Noise Evaluation of early images for Landsat 8 Operational Land Imager

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1 Noise Evaluation of early images for Landsat 8 Operational Land Imager Huazhong Ren 1, Chen Du 1, Rongyuan Liu 2, Qiming Qin 1,*, Guangjian Yan 2, Zhao-Liang Li 3, and Jinjie Meng 1 1 Institute of Remote Sensing and Geographic Information System, Peking University, Beijing , China 2 State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing , China 3 Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing , China * qmqinpku@163.com Abstract: This study performed an on-orbit evaluation of noise level for the Operational Land Imager (OLI) onboard Landsat 8 using early images over ground homogeneous sites. The signal-to-noise ratios (SNR) were higher than 160 of OLI nine bands at typical radiance level, while the noise equivalent radiance difference (NE L) and the noise equivalent reflectance difference (NE ρ) were respectively lower than 0.8 W/m 2 /µm/sr and Compared to pre-launch predictions, the on-orbit low noise and high SNR almost satisfied requirements for OLI bands, and can provide a prior knowledge for uncertainty analysis of OLI images in monitoring land surface, oceanic, and atmospheric status Optical Society of America OCIS codes: ( ) Remote sensing and sensors; ( ) Optical sensing and sensors; ( ) Noise in imaging systems; ( ) Image analysis. References and links 1. J. R. Irons, J. L. Dwyer, and J. A. Barsi, The next Landsat satellite: The Landsat Data Continuity Mission, Remote Sens. Environ. 122, (2012). 2. D. P. Roy, M. A. Wulder, T. R. Loveland, W. C.e, R. G. Allen, M. C. Anderson, D. Helder, J. R. Irons, D. M. Johnson, R. Kennedy, T. A. Scambos, C. B. Schaaf, J. R. Schott, Y. Sheng, E. F. Vermote, A. S. Belward, R. Bindschadler, W. B. Cohen, F. Gao, J. D. Hipple, P. Hostert, J. Huntington, C. O. Justice, A. Kilic, V. Kovalskyy, Z. P. Lee, L. Lymburner, J. G. Masek, J. McCorkel, Y. Shuai, R. Trezza, J. Vogelmann, R. H. Wynne, and Z. Zhu, Landsat-8: Science and product vision for terrestrial global change research, Remote Sens. Environ. 145, (2014). 3. G. Chander, B. L. Markhamb, and D. L. Helder, Summary of current radiometric calibration coefcients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors, Remote Sens. Environ. 113(5), (2009). 4. P. J. Curran and J. L. Dungan, Estimation of signal-to-noise: A new procedure applied to AVIRIS data, IEEE Trans. Geosci. Rem. Sens. 27(5), (1989). 5. B.-C. Gao, An operational method for estimating signal to noise ratios from data acquired with imaging spectrometers, Remote Sens. Environ. 43(1), (1993). 6. N. Fujimoto, Y. Takahashi, T. Moriyama, M. Shimada, H. Wakabayashi, Y. Nakatani, and S. Obayashi, Evaluation of SPOT HRV image data received in Japan, in Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS, 1989) 2: R. S. Luis, A. Ó. Teixeira, and P. Monteiro, Optical signal-to-noise ratio estimation using reference asynchronous histograms, J. Lightwave Technol. 27(6), (2009). 8. A. Barducci, D. Guzzi, P. Marcoionni, and I. Pippi, Assessment of signal-to-noise ratio of CHRIS/PROBA and other hyperspectral sensors using images acquired over the San Rossore (Italy) test site, Proc. SPIE 5982, (2005). 9. Z. Wan, Estimate of noise and systematic error in early thermal infrared data of the Moderate Resolution Imaging Spectroradiometer (MODIS), Remote Sens. Environ. 80(1), (2002). 10. B. L. Markham, J. L. Barker, E. Kaita, J. Seiferth, and R. Morfitt, On-orbit performance of the Landsat-7 ETM+ radiometric calibrators, Int. J. Remote Sens. 24(2), (2003). 11. B.-C. Gao, NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sens. Environ. 58(3), (1996). 12. V. Salomonson and I. Appel, Development of the Aqua MODIS NDSI fractional snow cover algorithm and validation results, IEEE Trans. Geosci. Rem. Sens. 44(7), (2006). 13. Y. Zha, J. Gao, and S. Ni, Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, Int. J. Remote Sens. 24(3), (2003). (C) 2014 OSA 3 November 2014 Vol. 22, No. 22 DOI: /OE OPTICS EXPRESS 27270

