Modelling aboveground biomass using MODIS FPAR/LAI data in alpine grasslands of the Northern Tibetan Plateau

Aboveground biomass (AGB) is a vital variable in global carbon cycling and plays important roles in ecosystem structure and function in grasslands. Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 leaf area index (LAI) and fractional photosynthetically active radiation absorbed by vegetation (FPAR) data were used to model AGB in alpine grasslands in the Northern Tibetan Plateau. MYD15A2H (derived from the Aqua MODIS with a temporal resolution of eight days) FPAR/LAI with lower RMSE values (<18 g m^?2) may have higher estimated accuracies than MOD15A2H (derived from the Terra MODIS with a temporal resolution of eight days), MCD15A2H (derived from the combination of Terra MODIS and Aqua MODIS with a temporal resolution of eight days) and MCD15A3H (derived from the combination of Terra MODIS and Aqua MODIS with a temporal resolution of four days) FPAR/LAI with higher RMSE values (>20 g m^?2). The RMSE values (11.42–13.27 g m^?2) in the open grazed areas were lower than those (17.46–17.56 g m^?2) in fenced areas for MYD15A2H. Therefore, the estimated accuracies of AGB may vary among the four MODIS FPAR/LAI products and land use types in alpine grasslands of the Northern Tibetan Plateau.     

Algorithms for estimating green leaf area index in C3 and C4 crops for MODIS,Landsat TM/ETM+, MERIS,Sentinel MSI/OLCI,and Venµs sensors

This study developed a set of algorithms for satellite mapping of green leaf area index (LAI) in C3 and C4 crops. In situ hyperspectral reflectance and green LAI data, collected across eight years (2001–2008) at three AmeriFlux sites in Nebraska USA over irrigated and rain-fed maize and soybean, were used for algorithm development. The hyperspectral reflectance was resampled to simulate the spectral bands of sensors aboard operational satellites (Aqua and Terra: MODIS, Landsat: TM/ETM+), a legacy satellite (Envisat: MERIS), and future satellites (Sentinel-2, Sentinel-3, and Venµs). Among 15 vegetation indices (VIs) examined, five VIs – wide dynamic range vegetation index (WDRVI), green WDRVI, red edge WDRVI, and green and red edge chlorophyll indices – had a minimal noise equivalent for estimating maize and soybean green LAI ranging from 0 to 6.5 m² m^?2. The algorithms were validated using MODIS, TM/ETM+, and MERIS satellite data. The root mean square error of green LAI prediction in both crops from all sensors examined in this study ranged from 0.73 to 0.95 m² m^?2 and coefficient of variation ranged between 17.0 and 29.3%. The algorithms using the red edge bands of MERIS and future space systems Sentinel-2, Sentinel-3, and Venµs allowed accurate green LAI estimation over areas containing maize and soybean with no re-parameterization.     

The potential of Sentinel-2 data for estimating biophysical variables in a boreal forest: a simulation study

In this study we use ground reference data from 962 forest plots to demonstrate the potential of Sentinel-2 (S2) bands in estimating canopy biophysical properties in boreal forests in Finland. We simulated canopy bidirectional reflectance factors (BRFs) using the PARAS model, which applies photon recollision probability. Results showed that the highest correlation between simulated S2 BRFs and fraction of absorbed photosynthetically active radiation (fPAR) was for the band combination band 7/band 9 (wavelengths 773–793 nm and 935–955 nm, respectively) (the coefficient of determination (R²) was 0.93). For effective leaf area index (LAI_e) the best band combination was band 8/band 4 (wavelengths 785–900 nm and 650–680 nm, respectively) (R² = 0.93). Based on this study, the above-ground biomass (AGB) and S2 band combinations did not show strong relationships (R² = 0.24). The new inverted red-edge chlorophyll index (IRECI) and Sentinel-2 red-edge position – index (S2REP) showed moderate relationships with fPAR (R² = 0.61 and R² = 0.45, respectively) and LAI_e (R² = 0.56 and R² = 0.30, respectively). This study demonstrated the potential of the S2 data to estimate canopy biophysical properties.     

