Location: Hydrology and Remote Sensing LaboratoryTitle: Evaluation of TSEB turbulent fluxes using different methods for the retrieval of soil and canopy component temperatures from UAV thermal and multispectral imagery
|NIETO, H. - Institute De Recerca I Tecnologia Agroalimentaries (IRTA)|
|Kustas, William - Bill|
|TORRES, A. - Utah State University|
|White, William - Alex|
|SONG, L. - Southwest University|
|ALSINA, M. - E & J Gallo Winery|
Submitted to: Irrigation Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/28/2018
Publication Date: 9/14/2018
Publication URL: http://handle.nal.usda.gov/10113/6456687
Citation: Nieto, H., Kustas, W.P., Torres, A., Alfieri, J.G., Gao, F.N., Anderson, M.C., White, W.A., Song, L., Alsina, M., Prueger, J.H., McKee, L.G. 2018. Evaluation of TSEB turbulent fluxes using different methods for the retrieval of soil and canopy component temperatures from UAV thermal and multispectral imagery. Irrigation Science. https://doi.org/10.1007/s00271-018-0585-9.
Interpretive Summary: The use of unmanned aerial vehicle (UAV) technology for estimating water use and crop stress has been gaining much interest in recent years with the tremendous increase in the availability of UAVs and advancement in sensor technology that supports UAV platforms. Their very high resolution data can provide estimates of both leaf canopy temperatures and background soil/ground cover temperatures. This information is important particularly for high valued crops, such as vineyards as well as orchards, in order to identify levels of plant stress and how stress varies at the vine/tree level over a field. Water deficits, nutrient deficiencies or disease/pest infestation, which all lead to plant stress, can be detected from elevated plant temperatures that deviate from the surrounding field. This allows for precision agricultural methods such as variable rate application of water, nutrients or fungicide/pesticide within a field. Very high spatial resolution remote sensing data from a UAV were collected over vineyards in California, as part of the Grape Remote sensing and Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) and used in four different modeling approaches to estimate the component soil and plant canopy temperatures, and evpotranspiration partitioning between soil and vegetation. The results showed that a simple contextual algorithm based on the inverse relationship of a vegetation index and land surface temperature to derive soil and canopy temperatures yielded the closest agreement with evapotranspiration measurements. The utility in very high resolution remote sensing data for estimating evpotranspiration and partitioning into soil and plant contributions provides unique information concerning crop water use and stress useful for sub-field scale management that satellite observations are unable to provide for precision agriculture applications.
Technical Abstract: The thermal-based Two Source Energy Balance (TSEB) model partitions the evapotranspiration (ET) and energy fluxes from vegetation and soil components providing the capability for estimating soil evaporation (E) and canopy transpiration (T). However, it is crucial for ET partitioning to retrieve reliable estimates of canopy and soil temperatures and net radiation, as the latter determines the available energy for water and heat exchange from soil and canopy sources. These two factors become especially relevant in row crops with wide spacing and strongly clumped vegetation such as vineyards and orchards. To better understand these effects, very high spatial resolution remote sensing data from an Unmanned Aerial Vehicle (UAV) were collected over vineyards in California, as part of the Grape Remote sensing and Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) and used in four different TSEB approaches to estimate the component soil and canopy temperatures, and ET partitioning between soil and canopy. Two approaches rely on the use of composite T_rad , and assumes initially that the canopy transpires at the Priestley-Taylor potential rate. The other two algorithms are based on the contextual relationship between optical and thermal imagery to derive soil and canopy temperatures. The results showed that a simple contextual algorithm based on the inverse relationship of a vegetation index and T_rad to derive soil and canopy temperatures yielded the closest agreement with flux tower measurements. The utility in very high resolution remote sensing data for estimating ET and E and T partitioning at the canopy level is also discussed