Eco-environmental changes due to human activities in the Erhai Lake Basin from 1990 to 2020.

Human activities have increased with urbanisation in the Erhai Lake Basin, considerably impacting its eco-environmental quality (EEQ). This study aims to reveal the evolution and driving forces of the EEQ using water benefit-based ecological index (WBEI) in response to human activities and policy variations in the Erhai Lake Basin from 1990 to 2020. Results show that (1) the EEQ exhibited a pattern of initial degradation, subsequent improvement, further degradation and a rebound from 1990 to 2020, and the areas with poor and fair EEQ levels mainly concentrated around the Erhai Lake Basin with a high level of urbanisation and relatively flat terrain; (2) the EEQ levels were not optimistic in 1990, 1995 and 2015, and areas with poor and fair EEQ levels accounted for 43.41%, 47.01% and 40.05% of the total area, respectively; and (3) an overall improvement in the EEQ was observed in 1995-2000, 2000-2005, 2005-2009 and 2015-2020, and the improvement was most significant in 1995-2000, covering an area of 823.95 km2 and accounting for 31.79% of the total area. Results also confirmed that the EEQ changes in the Erhai Lake Basin were primarily influenced by human activities and policy variations. Moreover, these results can provide a scientific basis for the formulation and planning of sustainable development policy in the Erhai Lake Basin.

Spatiotemporal change patterns and driving factors of land surface temperature in the Yunnan-Kweichow Plateau from 2000 to 2020.

In this study, the spatiotemporal change patterns and driving factors of land surface temperature (LST) on the Yunnan-Kweichow Plateau (YKP) during 2000-2020 are investigated by using the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST dataset provided by National Tibetan Plateau Data Center. The YKP LST spatiotemporal change patterns are revealed at annual, seasonal, monthly, and daily scales. Furthermore, seven driving factors such as air temperature, land cover types, normalized difference vegetation index, precipitation, solar radiation, elevation, and latitude are quantified the impacts on LST spatial heterogeneity at annual scale. The main findings are as follows: (1) Annual mean LST increases by 0.016 K/year. Annual mean daytime LST slightly decreases by 0.009 K/year. Annual mean nighttime LST significantly increases by 0.042 K/year. (2) The trend and seasonal components of the daily, daily mean daytime, and daily mean nighttime LST have five and four breakpoints respectively, indicating that the variation of LST is unstable during 2000-2020 on the YKP. (3) The LST lapse rates at nighttime are generally higher than those at daytime on the YKP at the annual, seasonal, and monthly scales. The LST maximum lapse rate is 0.59 K/100 m in summer nighttime, and the LST minimum lapse rate is 0.18 K/100 m in winter daytime. (4) The controlling effects of seven factors are generally stronger in the nighttime than those in the daytime. The factors of elevation and air temperature dominate the LST spatial distribution on the YKP, with a contribution rate of >70 %. In addition, the interactions among the seven factors are all enhancing the effects on the spatial distribution of annual mean LST, including bivariate enhancement and nonlinear enhancement. This study contributes to the mitigation and adaptation to climate change of LST in the plateau and plays a theoretical reference role in formulating corresponding policies for environmental protection.

Estimation of land surface temperature from three thermal infrared channels of MODIS data for dust aerosol skies.

Studies indicated that a root mean square error (RMSE) of 3.7 K was found if dust aerosol was not considered in the traditional land surface temperature (LST) retrieval algorithm. To reduce the influence of dust aerosol on LST estimation, a three-channel algorithm is proposed using MODIS channels 29, 31, and 32 with model coefficients irrelevant to the aerosol optical depth (AOD). Compared with actual and estimated LSTs, the RMSEs are 1.8 K and 1.6 K for dry and wet atmospheres, respectively, when the AOD is 1.0. Sensitivity analyses considering instrument noise, land surface emissivity uncertainties, and the algorithm error itself show that the LST errors are 2.5 K and 1.7 K for dry and wet atmospheres, respectively, when the AOD is 1.0. Finally, some in situ measured LSTs at the Jichanghuangmo, Huazhaizi, and Yingke sites in northwest China are taken as referenced LST values and compared with the MODIS LST products MOD11_L2/MYD11_L2 and those estimated with the proposed method. The results show that the proposed method can improve the LST retrieval accuracy from 1.4 K to 2.2 K in dust aerosol atmospheres.

