An Improved Method for Retrieving Aerosol Optical Depth Using Gaofen-1 WFV Camera Data

remote sensing

Article An Improved Method for Retrieving Aerosol Optical Depth Using Gaofen-1 WFV Camera Data

Fukun Yang 1 , Meng Fan 2,* and Jinhua Tao 2

1 College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China; [email protected] 2 State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China; [email protected] * Correspondence: [email protected]

Abstract: The four wide-field-of-view (WFV) cameras aboard the GaoFen-1 (GF-1) satellite launched by China in April 2013 have been applied to the studies of the atmospheric environment. To highlight the advantages of GF-1 data in the atmospheric environment monitoring, an improved deep blue (DB) algorithm using only four bands (visible–near infrared) of GF-1/WFV was adopted to retrieve the aerosol optical depth (AOD) at ~500 m resolution in this paper. An optimal reflectivity technique (ORT) method was proposed to construct monthly land surface reflectance (LSR) dataset through converting from MODIS LSR product according to the WFV and MODIS spectral response functions to make the relationship more suitable for GF-1/WFV. There is a good spatial coincidence between our retrieved GF-1/WFV AOD results and MODIS/Terra or Himawari-8/AHI AOD products at 550 nm, but GF-1/WFV AOD with higher resolution can better characterized the details of regional pollution. Additionally, our retrieved GF-1/WFV AOD (2016–2019) results showed a good agreement with AERONET ground-based AOD measurements, especially, at low levels of AOD. Based on the

same LSR dataset transmitted from 2016–2018 MODIS LSR products, RORT of 2016–2018 and 2019 GF-1/WFV AOD retrievals can reach up to 0.88 and 0.94, respectively, while both of RMSEORT are smaller than 0.13. It is indicated that using the ORT method to deal with LSR information can make GF-1/WFV AOD retrieval algorithm more suitable and ﬂexible. Citation: Yang, F.; Fan, M.; Tao, J. An Improved Method for Retrieving Keywords: GaoFen-1; aerosol optical depth; deep blue; optimal reﬂectivity technique; validation Aerosol Optical Depth Using Gaofen-1 WFV Camera Data. Remote Sens. 2021, 13, 280. https://doi.org/ 10.3390/rs13020280 1. Introduction

Received: 10 December 2020 A signiﬁcant portion of aerosols in the atmosphere sources from anthropogenic activi- Accepted: 12 January 2021 ties, including industrial processes, fossil fuel combustion, agricultural operation, construc- Published: 14 January 2021 tion and mining. In recent years, China suffered from frequently severe pollution events, especially in winter, which had strong impact on the air environment, climate change, Publisher’s Note: MDPI stays neu- and public health [1–4]. However, due to the heterogeneous distribution of sources, short tral with regard to jurisdictional clai- lifetime, and episodic features of emission events, aerosols exhibit high spatiotemporal ms in published maps and institutio- variability which can hardly be characterized by the sparsely ground-based measurements. nal afﬁliations. Therefore, aerosol optical depth (AOD) retrieved from satellite data has been increasingly used to estimate the surface-level particle concentrations or the air pollution level [5]. Although there are some uncertainties (e.g., differences in the sensor, calibration/ characterization, retrieval algorithm, pixel selection, cloud and other masking) in satellite Copyright: © 2021 by the authors. Li- censee MDPI, Basel, Switzerland. AOD retrievals, many relevant studies still been continually developed [6–10]. Dark target This article is an open access article (DT) and deep blue (DB) algorithms have been successfully applied to the Moderate distributed under the terms and con- Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer ditions of the Creative Commons At- Suite (VIIRS), and Medium Resolution Spectral Imager (MERSI) onboard the Chinese tribution (CC BY) license (https:// FengYun (FY) satellite series (i.e., FY-3A, FY-3B, FY-3C and FY-3D) and other sensors with creativecommons.org/licenses/by/ medium-low resolution [5,11–13]. MODIS AOD products (MOD/MYD04) provide daily 4.0/). global AOD distributions at 10 and 3 km spatial resolutions since 2000, and they are

