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Int J Appl Earth Obs Geoinformation

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Effects of prediction accuracy of the proportion of vegetation cover on landsurface emissivity and temperature using the NDVI threshold methodElnaz Neinavaza,⁎, Andrew K. Skidmorea,b, Roshanak Darvishzadehaa Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, 7500 AE, Enschede, theNetherlandsbDepartment of Environmental Science, Macquarie University, NSW, 2109, Sydney, Australia

A R T I C L E I N F O

Keywords:Proportion of vegetation coverThermal infrared remote sensingLand surface emissivityLand surface temperatureVegetation indexLandsat-8Artificial neural network

A B S T R A C T

Predicting land surface energy budgets requires precise information of land surface emissivity (LSE) and landsurface temperature (LST). LST is one of the essential climate variables as well as an important parameter in thephysics of land surface processes at local and global scales, while LSE is an indicator of the material composition.Despite the fact that there are numerous publications on methods and algorithms for computing LST and LSEusing remotely sensed data, accurate prediction of these variables is still a challenging task. Among the existingapproaches for calculating LSE and LST, particular attention has been paid to the normalised difference vege-tation index threshold method (NDVITHM), especially for agriculture and forest ecosystems. To apply NDVITHM,knowledge of the proportion of vegetation cover (PV) is essential. The objective of this study is to investigate theeffect of the prediction accuracy of the PV on the estimation of LSE and LST when using NDVITHM. In August2015, a field campaign was carried out in mixed temperate forest of the Bavarian Forest National Park, insoutheastern Germany, coinciding with a Landsat-8 overpass. The PV was measured in the field for 37 plots. Fourdifferent vegetation indices, as well as artificial neural network approaches, were used to estimate PV and tocompute LSE and LST. The results showed that the prediction accuracy of PV improved using an artificial neuralnetwork (R2CV=0.64, RMSECV= 0.05) over classic vegetation indices (R2CV= 0.42, RMSECV= 0.06). The resultsof this study also revealed that variation in the accuracy of the estimated PV affected calculation results of theLSE. In addition, our findings revealed that, though LST depends on LSE, other parameters should also be takeninto account when predicting LST, as more accurate LSE results did not increase the prediction accuracy of LST.

1. Introduction

Vegetation biophysical and biochemical variables have been widelyretrieved with differing degrees of success by means of remote sensingdata from various parts of the electromagnetic spectrum (Bacour et al.,2006; Baret and Guyot, 1991; Baret et al., 1989; Boegh et al., 2002;Broge and Mortensen, 2002; Brown et al., 2000; Darvishzadeh et al.,2019, 2008; Inoue et al., 2016; Neinavaz et al., 2017; Ullah et al., 2012;Verrelst et al., 2015; Zheng and Moskal, 2009). However, remote sen-sing data from the thermal infrared (TIR, 8–14 μm) spectrum have notbeen utilized as much for the estimation of vegetation variables, due totheir coarse resolution (60m-1 km) and the difficulty in deriving andseparating land surface emissivity (LSE) and land surface temperature(LST) (Dash, 2005). Despite all these challenges, TIR data have beenmore beneficial than visible/near-infrared (VNIR, 0.3–1.0 μm), andshortwave-infrared (SWIR, 1.0–2.5 μm) data for monitoring solar

effects day/night as well as diurnal and intra-annual temperature var-iations. In addition, TIR data have been shown to be complementary toother remote sensing data such as VNIR, and SWIR, and even to beunique in aiding the identification of surface materials and spectralcharacteristics for vegetation, rock types, and soil moisture (Prakash,2000; Ullah et al., 2012). The atmosphere is mostly transparent acrossthe TIR domain (Clerbaux et al., 2011), and TIR data can be measuredday and night, allowing diurnal cycles to be observed and monitored.TIR spectral data have the advantage that they can extend observationand monitoring options for many types of natural phenomena, such asland and ocean surface temperatures, in which negligible variationsmay have a significant impact on climate and ecosystems (Smith et al.,2008).