Feasibility of using mobile phone to estimate forest Leaf Area Index: a case study in Yunnan Pine

Remotely-sensed Leaf Area Index (LAI) is vital to describe the vegetation canopy and assess plant growth condition and healthy status. However, sufficient instant ground LAI samples are pre-required for calibration or validation, which is generally difficult to collect. We proposed a method, LAI-Mobile, to use mobile phones with low-cost fisheye lens to take fisheye photos and to invert LAIs, which may be popularized for ordinary people to generate big volume of LAI sample data. The feasibility of LAI-Mobile was tested by comparing with LAI?2200 and GF-1 satellite data (GF = high resolution) in a pest-invaded Yunnan pine forest area in Yunnan province of China. Results show significant correlation between LAI-2200 and LAI-Mobile data for forest plots with coefficient of determination (R²) = 0.706 and Root Mean Square Error (RSME) = 0.241, and GF-1 satellite images (R² = 0.659 and RMSE = 0.268). The linear regression shows a good agreement between the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product and the inverted GF-1 LAI, with R² = 0.649, RMSE = 0.795. Despite larger uncertainty for single fisheye image than LAI-2200, LAI-mobile can provide fast and convenient method to collect large amount of LAI, which will support remote sensing inversion of LAI at large scale.     

Validation of MODIS collection 6 FPAR/LAI in the alpine grassland of the Northern Tibetan Plateau

Leaf area index (LAI) and fractional photosynthetically active radiation absorbed by vegetation (FPAR) are two important parameters in global carbon cycling. Ground measured LAI (LAI_G) and calculated FPAR (FPAR_G) using LAI_G were compared with three MODIS Collection 6 FPAR/LAI products (i.e. MOD15A2H derived from the Terra MODIS; MYD15A2H derived from the Aqua MODIS; and MCD15A2H derived from the combination of Terra MODIS and Aqua MODIS) in the alpine grasslands of the Northern Tibetan Plateau. MODIS FPAR/LAI showed significant positive correlations with FPAR_G/LAI_G (coefficient of determination, R² = 0.48–0.82). MOD15A2H, MYD15A2H and MCD15A2H LAI overestimated LAI_G by 17%, 46% and 55% for fenced areas, and by 10%, 53% and 34% for open grazed areas, respectively. MOD15A2H, MYD15A2H and MCD15A2H FPAR overestimated FPAR_G by 24%, 37% and 44% for fenced areas and by 20%, 35% and 35% for open grazed areas, respectively. Our findings implied that Terra MODIS FPAR/LAI had the highest accuracy among the three MODIS FPAR/LAI products. The accuracies of MODIS FPAR/LAI also varied with land use types.     

Estimating leaf area index of maize using airborne full-waveform lidar data

The leaf area index (LAI) is a key input parameter in ecosystem models and plays a vital role in gas–vegetation exchange processes. Several studies have recently been conducted to estimate the LAI of low-stature vegetation using airborne discrete-return light detection and ranging (lidar) data. However, few studies have been carried out to estimate the LAI of low-stature vegetation using airborne full-waveform lidar data. The objective of this research is to explore the potential of airborne full-waveform lidar for LAI estimation of maize. First, waveform processing was conducted for better extraction of waveform-derived metrics for LAI estimation. A method of faint returns retrieval was also proposed to obtain ground returns. Second, the LAIs of maize were estimated based on the Beer–Lambert law. Finally, the LAI estimates were validated using field-measured LAIs in Huailai, Hebei Province of China. Results indicated that maize LAI could be successfully retrieved with high accuracy (R² = 0.724, RMSE = 0.449) using full-waveform lidar data by the method proposed in this study.     

Retrieving leaf area index using ICESat/GLAS full-waveform data

Leaf area index (LAI) is an important parameter controlling many biological and physical processes associated with vegetation on the Earth's surface. In this study, an algorithm for estimating LAI from the ICESat (Ice, Cloud and land Elevation Satellite)/GLAS (Geoscience Laser Altimeter System) data was proposed and applied to a forest area in the Tibetan Plateau. First, Gaussian decomposition of the GLAS waveform was implemented to identify the ground peaks and calculate the ground and canopy return energy. Second, the ground-to-total energy ratio (E _r) was computed as the ratio of the ground return energy to the total waveform return energy for each GLAS footprint. Third, a regression model between the E _r and the field-measured LAI was established based on the Beer–Lambert law. The coefficient of determination (R ²) of the model was 0.81 and the root mean square error (RMSE) is 0.35 (n?=?23, p < 0.001). Finally, the leave-one-out cross-validation procedure was used to assess the constructed regression model. The results indicate that the regression model is not overfitting the data and has a good generalization capability. We validated the accuracy of the GLAS-predicted LAIs using the other 15 field-measured LAIs (R ² = 0.84), and the result shows that the accuracy of the GLAS-predicted LAI is high (RMSE = 0.31).     