Remote Sens. 2021, 13, 280. https://doi.org/10.3390/rs13020280 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 280 2 of 19

currently most suitable for studies of global aerosol spatial distribution [14,15]. Due to their relative low spatial resolution, MOD/MYD04 AOD products have limited applications in the air quality monitoring on a city or smaller scale [14–16]. Although MODIS AOD products have been available for 1 km, the observation data itself is characterized by moderate resolution, and the AOD results with higher resolution cannot be obtained. Resulting from economic development, agricultural and industrial activities, urban sprawl, etc. China has been experiencing serious air pollution situation, especially atmospheric particulate pollution [15]. Therefore, it is in urgent need of AOD retrieval with higher resolution for capturing the characteristics of urban-scale particulate pollution [14–16]. With the development of satellite technology, the precision of AOD inversion from high-resolution satellite is required to be higher, which promotes the further research of algorithm. Many researchers have studied aerosols and atmospheric particles at small scales, and developed many high-resolution aerosol retrieval algorithms for Terra [17–20], NPP [21], Landsat [15,22–25], and Chinese resources and environment satellite series [16,19,26–28], and other satellites [29,30]. China has launched a major project to build a high-resolution Earth observation system, and plans to build its own maritime, land and atmospheric observation systems around 2020. As the first satellite of the observation system, GaoFen-1 (GF-1) has a great potential in aerosol detection. The four wide-field-of-view (WFV) cameras aboard GF-1 satellite, launched by the Chinese government in April 2013, can provide multi-spectral images from visible to near-infrared (NIR) channels, with a high spatial resolution of 16 m and a re-visiting period of 4 days [16]. Developing or improving the GF-1 aerosol retrieval ability can not only enhance the application of space infrastructure efficiency, enrich the AOD inversion data source effectively, and promote the domestic high series of satellite application in the atmospheric environment monitoring ability. For regions covered by dense vegetation, the land surface reflectance (LSR) information is relatively weak, so the LSR relationship between visible and infrared channels in the low reflection region (dark targets) can be used to carry out AOD inversion, which is the idea of DT algorithm [5,31]. Although the absence of SWIR band on GF-1/WFV makes it difficult to retrieve AOD using the DT method, the blue band of GF-1/WFV can be used for AOD inversion by DB algorithm. DB algorithm removes the surface contribution by using the feature that the reflection of blue light in the atmosphere is strong and the reflection of the surface is weak [12,13,32]. The determination of LSR is the precondition of the DB algorithm. AOD retrieval is influenced by different factors including calibration accuracy, the presence of clouds, the reflectance of the underlying surface and aerosol properties [33]. The Beijing–Tianjin–Hebei (BTH) urban agglomeration (Figure1) is one of regions in China with most severe air pollution. Due to the integration process progresses and the close connection between Beijing, Tianjin, and Hebei, frequent pollution events are always caused by the coal burning of industrial activities. Moreover, the complex underlying surface composition and the influence of monsoonal climate in this area make it difficult for the AOD retrieval over BTH region [21,34]. In this paper, our algorithm is based on the DB algorithm, with a focus on solving the problems of cloud screening and LSR determination of GF-1 satellite. To construct GF-1/WFV blue LSR, an optimal reflectance technique (ORT) method was applied in MODIS surface information to transform to the GF-1/WFV LSR using a channel transformation method. Based on the GF-1/WFV reflectance in blue channel, the DB algorithm was employed to retrieve AOD over BTH region in China. Then, the new WFV AOD from 2016 to 2019 was evaluated based on comparisons with AOD values from different sources. The main purpose of this paper is to verify that the AOD inversion based on a new cloud screening method and LSR database can be more suitable for BTH region. In this paper, Section2 introduces relevant satellite data and the AOD product, and Section3 introduces the main methodology. The validation of GF-1/WFV AODs against different satellite measurements and ground-based AErosol RObotic NETwork (AERONET) Remote Sens. 2021, 13, x FOR PEER REVIEW 3 of 19

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introduces the main methodology. The validation of GF‐1/WFV AODs against different satellite measurements and ground‐based AErosol RObotic NETwork (AERONET) obser‐ vationsobservations are described are described in Section in Section 4. Finally,4. Finally, we discuss we discuss the deficiencies the deﬁciencies of our AOD of our product AOD andproduct conclude and concludethe paper the in Section paper in 5. Section 5.

FigureFigure 1. 1. LocationsLocations of of AERONET AERONET ground ground-based‐based sites sites over over Beijing–Tianjin–Hebei Beijing–Tianjin–Hebei (BTH) (BTH) region. region. Elevation Elevation data data obtained obtained fromfrom the the Advanced Advanced Spaceborne Spaceborne Thermal Thermal Emission Emission and and Reflection Reflection Radiometer Radiometer Global Global Digital Digital Elevation Elevation Model Model (GDEM) (GDEM) at at 30 m spatial resolution. 30 m spatial resolution. 2. Data 2. Data 2.1.2.1. GF GF-1/WFV‐1/WFV TableTable 1 shows the specificspecific configurationconfiguration parametersparameters of GF-1/WFV.GF‐1/WFV. The data of each GFGF-1/WFV‐1/WFV image image includes includes four four files: files: (1) (1) The The GeoTIFF GeoTIFF file file is is used used to to store store image image data data in in a a 3232-bit‐bit integer integer format; format; (2) (2) JPG JPG file file is is used used to to display display thumbnails thumbnails of of images; images; (3) (3) XML XML files files includeinclude data data description description files, files, which which mainly mainly provide provide angle angle (Solar (Solar Zenith Zenith Angles, Angles, Satellite Satellite ZenithZenith Angles, Angles, Solar Solar Azimuth Azimuth Angles Angles and and Satellite Satellite Azimuth Azimuth Angles) Angles) information information of of image image centercenter point, point, transit transit time, time, etc.; etc.; (4) (4) The The RPB RPB file file mainly mainly provides provides the the geometric geometric positioning positioning informationinformation of of the the image, image, which which is is used used for for geometric geometric correction correction of of image. image.

