Consistency between In Situ, Model-Derived and High-Resolution-Image-Based Soil Temperature Endmembers: Towards a Robust Data-Based Model for Multi-Resolution Monitoring of Crop Evapotranspiration

Abstract

Due to their image-based nature, "contextual" approaches are very attractive to estimate evapotranspiration (ET) from remotely-sensed land surface temperature (LST) data. Their application is however limited to highly heterogeneous areas where the soil and vegetation temperature endmembers (Tends) can be observed at the thermal sensor resolution. This paper aims to develop a simple theoretical approach to estimate Tends independently from LST images. for the image-based and model-derived Tends, respectively. In light of the above results, Tends should be estimated a priori when contextual models are applied to low resolution images. Moreover, the consistency over highly heterogeneous areas between model-derived and high-resolution image-based Tends provides a meaningful basis for developing mixed modeling observational approaches.

A review of spatial downscaling of satellite remotely sensed soil moisture

“…The main sources of uncertainties when using DISPATCH are related to the modeling of SEE using two different information types: (1) the modeling of SEE as a function of LST and visible/near‐infrared reflectances and (2) the modeling of SEE as a function of SM. Further improvements of DISPATCH need revising the modeling of temperature end‐members [ Stefan et al , ], the topographic correction of LST including both elevation and illumination effects [ Malbéteau et al , ], and the modeling of SEE as a function of SM, soil properties, and atmospheric conditions [ Merlin et al , ].…”

Modeling soil evaporation efficiency in a range of soil and atmospheric conditions using a meta‐analysis approach

“…Another significant advantage of the formulation in SEE is the strong link with remote sensing variables available in the thermal and microwave frequencies. In particular, the SEE‐based representation of evaporation is fully consistent with both the thermal‐derived T normalized by wet/dry T endmembers [e.g., Nishida et al ., ; Stefan et al ., ], and the θ retrieved from microwave data [e.g., Prévot et al ., ; Simmonds and Burke , ; Zribi et al ., ].…”

“…Physically based soil water diffusion models [e.g., Tang and Riley , ] will be very helpful in that direction. given that a significant correlation exists between and sand and clay fractions, one could imagine a remote sensing approach for estimating surface soil texture from multi‐sensor/multi‐spectral remote sensing. In practice, several issues will need to be addressed beforehand such as the estimation of SEE from thermal infrared data [ Chanzy et al ., ; Stefan et al ., ], the downscaling of microwave‐derived θ [e.g., Merlin et al ., ], and the partitioning between soil evaporation and plant transpiration from available remote sensing data [e.g., Merlin et al ., ].…”

Smos based High Resolution Soil Moisture Estimates for Desert Locust Preventive Management

“…(2), a thermal-derived SM index (SEE) can be obtained by normalizing the Landsat LST from the theoretical dry and wet LST values estimated from the LST-NDVI feature space (e.g. Wan et al, 2004) or from an energy balance model (Stefan et al, 2015). Over a relatively flat terrain, in the absence of vegetation cover and under heterogeneous SM conditions (i.e.…”