Retrieving fPAR of maize canopy using artificial neural networks with airborne LiDAR and hyperspectral data

ABSTRACT

Accurate estimation of the fraction of absorbed photosynthetically active radiation (fPAR) is important for maize growth and yield estimations. Light detection and ranging (LiDAR)-derived canopy vertical structural and hyperspectral image-derived vegetation spectral information are complementary for vegetation fPAR estimation. This study explores the potential of artificial neural networks (ANNs) with two types of data to estimate maize fPAR. First, 45 metrics were derived from LiDAR data and 13 from a hyperspectral image. Then, the ANNs and stepwise multiple linear regression (SMLR) methods were used to estimate the fPAR. Finally, model validity was assessed using in-situ data. Results showed that the ANNs performed better in fPAR inversion (R ² = 0.910, adj. R ² = 0.921, RMSE = 0.046, RRMSE = 0.056, where R ² is the coefficient of determination, adj. R ² the adjusted coefficient of determination, RMSE the root mean squared error, and RRMSE the relative root mean squared error) than SMLR (R ² = 0.638, adj. R ² = 0.609, RMSE = 0.077, RRMSE = 0.092) and SMLR with the natural logarithm of data (R ² = 0.855, adj. R ² = 0.825, RMSE = 0.067, RRMSE = 0.081). This study is helpful for guiding the accurate estimation of maize fPAR using remote sensing techniques.     

Determinants of the reduction in B‐type natriuretic peptide after mitral valve replacement in patients with rheumatic mitral stenosis

Background: Plasma B‐type natriuretic peptide (BNP) levels are closely related to symptoms in left ventricle (LV) systolic heart failure, although marked regarding heterogeneity levels among subjects are reported. Aims: To assess the influence of right ventricle on plasma BNP in the patients with different grades of its overload secondary to severe mitral valve stenosis (MVS). Methods: Plasma BNP was evaluated in MVS patients (n = 27) before valve replacement and during follow‐up (FUV) 401 ± 42 days after operation. Results: Initial examination showed severe MVS (0.9 ± 0.2 cm²), left atrial enlargement (LAI 30 ± 4.5 mm m^?2), right ventricle diastolic dilatation (RVDI 16 ± 3.6 mm m^?2), normal LV size/function and elevated BNP levels (166 ± 137 pg ml^?1). FUV examination revealed a significant reduction in LAI (27 ± 2.2 mm m^?2), RVDI (14 ± 1.6 mm m^?2) and BNP levels (80 ± 35 pg ml^?1). The regression analysis of the initial parameters found RVDI to be the strongest predictor (R² = 0.61; P<0.0001) for BNP level, whereas RVDI reduction was the strongest factor for BNP decrease (R² = 0.65; P<0.0001) during FUV. Conclusions: Right ventricle should be taken into account as a potential important source of plasma BNP owing to the fact that LV size and function are well preserved in MVS patients. RVDI determines BNP plasma levels whereas after MVS removal, the RVDI reduction predicts BNP level decrease.     

Trends and relationship between chlorophyll-a and sea surface temperature in the central equatorial Indian Ocean

This work presents the trend analysis and relationship between chlorophyll-a (chl-a) concentration and sea surface temperature (SST) in the central equatorial Indian Ocean (CEIO) using Aqua MODIS chl-a Level-3 Standard Mapped Image (SMI) data for a period of 10 years (2002–2012). In order to understand the monsoonal variability of chl-a concentration and SST and to evaluate their relationships over the CEIO, trend analysis of chl-a values was carried out. The area average chl-a concentration in the region shows a weak annual cycle with high concentration during winter (October–December) and low in summer (June). High chl-a concentration (~0.22 mg m^?3) is observed during early winter in the region. Chl-a concentration starts decreasing from March onwards until the onset of summer monsoon. The data reveal low chl-a concentrations during summer period, i.e., from June to September, which is in accordance with several observations, and higher concentrations during October to December. The other reason is that satellite sensor may not capture chl-a variability more accurately because of cloud cover during summer monsoon time. A reasonably significant coefficient of determination (R² = 0.51; significant at p < 0.05 level) between SST and chl-a concentration is recorded. This study clearly suggests that the SST acts as a proxy for variables which cause high chl-a concentration in the CEIO.     

