Tracking radiometric responsivity of optical sensors ... - OSA Publishing

Tracking radiometric responsivity of optical sensors without on-board calibration systemscase of the Chinese HJ-1A/1B CCD sensors Jian Li,1 Xiaoling Chen,1 Liqiao Tian,1,* and Lian Feng1 1

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China * [email protected]

Abstract: The radiometric stability of satellite sensors is crucial for generating highly consistent remote sensing measurements and products. We have presented a radiometric responsivity tracking method designed especially for optical sensors without on-board calibration systems. Using a temporally stable desert site with high reflectance, the sensor responsivity was simulated using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model (RTM) with information from validated MODIS atmospheric data. Next, radiometric responsivity drifting was identified using a linear regression of the time series bidirectional reflectance distribution function (BRDF) normalized coefficients. The proposed method was applied to Chinese HJ-1A/1B charge-coupled device (CCD) sensors, which have been on-orbit operations for more than 5 years without continuous assessment of their radiometric performance. Results from the Dunhuang desert site between 2008 and 2013 indicated that the CCD sensors degraded at various rates, with the most significant degradation occurring in the blue bands, ranging from 2.8% to 4.2% yr−1. The red bands were more stable, with a degradation rate of 0.7-3.1% yr−1. A cross-sensor comparison revealed the least degradation for the HJ-1A CCD1 (blue: 2.8%; green: 2.8%; red: 0.7%; and NIR: 0.9% yr−1), whereas the degradation of HJ-1B CCD1 was most pronounced (blue: 3.5%; green: 4.1%; red: 2.3%; and NIR: 3.4% yr−1). The uncertainties of the method were evaluated theoretically based on the propagation of uncertainties from all possible sources of the RT simulations. In addition, a cross comparison with matchup ground-based absolute calibration results was conducted. The comparison demonstrated that the method was useful for continuously monitoring the radiometric performance of remote sensors, such as HJ1A/1B CCD and GaoFen (GF) series (China's latest high-definition Earth observation satellite), and indicated the potential use of the method for high-precision absolute calibration. ©2015 Optical Society of America OCIS codes: (280.0280) Remote sensing and sensors; (010.5620) Radiative transfer.

References and links 1. 2. 3. 4.

J. Guorui, Z. Huijie, and L. Hao, “Uncertainty propagation algorithm from the radiometric calibration to the restored earth observation radiance,” Opt. Express 22(8), 9442–9449 (2014). B. L. Markham, K. J. Thome, J. A. Barsi, E. Kaita, D. L. Helder, J. L. Barker, and P. L. Scaramuzza, “Landsat-7 ETM+ on-orbit reflective-band radiometric stability and absolute calibration,” IEEE Trans. Geosci. Rem. Sens. 42(12), 2810–2820 (2004). X. Xiong, A. Wu, J. A. Esposito, J. Sun, N. Che, B. Guenther, and W. L. Barnes, “Trending results of MODIS optics on-obit degradation,” Proc. SPIE 4814, 337–346 (2002). R. A. Barnes and E. F. Zalewski, “Reflectance-based calibration of SeaWiFS. I. Calibration coefficients,” Appl. Opt. 42(9), 1629–1647 (2003).

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Received 4 Nov 2014; revised 25 Dec 2014; accepted 27 Dec 2014; published 23 Jan 2015 26 Jan 2015 | Vol. 23, No. 2 | DOI:10.1364/OE.23.001829 | OPTICS EXPRESS 1829

5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32.