2 14. R. Morfitt, B. L. Markham, E. Micijevic, P. Scaramuzza, J. A. Barsi, R. Levy, L. Ong, and K. Vanderwerff, OLI Radiometric Performance On-Orbit, Remote Sens. in press. 1. Introduction Landsat 8 was launched on February 11, 2013 as the latest satellite in the Landsat Data Continuity Mission (LDCM) project. One important instrument onboard this satellite is the Operational Land Imager (OLI) that uses a push-broom method to collect surface reflected radiance at a resolution of 30 m. OLI has a broader spectral range than its predecessors Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM + ), i.e., nine bands in visible, near, and shortwave infrared wavelengths, including two additional bands: the coastal aerosol band ( μm), which is designed to investigate chlorophyll and suspended materials in coastal waters; and the cirrus band ( μm), which is designed to provide a comprehensive detection of cirrus cloud contamination [1,2]. With calibration before its launch, OLI satisfied the design requirements of radiometric characteristics and performed better than the TM and ETM + because it exceeds the 8-bit recording capacity of the TM and ETM + [3] and records data at 12 bits. However, the radiometric performance of OLI may be affected by any potential changes in its optical status caused by the rapid launch process and its long post-launch operation in the orbit. The uncertainty of OLI instrument might come from two main sources: (1) the dark current of the detector and electronics, and (2) the scattering signal from the instrument optical path [1]. Although the OLI will close a shutter wheel to prevent light from entering the instrument to remove the background bias before and after an orbit observation, the residual of the background bias will still exist. Since such random variability or noise associated with signals will reduce the image quality of the OLI and consequently increase the uncertainty in land surface reflectance after atmospheric correction and its further applications in land surface and ocean studies [4,5], the noise of OLI images should be estimated to optimize the use of OLI and also to ensure the data consistency of more than 40 years Landsat series images. For clarity, in this paper, signal is defined as the useful information collected by the sensor while noise is considered as the random error that contaminates the signal [5]. Therefore, noise level is related to image quality, stability, and uniformity. Signal-to-noise ratio (SNR) is always used to characterize the radiation response of an imaging instrument, and it is one of the most direct and accurate parameters used to evaluate the radiometric performance of an instrument. To characterize the possible post-launch degradation of the performance of OLI, radiometric calibration is undertaken on-orbit Landsat 8, in which OLI is recalibrated by observations of on-orbit stimulation lamps (once per day), solar diffuser panels (once per week) and lunar illuminations (once per month) [1]. This process updates the radiometric calibration coefficients from the least square regression of the measured average digital number (DN) and the known spectral radiance. However, this method smoothens unexpected noise and seldom provides the signal uncertainty of the instrument. It also becomes ineffective as the on-orbit lamps and solar diffuser aging. From this viewpoint, image noise and the ability of OLI to respond to input radiation should be determined to improve the calibration accuracy of this instrument and its application. The present study aims at evaluating the SNR and noise level included in the early images of OLI before the end of May 2014, and it is organized as follows: Section 2 introduces the method and the data used to estimate the two parameters. Section 3 presents results of the SNR and noise level of the OLI bands under different radiance conditions. Sections 4 and 5 contain the discussion and conclusions, respectively. 2. The method and data 2.1 Method The evaluation of SNR and noise has attracted great research attention, and several methods have been developed to estimate the two parameters from remotely sensed images and photos. Curran and Dungan proposed a geostatistical method that used a few narrow strips of (C) 2014 OSA 3 November 2014 Vol. 22, No. 22 DOI: /OE OPTICS EXPRESS 27271