LST, which is defined as the effective kinetic temperature of theEarth’s surface skin (Pinheiro et al., 2004), provides a valuable set ofobservations to characterise land surface states and land-atmosphere

https://doi.org/10.1016/j.jag.2019.101984Received 26 June 2019; Received in revised form 20 September 2019; Accepted 1 October 2019

⁎ Corresponding author.E-mail addresses: e.neinavaz@utwente.nl (E. Neinavaz), a.k.skidmore@utwente.nl (A.K. Skidmore), r.darvish@utwente.nl (R. Darvishzadeh).

Int J Appl  Earth Obs Geoinformation 85 (2020) 101984

Available online 18 October 20190303-2434/ © 2019 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

T

exchange. LST is recognised as an essential climate variable (ECV) andis acknowledged as an essential parameter for diagnosing the behaviourof the Earth system and evaluating Earth system models (Bojinski et al.,2014) . Furthermore, LST has proved to be important in various aspectsof crop management, such as stress detection, crop growth monitoring,yield forecasting, as well as in irrigation scheduling (Gonzalez-Dugoet al., 2013; Sruthi and Aslam, 2015; Tormann, 1986).

LSE plays an important role in computing LST (Gillespie et al., 1998;Sobrino et al., 2004), particularly when only a few TIR bands areavailable (Dash et al., 2002). The retrieval of LSE, which was defined bySobrino et al. (2001) as a measure of the inherent efficiency of thesurface in converting kinetic into radiant energy above the surface, isnot an easy task. The prediction of LSE from passive satellite mea-surements may encounter difficulties due to, for instance, atmosphericabsorption and emission, atmospheric contamination as well as surfacereflection (Li et al., 2013b). However, knowledge of LSE values is

required for estimating some of the vital vegetation parameters inbiodiversity monitoring, such as energy budget, evapotranspiration,water and energy balances (Jiménez-Muñoz et al., 2006; Seemannet al., 2008; Sobrino et al., 2002), as LSE has considerable control overthe amount of thermal radiation emitted (Dash et al., 2002; Jacob et al.,2004).

Many studies have been carried out to derive LSE and LST fromremotely sensed data (Becker and Li, 1995; Gillespie et al., 1998;Jiménez-Muñoz and Sobrino, 2003). A comprehensive overview on LSEand LST retrieval has been provided by Li et al. (2013a). Among theexisting approaches for LSE and LST retrieval, special consideration hasbeen given to the normalised difference vegetation index thresholdmethod (NDVITHM) (Neinavaz et al., 2019; Oguz, 2013; Rajeshwari andMani, 2014; Tang et al., 2015). NDVITHM was described by Van deGriend and Owe (1993) and has been further modified by Sobrino andRaissouni (2000). Despite the challenges of using NDVITHM for

Fig. 1. The location of the Bavarian Forest National Park, Germany, and the distribution of the sample plots.

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ecosystems covered by rocks, ice, and water, NDVITHM has becomewidely accepted as a practical approach to retrieving the LSE and LSTfor agriculture and forest ecosystems (Oltra-Carrió et al., 2012; Sobrinoet al., 2001; Tang et al., 2015). In order to use NDVITHM, the emissivityof bare soil and vegetation, as well as the proportion of vegetation cover(PV), must be known. PV is defined as the ratio of the vertical projectionarea of vegetation (containing leaves, stalks, and branches) on theground to the total vegetation area (Deardorff, 1978). PV is one of theimportant vegetation biophysical variables associated with the Earthsurface processes, as well as biodiversity monitoring, climate model-ling, and numerical weather prediction models (Gutman and Ignatov,1998).