New method to estimate surface upwelling long-wave radiation from MODIS cloud-free data.

As an important component in the surface radiation budget, surface upwelling longwave radiation (SULR) is an outcome of the land surface energy exchange and mainly represents the capability of thermal radiation from the surface of the Earth. Existing satellite-derived SULR products are too coarse to support high-resolution numerical models, and their accuracy needs to be improved. In this study, an equivalent temperature is introduced through which a "split-window" atmospheric correction algorithm is developed for MODIS data to estimate the instantaneous clear-sky SULR. It is a simple and feasible method that is particularly applicable to MODIS data to acquire relatively high precision SULR under clear skies from which qualified water vapor contents (WVC) and thermal channel brightness temperatures are available. The root mean square errors (RMSEs) are less than 13 W/m2 for all WVC sub-ranges with the viewing zenith angle (VZA) less than 30°, or for all sub-ranges with the VZA less than 60° and the WVC less than 3.5 g/cm2. Also, applications and comparisons with the LST-emissivity method are made by using ground measurements which are collected from the network of surface radiation budget network data (SURFRAD) at the moment of MODIS overpass. Results show that the proposed model has high computational efficiency to estimate SULR from MODIS cloud-free data.

Land surface temperature retrieved from airborne multispectral scanner mid-infrared and thermal-infrared data.

Land surface temperature (LST) is one of the key parameters in the physics of land surface processes at local/global scales. In this paper, a LST retrieval method was proposed from airborne multispectral scanner data comparing one mid-infrared (MIR) channel and one thermal infrared (TIR) channel with the land surface emissivity given as a priori knowledge. To remove the influence of the direct solar radiance efficiently, a relationship between the direct solar radiance and water vapor content and the view zenith angle and solar zenith angle was established. Then, LST could be retrieved with a split-window algorithm from MIR/TIR data. Finally, the proposed algorithm was applied to the actual airborne flight data and validated with in situ measurements of land surface types in the Baotou site in China on 17 October 2014. The results demonstrate that the difference between the retrieved and in situ LST was less than 1.5 K. The bais, RMSE, and standard deviation of the retrieved LST were 0.156 K, 0.883 K, and 0.869 K, respectively, for samples.

Daytime Land Surface Temperature Extraction from MODIS Thermal Infrared Data under Cirrus Clouds.

Simulated data showed that cirrus clouds could lead to a maximum land surface temperature (LST) retrieval error of 11.0 K when using the generalized split-window (GSW) algorithm with a cirrus optical depth (COD) at 0.55 µm of 0.4 and in nadir view. A correction term in the COD linear function was added to the GSW algorithm to extend the GSW algorithm to cirrus cloudy conditions. The COD was acquired by a look up table of the isolated cirrus bidirectional reflectance at 0.55 µm. Additionally, the slope k of the linear function was expressed as a multiple linear model of the top of the atmospheric brightness temperatures of MODIS channels 31-34 and as the difference between split-window channel emissivities. The simulated data showed that the LST error could be reduced from 11.0 to 2.2 K. The sensitivity analysis indicated that the total errors from all the uncertainties of input parameters, extension algorithm accuracy, and GSW algorithm accuracy were less than 2.5 K in nadir view. Finally, the Great Lakes surface water temperatures measured by buoys showed that the retrieval accuracy of the GSW algorithm was improved by at least 1.5 K using the proposed extension algorithm for cirrus skies.

[Research on the Thermal Infrared Polarization Properties of Fresh Snow].