3 relatively homogenous area that were manually selected from images to estimate the parameters [4]. Similarly, Fujimoto et al. initially computed the mean and the standard deviation of the signals in a homogenous area of an image and then calculated the ratio of the mean and the standard deviation (considered as noise) as the SNR of the image. Their method was therefore called homogenous area [6]. Gao introduced an operational method that used the concept of local means and local standard deviation of small imaging blocks and used a box counting procedure [4]. Recently, the two parameters have also been calculated using reference asynchronous histograms [7] and bit-plane methods [8]. According to the spectral band features of OLI, the homogenous area method proposed by Fujimoto et al. [6] is suitable for estimating noise and SNR because the method has clear definitions of signal and noise and can directly obtain results from images rather than statistical outcomes. Homogenous areas are needed in this method. The pixel radiance collected by an instrument at the top of the atmosphere (TOA) over the study area can be written as [9]: Lb (, c j) = Lb, surf () c + δlb, noise (, c j) + δlb, s a (, c j), (1) where b = 1 9 stands for the band number of OLI, and c is the detector number because OLI observes the surface with a push-broom method using a linear detector array for each of the bands, unlike TM and ETM + that employ the whisk-broom method. Radiance changes pixel by pixel if the radiative responses are not the same for all detectors. j is the pixel number over the study area. L b,surf (c) is the accurate band radiance at average surface and atmospheric conditions if noise is absent and no changes can be observed in the surface and atmosphere conditions at the study area. δl b,noise (c, j) is the systematic noise of the detector from the residual bias after removing dark current and optical path scattering signal, while δl b,s-a (c, j) is the effect of the variations in surface and atmospheric conditions. If the surface for a homogeneous area is assumed to be under the same illumination, and surface and atmospheric conditions do not spatially vary, that is, δl b,s-a (c, j) = 0, and also all detectors of each band are assumed to have the same radiometric characteristics, Eq. (1) will change to L ( j) = L + δ L ( j) (2) b b, surf b, noise The exact noise δl b,noise (j) for each pixel in Eq. (2) is difficult to determine, but the noise level may be evaluated by using the standard deviation σ of pixel radiance in the homogeneous area, that is, L 1 L j L 1 L j L n n 2 b, surf = b ( ), σ = δ b, noise = [ b ( ) b, surf ], n i= 1 n 1 i= 1 (3) where n is the number of pixels in the homogenous area. As a result, SNR in this paper is defined as L bsurf, SNR =. (4) σ The above method of estimating noise σ and SNR is generally operational because it only needs to find homogenous surfaces, which can be obtained from lakes, deep oceans, snowy regions, deserts, and areas with dense vegetation. However, finding an absolute homogenous surface at a large area in a satellite image is almost impossible, and internal variations in surface and atmospheric conditions between pixels are inevitable. Consequently, the noise level in Eq. (3) will be overestimated and SNR will be underestimated in theory. Furthermore, an instrument records its response to input radiation using DN, and it needs radiometric calibration coefficients to transfer DN to band radiance or reflectance. Consequently, the result will not be the exact value if the radiance is used to calculate the SNR like in Eq. (4) because of the effect of calibration coefficients. Therefore, in the discussion below, the measured DN is applied directly to the calculation of SNR, that is, the numerator and denominator of Eq. (4) are the average and standard deviation, respectively, of (C) 2014 OSA 3 November 2014 Vol. 22, No. 22 DOI: /OE OPTICS EXPRESS 27272

4 pixel DNs over the homogenous area. However, because the DN has no direct physical meaning for the radiometric performance, we consider the standard deviation of another two terms, radiance and reflectance that can be calculated from the DN with radiometric calibration coefficients, as the noise σ of OLI images. As a result, the noise σ in Eq. (3) becomes two terms with important physical meaning: noise equivalent radiance difference (NE L) and noise equivalent reflectance difference (NE ρ). 