1. Introduction

2. Checks of spectral calibration

2.2. Sensitivities and Influences of Assumptions

In the following, we analyze the sensitivity of the method and the influences of the assumptions made in Chapter 2.1, resulting in the expected uncertainty range of this approach.

Sensitivity to noise. For the analysis of sensitivity to noise of the estimated shifts, we expect that the spectra are predominantly affected by signal independent Gaussian noise and only to a small degree affected by signal dependent shot noise [22], because of the low signals, e.g. by comparing the optimally summed image spectra and the fitted reference spectra as illustrated in Figure 6. Furthermore, we expect that the noise is equally distributed to all bands, because, e.g. by examining half of the image spectra with the lower signals in the considered range, we obtain

• for VIS/NIR signals, averaged for each band, in the range of $[+2.8·{10}^{-6},+3.4·{10}^{-6}]$ W/m${}^2$/sr/nm with standard deviations in the range of $[7.1·{10}^{-6},7.9·{10}^{-6}]$ and
• for SWIR signals, averaged for each band, in the range of $[+2.7·{10}^{-5},+3.2·{10}^{-5}]$ W/m${}^2$/sr/nm with standard deviations in the range of $[2.4·{10}^{-5},2.5·{10}^{-5}]$,

namely, basically constant signals, averaged for each band, with constant standard deviations. Furthermore, for these image spectra, we obtain

• for VIS/NIR a signal, averaged for all bands, of $1.96·{10}^{-5}$ W/m${}^2$/sr/nm with a standard deviation of $1.35·{10}^{-5}$, namely an expected relative deviation of the signal of $0.69$, and
• for SWIR a signal, averaged for all bands, of $17.7·{10}^{-5}$ W/m${}^2$/sr/nm and a standard deviation of $4.50·{10}^{-5}$, namely an expected relative deviation of the signal of $0.26$.

Based on these estimates for the given case, the noise contribution is larger in VIS/NIR than in the SWIR.

For estimating the sensitivity, we add noise to the reference spectrum and change its shift to achieve an observed error and shift. In particular, if some ratio $r\ge 0.0$ of the signal of the normalized, not noisy, shifted by $c$nm, reference spectrum $r_{\left(1\right)},\dots ,r_{\left(b\right)}$ with $r_{\mathrm{sum}}={\sum }_{i=1}^b{r}_{\left(i\right)}$ is equally added to each of the b considered bands as noise, namely the noisy, shifted by $c$nm, reference spectrum $r_{\left(1\right)}+r·r_{\mathrm{sum}}/b,\dots ,r_{\left(b\right)}+r·r_{\mathrm{sum}}/b$ is considered, we obtain an observed error and shift.

To be more precise, we consider the mapping $f(r,c)=(e_{i_{0.01,\min }},d_{i_{0.01,\min }})$. This mapping f is not necessarily bijective, in particular not unambiguous, because different combinations of r and c may result in the same observed error $e_t$ and shift $d_t$, namely f is not injective as $\left\{f^{-1}(e_t,d_t)\right\}>1$, or no combinations of r and c may result in some observed error $e_t$ and shift $d_t$, namely f is not surjective as $\left\{f^{-1}(e_t,d_t)\right\}=0$. The mapping f is illustrated in Figure 8.

Consequently, the influence of noise for the observed errors and shifts in Section 2.1 is as follows. For the VIS/NIR, the estimates have minimal ambiguities $f^{-1}(0.023,+0.331)=(0.077,+0.329)$ and just a minor change in the spectral shift of $-0.012$nm. For the SWIR, ambiguities increase to $f^{-1}(0.117,-0.175)=(0.379,+0.194)$ and a related major change in the shift of $+0.369$ is observed.

