderson8, Maria Mar Alsina9, Austin Jensen4 and Mac McKee4, (1)IAS-CSIC, Laboratory for Research Methods in Quantitative Remote Sensing, Cordoba, Spain, (2)USDA Beltsville Agricultural Research Center, Hydrology and Remote Sensing Lab, Beltsville, MD, United States, (3)USDA Agricultural Research Service New England Plant, Soil and Water Research Laboratory, East Wareham, MA, United States, (4)Utah State University, Logan, UT, United States, (5)Beijing Normal University, Beijing, China, (6)USDA Beltsville Agricultural Research Center, Beltsville, MD, United States, (7)USDA ARS, Beltsville, MD, United States, (8)USDA ARS, Pendleton, OR, United States, (9)E & J Gallo Winery, Modesto, CA, United States
The thermal-based Two Source Energy Balance (TSEB) model partitions the water and energy fluxes from vegetation and soil components providing thus the ability for estimating soil evaporation (E) and canopy transpiration (T) separately. However, it is crucial for ET partitioning to retrieve reliable estimates of canopy and soil temperatures as well as the net radiation partitioning (ΔRn), as the latter determines the available energy for water and heat exchange from soil and canopy sources. These two factors become especially relevant in agricultural areas, with vegetation clumped along rows and hence only partially covering the soil surface for much of the growing season. The effects on radiation and temperature partitioning is extreme for vineyards and orchards, where there is often significant separation between plants, resulting in strongly clumped vegetation with significant fraction of bare soil/substrate. To better understand the effects of strongly clumped vegetation on radiation and Land Surface Temperature (LST) partitioning very high spatial resolution remote sensing data acquired from an Unmanned Aerial System (UAS) were collected over vineyards in Califronia, as part of the Grape Remote sensing and Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX).

The multi-temporal observations from the UAS and very high pixel resolution permitted the estimation of reliable soil and leaf temperatures using a contextual algorithm based on the inverse relationship between LST and a vegetation index. An improvement in the algorithm estimating the effective leaf area index explicitly developed for vine rows and ΔRn using the 4SAIL Radiative Transfer Model is as well developed. The revisions to the TSEB model are evaluated with in situ measurements of energy fluxes and transmitted solar radiation. Results show that the modifications to the TSEB resulted in closer agreement with the flux tower measurements compared to the original TSEB model formulations. The significant advantages in using very high resolution remote sensing data for ET monitoring in agricultural regions having strongly clumped vegetation will be discussed.