Estimation of Land Surface Energy Fluxes from Remote Sensing Using One-Layer Modeling Approaches

Author(s):  
Weiqiang Ma ◽  
Yaoming Ma ◽  
Hirohiko Ishikawa ◽  
Zhongbo Su
2009 ◽  
Vol 33 (2) ◽  
pp. 224-250 ◽  
Author(s):  
G. Petropoulos ◽  
T.N. Carlson ◽  
M.J. Wooster ◽  
S. Islam

Imagery from remote sensing systems, often combined with ancillary ground information, is able to provide repetitive, synoptic views of key parameters characterizing land surface interactions, including surface energy fluxes and surface soil moisture. Differing methodologies using a wide range of remote sensing data have been developed for this purpose. Approaches vary from purely empirical to more complex ones, including residual methods and those that have their basis in the biophysical properties characterizing a two-dimensional Ts/VI (surface temperature/ vegetation index) scatterplot domain derived from remote sensing observations. The present article aims to offer a comprehensive and systematic review of this latter group of methods, which differ in terms of the complexity and assumptions they entail as well as their requirement for field-based and other ancillary data. Prior to the review, the biophysical meanings and properties encapsulated in the Ts/VI feature space is elucidated, since these represent the building block upon which all the Ts/VI methods described herein are based. The overview of the Ts/VI methods is also very timely, as one such method is being scheduled in the operational retrieval of surface soil moisture content by the National Polar-orbiting Operational Environmental Satellite System (NPOESS), in a series of satellite platforms due to be launched in the next 12 years starting from 2016.


2014 ◽  
Vol 11 (18) ◽  
pp. 5021-5046 ◽  
Author(s):  
R. Guzinski ◽  
H. Nieto ◽  
R. Jensen ◽  
G. Mendiguren

Abstract. In this study we evaluate a methodology for disaggregating land surface energy fluxes estimated with the Two-Source Energy Balance (TSEB)-based Dual-Temperature Difference (DTD) model which uses day and night polar orbiting satellite observations of land surface temperature (LST) as a remotely sensed input. The DTD model is run with MODIS input data at a spatial resolution of around 1 km while the disaggregation uses Landsat observations to produce fluxes at a nominal spatial resolution of 30 m. The higher-resolution modelled fluxes can be directly compared against eddy covariance (EC)-based flux tower measurements to ensure more accurate model validation and also provide a better visualization of the fluxes' spatial patterns in heterogeneous areas allowing for development of, for example, more efficient irrigation practices. The disaggregation technique is evaluated in an area covered by the Danish Hydrological Observatory (HOBE), in the west of the Jutland peninsula, and the modelled fluxes are compared against measurements from two flux towers: the first one in a heterogeneous agricultural landscape and the second one in a homogeneous conifer plantation. The results indicate that the coarse-resolution DTD fluxes disaggregated at Landsat scale have greatly improved accuracy as compared to high-resolution fluxes derived directly with Landsat data without the disaggregation. At the agricultural site the disaggregated fluxes display small bias and very high correlation (r ≈ 0.95) with EC-based measurements, while at the plantation site the results are encouraging but still with significant errors. In addition, we introduce a~modification to the DTD model by replacing the "parallel" configuration of the resistances to sensible heat exchange by the "series" configuration. The latter takes into account the in-canopy air temperature and substantially improves the accuracy of the DTD model.


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