Among the existing approaches for estimation of PV from remotesensing data, vegetation indices, such as the normalised difference ve-getation index (NDVI), have been frequently used (Camacho-De Cocaet al., 2004; Gutman and Ignatov, 1998; Jiapaer et al., 2011; Zeng et al.,2000). NDVI is generally saturated in areas with relatively high vege-tation cover (Atzberger, 2004; Baret and Guyot, 1991; Danson andPlummer, 1995; Gitelson et al., 2002). Therefore, other vegetation in-dices such as variable atmospherically resistant index (VARIgreen)Gitelson et al. (2002) are used to estimate PV using remote sensing data(Jiménez-Muñoz et al., 2005, 2009). Jiménez-Muñoz et al. (2005)

revealed that VARIgreen could predict PV with higher accuracy than theNDVI-approach could. Additionally, the wide dynamic range vegetationindex (WDRVI), which was also developed by Gitelson (2004), and athree-band gradient difference vegetation index (TGDVI) (Tang et al.,2005) may also be used for the retrieval of PV. Theoretically, the TGDVImitigates the effect of the background to some extent and the influenceof cirrus cloud, while having a high saturation point.

Furthermore, the sensitivity of the WDRVI to a high LAI value(dense vegetation cover) is at least three times greater than the sensi-tivity of the NDVI (Gitelson, 2004). An alternative approach for esti-mating PV is an artificial neural network (ANN), in which spectral re-flectance is applied as an input layer (Boyd et al., 2002). To ourknowledge, the effect of prediction accuracy of the PV on the estimationof LSE has been explored over agricultural area (Jiménez-Muñoz et al.,2005), but has, to date, not been addressed mixed temperate forest.Therefore, in this study, we aim to assess the prediction accuracy of thePV calculated using vegetation indices and an artificial neural network,to evaluate the effect of the PV prediction accuracy obtained by variousmethods for calculating LSE and eventually LST in mixed temperateforest.

Table 1Summary statistics of the proportion of vegetation cover calculated using normalised difference vegetation index (PVNDVI), variable atmospherically resistant indexvalues (PVVARIgreen), the three-band gradient difference vegetation index (PVTGDVI), wide dynamic range vegetation index (PVWDRVI), artificial neural network (PVANN),and in situ measurements (PVIn situ) for 37 plots in Bavarian Forest National Park.

Variables Range Minimum Maximum Mean Std. Deviation Variance

Statistic Std. Error

PVNDVI 0.37 0.53 0.91 0.766 0.013 0.084 0.007PVVARIgreen 0.11 0.29 0.40 0.363 0.003 0.023 0.001PVTGDVI 0.09 0.12 0.20 0.137 0.003 0.018 0.000PVWDRVI 0.77 1.73 2.50 2.011 0.032 0.195 0.038PVANN 0.43 0.40 0.83 0.614 0.015 0.095 0.009PVIn situ 0.43 0.39 0.82 0.619 0.020 0.127 0.016

Fig. 2. Box plots are demonstrating the median, lower and upper quartile values of the proportion of vegetation cover calculated using normalised differencevegetation index (PVNDVI), variable atmospherically resistant index values (PVVARIgreen), the three-band gradient difference vegetation index (PVTGDVI), wide dynamicrange vegetation index (PVWDRVI), artificial neural network (PVANN), and in situ measurements (PVIn situ).

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2. Materials and methods

2.1. Study area

The Bavarian Forest National Park (BFNP) is situated in south-eastern Germany along the border with the Czech Republic (49˚3′19″ N,13˚12′9″E) (Fig. 1). The total area of the BFNP is 24,250 ha and has atemperate climate, with elevation ranging from 600 to 1453m (Huber,2005). There are three main forest types in the BFNP. Sub-alpinespruce, Norway spruce (Picea abies) and Mountain ash (Sorbus aucu-paria) can be found at high altitudes, above 1100m. On slopes and atthe lower altitudes, between 600 and 1100m, Norway spruce, Silver fir(Abies alba), European beech (Fagus sylvatica), and Norway maple (Acerpseudoplatanus) are visible. The valleys have Spruce, mixed with Birches(Betula pendula, and Betula pubescens), Norway spruce, and Mountainash. The dominant tree species in the BFNP is Norway spruce (67%),while European beech and Silver fir are found in 24.5%, and 26% of thepark’s area, respectively (Heurich et al., 2010).