Estimating the vertical distribution of chlorophyll in winter wheat based on multi-angle hyperspectral data

ABSTRACT

Chlorophyll plays an important role in crop photosynthesis, which is closely related to nitrogen (N). N deficiency first occurs in the lower leaves, but the spectral detection of the lower layer is insufficient due to leaf shading. The aim of this paper was to investigate the feasibility of estimating the chlorophyll content of leaves (LCC) and the vertical distribution of LCC in wheat using multi-angle hyperspectral data. Three winter wheat layers were divided, and the multi-angle hyperspectral data of the different layers were obtained by removing the leaves from the lower layer to the top layer. The multi-angle vegetation index and LCC linear models were established, and the estimated model based on nadir view angle (i.e., conventional observation angle) was compared. Results show that (1) the best observation angle for the first layer, the second layer, and the third layer are 60°, 60°, 50°, respectively. (2) The accuracy of multi-angle-based estimation models (R² = 0.87, RMSE = 2.86 μg cm^?2) are higher than nadir-based ones (R² = 0.72, RMSE = 4.24 μg cm^?2). This study proved that vertical distribution has a positive influence on the estimation results, and multi-angle hyperspectral data could be promising in improving estimation accuracy.     

Estimating discharge from the Godavari River using ENVISAT,Jason-2, and SARAL/AltiKa radar altimeters

The present work discusses the application of radar altimeters on-board Environmental Satellite (ENVISAT), Jason-2, and Satellite with Argos and AltiKa (SARAL) missions to estimate the Godavari River discharge draining into the Bay of Bengal for the period 2002–2014. Ranges retrieved from the ICE-1 and ICE-3 retracker algorithms were used to estimate the river heights, followed by important range corrections. In situ radar gauge data at Yanam station and river discharge data from Dowlaiswaram dam were used to construct rating curves. This technique was applied to the ENVISAT (2002–2010), SARAL (2013–2014) derived river heights at Yanam and ENVISAT (2002–2010), SARAL (2013–2014), Jason-2 (2008–2014) derived river heights at Bhadrachalam. A hydrodynamic model was used to validate the ENVISAT and SARAL derived heights at Yanam; found that the modelled data are highly correlated with correlation coefficient r = 0.9. We estimate the standard errors of river height for all three altimeters. The values range from 0.15 to 0.40 m, quite within the acceptable accuracy of altimetry for hydrology/river applications. The root mean square error (RMSE) between ENVISAT and in situ discharge is 366 and 253 m³ s^?1 at Yanam and Bhadrachalam, respectively. With Jason-2, it is around 620 m³ s^?1 at Bhadrachalam. The residual error in individual altimetry data estimates is of the order ±15–25% of the in situ river discharge. The standard errors from ENVISAT at Yanam (Bhadrachalam) for the years 2007, 2008, 2009, and 2010 are 0.10% (14.70%), 10.30% (2.80%), 8.20% (26.50%), and 25.30% (10.20%), respectively; which are within the range of acceptable errors for discharge measurements (10–25%).     

Latent heat flux estimation using trapezoidal relationship between MODIS land surface temperature and fraction of vegetation – application and validation in a humid tropical region

The present study was taken up with the objective of developing a methodology for estimation of actual evapotranspiration (AET) using only satellite data. Accordingly, an algorithm based on the popular Priestley–Taylor method was developed. While previous studies have assumed a triangular relationship between land surface temperature (LST) and fraction of vegetation (FV) to calculate the Priestley–Taylor parameter (φ), a trapezoidal relationship was adopted in the present study to enable applications in forested regions in the humid tropics. The developed algorithm was applied to the humid tropical Mae Klong region, Thailand, and latent heat flux (λ_ET) estimates were validated with measurements made at a flux tower located at the centre of the region. Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing satellite data products corresponding to the study area were used to derive various inputs required by the algorithm. Comparison of estimated and measured fluxes on five cloud-free days in 2003 yielded root mean square error (RMSE) of 64.73 W m^?2 which reduced to 18.65 W m^?2 when one day was treated as an outlier. The methodology developed in this study derived inputs only from satellite imagery and provided reasonably accurate estimates of latent heat flux at a humid tropical location.     