TableTable 1. 1. ParameterParameter configuration conﬁguration of of GF GF-1/WFV.‐1/WFV.

SensorSensor BandsBands (μm) (µ m)Central Central Wavelength Wavelength (μm) (µ m)View View Zenith Zenith Angle OverpassOverpass Time Time SpatialSpatial Resolution Resolution SwathSwath Revisit Revisit Period Period E1: 0.45~0.52 0.484 E1: 0.45~0.52 0.484 WFV2 and WFV3: E2: 0.52~0.59E2: 0.52~0.59 0.560 0.560 WFV2 and WFV3:◦ 0–24° WFVWFV 0–24 10:3010:30 LT LT 1616 m m 800800 km km 44 days days E3: 0.63~0.69E3: 0.63~0.69 0.665 0.665 WFV1WFV1 and andWFV4: WFV4: 24–40° E4: 0.77~0.89 0.800 24–40◦ E4: 0.77~0.89 0.800

2.2.2.2. Other Other Satellite Satellite Products Products TheThe DT DT (AOD_MODT) (AOD_MODT) and and DB DB (AOD_MODB) (AOD_MODB) AOD AOD datasets datasets from from Terra/MODIS Terra/MODIS aer‐ osolaerosol products products (MOD04), (MOD04), Himawari Himawari-8/AHI‐8/AHI aerosol aerosol product product (AOD_AHI) (AOD_AHI) [35], and [ ground35], and‐ basedground-based AOD observations AOD observations from AERONET from AERONET (AOD_AERO) (AOD_AERO) [36,37] were [36 used,37] for were the used valida for‐ tionthe validationof retrieved of GF retrieved‐1/WFV GF-1/WFV AOD (AOD_WORT) AOD (AOD_WORT) results at 550 results nm. Here, at 550 the nm. Ångström Here, the exponentÅngström (AE) exponent relationship (AE) relationshipwas applied wasfor the applied calculation for the of calculation AOD_AHI ofand AOD_AHI AOD_AERO and atAOD_AERO 550 nm. Terra/MODIS at 550 nm. Terra/MODIS surface reflectance surface product reﬂectance (MOD09A1) product (MOD09A1)[12,14,34], denoted [12,14, 34as], MODISdenoted LSR. as MODIS Detailed LSR. product Detailed information product informationis listed in Table is listed 2. in Table2.

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Table 2. MODIS, AHI, and AERONET products used in this work.

Item Product Name Quality Flag (QF) Spatial Resolution Usage Purpose Website https://ladsweb.modaps.eosdis. sur_refl_b03 (500 m Surface reflectance Band 3 500 m Reflectance Band MODIS LSR 500 m LSR determination nasa.gov/search/order/1/MOD04_ (459–479 nm)) Quality L2--61,MOD09A1--6 (NASA) MODIS DT AOD QF = 1,2,3 10 km Spatial distribution contrast https://ladsweb.modaps.eosdis. Corrected_Optical_Depth_Land (AOD_MODT) Comprision nasa.gov/search/order/1/MOD04_ QF = 3 10 km (2016–2018) L2--61 (NASA) MODIS DB AOD Deep_Blue_Aerosol_Optical_Depth_550_Land QF = 1,2,3 10 km Spatial distribution contrast (AOD_MODB) AHI AOD http://www.eorc.jaxa.jp/ptree/ AOT (Aerosol optical thickness at 500 nm) QA flag (all) 0.05 deg Spatial distribution contrast (AOD_AHI) index.html (JAXA) AERONET AOD Level 1.5 (cloud screened) – Validation (2019) https://aeronet.gsfc.nasa.gov/new_ Version 3 Aerosol Optical Depth (AOD_AERO) Level 2.0 (cloud screened Validation web/data.html – and quality assured) (2016–2018) (NASA and PHOTONS) Remote Sens. 2021, 13, 280 5 of 19

Retrieved GF-1/WFV AOD data was matched with the AERONET observations from 2016 to 2019 over BTH region. Within a sampling window of 25 km × 25 km centered in an AERONET site, more than 25% of the total valid pixels are required to calculate a mean value of GF-1/WFV AOD for the accuracy evaluation. And no less than 4 AERONET AODs at 550 nm with a time interval of 1 h centered at Terra overpass time were averaged as the corresponding ground-based measurements for our validation.