2.2. In situ measurement of the proportion of vegetation cover

The fieldwork was carried out in August 2015. The study area wasdivided into three stands, namely needle-leaf (conifer), broad-leaf aswell as mixed forest. Consistent with the random sampling strategy, 37plots were selected (plot size= 30×30m). The coordinates of thecenter location of each plot were recorded with an accuracy of 1musing a Leica system GPS 1200 (Leica Geosystems AG, Heerbrugg,Switzerland). Each plot was divided into four sub-plots using diagonallines. In this study, PV was measured for each sub-plot based on fiveupward-pointing digital hemispherical photography (DHP) according tothe guideline proposed by Zhou et al. (1998), using a Canon EOS 5Dequipped with a fish-eye lens (Sigma 8mm F3.5 EX DF), under clear skycondition. The camera was levelled on a tripod at about 1.3 m aboveground level (i.e., breast height) (Whitmore et al., 1993). In order toreduce subjective thresh-holding on the blue channel for all captured

images and to classify sky and canopy pixels, the two-corner classifi-cation procedure was applied (Macfarlane, 2011). Further, CAN_EYEsoftware was used to estimate PV. More information on the basicprinciple and specific features of CAN_EYE software can be found inWeiss and Baret (2017). The average of the PV’s estimated for each foursub-plots was considered to be the PV of that plot. The summary sta-tistics of the measured PV is presented in Table 1.

2.3. Satellite data and processing

Landsat-8 data were acquired on 9 August 2015. Digital numbers ofthe operational land imager (OLI) and thermal infrared (TIRS) imagesof Landsat-8 were converted to radiance value, using the coefficientsprovided by the United States Geological Survey (USGS, https://landsat.usgs.gov/using-usgs-landsat-8-product). For the OLI images,radiance was converted to reflectance using the Fast Line-of-sightAtmospheric Analysis of Spectral Hypercubes (FLAASH) module(Matthew et al., 2002). Since 2010, TIRS bands have been resampled byUSGS to 30m to match the OLI spectral bands’ resolution. In this study,we only considered band 10 of Landsat-8, as instability has been re-ported in the calibration of band 11 (Barsi et al. (2014).

2.4. Estimation of land surface emissivity and land surface temperature

One of the practical methods for determining LSE is the use ofstatistical relationships between NDVI derived from VNIR data and thevegetation and soil emissivity values (Van de Griend and Owe, 1993).This approach was later modified and called NDVITHM (Sobrino andRaissouni, 2000; Valor and Caselles, 1996). The LSE using NDVITHM iscalculated according to the following equation (Sobrino and Raissouni,2000; Sobrino et al., 2008):

=< +

++ × +

NDVI bNDVI

NDVI

0.2,0.5, d

0.2 0.5, P (1 P ) d

red

v

v v s v (1)

Fig. 3. Represents analysis of variance for the proportion of vegetation cover calculated using normalised difference vegetation index (PVNDVI), variable atmo-spherically resistant index values (PVVARIgreen), artificial neural network (PVANN), wide dynamic range vegetation index (PVWDRVI), the three-band gradient differencevegetation index (PVTGDVI), and in situ measurements (PVIn situ).

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where a and b denote channel-dependent regression coefficients; redis reflectance in the red region; v and s denote vegetation and baresoil emissivity over TIR band, respectively. In this study, the v and swere extracted from the MODIS emissivity library of the University ofthe California, Santa Barbara (USA) (Wan and Dozier, 1996). PV is theproportion of vegetation cover; dε is cavity effect, which is incon-sequential for flat surfaces, however, for diverse and rough surfaces canreach a value of 2% (Sobrino, 1989; Sobrino et al., 1990). dε can becomputed by using the following equation:

=d P F(1 )(1 )s v v (2)

where F denotes shape factor, the mean value of which, assuming di-verse geometrical distributions, is 0.55 (Sobrino et al., 1990, 2004).

