“…The annual average wind speed was 2.5 m·s −1 , and the instantaneous maximum wind speed was 24.0 m·s −1 (Fig. 5) 27 .Figure 5FTIR observed sites along two desert highways. The map is generated with ArcMap Version 10.0(Image download from Google Earth) (http://www.esri.com/en/arcgis/arcgis-for-desktop/).…”
Section: Conclusion and Discussionmentioning
confidence: 84%
“…Li et al . 22 pointed out that MODIS emissivity product data accuracy is preferably when FTIR measurement data is used and the coefficients of the formula (9) are fitted by using 10 measurement values as follows (Eq. 1):…”
Section: Resultsmentioning
confidence: 99%
“…The soil composition and humidity change are small due to the single type of desert surface; thus, the suitable site was chosen every 50–100 km and the observed sites were increased in the oasis transition zone at the edge of the desert. In order to have effective observation data, the measurement was carried out on sunny days 22 . The field data collection was from Oct 16 to Oct 19, 2013, for the sites in the Luntai-Niya Desert Highway and from Sep 25 to Sep 27, 2014, for the sites in the Alar-Hetian Desert Highway.…”
Section: Conclusion and Discussionmentioning
confidence: 99%
“…Therefore, the thermal infrared wave points were chosen band 29 (8.400–8.700 μm), band 31 (10.780–11.280 μm) and band 32 (11.770–12.270 μm). In addition, the 7th band (2.105–2.155 μm) in the near-infrared spectra is the reflection of surface and clouds 29 which can represent the surface soil properties 30 , and thus the band 7 has the highest correlation with the broadband emissivity in all MODIS reflection channels 27 . The soils and rocks surface reflectivity depends on their mineral composition and emissivity.…”
In this paper, the Taklimakan Desert land surface emissivity measured by portable Fourier Transform thermal InfraRed spectroscopy (FTIR) was used to re-estimate Global LAnd Surface Satellite (GLASS) BroadBand Emissivity (BBE) and Moderate Resolution Imaging Spectroradiometer (MODIS) BBE optimal coefficients equations. In addition, the revised BBE equations of both GLASS and MODIS with the optimized coefficients were obtained. Comparing the FTIR and MODIS BBE data with the values retrieved before and after the revised GLASS BBE equation, it was found that the revised GLASS BBE equation is more accurate than the original one. First, according to the error analysis with FTIR data, the value of R2 is increased from 0.4 to 0.9, and the Root Mean Square Error (RMSE) and Bias are reduced by 1 and 3 magnitude orders, respectively. Second, with MODIS BBE data, the value of R2 is increased from 0.6 to 0.9, the RMSE and Bias are reduced by 1 and 2 magnitude orders, respectively. Finally, the Taklimakan Desert BBE was calculated using the revised GLASS BBE equation. The results showed that the BBE values are between 0.890 and 0.920 in the desert center, between 0.920 and 0.950 in the sparse vegetation areas, and between 0.950 and 0.980 in the oasis edges.