Because of the observations on noise, namely accounting for the expected relative deviations of the signals, averaged for all bands, of $0.69$ for VIS/NIR and $0.26$ for SWIR, we consider ratios $r\in R=[0.0,0.077·(1.0+0.69)=0.130]$ for VIS/NIR and $r\in R=[0.0,0.379·(1.0+0.26)=0.478]$ for SWIR. In other words, we consider some more added noise to the reference spectrum. Hence, at most all $d_i$ for $i\ge i_{0.01}$, namely $s_i$ is not considered as outlier, and where $f^{-1}(e_i,d_i)=(r,c)$ for $r\in R$ and any c, are shifts not violating the expected deviations of the observed errors. We obtain ranges $D=[+0.326\mathrm{nm},+0.370\mathrm{nm}]$ for VIS/NIR and $D=[-0.710\mathrm{nm},+0.026\mathrm{nm}]$ for SWIR and by considering any c, where $f^{-1}(e_t,d_t)=(r,c)$ for $r\in R$, $d_t\in D$ and any $e_t$, we obtain results on uncertainties based on sensitivity as stated in Table 1. We remark, at least for the considered ranges, the values of $e_t$ and r are strongly correlated by a constant positive factor. Because, e.g., $e_{i_{0.01,\min }}·(1.0+0.69)=0.040$ for VIS/NIR and $e_{i_{0.01,\min }}·(1.0+0.26)=0.148$ for SWIR, Figure 7 (lines 1 and 2) indicates these ranges of D and Figure 8 indicates the resulting differences, namely uncertainties. We remark, $0.0$nm is always contained in the ranges, here, because for $r=0.0$ the noisy and not noisy spectra equal.

A1—influence of lighting types. Let us consider assumption A1 and each of the seven spectra measured by [8] as reference spectrum ($3\times$HPS, $3\times$MH, $1\times$LPS). We obtain shifts of:

	reference
	HPS 1	HPS 2	HPS 3	MH 1	MH 2	MH 3	LPS
VIS/NIR	$+0.331$nm	$+0.413$nm	$-0.111$nm	$+0.402$nm	$+0.904$nm${}^{\left(2\right)}$	$+0.051$nm	$+0.422$nm${}^{\left(1\right)}$
SWIR	$-0.175$nm	$-0.111$nm	$+0.290$nm	$-0.030$nm	$+0.006$nm	$-1.573$nm${}^{\left(2\right)}$	$+0.700$nm${}^{\left(1\right)}$

Because of lower signals for LPS in VIS/NIR and SWIR, and as mixtures of such spectra are expected, we do not account for (and to avoid drifts both) the minimum shifts, marked by (1), and maximum shifts, marked by (2). As expected, the differences in the shifts are marginal and we obtain results on the uncertainties based on A1 as stated in Table 1. We remark, that the average shifts are $+0.324$nm for VIS/NIR and $-0.004$nm for SWIR and, in particular for VIS/NIR, these shifts are close to the shifts for the reference spectrum.

A2—influence of surface types and upward vs. downward illuminations. Let us consider assumption A2 and a smooth surface reflectance, absorption depths $a_d$ of low (00.1%), medium (01.0%), and high (10.0%) and absorption widths $a_w$ of low (001nm), medium (010nm), and high (100nm) applied to the constant, positive surface reflectance. In other words, if a signal is a combination of, e.g., 90% directly observed light emission and 10% indirectly observed light emission via a surface with a positive reflectance, and now a high absorption depth of 10.0% is applied to the surface, namely the surface reflectance is changed by a factor of 90.0%, then a signal of $90\%+10\%·90.0\%=99\%$ results. We remark, that the result is the same as for, e.g., 0% directly and 100% indirectly observed light emission and applying a medium absorption depth of 01.0%. To be more precise, we model absorption of $a_d$ at a specific wavelength of $a_c$nm by a linearly descending absorption to the constant, positive surface reflectance by a factor of $100\%-a_d$ at $a_c$nm to $100\%$ for increasing distances to $a_c$nm, where the Full Width at Half Maximum is given by $a_w$. Considering all $a_c$nm, we obtain shifts of:

VIS/NIR	low depth (00.1%)	medium depth (01.0%)	high depth (10.0%)
low width (001nm)	$[+0.331\mathrm{nm},+0.332\mathrm{nm}]$	$[+0.328\mathrm{nm},+0.334\mathrm{nm}]$	$[+0.300\mathrm{nm},+0.359\mathrm{nm}]$
medium width (010nm)	$[+0.330\mathrm{nm},+0.333\mathrm{nm}]$	$[+0.325\mathrm{nm},+0.338\mathrm{nm}]$	$[+0.272\mathrm{nm},+0.390\mathrm{nm}]$
high width (100nm)	$[+0.331\mathrm{nm},+0.332\mathrm{nm}]$	$[+0.330\mathrm{nm},+0.332\mathrm{nm}]$	$[+0.323\mathrm{nm},+0.340\mathrm{nm}]$