3. AOD Retrieval Algorithm 3.1. Deep Blue (DB) Algorithm In the case of an infinite uniform Lambertian and homogeneous target of surface s T reflectance ρλ, the reflectance of the top of the atmosphere ρλ at a particular wavelength λ can be written as follows [31,38,39]:

↓ ↑ s T a Tλ (µs)Tλ (µv)ρλ ρλ (µs, µv, φ) = ρλ(µs, µv, φ)+ s (1) 1 − ρλSλ a where ρλ is the atmospheric path reflectance, µs is the cosine of the solar zenith angle (θs), µv is the cosine of the view zenith angle (θv), φ is the relative azimuth angle (the ↑ difference between the solar azimuth angle and the satellite azimuth angle), Tλ (µv) is the ↓ total upward transmission in the direction of the satellite field of view, Tλ (µs) is the total downward atmospheric transmission, and Sλ is the spherical albedo of the atmosphere for illumination from below (atmospheric backscattering ratio). On the left-hand side of T Equation (1), ρλ can be obtained from satellite observation data. Except for the surface s reflectance ρλ, each term on the right-hand side of Equation (1) is a function of the aerosol type and loading optical thickness τ. Using the radiative transport model Second Simulation of Satellite Signal in the Solar Spectrum (6S) [39,40], the relationship between aerosol optical depth and parameters (i.e., atm ↓ ↑ Sλ ρλ , Tλ (µs)Tλ (µv)) was calculated under different atmospheric aerosol modes and observation conditions. A set of sun-satellite geometries, atmospheric parameters, and aerosol information listed in Table3 was applied to build a look-up table (LUT), which would be used in the AOD retrieval to speed up the calculation processes.

Table 3. Input variables for the calculation of the look-up table.

Input Variables Setting Solar Zenith Angle From 0◦ to 72◦ in increments of 6◦ View Zenith Angle From 0◦ to 72◦ in increments of 6◦ Relative Azimuth Angle From 0◦ to 180◦ in increments of 10◦ Atmospheric Model Midlatitude winter/summer Aerosol Optical Depth 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 1.7, 2.0 6S: continental model and urban model Aerosol Model Five models used in the VIIRS over-land: Dust, Smoke-low absorption, Smoke-high absorption, Urban-clean absorption and Urban-polluted absorption Land Surface Reﬂectance From 0 to 0.15 in increments of 0.01

The Rayleigh scattering has signiﬁcant impacts on the visible channels, especially for the blue band with short wavelength [15]. In this study, the method proposed by Levy et al. was used to correct the altitude of atmospheric molecule scattering [5]:

−Z τ = 0.00877λ−4.05exp (2) λ,Z 8.5

where τλ,z is the Rayleigh optical depth (ROD), λ is the wavelength (µm), Z is the elevation of the surface target obtained from GDEM, and 8.5 km is the exponential scale height of the atmosphere. Remote Sens. 2021, 13, 280 6 of 19

An overview of the retrieval algorithm is shown in the dataflow diagram in Figure2 . The original GF-1/WFV data was sampled with 32 × 32 pixels to obtain a new image with a resolution of 512 m (~500 m, regarded as 500 m). Then, angle calculation (including view zenith angle, solar zenith angle and relative azimuth angle) and pixel selection were pre-processed before the AOD retrieval. GF-1/WFV surface reflectance converted from the MODIS LSR data and seven standard aerosol models were used to identify the land surface information and aerosol type in the GF-1/WFV AOD calculation, respectively. Here, the above seven aerosol models involved two standard aerosol models (urban and continental models) in 6S [39,40] and five models (dust, smoke-high absorption, smoke-low absorption, Remote Sens. 2021, 13, x FOR PEER REVIEWurban-clean and urban-polluted models) in the VIIRS over-land aerosol retrieval [41].6 Once of 19

the calculated spectral reﬂectance makes the best match with those that are measured, the aerosol model is determined and the AOD value will be reported.

FigureFigure 2. 2.Flowchart Flowchart for for GF-1/WFV GF‐1/WFV aerosolaerosol opticaloptical depthdepth retrievalretrieval overover land.land.