“…The annual average wind speed was 2.5 m·s −1 , and the instantaneous maximum wind speed was 24.0 m·s −1 (Fig. 5) 27 .Figure 5FTIR observed sites along two desert highways. The map is generated with ArcMap Version 10.0(Image download from Google Earth) (http://www.esri.com/en/arcgis/arcgis-for-desktop/).…”
Section: Conclusion and Discussionmentioning
confidence: 84%
“…Li et al . 22 pointed out that MODIS emissivity product data accuracy is preferably when FTIR measurement data is used and the coefficients of the formula (9) are fitted by using 10 measurement values as follows (Eq. 1):…”
Section: Resultsmentioning
confidence: 99%
“…The soil composition and humidity change are small due to the single type of desert surface; thus, the suitable site was chosen every 50–100 km and the observed sites were increased in the oasis transition zone at the edge of the desert. In order to have effective observation data, the measurement was carried out on sunny days 22 . The field data collection was from Oct 16 to Oct 19, 2013, for the sites in the Luntai-Niya Desert Highway and from Sep 25 to Sep 27, 2014, for the sites in the Alar-Hetian Desert Highway.…”
Section: Conclusion and Discussionmentioning
confidence: 99%
“…Therefore, the thermal infrared wave points were chosen band 29 (8.400–8.700 μm), band 31 (10.780–11.280 μm) and band 32 (11.770–12.270 μm). In addition, the 7th band (2.105–2.155 μm) in the near-infrared spectra is the reflection of surface and clouds 29 which can represent the surface soil properties 30 , and thus the band 7 has the highest correlation with the broadband emissivity in all MODIS reflection channels 27 . The soils and rocks surface reflectivity depends on their mineral composition and emissivity.…”
In this paper, the Taklimakan Desert land surface emissivity measured by portable Fourier Transform thermal InfraRed spectroscopy (FTIR) was used to re-estimate Global LAnd Surface Satellite (GLASS) BroadBand Emissivity (BBE) and Moderate Resolution Imaging Spectroradiometer (MODIS) BBE optimal coefficients equations. In addition, the revised BBE equations of both GLASS and MODIS with the optimized coefficients were obtained. Comparing the FTIR and MODIS BBE data with the values retrieved before and after the revised GLASS BBE equation, it was found that the revised GLASS BBE equation is more accurate than the original one. First, according to the error analysis with FTIR data, the value of R2 is increased from 0.4 to 0.9, and the Root Mean Square Error (RMSE) and Bias are reduced by 1 and 3 magnitude orders, respectively. Second, with MODIS BBE data, the value of R2 is increased from 0.6 to 0.9, the RMSE and Bias are reduced by 1 and 2 magnitude orders, respectively. Finally, the Taklimakan Desert BBE was calculated using the revised GLASS BBE equation. The results showed that the BBE values are between 0.890 and 0.920 in the desert center, between 0.920 and 0.950 in the sparse vegetation areas, and between 0.950 and 0.980 in the oasis edges.
“…In addition, the surface broadband emissivity was defined as 0.905 in the Taklimakan Desert using FTIR and MODIS according to the method of Li et al [28]. We modified the monthly average albedo and surface broadband emissivity of the 19 th vegetation type of Noah.…”
Section: Modification Of Surface Parametersmentioning
This study evaluated the Noah land-surface model performance to simulate the land-surface process during different weather conditions in the hinterland of the Taklimakan Desert. This study is based on observation data from the Taklimakan Desert Meteorology Field Experiment Station in 2014. The results illustrated that the energy-exchange process between the land surface and the atmosphere in the drifting desert can be simulated by Noah effectively. However, the effects of soil moisture and latent heat flux were very poor. For sunny days, the soil temperature and heat flux were underestimated significantly in the nighttime and overestimated in the daytime. The simulation results are very good in sand-dust weather. The simulation of heat flux and net radiation is very consistent with the observation during cloudy days. For rainy days, the model can successfully model the diurnal variation of soil moisture, but it has obvious deviations in the net radiation, heat flux, and soil heat flux.
Broadband emissivity is a crucial parameter for calculating the radiation budget, still, it adopts a constant value in land surface models due to a lack of adequate observations. Arid regions have complex underlying surfaces and estimations of the broadband emissivity in such areas suffer from high spatial variation and uncertainty. Here, we propose a novel method for estimating broadband emissivity in the 8–14 µm range based on Fourier-transform infrared spectroscopy (FTIR) observations, moderate resolution imaging spectrometer (MODIS) emissivity, the leaf area index (LAI) and reflectance products. The proposed method exploits FTIR observations, MODIS single-channel emissivity, reflectance and the LAI to fit a linear regression of the broadband emissivity, so the optimal equation includes emissivity, reflectance and the LAI, with an R2 and root-mean-squared error of 0.942 and 0.08. Then we used the proposed method to generate a broadband emissivity map of Northwest of China, the broadband emissivity estimated by the method showed higher variations and finer distribution in arid areas and sparsely vegetated regions compared to data from the global land surface satellite and land model. An analysis of the relationship between the broadband emissivity, land-use type and soil moisture found an existing but not linear relationship, which indicated that the relationship was complicated under the inhomogeneous surface of wetness and vegetation. In conclusion, our results suggest that the proposed method can accurately estimate the broadband emissivity in arid regions. In future research, we will test the data in a land model.
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