D. Koslowsky, “Signal degradation of the AVHRR shortwave channels of NOAA 11 and NOAA 14 by daily monitoring of desert targets,” Adv. Space Res. 19(9), 1355–1358 (1997). D. L. Smith, C. T. Mutlow, and R. C. Nagaraja, “Calibration monitoring of the visible and near-infrared channels of the Along-Track Scanning Radiometer-2 by use of stable terrestrial sites,” Appl. Opt. 41(3), 515–523 (2002). B. Fougnie, G. Bracco, B. Lafrance, C. Ruffel, O. Hagolle, and C. Tinel, “PARASOL in-flight calibration and performance,” Appl. Opt. 46(22), 5435–5451 (2007). S. Hlaing, A. Gilerson, R. Foster, M. Wang, R. Arnone, and S. Ahmed, “Radiometric calibration of ocean color satellite sensors using AERONET-OC data,” Opt. Express 22(19), 23385–23401 (2014). F. Mélin and G. Zibordi, “Vicarious calibration of satellite ocean color sensors at two coastal sites,” Appl. Opt. 49(5), 798–810 (2010). G. M. Jiang, “Intercalibration of infrared channels of polar-orbiting IRAS/FY-3A with AIRS/Aqua data,” Opt. Express 18(4), 3358–3363 (2010). E. J. Kwiatkowska, B. A. Franz, G. Meister, C. R. McClain, and X. X. Xiong, “Cross calibration of ocean-color bands from Moderate Resolution Imaging Spectroradiometer on Terra platform,” Appl. Opt. 47(36), 6796–6810 (2008). G. Chander, X. X. Xiong, T. Y. Choi, and A. Angal, “Monitoring on-orbit calibration stability of the Terra MODIS and Landsat 7 ETM+ sensors using pseudo-invariant test sites,” Remote Sens. Environ. 114(4), 925–939 (2010). W. Xu, J. Gong, and M. Wang, “Development, application, and prospects for Chinese land observation satellites,” Geo-spatial Information Science 17(2), 102–109 (2014). Q. Wang, C. Wu, Q. Li, and J. Li, “Chinese HJ-1A/B satellites and data characteristics,” Sci. China Earth Sci. 53(S1), 51–57 (2010). Z. Sun, W. Shen, B. Wei, X. Liu, W. Su, C. Zhang, and J. Yang, “Object-oriented land cover classification using HJ-1 remote sensing imagery,” Sci. China Earth Sci. 53(S1), 34–44 (2010). Z. Guo, H. Chi, and G. Sun, “Estimating forest aboveground biomass using HJ-1 Satellite CCD and ICESat GLAS waveform data,” Sci. China Earth Sci. 53(S1), 16–25 (2010). R. Zhang, R. Sun, J. Du, T. Zhang, Y. Tang, H. Xu, S. Yang, and W. Jiang, “Estimations of net primary productivity and evapotranspiration based on HJ-1A/B data in Jinggangshan city, China,” J. Mt, Sci-Engl. 10, 777–789 (2013). Y. Li, Y. Xue, X. He, and J. Guang, “High-resolution aerosol remote sensing retrieval over urban areas by synergetic use of HJ-1 CCD and MODIS data,” Atmos. Environ. 46, 173–180 (2012). Z. T. Wang, Q. Li, S. S. Li, L. F. Chen, C. Y. Zhou, Z. F. Wang, and L. J. Zhang, “The Monitoring of Haze from HJ-1],” Guang Pu Xue Yu Guang Pu Fen Xi 32(3), 775–780 (2012). Z. Yu, X. Chen, B. Zhou, L. Tian, X. Yuan, and L. Feng, “Assessment of total suspended sediment concentrations in Poyang Lake using HJ-1A/1B CCD imagery,” Chin. J. Oceanology Limnol. 30(2), 295–304 (2012). T. Cui, J. Zhang, L. E. Sun, Y. Jia, W. Zhao, Z. Wang, and J. Meng, “Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation,” Int. J. Remote Sens. 33(17), 5513–5527 (2012). L. Chen, L. Tian, F. Qiu, and X. Chen, “Water Color Constituents Remote Sensing in Wuhan Donghu Lake Using HJ-1A/B CCD Imagery,” Geomatics and Information Science of Wuhan University 36, 1280–1283 (2011). H. Jiang, Q. Qin, J. Li, S. Zhao, H. Dong, W. Yuan, and R. Cui, “Validation for the absolute radiometric calibration of the HJ-1B CCD sensors of China,” in Geoscience and Remote Sensing Symposium (IGARSS), (IEEE International, 2010), 2876–2879. G. Chen, Z. Chen, L. Ma, and H. Zhang, “Monitoring and assessment on radiometric stability of HJ-1A CCD using MODIS data,” in Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, (International Society for Optics and Photonics, 2013), 89211I. Z. Chen, B. Zhang, H. Zhang, W. Zhang, and Y. Zhang, “HJ-1A multispectral imagers radiometric performance in the first year,” in Geoscience and Remote Sensing Symposium (IGARSS), (IEEE International, 2010), 4264– 4267. S. Sterckx, S. Livens, and S. Adriaensen, “Rayleigh, deep convective clouds, and cross-sensor desert vicarious calibration validation for the PROBA-V mission,” IEEE Trans. Geosci. Rem. Sens. 51(3), 1437–1452 (2013). Z. Wang, P. Xiao, X. Gu, X. Feng, X. Li, H. Gao, H. Li, J. Lin, and X. Zhang, “Uncertainty analysis of crosscalibration for HJ-1 CCD camera,” Sci. China Technol. Sci. 56(3), 713–723 (2013). B. Jiang, S. Liang, J. R. Townshend, and Z. M. Dodson, “Assessment of the Radiometric Performance of Chinese HJ-1 Satellite CCD Instruments,” IEEE J. STARS 6, 840–850 (2013). E. F. Vermote, D. Tanré, J. L. Deuze, M. Herman, and J.-J. Morcette, “Second simulation of the satellite signal in the solar spectrum, 6S: An overview,” IEEE Trans. Geosci. Rem. Sens. 35(3), 675–686 (1997). S. M. Vicente-Serrano, F. Pérez-Cabello, and T. Lasanta, “Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images,” Remote Sens. Environ. 112(10), 3916–3934 (2008). R. Latifovic, J. Cihlar, and J. Chen, “A comparison of BRDF models for the normalization of satellite optical data to a standard Sun-target-sensor geometry,” IEEE Trans. Geosci. Rem. Sens. 41(8), 1889–1898 (2003). J. Cihlar, H. Ly, Z. Li, J. Chen, H. Pokrant, and F. Huang, “Multitemporal, multichannel AVHRR data sets for