3.2.3.2. DataData PreprocessingPreprocessing 3.2.1.3.2.1. AngleAngle CalculationCalculation ForFor the original GF GF-1/WFV‐1/WFV data, data, only only the the angle angle information information in the in center the center of the of satel the‐ satellitelite image image is provided. is provided. The difference The difference in the in observation the observation angle angle between between the image the image edge edge pixel and the central pixel can cause a large error to the quantitative application. The pixel and the central pixel can cause a large error to the quantitative application. The var‐ variations of apparent reflectance changed with the observation geometry and AOD were iations of apparent reflectance changed with the observation geometry and AOD were simulated by the 6S model, and the results are shown in Figure3. Figure3c,d indicate that simulated by the 6S model, and the results are shown in Figure 3. Figure 3c,d indicate that the change of relative azimuth angle has little influence on the apparent reflectance for a the change of relative azimuth angle has little influence on the apparent reflectance for a fixed AOD, so that the entire image can utilize the uniform value of relative azimuth angle fixed AOD, so that the entire image can utilize the uniform value of relative azimuth angle provided by the XML file. However, based on the simulations shown in Figure 3a,b, the satellite zenith angles of the satellite image need to be calculated by interpolation of cam‐ era field angles due to the strong effect of satellite zenith angle on the apparent reflectance. According to Jacobson (2005) [42], the solar zenith angle can be calculated by using the satellite transit time, longitude and latitude of the corresponding satellite image pixel, as follows: 𝑐𝑜𝑠𝜃 𝑠𝑖𝑛𝑙𝑎𝑡 𝑠𝑖𝑛 𝛿 𝑐𝑜𝑠𝑙𝑎𝑡 𝑐𝑜𝑠𝛿𝑡 (3) where δ is the declination of the sun, t is the solar hour angle, and lat is the latitude of the pixel. The longitude and latitude of the image elements can be bilinearly interpolated by the longitude and latitude values of four image corners.

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provided by the XML file. However, based on the simulations shown in Figure3a,b , the satellite zenith angles of the satellite image need to be calculated by interpolation of camera field angles due to the strong effect of satellite zenith angle on the apparent reflectance. According to Jacobson (2005) [42], the solar zenith angle can be calculated by using the satellite transit time, longitude and latitude of the corresponding satellite image pixel, as follows: cos(θs) = sin(lat) × sin δ + cos(lat) × cos δ × cos t (3) where δ is the declination of the sun, t is the solar hour angle, and lat is the latitude of the Remote Sens. 2021, 13, x FOR PEER REVIEW 7 of 19 pixel. The longitude and latitude of the image elements can be bilinearly interpolated by the longitude and latitude values of four image corners.

(a) (b)

FigureFigure 3.3. TheThe changechange ofof simulatedsimulated apparentapparent reflectance reflectance in in GF-1/WFV GF‐1/WFV blue band withwith solarsolar zenithzenith angleangle ((aa),), satellitesatellite zenithzenith angle (b), relative azimuth angle (c) for different AODs. The change of apparent reflectance with different relative azimuth angle (b), relative azimuth angle (c) for different AODs. The change of apparent reflectance with different relative azimuth angle (d). In the simulation, the view geometry, atmospheric model, and aerosol model are set as shown in figure by 6S. angle (d). In the simulation, the view geometry, atmospheric model, and aerosol model are set as shown in figure by 6S. 3.2.2. Appropriate Pixel Selection 3.2.2.In Appropriate this paper, Pixelsome Selectionobstructions such as cloud, water, and snow/ice pixels need to be identifiedIn this before paper, AOD some retrieval. obstructions The suchpremise as cloud, of AOD water, retrieval and snow/ice is the accurate pixels isolation need to be of identifiedcloud and before clear regions. AOD retrieval. Due to Thethe limitation premise of of AOD GF‐1/WFV retrieval band, is the it accurate is difficult isolation to effec of‐ cloudtively andremove clear regions.cloud pixels Due tobut the maintain limitation heavy of GF-1/WFV aerosol band,ones. itFigure is difficult 4 shows to effectively the GF‐ remove1/WFV cloud pixelsrecognition but maintain flow, which heavy is aerosol described ones. in Figure detail 4below. shows the GF-1/WFV cloud recognitionThe multi flow,‐band which threshold is described approach in detail of cloud below. removing, which is combined with vis‐ ible bandsThe multi-band and infrared threshold bands approach and commonly of cloud applied removing, to the which MODIS is combined or Landsat with data, visible is bandsnot suitable and infrared for the GF bands‐1/WFV and with commonly only four applied bands. to Bright the MODIS clouds or characterized Landsat data, by is high not suitablereflectivity for in the green GF-1/WFV and red with channels, only fourcan lead bands. to the Bright signal clouds oversaturation characterized and byeven high an reflectivityupward trend in green of reflectance and red channels,spectrum. can Therefore, lead to a the combination signal oversaturation approach of and multi even‐band an threshold method and reflectance variation characteristics with the increasing wavelength can be used for the cloud identification of GF‐1/WFV. Due to differences in the microphysical and chemical properties between cloud and aerosol, cloud exhibit a more complex spatial behavior than aerosol [43]. In our study, interesting regions, including thick cloud, thin cirrus cloud and clear sky, were extracted by a supervised classification method [44]. Due to the difference in the apparent reflec‐ tance of the image under different backgrounds, histogram statistics were conducted for exploring the pixel distribution to determine the threshold for dividing clouds and non‐ clouds. The spatial variation of the apparent reflectance at visible wavelength is relatively uniform for clear sky pixels, while that of cloud pixels is extremely mutational. And with the comparison of clear‐sky condition, the amplitude of spatial variation of apparent re‐