2.2 Landsat 8 image data The selection of OLI images at different radiance levels for the nine bands is necessary because SNR always increases with spectral radiance [10]. As stated above, this study is focused on surface covers, including lakes, deep oceans, desert and Gobi, snowy regions, and dense vegetation areas, because these kinds of land covers can be distributed homogenously at a large area in satellite images. Figure 1 displays the spectral reflectance of vegetation, soil, sandstone, snow, and water, as well as the location of the central wavelengths of the OLI spectral bands (see Table 1). This figure indicates that bands 1 to 6 and band 9 can achieve high reflectance on a snowy surface; middle reflectance on vegetation, soil, and sandstone surfaces; and low reflectance on water body surfaces. Meanwhile, band 7 presents high reflectance on sandstone and soil, middle reflectance on vegetation and snow surfaces, and low reflectance on water surfaces. High reflectance corresponds to high spectral radiance if the solar illumination is assumed to be the same, and vice versa. Fig. 1. Reflectance spectra of several land covers and the locations of OLI spectral bands (except the panchromatic band 8). Table 1. The spectral ranges and ground spatial resolutions of the OLI instrument No. Wavelength range(μm) Resolution(m) We chose 43 ground sites (see Fig. 2) that included 2 lake sites, 20 deep oceans sites, 11 snow sites, 4 desert or Gobi sites, and 8 dense vegetation sites, and we downloaded 339 OLI images under clear sky conditions from the United States Geological Survey (USGS) website before the end of May The background image in Fig. 2 was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) global (C) 2014 OSA 3 November 2014 Vol. 22, No. 22 DOI: /OE OPTICS EXPRESS 27273

5 normalized difference vegetation index (NDVI) product at a spatial resolution of 0.05 degrees. Among those sites, four desert or Gobi sites (see red squares), including the Dunhuang Gobi sites (Lon = 94.0 E, Lat = 40.1 N) in China, the Sahara desert site (Lon = 9.0 E, Lat = 20.2 N) in Niger, and the Uyuni Salt Flat site (Lon = 68.0 W, Lat = 20.0 S) in Bolivia, were chosen from the USGS Radiometry Test Site Gallery ( because of their flat surface at a large area. Figure 3 shows the detailed images of three desert or Gobi sites and their typical TOA reflectance. The figure indicates that their reflectance varied with the OLI bands. The reflectance was always higher in near-infrared wavelengths (e.g., bands 5 and 6) than in visible wavelengths (e.g., bands 1 and 2) and was at its lowest in band 9. The snow sites were mainly close to the North Polar Region and the dense vegetation sites was mainly distributed in the Amazon region and southern Africa. As for the lake sites, the Qinghai Lake (Lon = E, Lat = 36.8 N) in China, often used for satellite calibrations, and the Gulf of Bahrayn (Lon = 50.8 E, Lat = 24.9 N) were chosen for this study. The ocean sites near some small islands were used for the investigation because we have no access rights to download ocean images from the USGS website as public users. Fig. 2. Distributions of the ground sites. The background image was obtained from the MODIS global NDVI products at day 257, 2013 with a spatial resolution of 0.05 degrees. Homogenous subareas were manually selected near the ground sites in the images. However, the size of the subareas could not be small because such size would reduce the representativeness of the result or too large because variations in surface and atmospheric conditions would increase as size increases. We found through trial and error that a size of 30 samples 30 lines (approximately 900 pixels and an area of 900 m 900 m) is reasonable and that meaningful noise and SNR results can be obtained from Eqs. (3) and (4). Furthermore, the NDVI, the normalized difference water index (NDWI) [11], the normalized difference snow index (NDSI) [12], and the normalized difference build-up index (NDBI) [13] of each pixel in the subareas were estimated from the OLI TOA reflectance. With dense vegetation as an example, the pixel with an NDVI out of the range [ x 2δ, x + 2δ ] (where x is the average of the NDVI, and δ is the standard deviation of the NDVI) was removed before calculating noise to ensure that the subareas were as uniform as possible. Some subareas had been abandoned because their removed pixels covered more than 5% of the total pixels of the subarea. The same threshold method was applied to check the surface condition variations in the lake and deep oceans from the NDWI, snow from the NDSI, and desert and Gobi from the NDBI. Note that, because no atmospheric correction was made to OLI image, the spatial variation of those indices may be reduced by the atmospheric scattering and attenuation. But this effect may be not very remarkable for our selected clear-sky images with small aerosol loading. (C) 2014 OSA 3 November 2014 Vol. 22, No. 22 DOI: /OE OPTICS EXPRESS 27274