Snow can directly affect the surface energy balance and climate change and has a significant impact on human life and production. It is therefore of great significance to study the fresh snow emission spectroscopy properties by using the thermal infrared Polarization technique. This can provide a basis for quantitative thermal infrared remote sensing monitoring of snow as well as a deeper understanding of global warming and appropriate countermeasures. This paper focuses on the investigation of the thermal infrared polarization properties of the fresh snow. The results show that the thermal emissive polarization properties of fresh snow depend significantly on the wavelengths (channels) and view angles used to measure them. Four channels are considered in this study, their spectral response ranges are 8-14 microm for channel 1 (CH1), 11.5-12.5 microm for channel 2 (CH2), 10.3-11.5 microm for channel 3 (CH) and 8.2-9.2 microm for channel 4 (CH4). The snow polarized radiance (L) and its polarized brightness temperature (T) manifest as L(CH1) >L(CH3) > L(CH4) > L(CH2) and T(CH4) > T(CH1) > T(CH2) > TCH3, respectively, while the degree of polarization (P) manifests as P0 > P30 > P40 > P20 > P0 > P50 where the subscript of P denotes the view angle. The maximum of both L and T occurs at the view angle of 50 degree and polarization angle of 90 degree while their minimum appears at the view angle of 30 degree and polarization angle of 75 degree for each channel. In addition, the results show that: CH3 is more appropriate for better investigation of the emissive polarization properties of snow. Linear relationship is found between the fresh snow polarized T and the polarization angle with the coefficient of determination larger than 0.77 for all four channels. The polarized brightness temperature of the fresh snow is found to be increased about 0.003 K per polarization angle within 0-135 degree. The degree of polarization of snow is almost independent of the channels we used (CH1 to CH4). The snow emissive polarization is isotropic and the relative azimuth view angle has no significant impact on the snow emissive polarization properties. The impact of the polarization angle may be neglected if the investigation of the relative azimuth view angle on the fresh snow thermal emissive polarization is conducted. The difference of the fresh snow emissive polarization properties mainly comes from the snow surface roughness and structure.

Atmospheric corrections of passive microwave data for estimating land surface temperature.

Quantitative analysis of the atmospheric effects on observations made by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) has been performed. The differences between observed brightness temperatures at the top of the atmosphere and at the bottom of the atmosphere were analyzed using a database of simulated observations, which were configured to replicate AMSR-E data. The differences between observed brightness temperatures at the top of the atmosphere and land surface-emitted brightness temperatures were also computed. Quantitative results show that the atmosphere has different effects on brightness temperatures in different AMSR-E channels. Atmospheric effects can be neglected at 6.925 and 10.65 GHz, when the standard deviation is less than 1 K. However, at other frequencies and polarizations, atmospheric effects on observations should not be neglected. An atmospheric correction algorithm was developed at 18.7 GHz vertical polarization, based on the classic split-window algorithm used in thermal remote sensing. Land surface emission can be estimated with RMSE = 0.99 K using the proposed method. Using the known land surface emissivity, Land Surface Temperature (LST) can be retrieved. The RMSE of retrieved LST is 1.17 K using the simulated data.

Land surface reflectance retrieval from hyperspectral data collected by an unmanned aerial vehicle over the Baotou test site.

To evaluate the in-flight performance of a new hyperspectral sensor onboard an unmanned aerial vehicle (UAV-HYPER), a comprehensive field campaign was conducted over the Baotou test site in China on 3 September 2011. Several portable reference reflectance targets were deployed across the test site. The radiometric performance of the UAV-HYPER sensor was assessed in terms of signal-to-noise ratio (SNR) and the calibration accuracy. The SNR of the different bands of the UAV-HYPER sensor was estimated to be between approximately 5 and 120 over the homogeneous targets, and the linear response of the apparent reflectance ranged from approximately 0.05 to 0.45. The uniform and non-uniform Lambertian land surface reflectance was retrieved and validated using in situ measurements, with root mean square error (RMSE) of approximately 0.01-0.07 and relative RMSE of approximately 5%-12%. There were small discrepancies between the retrieved uniform and non-uniform Lambertian land surface reflectance over the homogeneous targets and under low aerosol optical depth (AOD) conditions (AOD = 0.18). However, these discrepancies must be taken into account when adjacent pixels had large land surface reflectance contrast and under high AOD conditions (e.g. AOD = 1.0).

Practical retrieval of land surface emissivity spectra in 8-14 µm from hyperspectral thermal infrared data.