To calculate LST using TIR data, the brightness temperature (BT)should be calculated, using the spectral radiance of TIR bands, andthermal constants (Markham, 1986):

=+( )BT K

In 1KL

21

(3)

where L is spectral radiance at the top of the atmosphere, and K1 andK2 denote bands-specific thermal conversion constants that are avail-able at the Landsat-8 image metadata file. Eventually, the LST wascomputed applying the following equation, suggested by Stathopoulouand Cartalis (2007):

=+ ××{ }LST BT

Ln1 ( )W BTp i (4)

where W denotes wavelength of emitted radiance, i stands as LSE andp corresponds to 1.438 × 10−2 mK. As LST is computed in Kelvin, it isconverted to Celsius by subtracting 273.15. The estimated LSE and LSTwere subsequently evaluated for 37 plots.

2.4.1. Estimating the proportion of vegetation coverAs can be observed from Eq. (2), PV is an essential parameter for

calculating LSE according to NDVITHM. There are several methods forestimating PV using empirical models. In this study, PV was estimatedfollowing the NDVI traditional method (Rouse et al., 1974) and then

Fig. 4. Scatterplots of the measured versus the predicted proportion of vegetation cover calculated using artificial neural network (a), variable atmosphericallyresistant index (b), normalised difference vegetation index (c), the three-band gradient difference vegetation index (d), and wide dynamic range vegetation index (e).

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compared to the VARIgreen (Gitelson et al., 2002), WDRVI (Gitelson,2004), and TGDVI (Tang et al., 2005) approaches, as well as to an ar-tificial neural network. PV can be predicted using NDVI, VARIgreen,and WDRVI as follows:

=P NDVI NDVINDVI NDVIV

soil

veg soil (5)

=PVARIgreen VARIgreen

VARIgreen VARIgreenVsoil

veg soil (6)

= × × +WDRVI Ref Red Ref Ref( )/( )NIR Red NIR Red (7.a)

=P WDRVI WDRVIWDRVI WDRVI( )

( )Vsoil

veg soil (7.b)

where vegetation indexveg and vegetation indexsoil represent the fullyvegetated and bare soil pixels of the considered index, respectively.RefNIR and RefRed are reflectance values on near infrared, and redbands, respectively. Parameter α in Eq. (7.a), was empirically set to 0.3(Jia et al., 2017). More details on estimating PV using NDVI, VARIgreen,and WDRVI can be found in Gutman and Ignatov (1998), Carlson andRipley (1997), and Jia et al. (2017), respectively.

The estimation of PV using TGDVI is put forward as follows, ac-cording to Tang et al. (2005):

=

=

TGDVIRef Ref Ref Ref

P TGDVITGDVI

NIR Red

NIR Red

Red Green

Red Green

Vmax (8)

Where RefGreen is the green band reflectance, and λNIR, λRed, and λGreenare corresponding wavelengths for RefNIR, RefRed, and RefGreen, re-spectively. TGDVImax stands for the maximal three-band gradient dif-ference vegetation index. Finally, an ANN was used to estimate PV. Inthis case, the surface reflectance values from OLI bands were extractedfor each plot and used for PV estimation as an input layer. For networktraining, one of the common training algorithms in back-propagationnetworks was used: the Levenberg-Marquardt algorithm (Hagan and

Menhaj, 1994). As the number of neurons in the hidden layer influencesthe performance of an ANN prediction, the ideal ANN size was de-termined by examining different numbers. The early stopping techniquewas used to avoid over-fitting.

The estimated PV obtained from the different approaches was thenapplied to compute the LSE according to Eq. (1) and eventually the LST.Thus, the LSE and LST values were extracted for the 37 plots. PV esti-mated using NDVI, VARIgreen, WDRVI, TGDVI, and an ANN, is referredto as PVNDVI, PVVARIgreen, PVWDRVI, PVTGDVI, and PVANN, respectively.Hereafter, the LSE and LST, which have been calculated using PVANN,PVNDVI, PVVARIgreen, PVWDRVI, PVTGDVI, and PVIn situ, respectively, arereferred to as LSEPVANN, LSEPVNDVI, LSEPVVARIgreen, LSEPVWDRVI,LSEPVTGDVI and LSEPVIn situ, and as LSTPVANN, LSTPVNDVI, LSTPVVARIgreen,LSTPVWDRVI, LSTPVTGDVI, and LSTPVIn situ, respectively.