6 3. Results of SNR and noise Using the above images, the apparent spectral radiance of nine OLI bands at the TOA was calculated from the DN as Lλ = aλ DN + bλ, where aλ and bλ are radiometric calibration coefficients included in the auxiliary files. TOA reflectance ρλ was also calculated from the DN with the similar method. Table 2 shows the TOA radiance and reflectance ranges of all bands based on the image of all ground sites shown in Fig. 2. This table indicates that the TOA reflectance for all bands mainly ranged from to The reflectance of band 9 ( μm) was very low because this band is designed to detect cirrus clouds and the background appears dark in the images. The TOA reflectance was within the typical range of surface reflectance according to the spectral reflectance ranges of different land covers shown in Fig. 1. This condition indicates that the result of this work is representative. Besides, for comparison in the following discussion, Table 2 also lists the Typical radiance (hereafter denoted as LTypical) and the High radiance (hereafter denoted as LHigh) specified in the OLI SNR design requirement [14]. Fig. 3. Local false-color images of three barren sites and their typical TOA reflectance. (a) Dunhuang calibration site in China; (b) Sahara desert site in Niger; (c) Uyuni salt flat site in Bolivia. # $15.00 USD Received 4 Sep 2014; revised 17 Oct 2014; accepted 17 Oct 2014; published 27 Oct 2014 (C) 2014 OSA 3 November 2014 Vol. 22, No. 22 DOI: /OE OPTICS EXPRESS 27275

7 Table 2. Maximum, minimum, median, and average of TOA apparent radiance and reflectance for OLI nine bands from all ground sites. L Typical and L High are the Typical and High radiances specified in OLI SNR design requirement (unit of L λ is W/m 2 /μm/sr). Band No. MIN L λ MAX L λ Median L λ AVG L λ L Typical L High MIN ρ λ MAX ρ λ AVG ρ λ N/A SNR of OLI images SNR was calculated using Eqs. (3) and (4) from the measured DN of the clear-sky images in each subarea. In order to investigate the SNR variation with the radiance, we divided the radiance ranges (see Table 2) into several subranges, and obtained the average SNR in each radiance subrange, and finally presented the radiance-dependent SNR for nine OLI bands (Fig. 4). In this figure, the SNR generally increased with the increase of radiance for all the bands except band 9. This result is similar to the laboratory measurement and turns out to be reasonable because the noise of an instrument remains at the same level (as shown in Fig. 6) while the radiance changes and large radiance includes more useful signals in the measurement. However, the SNR locally decreased in some middle radiance levels with the increase in radiance. By checking the radiance distribution of the used OLI images across all ground sites, we found that the samples numbers of the middle radiance levels were relatively smaller than those of the other radiance levels. Consequently, the random error perhaps caused great uncertainty to the SNR result at those radiance levels. The SNR of band 9 presented a totally different variation from the other bands, as shown in Fig. 4 (i), that is, SNR decreased with the increase in radiance. As a newer band compared with previous Landsat satellites, OLI band 9 was designed around the water vapor absorption (1.94 μm) wavelength ranges. It is used to detect cirrus clouds in the OLI images because such cloud type always appears bright while most land surfaces appear dark in cloud-free atmospheres containing water vapor. Therefore, the image of this band will show a significant contrast if both clouds and background land surfaces exist. According to the ground sites distribution in Fig. 2, the images we used were only those obtained from the ground surface under a cloud-free atmosphere. Hence, the TOA apparent radiance and reflectance were very low (Table 2). The SNR of band 9, as shown in Fig. 4 (i), actually corresponded to the result for the low or middle radiance (excluding high radiance). We attempted to find other image samples under cirrus cloud covers to complete the investigation of the SNR of this band. Unfortunately, cirrus clouds caused obvious shadows in the images and made the top of the clouds not as flat as the land surfaces, such as lakes, deep oceans, and deserts, because cirrus clouds always show a strong three-dimensional vertical structure. As a result, we had to shift our focus from cloud covers.. (C) 2014 OSA 3 November 2014 Vol. 22, No. 22 DOI: /OE OPTICS EXPRESS 27276