#226230 - $15.00 USD © 2015 OSA


land biosphere studies—Artifacts and corrections,” Remote Sens. Environ. 60(1), 35–57 (1997). 33. A. Angal, X. Xiong, T. Choi, G. Chander, N. Mishra, and D. L. Helder, “Impact of Terra MODIS Collection 6 on long-term trending comparisons with Landsat 7 ETM+ reflective solar bands,” Romote Sens. Lett. 4(9), 873– 881 (2013). 34. X. Hu, Y. Zhang, Z. Liu, G. Zhang, Y. Huang, K. Qiu, Y. Wang, L. Zhang, X. Zhu, and Z. Rong, “Optical characteristics of China Radiometric Calibration Site for Remote Sensing Satellite Sensors (CRCSRSSS),” in Second International Asia-Pacific Symposium on Remote Sensing of the Atmosphere, Environment, and Space, (International Society for Optics and Photonics, 2001), pp. 77–86. 35. C. Cao, L. Ma, S. Uprety, C. Li, and L. Tang, “Spectral characterization of the Dunhuang calibration/validation site using hyperspectral measurements,” Proc. SPIE 7862, 78620J (2010). 36. C. Gao, X. Jiang, X. Li, and X. Li, “The cross-calibration of CBERS-02B/CCD visible-near infrared channels with Terra/MODIS channels,” Int. J. Remote Sens. 34(9-10), 3688–3698 (2013). 37. D. L. Helder, B. Basnet, and D. L. Morstad, “Optimized identification of worldwide radiometric pseudoinvariant calibration sites,” Can. J. Rem. Sens. 36(5), 527–539 (2010). 38. X. Hu, J. Liu, L. Sun, Z. Rong, Y. Li, Y. Zhang, Z. Zheng, R. Wu, L. Zhang, and X. Gu, “Characterization of CRCS Dunhuang test site and vicarious calibration utilization for Fengyun (FY) series sensors,” Can. J. Rem. Sens. 36(5), 566–582 (2010). 39. Z. Zhang, Y. Qiu, K. Hu, X. Zhang, and J. Li, “Radiometric calibration on orbit for FY-2B meteorological satellite's visible channels with the radiometric calibration site of Dunhuang,” J. Appl. Meteorol. 3, 001 (2004). 40. L. Sun, X. Hu, M. Guo, and N. Xu, “Multisite Calibration Tracking for FY-3A MERSI Solar Bands,” IEEE Trans. Geosci. Rem. Sens. 50(12), 4929–4942 (2012). 41. Dunhuang radiometric calibration site of China and China Meteorological Administration, Spectral Data Sets for Satellite Calibration Site and Typical Earth Objects (China Meteorological, Beijing, 2008). 42. H. L. Gao, X. F. Gu, T. Yu, X. Y. Li, H. Gong, J. G. Li, and G. H. Zhu, “HJ1A/HSI radiometric calibration and spectrum response function sensitivity analysis,” Spectrosc. Spect. Aanl. 30(11), 3149–3155 (2010). 43. B. Gao and Y. J. Kaufman, “Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared channels,” J. Geophys. Res. 108(D13), 4389 (2003). 44. A. M. Sayer, N. C. Hsu, C. Bettenhausen, and M. J. Jeong, “Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data,” J. Geophys. Res. 118, 7864–7872 (2013). 45. J. L. Roujean, M. Leroy, and P. Y. Deschamps, “A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data,” J. Geophys. Res. 97(D18), 20455–20468 (1992). 