2.5. Validation

Two-way analysis of variance (ANOVA) was applied to explorewhether the mean values of the estimated PV’s obtained with the var-ious methods differed significantly. For PVANN, linear regression ana-lyses were performed between the predicted and the measured PV toidentify the best ANN model. The reliability of the ANN in the esti-mation of PV was evaluated using the cross-validated coefficient ofdetermination, and cross-validated root mean squared error. In addi-tion, the cross-validation procedure was reiterated 1000 times, andresults were averaged to minimize unwanted effects from the randominitialization of the optimization routine. The estimated PV, LSE, andLST were evaluated using the cross-validated procedure to select thebest model based on its predictive ability (Duda and Hart, 1973; Shao,1993).

3. Results

3.1. Measured and estimated proportion of vegetation cover

The in situ measured PV ranged from 0.39 to 0.82 and has a meanvalue of 0.61 for the 37 plots. Estimated PVANN and PVWDRVI ranged

Fig. 5. Box plots of the land surface emissivity using proportion of vegetation cover in which estimated using normalised difference vegetation index (LSEPvNDVI),variable atmospherically resistant index values (LSEPvVARIgreen), the three-band gradient difference vegetation index (LSEPvTGDVI), wide dynamic range vegetationindex (LSEPvWDRVI), artificial neural network (LSEPvANN), and in situ measurements (LSEPvIn situ) approaches.

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widely compared with the PV predicted using other approaches (i.e.,PVNDVI, PVVARIgreen, and PVTGDVI) (Table 1). In addition, the boxplotshows that the PVWDRVI is overestimated, while PVANN and PVNDVI arecloser in range to the PvIn situ (Fig. 2). As can be seen in Fig. 3, there aresignificant differences between PVIn situ and PV calculated using

empirical approaches (i.e., Vegetation indices); however, there is nosignificant difference between PVIn situ and PVANN.

The relationships between the estimated PV and the measured PV arepresented in Fig. 4. As can be seen, PVANN is estimated with slightlygreater accuracy (R2CV=0.64, RMSECV= 0.05) than PVVARIgreen

Fig. 6. Land surface emissivity calculated over Bavaria Forest National Park using PVNDVI (a), PVVARIgreen (b), PVTGDVI (c), and PVWDRVI (d).

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(R2CV=0.42, RMSECV=0.06) and PVNDVI (R2CV= 0.408,RMSECV= 0.06). Fig. 4a also demonstrates that the ANN model is lessinsensitive to greater vegetation cover values (greater than 0.7) com-pared with other approaches. Additionally, the scatter plot in Fig. 4bindicates that estimated PVVARIgreen may be underestimated. The resultsalso showed that PV is predicted with low accuracy when using WDRVIand TGDVI approaches (Fig. 4d & e).

3.2. Retrieval of land surface emissivity

The results indicate that the estimated ranges of LSEPVANN andLSEPVNDVI are closer to the LSEPVIn situ than those of LSEPVNDVI andLSEPVVARIgreen for the 37 plots over BFNP (Fig. 5). The LSEPVWDRVI'sestimated range is wider and set much further away from the LSEPVInsitu range. The results show that the LSE, estimated using PVNDVI andPVVARIgreen, disclosed a similar pattern over the BFNP, whereas theLSEPVTGDVI and LSEPVWDRVI displayed a differing pattern to theLSEPVNDVI, and LSTPVVARIgreen (Fig. 6). The two-way analysis of variance

reveals that estimated LSE using different methods are significantlydifferent, except for LSEPVNDVI and LSEPVVARIgreen, which are not stati-cally significantly different from each other. The relationship betweenLSEPVInsitu and the estimated LSEPVANN, LSEPVNDVI, LSEPVVARIgreen,LSEPVTGDVI, and LSEPVWDRVI for the 37 plots is presented in Fig. 7. Ascan be seen, LSEPVANN was predicted with reasonably high accuracy.LSEPVNDVI and LSTPVVARIgreen were estimated with almost similar ac-curacy, although LSEPVNDVI was slightly overestimated, andLSEPVVARIgreen slightly underestimated (Fig. 7b and c). The resultsshowed that the prediction accuracy for LSEPVTGDVI and LSEPVWDRVI wasvery low.