8 Fig. 4. The radiance-dependent SNR of OLI nine bands. Based on the typical radiance (L Typical ) shown in Table 2, we first selected the ground homogenous subareas with TOA radiance in the range of [0.8 L Typical, 1.2 L Typical ], and then calculated the SNR average of those subareas as the final SNR of L Typical. Similarly, we also obtained the on-orbit spectral SNR for high radiance level (L High ), and finally displayed their results in Fig. 5 (a) and (b). Moreover, for comparison to pre-launch predictions, the required SNR and the laboratory-measured SNR for L Typical and L High were also presented in this figure. We know from the figures that at an on-orbit status, the SNR of all bands was larger than 160 for L Typical level, which satisfied the design requirement of the instrument response and even higher than the laboratory measurement in the first three band. Furthermore, the SNR spectral variation was similar to that of the laboratory measurement. Band 2 (blue band, μm) had the highest SNR while band 5 (near infrared band, μm) had the lowest SNR. Although band 9 had a decreasing variation with the increase of radiance, its SNR of L Typical and other radiances also exceeded the requirement (i.e., 50). As for the case of L High in Fig. 5 (b), we found the observed on-orbit SNR was generally larger than the requirement for all band except band 6 (shortwave infrared band, μm). However, the observed SNR was totally smaller than the laboratory measurement, which was probably caused by the averaging process in the calculation. To clarify this problem, we obtained the largest observed SNR in L High and found that those SNRs were closed to the laboratory measurement as shown in Fig. 5 (b). The results of band 9 for L High level was not displayed in this figure because the L High of this band was not specified in the design requirement as shown in Table 2. Based on the observed results in both Figs. (4) and (5), we think that the OLI instrument response well at on-orbit conditions and its SNRs for all nine bands generally met the design (C) 2014 OSA 3 November 2014 Vol. 22, No. 22 DOI: /OE OPTICS EXPRESS 27277

9 requirement, but a conclusion of the status of radiometric performance needs more discussion on the noise level contained in the OLI images. Fig. 5. Spectral SNR of OLI bands at design requirement, laboratory measurement and on-orbit measurement for (a) typical radiance LTypical and (b) high radiance LHigh, respectively. 3.2 Noise of OLI images We first calculated the standard deviation of the TOA band radiance and reflectance in each homogeneous subarea using Eq. (3) and the calibration coefficients of every clear-sky image, and then took their average in all ground sites and images as NE L and NE ρ. Besides, as stated above, we also estimated the NE L and NE ρ for LTypical and LHigh using the ground samples with TOA radiance in the range of [0.8 LTypical, 1.2 LTypical] and [0.8 LHigh, 1.2 LHigh], respectively. As a result, their band-dependent values were presented in Fig. 6. Figure 6(a) shows that the radiance noise (NE L) for all nine bands was less than 0.8 W/m2/µm/sr and that the values of visible and near-infrared bands (VNIR, bands 1 to 5 and 8) were relatively larger than those of the remaining shortwave infrared bands (SWIR, bands 6, 7 and 9), probably because of the intrinsic characteristics of the band noise itself and/or because of the strong solar spectral illumination in VNIR bands. However, the corresponding reflectance noise (NE ρ) shown in Fig. 6 (b) presented different results, that is, NE ρ in bands 5 and 6 increased and exceeded that of other bands. Because the calculation from TOA radiance to TOA apparent reflectance, usually as Eq. (5) [3], needs TOA band solar irradiance (Esun) and the Esun has strong variation in different bands, the band-dependent NE L and NE ρ finally had different patterns to each other. ρ= πd2 L, and NE Δρ = πd2 NE ΔL. (5) Esun cos θ s Esun cos θ s In Eq. (5), d is the correction factor for the earth sun distance in different days, and cos(θs) is the correction for the solar zenith angle θs. However, NE ρ was more comparable than NE L between different bands and became more useful in characterizing the uncertainty of a specified band because