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upward trend of reflectance spectrum. Therefore, a combination approach of multi-band threshold method and reflectance variation characteristics with the increasing wavelength can be used for the cloud identification of GF-1/WFV. Due to differences in the microphysical and chemical properties between cloud and aerosol, cloud exhibit a more complex spatial behavior than aerosol [43]. In our study, interesting regions, including thick cloud, thin cirrus cloud and clear sky, were extracted by a supervised classification method [44]. Due to the difference in the apparent reflectance of the image under different backgrounds, histogram statistics were conducted for exploring the pixel distribution to determine the threshold for dividing clouds and non-clouds. The Remote Sens. 2021, 13, x FOR PEER REVIEWspatial variation of the apparent reflectance at visible wavelength is relatively uniform8 of 19

for clear sky pixels, while that of cloud pixels is extremely mutational. And with the comparison of clear-sky condition, the amplitude of spatial variation of apparent reflectance can be further deduced for heavy aerosol-loaded pixels due to the stronger scattering and flectance can be further deduced for heavy aerosol‐loaded pixels due to the stronger scat‐ absorption led by aerosol particles. Therefore, the standard deviation of band 1 (0.484 µm) tering and absorption led by aerosol particles. Therefore, the standard deviation of band TOA reflectance within a window of 3 × 3 pixels (3 × 3-STD) is higher for cloud pixels than 1 (0.484 μm) TOA reflectance within a window of 3 × 3 pixels (3 × 3‐STD) is higher for that for non-cloud pixels [34]. To eliminate the misclassification of cloud pixels, isolated cloud pixels than that for non‐cloud pixels [34]. To eliminate the misclassification of cloud cloud pixels were regrouped into cloud-free type if the proportion of cloud pixel number pixels, isolated cloud pixels were regrouped into cloud‐free type if the proportion of cloud within a window of 3 × 3 pixels is less than 25%. A buffer zone with a distance of 3-pixel pixel number within a window of 3 × 3 pixels is less than 25%. A buffer zone with a dis‐ size was used for expanding the initially recognized cloud regions to guarantee that the tance of 3‐pixel size was used for expanding the initially recognized cloud regions to guar‐ cloud pixels in the satellite image can be removed completely. antee that the cloud pixels in the satellite image can be removed completely.

Figure 4. GF‐1/WFV cloud recognition flow. Figure 4. GF-1/WFV cloud recognition ﬂow.

BecauseBecause thethe algorithmalgorithm isis sensitivesensitive toto snow/ice snow/ice and water, thethe normalizednormalized differencedifference vegetationvegetation index index (NDVI) (NDVI) is is aa simplesimple graphicalgraphical indicatorindicator that that can can bebe furtherfurther usedused toto removeremove thosethose non-inversionnon‐inversion pixels. pixels. Cloud,Cloud, water,water, and and snow/ice snow/ice pixels cancan bebe also identiﬁedidentified basedbased onon aa rulerule ofof NDVINDVI << 0.

3.3.3.3. ReﬂectanceReflectance RelationshipRelationship TransformationTransformation InIn thisthis paper,paper, aa newnew aerosol aerosol retrieval retrieval algorithm algorithm was was proposed proposed to to retrieve retrieve GF-1/WFV GF‐1/WFV AODAOD basedbased onon thethe MODISMODIS LSRLSR data.data. AsAs aa keykey process,process, thethe transformationaltransformational relationshiprelationship betweenbetween thethe surfacesurface reﬂectancereflectance of of GF-1/WFV GF‐1/WFV and MODIS was implemented throughthrough twotwo main steps: the building of monthly MODIS LSR dataset and the establishment of LSR transfer coefficients. MODIS LSR data were obtained from eight‐day synthesized MODIS 500 m surface reflectance product after atmospheric corrections for gaseous scattering and absorption, aerosol scattering and absorption, cirrus contamination, BRDF coupling, and the adja‐ cency effect [40]. In the first step, the dataset of 500 m monthly MODIS LSR in blue wave‐ length was constructed by using the traditional minimum reflectivity technique (MRT) and the optimal reflectivity technique (ORT) method. The MRT method considers a cer‐ tain number of images at different times, and seeks the minimum value in the same posi‐ tion pixel as the target value [25,45]. Although the monthly LSR data constructed by the MRT method is the LSR minimum of each pixel in one month, the actual land surface reflectance may be underestimated. By comparison, although the ORT method cannot