3.3. Land surface temperature retrieval

In general, the results across BFNP show that LSTs estimated usingdifferent approaches exhibit very similar patterns despite having dif-ferent ranges (Fig. 8). The pattern and range of the LSTPVNDVI andLSTPVVARIgreen were similar, while the LSTPVWDRVI and LSTPVTGDVI

Fig. 7. Scatterplots of the land surface emissivity calculated from in situ proportion of vegetation cover and the one estimated using artificial neural network (a),normalised difference vegetation index (b), variable atmospherically resistant index (c), the three-band gradient difference vegetation index (d), and wide dynamicrange vegetation index (e).

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Fig. 8. Land surface temperature (LST) calculated over Bavaria Forest National Park using LSEPVNDVI (a), PVVARIgreen (b), LSEPVTGDVI (c), and LSEPVWDRVI (d).

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showed a different range. Additionally, the maximum value of the LSTwas calculated using PVWDRVI (LSTPVWDRVImax= 29.16 °C). As can beseen in Fig. 8, LSTPVWDRVI and LSTPVTGDVI appear to be overestimated incomparison to LSTPVNDVI and LSTPVVARIgreen. The difference in surfacetemperature range between LSTPVNDVI and LSTPVVARIgreen is negligible(Fig. 9a), whereas the differences between LSTPVNDVI and both

LSTPVWDRVI and LSTPVTGDVI are greater. For instance, the differencebetween LSTPVNDVI and LSTPVWDRVI is 0.54 °C, which is remarkable(Fig. 9b). In addition, LSTPVANN is predicted with higher accuracy(R2CV= 0.92, RMSECV=0.16) (Fig. 10). The prediction accuracy ofLSTPVNDVI and LSTPVVARIgreen is similar (R2CV= 0.21, RMSECV= 0.70).However, surprisingly, LSTPVWDRVI (R2CV=0.61, RMSECV= 0.48) and

Fig. 9. The difference in land surface temperature between LSTPVNDVI and (a) LSTPVVARIgreen, (b) LSTPVTGDVI, and (c) LSTPVWDRVI.

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LSTPVTGDVI (R2CV=0.61, RMSECV=0.46) have been predicted withgreater accuracy than LSTPVNDVI and LSTPVVARIgreen (Fig. 10).

4. Discussion

Previously, Sobrino et al. (2002) discussed how NDVITHM yieldedpromising results for retrieving LSE and LST. To apply NDVITHM, priorknowledge of soil and vegetation emissivities is essential (Becker andLi, 1995; Li et al., 2013b; Van de Griend and Owe, 1993). Notwith-standing the advantages of the NDVITHM, such as obtaining high-re-solution LSE and LST maps, as well as not requiring a sensor with alarge number of TIR bands (Sobrino et al., 2008), some issues shouldstill be taken into consideration. Our results reveal that in addition toinformation on soil and vegetation emissivity values, it is essential toestimate the PV with high accuracy in order to obtain LSE usingNDVITHM. Our findings show that PV can be predicted with different

degrees of accuracy depending on the applied method. For instance,using NDVI and VARIgreen resulted in near- similar results regardingthe prediction accuracy of the PV, which is contrary to the findings ofJiménez-Muñoz et al. (2005; 2009), who suggested that VARIgreencould retrieve PV more accurately than NDVI in agricultural areas (e.g.,corn, alfalfa). In our study, PV is estimated with higher accuracy using amachine learning (ANN) approach (R2CV=0.64, RMSECV= 0.05) thanusing an empirical approach, such as VARIgreen (R2CV=0.42,RMSECV=0.06) and NDVI (R2CV=0.40, RMSECV=0.06). This resultconfirms the earlier results of Boyd et al. (2002), who recommended theANN approach as the preferred option for the retrieval of PV, becausethe machine learning technique offers a powerful means of analyzingwithout making any assumptions, even for complex datasets. In addi-tion, PV has been estimated with low accuracy using WDRVI, andTGDVI approaches, whereas it has been suggested previously that PVcan be retrieved with reasonable accuracy over agricultural area with