Parameter selection in permutation entropy for an electroencephalographic measure of isoflurane anesthetic drug effect

The permutation entropy (PE) of the electroencephalographic (EEG) signals has been proposed as a robust measure of anesthetic drug effect. The calculation of PE involves the somewhat arbitrary selection of embedding dimension (m) and lag (τ) parameters. Previous studies of PE include the analysis of EEG signals under sevoflurane or propofol anesthesia, where different parameter settings were determined using a number of different criteria. In this study we choose parameter values based on the quantitative performance, to quantify the effect of a wide range of concentrations of isoflurane on the EEG. We analyzed a set of previously published EEG data, obtained from 29 patients who underwent elective abdominal surgery under isoflurane general anesthesia combined with epidural anesthesia. PE indices using a range of different parameter settings (m = 3–7, τ = 1–5) were calculated. These indices were evaluated as regards: the correlation coefficient (r) with isoflurane end-tidal concentration, the relationship with isoflurane effect-site concentration assessed by the coefficient of determination (R ²) of the pharmacokinetic–pharmacodynamic models, and the prediction probability (P_K). The embedding dimension (m) and lag (τ) have significant effect on the r values (Two-way repeated-measures ANOVA, p < 0.001). The proposed new permutation entropy index (NPEI) [a combination of PE(m = 3, τ = 2) and PE(m = 3, τ = 3)] performed best among all the parameter combinations, with r = 0.89(0.83–0.94), R ² = 0.82(0.76–0.87), and P_K = 0.80 (0.76–0.85). Further comparison with previously suggested PE measures, as well as other unrelated EEG measures, indicates the superiority of the NPEI. The PE can be utilized to indicate the dynamical changes of EEG signals under isoflurane anesthesia. In this study, the NPEI measure that combines the PE with m = 3, τ = 2 and that with m = 3, τ = 3 is optimal.     

Estimating potato leaf chlorophyll content using ratio vegetation indices

Chlorophyll content at leaf level is an important variable because of its crucial role in photosynthesis and in understanding plant functioning. In this study, we tested the hypothesis that the ratio of a vegetation index (VI) for estimating canopy chlorophyll content (CCC) and one for estimating leaf area index (LAI) can be used to derive chlorophyll content at the leaf level. This hypothesis for estimating chlorophyll content at the leaf level was tested using simulations with the PROSAIL radiative transfer model and field spectroradiometry measurements in five consecutive years (2010–2014) for potato crops on experimental fields. During the growing season, in-situ field measurements of LAI and leaf chlorophyll content (LCC) were performed. Results showed that good estimates of LCC were feasible using ratio vegetation indices (VIs). This was tested at satellite level using RapidEye images. This letter presents a proof of concept for estimating LCC using Sentinel-2 data. Results confirm the importance of the red-edge bands for agricultural applications, but also showed that indices using the red-edge bands may be replaced by indices using green bands. It should now be tested with real Sentinel-2 data whether its spectral bands at 10 m spatial resolution are suitable for estimating LCC, avoiding the need for red-edge bands that only are available at 20 m.     

A simple technique for co-registration of terrestrial LiDAR observations for forestry applications

Light detection and ranging (LiDAR) from terrestrial platforms provides unprecedented detail about the three-dimensional structure of forest canopies. Although airborne laser scanning is designed to yield a relatively homogeneous distribution of returns, the radial perspective of terrestrial laser scanning (TLS) results in a rapid decrease of number of returns with increasing distance from the instrument. Additionally, when used in forested environments, significant parts of the area under investigation may be obscured by tree trunks and understorey. A possible approach to mitigate this effect is to combine TLS observations acquired at different locations to obtain multiple perspectives of an area under investigation. The denser and more evenly distributed observations then allow a spatially explicit and more comprehensive study of forest characteristics. This study demonstrates a simple approach to combine TLS observations made at multiple locations using bright reference targets as tie-points. Results show this technique was able to accurately combine the different TLS data sets (root mean square error (RMSE): 0.04–0.7 m, coefficient of determination (R ²): 0.70–0.99). Terrain elevations from TLS system were highly correlated with field-measured terrain heights (R ²: 0.70–0.98).     