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main steps: the building of monthly MODIS LSR dataset and the establishment of LSR transfer coefficients. MODIS LSR data were obtained from eight-day synthesized MODIS 500 m surface reflectance product after atmospheric corrections for gaseous scattering and absorption, aerosol scattering and absorption, cirrus contamination, BRDF coupling, and the adjacency effect [40]. In the first step, the dataset of 500 m monthly MODIS LSR in blue wavelength was constructed by using the traditional minimum reflectivity technique (MRT) and the optimal reflectivity technique (ORT) method. The MRT method considers a certain number of images at different times, and seeks the minimum value in the same position pixel as the target value [25,45]. Although the monthly LSR data constructed by the MRT method is the LSR minimum of each pixel in one month, the actual land surface reflectance may be underestimated. By comparison, although the ORT method cannot guarantee the existence of every pixel, the generated LSR data is more rigorous to ensure the accuracy of AOD inversion. The determination rules of ORT method are as follows: MOD09A1 data from 2016 to 2018 were selected, and pixels with view zenith angle greater than 30 degree were removed. Each pixel is sorted from low to high values, then set the rules:

[L1(i, j), L2(i, j), L3(i, j),..., Ln(i, j)] = sort_MINtoMAX[I1(i, j), I2(i, j), I3(i, j),..., In(i, j)](n(i, j) ≤ N(i, j)) (4)

where (i,j) is the position of a single pixel, L(i,j) is the unsorted LSR value, and I(i,j) is the sorted LSR value. N is the number of primitive values per month for the same location, n is the number of values processed (view zenith angle ≤ 30◦) per month at the same location. S(i,j) is the ﬁnal MODIS LSR which can be determined as follows:

 S(i, j) == Invalid Value, (n ≤ 2)    Mk = [L k(i, j), L k− (i, j), L k− (i, j)]   3 3 1 3 2  i f Minimum[Variance(M )] (5) k ≤ ≤ N −  , 3 n 2 1   then   S(i, j) == Average[Mk]

where Mk is an array containing three elements L3k(i, j), L3k−1(i, j) and L3k−2(i, j). According to the significant difference between MODIS and GF-1/WFV bands in the wavelength range and spectral response function (SRF) shown in Figure5, the surface reflectance of GF-1/WFV can be inherited from MODIS LSR, but cannot be replaced directly. Theoretically, the Earth observation of the four WFV sensors aboard on GF-1 satellite is simultaneous, meaning that the observation geometries of four WFVs are not the same. However, our simulated results in Figure5b indicate that for the blue band, the impact of four different WFV cameras on the apparent reflectance can be ignored for a single observation of GF-1/WFV. Therefore, the spectral differences among the four cameras were not considered into the transformation method of surface reflectance, and a single linear relationship between MODIS and GF-1/WFV surface reflectance was assumed in the second step. s_WFV s_MODIS ρλ = A + B × ρλ (6) s_MODIS s_WFV where A and B are two coefficients of the linear formula, and ρλ and ρλ are surface reflectance values observed by MODIS and WFV, respectively, which can be derived by: R λ2 S(λ)Γ(λ)dλ λ1 ρλ = (7) R λ2 Γ(λ)dλ λ1

where Γ(λ) is the SRF. λ1 and λ2 are the upper and lower limits of integration, respectively, which deﬁne the band width, and S(λ) is the spectral curve corresponding to central wavelength λ. Remote Sens. 2021, 13, 280 10 of 19

After differentiation, Equation (6) can be rewritten as:

− ∑N 1 S(λ )Γ(λ )∆λ = i=0 i i ρλ N−1 (8) ∑ = Γ(λi)∆λ Remote Sens. 2021, 13, x FOR PEER REVIEW i 0 10 of 19

where ∆λ = 0.0001.

(a) (b)