Fig. 10. Scatterplots of land surface temperature calculated from in situ proportion of vegetation cover and the one which is estimated using artificial neural network(a), normalised difference vegetation index (b), variable atmospherically resistant index (c), the three-band gradient difference vegetation index (d), and widedynamic range vegetation index (e).

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both approaches (Gitelson, 2004; Tang et al., 2005). This study showsthat PV might be predicted with reasonable accuracy over the agri-cultural area using vegetation indices; however, these approaches arenot necessarily reliable for estimating PV over forest ecosystems.

The results of our study reveal that differences in prediction accu-racy of the PV (i.e., empirical and machine learning approaches) maylead to different results for the estimation of LSE. It should be noted that1% uncertainty in prediction of the LSE can lead to an error of ap-proximately 0.5 K in the calculation of the LST under moderate condi-tions (Li et al., 2013a), with an obvious error of roughly 1 K expected inwarmer and less humid environments (Chen et al., 2016). Therefore, fora heterogeneous ecosystem such as a forest, accurate estimation of thePV is paramount for computing LSE using NDVITHM, as LSE constitutesthe primary source of error in the LST calculation (Jiménez-Muñoz andSobrino, 2006).

The findings of this study reveal that, although the LSE is an im-portant variable for LST calculation, LSE is not the only factor, whichmay affect the prediction accuracy of LST. Unexpectedly, LSTPVWDRVI

and LSTPVTGDVI were estimated with higher accuracy than LSTPVNDVI

and LSTPVVARIgreen, therefore suggesting that in addition to LSE and theatmospheric parameters on which LST depends, attention should bepaid to other parameters, such as elevation (i.e., an abiotic variable)(Peng et al., 2017) and soil moisture (i.e., abiotic variable) (Sun andPinker, 2004),. which have previously been suggested to influence LST.

5. Conclusion

This study aimed to explore the effect of the prediction accuracy ofPV on computing LSE and LST using NDVITHM. PV, as one of the es-sential parameters for calculating LSE via NDVITHM, was estimated withreasonable accuracy using an ANN technique. The ANN-based modelscan be seen as a reliable approach for estimating PV by means ofLandsat-8 data. Our findings have revealed that there is surprisingly nodifference in the prediction accuracy of the PV between using NDVI andVARIgreen methods (empirical models) over the mixed temperateforest. This is in contradiction with previous studies and needs to beexplored further and across different ecosystems. In addition, this studyrevealed that the WDRVI and TGDVI are not reliable approaches forestimating PV over mixed temperate forest. Our findings establishedthat NDVITHM can still be considered one of the most practical methodsfor estimating LST and LSE; however, accurate prediction of the PVremains an important factor as inaccuracy and uncertainty in the pre-diction of the PV could lead to a significant error in the calculation ofLSE. Additionally, in our view, the results of this study suggest that tocalculate LST using NDVITHM more parameters need to be considered,as LSE is not the only variable capable of influencing LST prediction.

Declaration of Competing Interest

None.

Acknowledgments

This research received financial support from the EU ErasmusMundus External Cooperation Window (EM8) Action 2 and was co-funded by the Natural Resources Department, Faculty of Geo-Information Science and Earth Observation, University of Twente, theNetherlands and Bavarian Forest National Park, Germany. The authorsextend their appreciation for the data pool initiative for the BohemianForest Ecosystem and the excellent support received during fieldworkby Bavarian Forest National Park management.

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