Sentence examples for land surface thermal from inspiring English sources

Further, the physics underlying this model are complementary to the physics of land surface thermal remote sensing.

Landsat data were used to assess urbanization-induced dynamics in Land use/cover (LULC), surface thermal intensity, and its relationships with urban biophysical composition.

We derived measures of the vegetated land surface and the thermal regime of the growing season at 8-day intervals using the Normalized Difference Vegetation Index (NDVI) and Accumulated Growing Degree-Days (AGDD).

The ones which we will consider in this paper are land surface temperature from thermal infrared data, surface soil moisture from passive microwave data, snow cover using both visible and microwave data, water quality using visible and near-infrared data and estimating landscape surface roughness using lidar.

This work aims to assess a diagnostic approach which links evapotranspiration (ET) to land surface temperature (LST) measured by thermal remote sensing in the Alps.

Thermal domain (TD) deals with land surface temperature which offers valuable information in terms of thermal aberrations that can easily be detected in thermal infrared remotely sensed data.

Over 10 years ago, John Norman and co-authors proposed a thermal-based land surface modeling strategy that treated the energy exchange and kinetic temperatures of the soil and vegetated components in a unique "Two-Source Model" (TSM) approach.

Measurements of land surface temperature (LST) performed in the thermal infrared (TIR) domain are prone to strong directional anisotropy.

Remote sensing techniques were applied to derive information on land use/cover and land surface temperatures and to assess the thermal response patterns of land cover types.

In this paper we propose a minimum multispectral configuration to allow the retrieval of both land surface temperature (LST) and emissivity from thermal infra-red (TIR) data.

This paper utilizes the land surface temperature (LST) derived from the thermal band of Landsat images and Normalized Difference Vegetation Index (NDVI) derived from its red and near-infrared bands to further improve land-cover and surface microclimate mapping.