46. N. C. Hsu, S. Tsay, M. D. King, and J. R. Herman, “Deep blue retrievals of Asian aerosol properties during ACE-Asia,” IEEE Trans. Geosci. Rem. Sens. 44(11), 3180–3195 (2006). 47. Y. Xie, Y. Zhang, X. Xiong, J. J. Qu, and H. Che, “Validation of MODIS aerosol optical depth product over China using CARSNET measurements,” Atmos. Environ. 45(33), 5970–5978 (2011). 48. X. Li, X. Xia, S. Wang, J. Mao, and Y. Liu, “Validation of MODIS and Deep Blue aerosol optical depth retrievals in an arid/semi-arid region of northwest China,” Particuology 10(1), 132–139 (2012). 49. Y. Shi, J. Zhang, J. S. Reid, E. J. Hyer, and N. C. Hsu, “Critical evaluation of the MODIS Deep Blue aerosol optical depth product for data assimilation over North Africa,” Atmos. Meas. Tech. 5(5), 7815–7865 (2012). 50. X. Hu, Y. Zhang, G. Zhang, Y. Huang, and Y. Wang, “Measurements and Study of Aerosol Optical Characteristics in China Radiometric Calibration Sites,” J. Appl. Meteorol. 03, 257–266 (2001). 51. X. A. Xia, M. X. Wang, and Y. S. Wang, “Automatic and continuous measurement of aerosol properties in Dunhuang, China,” J. Environ. Sci. (China) 16(1), 40–43 (2004). 52. S. Y. Kotchenova, E. F. Vermote, R. Matarrese, and F. J. Klemm, Jr., “Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: path radiance,” Appl. Opt. 45(26), 6762–6774 (2006). 53. D. L. Helder, T. A. Ruggles, J. D. Dewald, and S. Madhavan, “Landsat-5 Thematic Mapper reflective-band radiometric stability,” IEEE Trans. Geosci. Rem. Sens. 42(12), 2730–2746 (2004). 54. C. B. Schaaf, F. Gao, A. H. Strahler, W. Lucht, X. Li, T. Tsang, N. C. Strugnell, X. Zhang, Y. Jin, J.-P. Muller, P. Lewis, M. Barnsley, P. Hobson, M. Disney, G. Roberts, M. Dunderdale, C. Doll, R. P. d’Entremont, B. Hu, S. Liang, J. L. Privette, and D. Roy, “First operational BRDF, albedo nadir reflectance products from MODIS,” Remote Sens. Environ. 83(1-2), 135–148 (2002). 55. N. Flood, “Testing the local applicability of MODIS BRDF parameters for correcting Landsat TM imagery,” Romote Sens. Lett. 4(8), 793–802 (2013).

1. Introduction Continuous and consistent multi-temporal/sensor remote sensing measurements are crucial for observing land surfaces, the atmosphere and water dynamics. However the satellite measurements and products are very sensitive to uncertainties related to sensor degradation due to the aging of components in the harsh conditions of space [1]. For sensors with onboard calibration systems, such as Landsat 7 ETM + and MODIS [2, 3], degradation is tracked and adjusted to achieve high quality satellite measurements and products, such as surface reflectance and NDVI trends. However, for sensors without onboard calibration capabilities, #226230 - $15.00 USD © 2015 OSA