SWIR	low depth (00.1%)	medium depth (01.0%)	high depth (10.0%)
low width (001nm)	$[-0.176,\mathrm{nm},-0.174\mathrm{nm}]$	$[-0.180\mathrm{nm},-0.172\mathrm{nm}]$	$[-0.245\mathrm{nm},-0.146\mathrm{nm}]$
medium width (010nm)	$[-0.177\mathrm{nm},-0.173\mathrm{nm}]$	$[-0.194\mathrm{nm},-0.157\mathrm{nm}]$	$[-0.400\mathrm{nm},+0.001\mathrm{nm}]$
high width (100nm)	$[-0.176\mathrm{nm},-0.174\mathrm{nm}]$	$[-0.179\mathrm{nm},-0.171\mathrm{nm}]$	$[-0.216\mathrm{nm},-0.139\mathrm{nm}]$

As expected, the extends of the ranges increase by increasing the absorption depths and are larger for medium widths, because for low widths the influence to a single band is limited and for large widths the influence to all bands is similar. We account for the minimum and maximum shifts for all considered combinations of $a_d$ and $a_w$ and we obtain results on the uncertainties based on A2 as stated in Table 1.

A3—influence of atmospheres. Let us consider assumption A3 and next to the default atmosphere, atmospheres with a maximal (but not cloudy) and a minimal water vapour of 6651.1atm-cm and 1817.9atm-cm, that is half of the default water vapour, as illustrated in Figure 4. Because the water vapour has the highest influence on the atmospheric absorption, we do not consider changes of the other parameters. We obtain shifts of $+0.301$nm and $+0.355$nm for VIS/NIR, and $-0.395$nm and $-0.030$nm for SWIR. In particular for VIS/NIR, the shifts are close to the ones for the default atmosphere, because the dynamics of the atmosphere are limited in this wavelength range. And we obtain results on the uncertainties based on A3 as stated in Table 1. We remark, if we do not account for the atmosphere at all, shifts of $+0.428$nm for VIS/NIR and $+0.480$nm for SWIR result, that are as expected close to the shifts considering the default atmosphere.

A4—influence of range of bands around the lighting emission peaks. Let us consider assumption A4 and next to the default range of $\pm 3$ bands, $\pm 2$ bands and $\pm 4$ bands around the lighting emission peaks, namely 819nm for VIS/NIR and 1,139nm for SWIR. We obtain shifts of $+0.359$nm and $+0.351$nm for VIS/NIR and $-0.388$nm and $-0.030$nm for SWIR. Again, in particular for VIS/NIR, these are close to the shifts for the default range of bands. And we obtain results on the uncertainties based on A4 as stated in Table 1. This assumption is already incorporated by the considerations of sensitivity, namely changes of the considered bands will result in corresponding changes in the sensitivity, and therefore, not relevant for uncertainty estimations. However, the results illustrate the robustness of the considered method.

A5—influence of distance metrics. Let us consider assumption A5 and for normalized image spectra $s_{\left(1\right)},\dots ,s_{\left(b\right)}$ and reference spectra $r_{\left(1\right)},\dots ,r_{\left(b\right)}$ next to the default Euclidean distance ${\left({\sum }_{i=1}^b{(s_{\left(i\right)}-r_{\left(i\right)})}^2\right)}^{1/2}$, the Manhattan distance ${\sum }_{i=1}^b{s}_{\left(i\right)}-r_{\left(i\right)}$ and the distance ${\left({\sum }_{i=1}^b{(s_{\left(i\right)}-r_{\left(i\right)})}^4\right)}^{1/4}$. We obtain shifts of $+0.315$nm and $+0.404$nm for VIS/NIR and $-0.413$nm and $-0.274$nm for SWIR, namely, in particular for VIS/NIR, close to the shifts for the default distance measure. And we obtain results on the uncertainties based on A5 as stated in Table 1. This assumption is already incorporated by the considerations of sensitivity, namely changes of the considered distance measure will result in corresponding changes in the sensitivity, and therefore, not relevant for uncertainty estimations, here. However, the results illustrate the robustness of the considered method.