this parameter removed the influence of solar irradiance and was normalized to a dimensionless number. Furthermore, Fig. 6(b) also illustrates that all the NE ρ values were lower than 0.002, and the noise of most bands was better than that of ETM + (see Table 1 of [10]). Besides, comparison between the average noise of all radiance with those of LTypical and LHigh indicated that the noise level had a slight change with radiance but generally remained a stable value for different radiance. Therefore, we can draw a cautious conclusion from Fig. 6(b) that all bands performed generally well in the early images. # $15.00 USD Received 4 Sep 2014; revised 17 Oct 2014; accepted 17 Oct 2014; published 27 Oct 2014 (C) 2014 OSA 3 November 2014 Vol. 22, No. 22 DOI: /OE OPTICS EXPRESS 27278

10 Fig. 6. The band-dependent (a) noise equivalent radiance difference (NE L, unit: W/m 2 /µm/sr) and (b) noise equivalent reflectance difference (NE ρ) for all radiance, Typical radiance (L Typical ) and High radiance (L High ) of OLI. As seen from the SNR in Fig. 4 and the NE ρ in Fig. 6(b), the noise of band 9 was low, and its SNR was high. The high SNR under low radiance could enhance the ability of this band to detect the background surface. The comparison of Figs. 5(a) and 6(b) shows that a low NE ρ almost corresponds to a high SNR. At last, we know from the above discussion that the early OLI images showed small noise and high SNR, and performed better than expected at least at L Typical level. This superiority was achieved from the 12-bit quantized data on the OLI images and from the dynamic calibration using the on-orbit internal calibrator. Moreover, OLI was operated to monitor the surface with a push-broom scanner that can receive a stronger signal compared with a whisk-broom scanner used for other Landsat satellites because the push-broom scanner looked at each pixel for longer periods. The high SNR, which corresponds to low noise, can promote the accurate applications of OLI images in monitoring land surface, oceanic and atmospheric status, and can also provide a prior knowledge for uncertainty analysis in estimating leaf area index, albedo, water resource management, and atmospheric parameter retrieval for aerosol and cirrus clouds [2]. 4. Discussions We obtained the noise level and SNR for the OLI images. As stated above, an important assumption used for evaluating noise (NE L and NE ρ) and SNR is that the surface and atmospheric conditions are uniform. However, changes in surface and atmospheric conditions were inevitable despite our careful selection of the homogenous subarea and our use of some spectral indices (such as NDVI, NDWI, NDSI, and NDBI) to remove some invalid pixels. As a result, the standard deviation in Eq. (3) actually resulted from two factors: image noise and surface and atmosphere variation. NE L and NE ρ were overestimated while the SNR was underestimated in theory because we could not totally remove the surface and atmosphere variations. Furthermore, two other aspects should be considered. The first is the time variation of the radiometric response of the instrument to input energy. The OLI instrument was designed to provide reñective multispectral image data that is no less than five years old. The absolute radiometric calibration coefficients and the noise level should be updated frequently because the instrument response to input energy always appears to have obvious time variation due to aging detector, mechanical vibration, and the change in ambient environment. The present work focused on evaluating the noise level and SNR of OLI bands using on-orbit images of different ground sites (obtained before the end of May 2014) while ignoring the time variation of the two parameters. This approach was reasonable at the beginning of the OLI mission. However, in the future, especially toward the end of the mission when the instrument will show great uncertainty and instability, much attention should be paid to such time variation in calibration coefficients and noise level. The second is the pixel-to-pixel radiometric difference in the linear-array detector system. As shown in Eq. (3), we used the spatial standard deviation in one homogeneous subarea to calculate the noise level with an important assumption that all detectors of the same band have (C) 2014 OSA 3 November 2014 Vol. 22, No. 22 DOI: /OE OPTICS EXPRESS 27279