Patient–ventilator interaction in ARDS patients with extremely low compliance undergoing ECMO: a novel approach based on diaphragm electrical activity

Purpose

Patients with acute respiratory distress syndrome (ARDS) requiring extracorporeal membrane oxygenation (ECMO) usually present very low respiratory system compliance (Cst_rs) values (i.e., severe restrictive respiratory syndrome patients). As a consequence, they are at high risk of experiencing poor patient–ventilator interaction during assisted breathing. We hypothesized that monitoring of diaphragm electrical activity (EAdi) may enhance asynchrony assessment and that neurally adjusted ventilatory assist (NAVA) may reduce asynchrony, especially in more severely restricted patients.

Methods

We enrolled ten consecutive ARDS patients with very low Cst_rs values undergoing ECMO after switching from controlled to pressure support ventilation (PSV). We randomly tested (30 min) while recording EAdi: (1) PSV30 (PSV with an expiratory trigger at 30 % of flow peak value); (2) PSV1 (PSV with expiratory trigger at 1 %); (3) NAVA. During each step, we measured the EAdi-based asynchrony index (AI_EAdi) = flow-, pressure- and EAdi-based asynchrony events/EAdi-based respiratory rate × 100.

Results

AI_EAdi was high during all ventilation modes, and the most represented asynchrony pattern was specific for this population (i.e., premature cycling). NAVA was associated with significantly decreased, although suboptimal, AI_EAdi values in comparison to PSV30 and PSV1 (p < 0.01 for both). The PSV30–NAVA and PSV1–NAVA differences in AI_EAdi values were inversely correlated with patients’ Cst_rs (R ² = 0.545, p = 0.01 and R ² = 0.425, p < 0.05; respectively).

Conclusions

EAdi allows accurate analysis of asynchrony patterns and magnitude in ARDS patients with very low Cst_rs undergoing ECMO. In these patients, NAVA is associated with reduced asynchrony.     

Assessing water quality in the northern Adriatic Sea from HICO™ data

This letter focuses on water-quality estimation in the northern Adriatic Sea using physically-based methods applied to image obtained with the Hyperspectral Imager for the Coastal Ocean (HICO?). Optical properties of atmosphere and water were synchronously measured to parameterise such methods. HICO?-derived maps of chlorophyll-a (chl-a) and suspended particulate matter (SPM) indicated low values, in the range of 0–3 mg m^?3 and 0–4 g m^?3, respectively, correlating significantly with field data (R² = 0.71 for chl-a and R² = 0.85 for SPM). The results, on analysis, identify clear waters in the open sea and moderately turbid waters near the coast due to river sediment discharge and organic matter from coastal lagoons. These findings support the use of HICO? data to assess water-quality parameters in coastal zones and suggest the feasibility of integrating them with future-generation space-borne hyperspectral images.     

Wood density at breast height as estimate of the whole tree density in Grevillea robusta

To validate the relationship between whole-tree average density and wood density at breast height, 60 Grevillea robusta trees were sampled from north-eastern Argentina plantations. Wood basic density was measured at five height levels in each tree. A weighted average density was calculated for each tree and a linear regression was calculated using whole-tree density as the independent variable and wood density at breast height as the regressor. The coefficient of determination (R²), standard error and mean percentage error were 81%, 12.7 kg m^–3 and 10.2% respectively, showing a close association between variables. Wood density at breast height is useful for estimating wood density of the whole tree in G. robusta.     

Validation of the generalized single-channel algorithm using Landsat 8 imagery and SURFRAD ground measurements

Land surface temperature (LST) is a key parameter in land surface physics and depends on energy and water exchange processes with the atmosphere. The generalized single-channel (GSC) algorithm is an operational approach for retrieving LST from satellite sensors with only one thermal infrared channel. Until now, validations of the GSC algorithm using in situ data were limited. In this study, Landsat 8 imagery and SURFace RADiation budget network (SURFRAD) ground observations were employed to test the accuracy of the GSC algorithm. The validation results show that the GSC algorithm can obtain high LST retrieval accuracy when the total atmospheric water vapour content is between 0.5 and 3 g cm^?2, with coefficient of determination (R²) between the retrieved LST and the ground LST greater than 0.97 and mean absolute error (MAE) and root mean square error (RMSE) values of 1.57 K and 1.96 K, respectively.