A6—influence of image spectra selections. Let us consider assumption A6 and next to the default relative deviation in the error of $e=0.01$, $e=0.005$ and $e=0.05$—as well as $e=0.001$, $e=0.1$, and $e=1.0$ for later investigations—, namely lower and higher values as the default. We obtain shifts of:

Because $i_{0.005}=i_{0.005,\min }$ for VIS/NIR, this may be seen as some indicator, that $e=0.005$ is a too conservative value, here, but nevertheless the shift for $e=0.005$ is close to the one for $e=0.01$ and for SWIR $d_{0.005,\min }=d_{0.01,\min }$. Furthermore, for $e=0.05$ we obtain $d_{0.05,\min }=d_{0.01,\min }$ for VIS/NIR and SWIR. And we obtain results on uncertainties based on A6 as stated in Table 1. Because all these observed errors and shifts are already incorporated by the considerations of sensitivity, this assumption is not relevant for uncertainty estimations, here. We remark, if we consider $e=0.1$ and $e=1.0$, namely we do not consider e at all, but any optimum, we obtain the same shifts as for $e=0.01$. Furthermore, because $i_{0.001}=i_{0.001,\min }$ for VIS/NIR and SWIR, this may be seen as strong indicator, that $e=0.001$ is a too conservative value, here.

Summary of A1 to A6. Assuming to the worst that all these estimated uncertainties typeset bold in Table 1 are correlated, namely the ranges need to be added, we obtain total uncertainties as stated in Table 1, namely expected shifts of $+0.331$nm in the range $[-0.049\mathrm{nm},+0.549\mathrm{nm}]$ for VIS/NIR and $-0.175$nm in the range $[-1.340\mathrm{nm},+0.816\mathrm{nm}]$ for SWIR. These values—even the estimated shifts and not considering the estimated uncertainties—are well within the accuracy of $0.5$nm for the spectral calibration based on laboratory calibrations and dedicated satellite equipment [24]. We remark, assuming shifts of $+0.250$nm for VIS/NIR and $-0.262$nm for SWIR lead to symmetric uncertainties of $\pm 0.299$nm for VIS/NIR and $\pm 1.078$ for SWIR.

In addition to the investigation of other spectral features of light emissions, future research may consider atmospheric absorption features such as the Oxygen A band absorption at 760nm or the CO${}_2$ absorption at 2,060 for homogeneous light or thermal emissions in that range.

Figure 9 illustrates for the complete EnMAP tile the integrated signals for each pixel accounting for the bands in the considered range, where for illustration purposes instead of signal L the value $L^{1/4}$ is considered. We remark, for VIS/NIR, marginal systematic striping effects in along-track direction and for SWIR, marginal systematic higher signals at the borders compared to the center in across-track direction are visible.

Spectral Along-Track Stability. Another aspect is to check the spectral stability over short time intervals, i.e. in along-track direction. If we split the EnMAP tile into the first half and second half in along-track direction, we observe that pixel with high signals are located in both halves and we obtain shifts of $+0.585$nm (with error $0.076$) and $+0.320$nm (with error $0.025$) for VIS/NIR and $-0.392$nm (with error $0.122$) and $-0.113$nm (with error $0.116$) for SWIR. All these estimated shifts are close to the estimated shifts for the complete EnMAP tile and, in particular, well within the estimated uncertainties. Thus for this given scene the described approach allows for the estimation of short time stability, which is given for EnMAP.

Spectral Across-Track Characteristics. As hyperspectral push-broom sensors often exhibit a slight change in center wavelengths in across-track direction (often denoted as spectral smile), this sensor property is also assessed with this method. For the EO tile, we observe that pixel with high signals are located, in particular, between approx. pixel 570 and 630 in across-track direction or of the sensor. To be more precise, for $i_{0.01,\min }$ the average—weighted based on the considered signals of the considered pixel—sensor pixel is $615.7$ for VIS/NIR and $583.9$ for SWIR. For these sensor pixel shifts of $-0.029$nm for VIS/NIR and $-0.068$nm for SWIR are expected according to the spectral calibration based on laboratory characterization. Therefore, accounting for the shifts of these sensor pixel, too, we assume shifts of $+0.360$nm for VIS/NIR and $-0.107$nm for SWIR.

To analyze the spectral smile, we separate the 1,000 valid sensor pixel per band to 4 parts of 250 pixel each. We consider the two parts with lowest fitting error. And we obtain shifts of $+0.296$nm (with error $0.025$) and $+0.523$nm (with error $0.071$) for the 3^rd and 4^th part of VIS/NIR (all other parts have errors of at least $0.1$) and $-0.030$nm (with error $0.116$) and $-0.744$nm (with error $0.161$) for the 3^rd and 4^th part of SWIR (all other parts have errors of at least $0.2$). Figure 10 illustrate these results. All these results are well within the estimated uncertainties.

For VIS/NIR, the estimated smile is highly consistent, being close to the estimate using a across-track constant fitting, and showing the same across-track slope as the smile based on laboratory calibrations. For SWIR, where the uncertainties are estimated to be much larger as for VIS/NIR, the estimated shifts are decreasing with increasing across-track pixel as for the spectral calibration based on laboratory calibrations, but the magnitudes of changes are not consistent.