such as SeaWiFS [4] and NOAA AVHRR instruments [5], vicarious calibration (VC) approach is required to monitor their on-orbit radiometric performance. The VC approach is based on spectrally stable calibration sites in high-reflectance desert regions [6, 7] or on the homogeneous and clear ocean [8, 9] (both regarded as pseudo-invariant targets). In addition, VC provides an independent approach for the cross calibration of different sensors [10, 11] and for identifying the radiometric stability of onboard calibration systems (i.e., for MODIS and Landsat ETM + ) [12], which guarantees that consistent products are obtained from multi-sensor or multi-temporal measurements. In China, remote sensing earth observations are rapidly developing, with a progressively greater number of satellites sensors that provide unparalleled data at multi spectral, spatial and temporal resolutions [13]. On September 6, 2008, China successfully launched environment and disaster monitoring and forecasting satellites (HJ-1A/1B). The payloads of the HJ-1A and HJ-1B satellites included two charge-coupled device (CCD) cameras that were designed on similar principles, including their radiometric resolutions and spectral response characteristics. The CCD cameras capture four spectral bands (430–520, 520–600, 630–690, and 760–900 nm) with a scan swath of 360 km (700 km with 2 sensors combined) and record data at 8 bits [14]. The constellations of the two satellites generate multispectral images with high spatial resolution (30 m) and a short revisit interval (2 days). The HJ-1A/1B CCD data have been widely used for environmental issues, disaster detection, and natural resource monitoring, including land-use and land-cover change analyses (LULCC) [15], net primary productivity or biomass estimations [16, 17], and air quality monitoring (haze or aerosol retrieval) [18, 19]. Although the primary purpose is for land observation, the HJ-1A/1B CCD data collected over coastal and inland waters have successfully been used to detect multiple features, such as water suspended sediment concentrations [20], green microalgae blooms [20, 21], Chl-a and CDOM [22]. In addition, motivated by the wide range of applications of the HJ-1A/1B CCD data and the nearly identical spectral coverage and spatial resolution of Landsat TM/ETM + , the potential of extending the Landsat series Earth observations with HJ-1A/1B CCD data has been studied [18–21], and the results have shown that HJ-1A/1B CCD and Landsat 5 TM imageries are comparable and complementary. The constellation of HJ-1A/1B satellites has operated on orbit for more than 5 years and continues to serve social and scientific communities despite being designed for a two-year lifespan. However, it is important to identify the on-orbit radiometric stability of the HJ1A/1B CCD sensors when time-series data are used and compared with measurements from other sensors. Unfortunately, HJ-1A/1B satellites do not have onboard calibration systems for tracking the optical performance of the CCD sensors throughout the mission life. As an alternative, the China Center for Resources Satellite Data and Application (CRESDA) has conducted annual calibration efforts at the Dunhuang Calibration Site using the reflectancebased method since its launch [23]. In addition, the cross-calibration technique [24, 25] was also used to investigate the radiometric performances of the HJ-1A and HJ-1B CCD sensors. However, both the field-based and cross-calibration efforts are constrained by limited frequency of valid measurements and are inadequate for providing continuous tracking of the long-term sensor performance. For example, the field-based calibration could only be conducted once a year for China HJ-1A/1B CCD sensors due to the high cost of filed measurements. On the other hand, cross-calibration are sensitive to the selection of matchups between observation and reference sensors (well-calibrated), which require (1) Simultaneous observations of the same location with identical or similar sun-target-view geometry from the two sensors, and (2) Clear and stable atmospheric conditions to minimize uncertainties due to atmospheric variations between observation and reference [26, 27]. Previous study proven that only 13 matchups were obtained for cross-calibration between the HJ-1A/1B CCD and Landsat TM from 2009 to 2011 [28], and 13 matchups for HJ-1A/1B CCD and Terra/MODIS from 2009 to 2012 [24], which means there are only 3 or 4 valid measurements for cross