11 the same radiometric characteristics. OLI utilizes a push-broom scanner with linear array detectors unlike the TM and ETM +, which observes the Earth s surface with a whisk-broom (also called across-track) scanner equipped with only one detector for one scanning. The push-broom scanner ensures that all the pixels in the image are collected in a line measured simultaneously, but the drawback of push-broom instruments is that detectors in the linear array can have varying sensitivities. The measured data will appear as obvious stripes in the image, and our assumption will not work well if the detectors are not perfectly calibrated detector by detector. Fortunately, we did not find significant data strips in the OLI images. Consequently, the problem of the pixel-to-pixel radiative difference was not considered serious, and the noise and SNR we obtained can be considered as the result for all detectors. However, some quantitative looking at the mean radiance in at least a few of the backgrounds as a function of detector is still planned in our coming work in order to check the nonuniformity of pixel-to-pixel detectors response. Besides, this problem also requires attention in future studies because of aging detectors and other unexpected reasons. 5. Conclusions The noise level in radiometric calibration of remotely sensed images is crucial for assessing the uncertainty involved in the applications of these images. In this work, we used a similar homogeneous area method to estimate the SNR and noise level (NE L and NE ρ) of OLI instrument for the first time by using on-orbit early images at nine bands based on the lake, deep oceans, snow, desert and Gobi, and dense vegetation subareas in 339 clear-sky images of 43 ground sites collected by OLI instrument onboard Landsat 8. Result showed that the SNR of the nine OLI bands was greater than 160 for typical radiance level, thus satisfying the design requirement and leading to an increased value that was closed to the laboratory measurement. The SNR generally increased with band radiance for all bands except band 9 (cirrus detecting band). Moreover, the NE L and NE ρ for OLI nine bands were smaller than 0.8 W/m 2 /µm/sr and 0.002, respectively, and they almost remained stable for different radiance levels. The visible and near-infrared bands (bands 1 to 4) generally showed lower noise compared with the shortwave infrared bands (bands 5 to 7). As the early OLI images in this work generally showed low noise and high SNR, OLI images are expected to play an important role in the mission of the LDCM project to retrieve land surface, oceanic, and atmospheric parameters, and the noise level observed in this work can provide a prior knowledge for uncertainty analysis of the above application of OLI images. However, because this work used the spatial information to estimate the noise and SNR, and the variation of surface and atmospheric conditions cannot be totally removed although the clearsky images over hundreds of uniform areas were used, the finally observed results were consequently overestimated slightly. The optimal method for estimating such radiometric noise is to use the images observed on the internal stimulation lamps, solar diffuser panels and lunar illuminations that are almost close to uniform and without atmospheric variation, and its result can be used to validate the result obtained from ground images as did in this work. Furthermore, for a long-term stable data set from OLI observation, future work should consider aging detectors and pay attention to the time variation of noise and to pixel-to-pixel radiometric calibration to improve image quality. Acknowledgments The authors would like to thank the two anonymous reviewers for their constructive and thoughtful comments and suggestions, and also thank Dr. Julia A. Barsi from Science Systems and Applications, Inc., NASA Goddard Space Flight Center for providing us the data of typical and high radiances of OLI requirement. This work was supported by the program of the National Natural Science Foundation of China (Grant No , , , and ), the China Post-doctoral Fund (Grant No. 2014M550551), the National Basic Research Program of China (Grant No. 2013CB733402), and the High Resolution Earth Observation Systems of National Science and Technology Major Projects. The Landsat 8 images were downloaded from USGS image achieves. (C) 2014 OSA 3 November 2014 Vol. 22, No. 22 DOI: /OE OPTICS EXPRESS 27280