English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Validation and performance evaluations are beneficial for developing methods that estimate the remotely sensed land surface temperature (LST). However, such evaluations for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data are rare. By selecting the middle reach of the Heihe River basin (HRB), China, as the study area, the atmospheric correction (AC), mono-window (MW), single-channel (SC), and  split-window (SW) methods were evaluated based on in situ measured LSTs. Results demonstrate that the influences of surface heterogeneity on the validation are significant in the study area. For the AC, MW, and SC methods, the LSTs estimated from channel 13 are more accurate than those from channel 14 in general cases. When the in situ measured atmospheric profiles are available, the AC method has the highest accuracy, with a  root-mean squared error (RMSE) of about 1.4–1.5 K at the homogenous oasis sites. In actual application without sufficient in situ measured inputs, the MW method is highly accurate; the RMSE is around 1.5–1.6 K. The SC method systematically overestimates LSTs and it is sensitive to error in the water vapor content. The two SW methods are simple to use but their performances are limited by accuracies, revealed by the simulation dataset. Therefore, when the in situ atmospheric profiles are available, the AC method is recommended to generate reliable ASTER LSTs for modeling the eco-hydrological processes in the middle reach of the HRB. When sufficient in situ measured inputs are not available, the MW method can be used instead

remote sensing

Validation and Performance Evaluations of Methods for Estimating Land Surface Temperatures from ASTER Data in the Middle Reach of the Heihe River Basin, Northwest China