A practical physics-based regression method was developed and evaluated for nearly real time estimate of land surface emissivity spectra in 8-14 µm from hyperspectral thermal infrared data. Two spectral emissivity libraries and one atmospheric profile database fully covering all the possible situations for clear sky conditions were elaborately selected to simulate the radiances at the top of the atmosphere (TOA). The regression coefficients were determined by the main principal components of emissivity spectra and those of simulated brightness temperature at TOA using a ridge regression method. The experience with the simulated Interferometer Atmospheric Sounding Instrument (IASI) data showed that the emissivity spectra could be retrieved under clear sky conditions with root mean square errors of 0.015 and 0.03 for 714-970 cm(-1) (10.3-14.0 µm) and 970-1250 cm(-1) (8.0-10.3 µm), respectively, for various land surface and atmospheric conditions. This indicates the proposed method may be robust and applicable for all hyperspectral infrared sensors.

Validation of MODIS-derived bidirectional reflectivity retrieval algorithm in mid-infrared channel with field measurements.

This work addressed the validation of the MODIS-derived bidirectional reflectivity retrieval algorithm in mid-infrared (MIR) channel, proposed by Tang and Li [Int. J. Remote Sens. 29, 4907 (2008)], with ground-measured data, which were collected from a field campaign that took place in June 2004 at the ONERA (Office National d'Etudes et de Recherches Aérospatiales) center of Fauga-Mauzac, on the PIRRENE (Programme Interdisciplinaire de Recherche sur la Radiométrie en Environnement Extérieur) experiment site [Opt. Express 15, 12464 (2007)]. The leaving-surface spectral radiances measured by a BOMEM (MR250 Series) Fourier transform interferometer were used to calculate the ground brightness temperatures with the combination of the inversion of the Planck function and the spectral response functions of MODIS channels 22 and 23, and then to estimate the ground brightness temperature without the contribution of the solar direct beam and the bidirectional reflectivity by using Tang and Li's proposed algorithm. On the other hand, the simultaneously measured atmospheric profiles were used to obtain the atmospheric parameters and then to calculate the ground brightness temperature without the contribution of the solar direct beam, based on the atmospheric radiative transfer equation in the MIR region. Comparison of those two kinds of brightness temperature obtained by two different methods indicated that the Root Mean Square Error (RMSE) between the brightness temperatures estimated respectively using Tang and Li's algorithm and the atmospheric radiative transfer equation is 1.94 K. In addition, comparison of the hemispherical-directional reflectances derived by Tang and Li's algorithm with those obtained from the field measurements showed that the RMSE is 0.011, which indicates that Tang and Li's algorithm is feasible to retrieve the bidirectional reflectivity in MIR channel from MODIS data.

Estimation of broadband surface emissivity from narrowband emissivities.

This work analyzed and addressed the estimate of the broadband emissivities for the spectral domains 3-14µm (ε(3-14)) and 3-∞µm (ε(3-∞). Two linear narrow-to-broadband conversion models were proposed to estimate broadband emissivities ε(3-14) and ε(3-∞) using the Moderate Resolution Imaging Spectroradiometer (MODIS) derived emissivities in three thermal infrared channels 29 (8.4-8.7µm), 31 (10.78-11.28µm) and 32 (11.77-12.27µm). Two independent spectral libraries, the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) spectral library and the MODIS UCSB (University of California, Santa Barbara) emissivity library, were used to calibrate and validate the proposed models. Comparisons of the estimated broadband emissivities using the proposed models and the calculated values from the spectral libraries, showed that the proposed method of estimation of broadband emissivity has potential accuracy and the Root Mean Square Error (RMSE) between estimated and calculated broadband emissivities is less than 0.01 for both ε(3-14) and ε(3-∞).

Estimation of land surface directional emissivity in mid-infrared channel around 4.0 microm from MODIS data.

This work addressed the estimate of the directional emissivity in the mid-infrared (MIR) channel around 4.0 microm from MODIS data. A series of bidirectional reflectances in MODIS channel 22 (3.97 mum) were retrieved using the method developed by Tang and Li (Int. J. Remote Sens. 29, 4907, 2008) and then were used to estimate the directional emissivity in this channel with the aid of the BRDF model modified by Jiang and Li (Opt. Express 16, 19310, 2008). To validate the estimated directional emissivity, a cross-comparison of MODIS derived emissivities in channel 22 using the proposed method were performed with those provided by the MODIS land surface temperature/emissivity product MYD11B1 data. The results show that the proposed method for estimating the directional emissivity in MIR channel gives results comparable to those of MYD11B1 product with a Mean Error of -0.007 and a Root Mean Square Error of 0.024.