3. Identifications of lighting types

4. Conclusions

References

1. Bachmann, M.; Alonso, K.; Carmona, E.; Gerasch, B.; Habermeyer, M.; Holzwarth, S.; Krawczyk, H.; Langheinrich, M.; Marshall, D.; Pato, M.; Pinnel, N.; de los Reyes, R.; Schneider, M.; Schwind, P.; Storch, T. Analysis-Ready Data from Hyperspectral Sensors—The Design of the EnMAP CARD4L-SR Data Product. Remote Sensing 2023, 4536(1), 1–19. [Google Scholar]
2. Bennett, M.M.; Smith, L.C. Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics. Remote Sensing of Environment 2017, 192, 176–197. [Google Scholar]
3. Barentine, J.C.; Walczak, K.; Gyuk, G.; Tarr, C.; Longcore, T. A Case for a New Satellite Mission for Remote Sensing of Night Lights. Remote Sensing 2021, 13(12), 2294. [Google Scholar]
4. Chrien, T.G.; Green, R. Using Nighttime Lights to Validate the Spectral Calibration of Imaging Spectrometers. Physics 2000. [Google Scholar]
5. Clark, R.N.; et al. Environmental mapping of the world trade center area with imaging spectroscopy after the September 11, 2001 attack: The Airborne Visible/InfraRed Imaging Spectrometer mapping. ACS Publications 2005, 66–83. [Google Scholar]
6. Deborah, H.; Richard, N.; Hardeberg, J.Y. A comprehensive evaluation of spectral distance functions and metrics for hyperspectral image processing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2015, 8(6), 3224–3234. [Google Scholar]
7. Elvidge, C.D.; Green, R. High- and low-altitude AVIRIS observations of nocturnal lighting. Environmental Science 2005. [Google Scholar]
8. Elvidge, C.D.; D.M. Keith; Tuttle, B.T.; Baugh, K.E. Spectral identification of lighting type and character. Sensors 2010, 10(4), 3961–3988.
9. Elvidge, C.D.; Zhizhin, M.; Hsu, F.-C.; Baugh, K.E. VIIRS Nightfire: Satellite Pyrometry at Night. Remote Sensing 2013, 5(9), 4423–4449. [Google Scholar] [CrossRef]
10. Ghosh, T.; Hsu, F.C. Advances in Remote Sensing with Nighttime Lights. Remote Sensing 2019, Special Issue.
11. Guanter, L.; Kaufmann, H.; Segl, K.; Foerster, S.; Rogass, C.; Chabrillat, S.; Küster, T.; Hollstein, A.; Rossner, G.; Chlebek, C.; Straif, C.; Fischer, S.; Schrader, S.; Storch, T.; Heiden, U.; Müller, A.; Bachmann, M.; Mühle, H.; Müller, R.; Habermeyer, M.; Ohndorf, A.; Hill, J.; Buddenbaum, H.; Hostert, P.; van der Linden, S.; Leitao, P.J.; Rabe, A.; Doerffer, R.; Krasemann, H.; Xi, H.; Mauser, W.; Hank, T.; Locherer, M.; Rast, M.; Staenz, K.; Sang, B. The EnMAP spaceborne imaging spectroscopy mission for earth observation. Remote Sensing 1015, 7(7), 8830–8857. [Google Scholar]
12. Kruse, F.A., Elvidge, C.D. Identifying and mapping night lights using imaging spectrometry. In Proc. IEEE Aerospace Conference, 2011, USA.
13. Kyba, C.C.M.; Pritchard, S.B.; Ekirch, A.R.; Eldridge, A.; Jechow, A.; Preiser, C.; Kunz, D.; Henckel, D.; Hölker, F.; Barentine, J.; Berge, J.; Meier, J.; Gwiazdzinski, L.; Spitschan, M.; Milan, M.; Bach, S.; Schroer, S.; Straw, W. Night Matters—Why the Interdisciplinary Field of "Night Studies" Is Needed. J 2020, 3(1), 1–6. [Google Scholar]
14. Levin, N.; Kyba, C.C.M.; Zhang, Q.; Sánchez de Miguel, A.; Román, M.O.; Li, X.; Portnov, B.A.; Molthan, A.L.; Jechow, A.; Miller, S.D.; Wang, Z.; Shrestha, R.M.; Elvidge, C.D. Remote sensing of night lights: A review and an outlook for the future. Remote Sensing of Environment 2020, 237, 111443. [Google Scholar]