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calibration each year. Therefore, uncertainties from field measurements, atmospheric variation, and discrepancy in view geometries, spectral band mismatching, and discrepancy in relative spectral response (RSR) functions etc. would pronounce more errors through propagation, which is quite difficult to eliminate due to limited matchups. Consequently, comprehensive analyses with long-term optical performance monitoring are lacking for the HJ-1A/1B CCD sensors. In this paper, a practical method is proposed to track the continuous orbit radiometric performance of the Chinese HJ-1A/1B CCD sensors or other similar optical sensors without on-board calibration components. The efficiency of the method was evaluated theoretically based on the propagation of uncertainties from all possible sources and by comparing with the corresponding ground-based absolute calibration results. The results for the HJ-1A/1B CCD’s optical performance tracking and potential applications in other sensors were also discussed 2. Method for tracking radiometric responsivity The objective of this study is to track the continuous on-orbit optical performance of HJ1A/1B CCD sensors using radiometrically stable bright targets in the Dunhuang desert site. All available HJ-1A/1B CCD collections over Dunhuang site are acquired and screened for cloud-free high quality images. The acquisition time, the sun-target-satellite geometric angles are extracted from the metafile for each selected imagery, and the averaged digital number (DN) values over Dunhuang site are calculated. Then, two critical issues are addressed before a continuous and objective radiometric trend can be established from the long-term satellite measurements. First, time series TOA radiance are required to simulate the signal for each HJ-1A/1B CCD acquisition, according to the specific sun-target-satellite geometry, and corresponding atmospheric parameters. The Second Simulation of the Satellite Signal in the Solar Spectrum (6S) model is selected for its high accuracy and reasonable computation efficiency, using a successive orders method of scattering approximation. Moreover, the 6S code provides a more flexible approach for batching of time series data with restricted number of inputs and constants [29, 30]. Second, impacts of bidirectional reflectance distribution function (BRDF) effects, due to variations in sun-target-satellite geometries, on the time series radiometric response coefficients must be mitigated. To avoid excessive field measurements of physical BRDF models, a semiempirical kernel-driven BRDF model is adopted to normalize the illumination and viewing geometry variability between multi temporal remote sensing images [31, 32]. After that, time series radiometric responsivity is fitted using the normalized coefficients for trending estimation. The flowchart is shown in Fig. 1.

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Fig. 1. Flowchart for tracking the radiometric responsivity trends of the HJ-1A/1B CCD sensors.

2.1 Screening and processing of satellite data The HJ-1A/1B CCD images over the Dunhuang site from September 2008 to September 2013 were selected based on rigorous criteria. Generally, clouds, precipitation and sandstorms contaminated the images and violate the assumption of site stability. The coefficient of variation (CV), which is calculated as the ratio of the standard deviation (Std) to the average (Mean), provides a direct indication of radiometric spatial uniformity. Because the pixels affected by clouds or sandstorms would significantly increase the CV values, a CV threshold of 5% was used to exclude contaminated images. Furthermore, the images that were potentially affected by sandstorms, rainfall, or snow were further screened to avoid the subsequent effects of atmospheric particles or aerosols and the moisture content of the surface sand. The newly released MODIS Collection 6 (C6) Level 1B (L1B) products with improved long-term calibration stability [33] were obtained to distinguish the days that were affected by sandstorms, rainfall, and snow. Overall, 408 cloud-free MODIS L1B images from 2009 to 2013 were selected, and the statistics obtained from the time-series TOA reflectance were used as a baseline for the radiometric performance of the site. Thresholds for the TOA reflectance of the MODIS 465 nm band were determined as the mean plus (the upper threshold) and minus (the lower threshold) 3 times of the standard deviation, which were approximately 0.179 and 0.262, respectively. Because the site and MODIS measurements were both considered stable over time, any statistical anomalies should be excluded. Therefore, this pair of thresholds was assumed time-independent for distinguishing the images affected by sandstorms, rainfall, and snow. To constrain the BRDF effects from the varied sun-target-satellite geometry, the HJ1A/1B CCD sensors were arranged to enlarge the field of view to ± 31° from nadir, and the changes in the solar zenith angle (SZA) were confined to between approximately 20 and 60°. Overall, 155, 97, 154, and 82 images were selected to track the radiometric trends of the HJ1A CCD1, HJ-1A CCD2, HJ-1B CCD1, and HJ-1B CCD2 sensors, respectively. All images

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were geo-referenced using the nearest-neighbor approach referring to Landsat TM images (UTM WGS-84), with the RMSE within half a pixel, and then a commom area of 90*120 pixels were selected over Dunhuang site to calculate the average DN values. 2.2 Simulation of TOA radiance For each selected HJ-1A/1B imagery over the Dunhuang site, the TOA reflectance was simulated using 6S under specified sun-target-satellite geometry, surface and atmospheric parameters by Eq. (1): ρTOA (θ s , θ v ,ψ ) = ρ path (θ s , θ v ,ψ ) + ρtarget ∗ T (θ s ) ∗ T (θ v ) / (1 − ρtarget ∗ S )