15. Martin, R.V. Satellite remote sensing of surface air quality. Atmospheric Environment 2008, 42(34), 7823–7843. [Google Scholar]
16. Mayer, B.; Kylling, A. The libRadtran software package for radiative transfer calculations-description and examples of use. Atmospheric Chemistry and Physics 2005, 5(7), 1855–1877. [Google Scholar]
17. de Meester, J.; Storch, T. Optimized Performance Parameters for Nighttime Multispectral Satellite Imagery to Analyze Lightings in Urban Areas. Sensors 2020, 20, 3313. [Google Scholar]
18. de Miguel, A.S.; Kyba, C.C.M.; Aubé, M.; Zamorano, J.; Cardiel, N.; Tapia, C.; Bennie, J.; Gaston, K.J. Colour remote sensing of the impact of artificial light at night (I): The potential of the International Space Station and other DSLR-based platforms. Remote Sensing of Environment 2019, 224, 92–103. [Google Scholar]
19. de Miguel, A.S.; Zamorano, J.; Aubé, M.; Bennie, J.; Gallego, J.; Ocaña, F.; Pettit, D.R.; Steffanov, W.L.; Gaston, K.J. Colour remote sensing of the impact of artificial light at night (II): Calibration of DSLR-based images from the International Space Station. Remote Sensing of Environment 2021, 264, 112611. [Google Scholar]
20. Miller, S.D.; Straka, W.; Mills, S.P.; Elvidge, C.D.; Lee, T.F.; Solbrig, J.; Walther, A.; Heidinger, A.K.; Weiss, S.C. Illuminating the capabilities of the Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band. Remote Sensing 2013, 5(12), 6717–6766. [Google Scholar]
21. Ou, J.; Liu, X.; Li, X.; Li, M.; Li, W. Evaluation of NPP-VIIRS nighttime light data for mapping global fossil fuel combustion CO2 emissions: a comparison with DMSP-OLS nighttime light data. PloS one 2015, 10(9), e0138310. [Google Scholar]
22. Rasti, B.; Scheunders, P.; Ghamisi, P.; Licciardi, G.; Chanussot, J. Noise Reduction in Hyperspectral Imagery: Overview and Application. Remote Sensing 2018, 10(3), 482. [Google Scholar]
23. Storch, T.; Aubé, M.; Bara, S.; Falchi, F.; Kuffer, M.; Kyba, C.; Levin, N.; Oszoz, A.; Román, M.O.; de Miguel, A.S.; Schrott, L.; Münzenmayer, R.; Riel, S.; Gaston, K.J. N8—Global Environmental Effects of Artificial Nighttime Lighting. ESA Living Planet Symposium 2022, Germany. [Google Scholar]
24. Storch, T.; Honold, H.-P.; Chabrillat, S., Habermeyer, M.; Tucher, P.; Brell, M.; Ohndorf, A., Wirth, K.; Betz, M.; Kuchler, M.; Mühle, H.; Carmona, E.; Baur, S.; Mücke, M.; Löw, S.; Schulze, D.; Zimmermann, S.; Lenzen, C.; Wiesner, S.; Aida, S.; Kahle, R.; Willburger, P.; Hartung, S.; Dietrich, D.; Pleasia, N.; Tegler, M.; Schork, K.; Alonso, K.; Marshall, D.; Gerasch, B.; Schwind, P.; Pato, M.; Schneider, M.; de los Reyes, R.; Langheinrich, M., Wenzel, J.; Bachmann, M.; Holzwarth, S.; Pinnel, N.; Guanter, L.; Segl, K.; Scheffler, D.; Foerster, S.; Bohn, N.; Bracher, A.; Soppa, M.A.; Gascon, F.; Green, R.; Kokaly, R.; Moreno, J.; Ong, C.; Sornig, M.;Wernitz, R.; Bagschik, K.; Reintsema, D.; La Porta, L.; Schickling, A.; Fischer, S. The EnMAP imaging spectroscopy mission towards operations. Remote Sensing of Environment, 2021, 263, 112557.
25. Wang, Z.; Román, M.O.; Kalb, V.L., Miller, S.D.; Zhang, J.; Shrestha, R.M. Quantifying uncertainties in nighttime light retrievals from Suomi-NPP and NOAA-20 VIIRS Day/Night Band data. Remote Sensing of Environment, 2021 263, 112557.