(1)

where ρTOA (θ s , θ v ,ψ ) is the top-of-atmosphere reflectance received by the sensor(unitless), ρ path (θ s ,θ v ,ψ ) is the path reflectance due to Rayleigh and aerosol scattering, T (θ s ) and T (θ v ) are the downward atmospheric transmittance from the sun to the surface and the upward atmospheric transmittance from the surface to the satellite viewing direction, ρtarget is

the surface reflectance (assuming a Lambertian surface during the simulation), and S is the diffuse reflectance of the atmosphere. The input parameters for implementing the RT simulation include the (1) surface reflectance, (2) atmospheric parameters (ozone, total water vapor, aerosol information), and (3) sun-target-satellite geometry and sensor response function. (1) Surface reflectance The Dunhuang calibration/validation site is selected as a temporal stable reference endorsed by the Committee on Earth Observation Satellites (CEOS) and the Working Group on Cal/Val (WGCV). The site has a typical continental arid climate with a low aerosol loading [34], and the surface is covered by cemented gravel with almost no vegetation throughout the year [35, 36]. Therefore, the site provides great opportunity for radiometric calibration and responsivity tracking. Helde et al. observed that the temporal variability at the Dunhuang site is approximately 1% in the VIS-NIR bands, which is comparable to the Saharan and Arabian sites [37]. In addition, Hu et al. showed that the multi-year CV for the Dunhuang site is approximately 3% for the 7-year MODIS surface albedo product and multiple ground-based measurement in 10 years [38]. Thus, the site has been extensively used for VC activities among various sensors, including the NOAA/AVHRR, EOS/MODIS, FY-2B, CBERS02B/CCD, HJ-1A/1B CCD, and HJ-1A HSI sensors [23, 25, 36, 39], and for tracking instrument degradation ATSR-2 from 1995 to 2000 [6] and FY-3A MERSI from 2008 to 2011 [40]. To further evaluate the temporal stability of the surface properties of the Dunhuang site during the study period, the BRDF-adjusted MODIS reflectance product MCD43C was acquired for Dunhuang site from 2009 to 2013. As shown in Fig. 2(a), the time-series MODIS reflectance was stable, with seasonal fluctuations mainly resulting from the periodic variations of the SZA and variability of less than 2% (1 sigma) for all bands, which was consistent with the previous results of Helder and Hu [37, 38]. Overall, the Dunhuang site is capable of providing spatial uniformity and temporal stability for long-term monitoring of satellite sensor calibration. The surface reflectance of the Dunhuang site in this study was obtained from ground measurements that were conducted in 1999, 2000, 2002, 2004, 2005, 2006 and 2008 using ASD FieldSpec Spectral Radiometers with a 3 nm spectral resolution from 350 to 1,000 nm [41]. In addition, the field measurements conducted on August 26, 2009 from Gao et al. [42] and the field data collected in August 2011 from Sun et al. [40] were also adopted. The site has a stable spectral reflectance spectrum that ranges from 18% to 25% in the visible and near-infrared (NIR) regions [Fig. 2(b)]. These data both showed the stability of the ground surface at the Dunhuang site [Fig. 2(b)]. Overall, 2,135 samples were collected over the site,

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and the mean spectral reflectance data were determined from the multi-year measurements as the input for surface reflectance in 6S model.

Fig. 2. (a) Trends of the MODIS reflectance product, MCD43A4, over the Dunhuang site from 2009 to 2013 and (b) Field measurements of the spectral reflectance for the Dunhuang site.

(2) Atmospheric parameters Atmospheric parameters for 6S including ozone, total water vapor, aerosol AOD. Ozone data were obtained from the OMI Satellite Level 3e and daily-averaged ozone data from the NASA Space-Based Measurement of Ozone and Air Quality Website with a grid of 0.25 × 0.25° (http://ozoneaq.gsfc.nasa.gov). The MODIS Level 2 perceptible water vapor (PWV) products from Terra MODIS Collection 5 (MOD05_L2) were obtained at a spatial resolution of 1 km using the NIR algorithm during the day from the Goddard Earth Sciences Distributed Active Archive Center (DAAC) [43]. The water content was assumed stable because the time intervals between the HJ-1A/1B and Terra overpasses are within 30 min. AOD are critical for simulating the signals received by remote sensors. Because a lack of simultaneous measurements corresponded to HJ-1A/1B overpasses, the Aqua MODIS Collection 6 “Deep Blue” aerosol data were used as an alternative for the AOD estimations (http://ladsweb.nascom.nasa.gov/data/search.html) in the 6S simulation [44]. The quality of MODIS AOD are validated and improved using matched ground-based AOD data from the Dunhuang and Dunhuang_LZU AERONET sites, and details are provided in Section 4.1. To facilitate the simulation, the 6S code is modified to include the RSRs of the HJ-1A/1B CCD sensors, and the image specified sun-target-satellite geometry is extracted in batch. The TOA radiance is then calculated using Eq. (2). LTOA = ρTOA * E0 *cos(θ s ) / π * d 2

(2)

R( λ ) = DN ave / ( LTOA (λ ) − Ldark (λ ))

(3)

Here, LTOA is the theoretically measured radiance from the sensor under the specified suntarget-satellite geometry, E0 is the band-dependent extraterrestrial solar irradiance, and d is the Earth–Sun distance in astronomical units (AU). Then, the radiometric response coefficient R( λ ) (DN/W/m2/sr/μm) was obtained from the digital numbers (DNs) extracted from the homogeneous desert pixels and the simulated radiance for each HJ-1A/1B CCD acquisition (according to Eq. (3)), In addition, the reference radiance of the dark current or noise of the instrument were determined from the pre-launch calibration of the HJ-1A/1B CCD sensors provided by the CRESDA.

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2.3 Responsivity trends fitting Although images were rigorously selected to suppress BRDF effects, changes in sensor and solar view geometry must be addressed prior to long-term monitoring of the radiometric performance of the HJ-1A/1B CCD sensors. Without the bidirectional surface parameters, the Lambertian surface reflectance of the Dunhuang site was used to simulate the TOA radiance, which inevitably introduced uncertainties from the surface and atmospheric BRDF due to variations in the sun-target-satellite geometry. Since the physical BRDF models require excessive field measurements to specify the surface parameters that may vary over time [31, 32], making them difficult to implement. In this study, the semiempirical kernel-driven BRDF model of Roujean et al. [45] was adopted to normalize the time-series sensor response coefficients to a common illumination and viewing geometry, to facilitate trending analysis of the radiometric response. The modeled responsivity coefficients were assumed to consist of three additive kernels that described the isotropic scattering, geometric shadowing, and volume scattering in the Roujean model, as expressed in Eq. (4): R (λ )imodeled (θ s , θ v , φ ) = α 0 + α1 ƒ1 (θ s , θ v , φ ) + α 2 ƒ 2 (θs , θ v , φ ) min =  ([ R (λ )i

measured

− R (λ )i

modeled

])

2

(4) (5)

where θs, θv, and φ are the sun zenith angle (SZA), view zenith angle (VZA), and relative azimuth angle, respectively; f1 and f2 are the model kernels representing the volume scattering component and the surface scattering component, which are directly calculated as functions of the solar and view geometric angles following Roujean et al. [31, 45]. Then, parameters of α 0 , α1 , and α 2 were determined using multiple linear regression based on the least squares method to minimize the differences between time series measured and modeled responsivity coefficients over a wide range of view geometry parameters from Eq. (5), where i refers to the days of the image. Next, the BRDF normalized coefficients were utilized to determine longterm sensor responsivity drifting at a high frequency based on the linear regression statistics of the time-series results according to Eq. (6). R (λ )imodeled = slope * Days (i ) + intercept

(6)

Here, Days (i ) is the number of days on orbit, and slope and intercept indicate the daily drifting rate of the sensor responsivity coefficient for band λ . To investigate whether the sensor responsivity trend was statistically significant, the slope of the regression line was tested using Student’s t-test. The null hypothesis (H0: slope = 0) indicates that the long-term responsivity is statistically stable. The p-value was used to estimate the probability of rejecting the null hypothesis (H0), which was set at the 5%, 1% and 0.1% level. If the p-value is