Figure 5. (a) The spectrum response function (SRF) of WFV and MODIS blue bands. The dash line line is the SRF of MODIS bands 3 (0.466 μm), and the solid lines are for WFV1‐WFV4 band 1(0.484 μm). All the spectrum response functions use bands 3 (0.466 µm), and the solid lines are for WFV1-WFV4 band 1 (0.484 µm). All the spectrum response functions use the the same wavelength interval (0.0001 μm) by interpolation. (b) The change of simulated apparent reflectance in blue band same wavelength interval (0.0001 µm) by interpolation. (b) The change of simulated apparent reflectance in blue band with with different AOD and WFVs. different AOD and WFVs. In this study, the spectral library established in this paper is obtained by using the In this study, the spectral library established in this paper is obtained by using the remote sensing software ENVI. The spectral data of 50 typical features (construction: 15 remote sensing software ENVI. The spectral data of 50 typical features (construction: types, soils: 20 types, and vegetation: 15 types) in the ENVI spectrum database (ENVI 15 types, soils: 20 types, and vegetation: 15 types) in the ENVI spectrum database (ENVI standard spectral library (SLI) files, THOR Metadata Rich Spectral Library (MRSL) output, standard spectral library (SLI) files, THOR Metadata Rich Spectral Library (MRSL) output, andand Analytical Spectral DevicesDevices (ASD) (ASD) spectrometers spectrometers output) output) [34 [34]] was was selected selected to to calculate calcu‐ latethe reflectancethe reflectance of ground of ground objects objects in blue in blue band band for bothfor both GF-1/WFV GF‐1/WFV and and MODIS MODIS sensors. sensors. As Asshown shown in Figurein Figure6 and 6 and Table Table4, the 4, surfacethe surface reflectance reflectance of GF-1/WFV of GF‐1/WFV is higher is higher than than that that of ofMODIS, MODIS, and and there there is is little little difference difference in in the the transformation transformation parameters parameters ofof thethe four WFV cameras.cameras. Then, Then, by by fitting fitting all the scattered points in FigureFigure6 6,, the the GF-1/WFV GF‐1/WFV blueblue surfacesurface reflectancereflectance relationship relationship can be available from Equation (9). ρs_WFV=0.00109+1.09619× ρs_MODIS s_WFVblue blues_MODIS (9) ρblue = 0.00109+1.09619 × ρblue (9)

Table 4. Statistical information of blue band LSR between MODIS and different WFV cameras of GF-1.

AB R2 RMSE MODIS-WFV1 0.00058 1.08407 0.98665 0.00476 MODIS-WFV2 0.00153 1.10921 0.98064 0.00589 MODIS-WFV3 0.00177 1.09950 0.98347 0.00538 MODIS-WFV4 0.00047 1.09200 0.98483 0.00512 MODIS-WFV 0.00109 1.09619 0.98303 0.00536

Figure 6. Linear relationships of blue band LSR between MODIS and different WFV cameras of GF‐1.

Remote Sens. 2021, 13, x FOR PEER REVIEW 10 of 19

(a) (b) Figure 5. (a) The spectrum response function (SRF) of WFV and MODIS blue bands. The dash line is the SRF of MODIS bands 3 (0.466 μm), and the solid lines are for WFV1‐WFV4 band 1(0.484 μm). All the spectrum response functions use the same wavelength interval (0.0001 μm) by interpolation. (b) The change of simulated apparent reflectance in blue band with different AOD and WFVs.

In this study, the spectral library established in this paper is obtained by using the remote sensing software ENVI. The spectral data of 50 typical features (construction: 15 types, soils: 20 types, and vegetation: 15 types) in the ENVI spectrum database (ENVI standard spectral library (SLI) files, THOR Metadata Rich Spectral Library (MRSL) output, and Analytical Spectral Devices (ASD) spectrometers output) [34] was selected to calcu‐ late the reflectance of ground objects in blue band for both GF‐1/WFV and MODIS sensors. As shown in Figure 6 and Table 4, the surface reflectance of GF‐1/WFV is higher than that Remote Sens. 2021, 13, x FOR PEER REVIEWof MODIS, and there is little difference in the transformation parameters of the four11 of WFV 19 cameras. Then, by fitting all the scattered points in Figure 6, the GF‐1/WFV blue surface Remote Sens. 2021, 13, 280 reflectance relationship can be available from Equation (9). 11 of 19 Table 4. Statistical information of blueρs_WFV band=0.00109+1.09619× LSR between MODIS ρs_MODIS and different WFV cameras of blue blue (9) GF‐1.

A B R2 RMSE MODIS‐WFV1 0.00058 1.08407 0.98665 0.00476 MODIS‐WFV2 0.00153 1.10921 0.98064 0.00589 MODIS‐WFV3 0.00177 1.09950 0.98347 0.00538 MODIS‐WFV4 0.00047 1.09200 0.98483 0.00512 MODIS‐WFV 0.00109 1.09619 0.98303 0.00536

4. Results and Analysis Here, ORT LSR results were firstly compared with the MRT LSR data. Then, GF‐ 1/WFV AODs over BTH from 2016 to 2019 were retrieved based on the new algorithm mentioned in Section 3. To assess the accuracy of our retrieved AOD_WORT, 550 nm AODs obtained from AERONET ground‐based measurements and different satellite ob‐ servations were used for the validation.

4.1. Land Surface Reflectance In order to verify the role of LSR data in the GF‐1/WFV AOD retrieval, a sensitive analysis was carried out by simulating the apparent reflectance in GF‐1/WFV blue band under different AOD and LSR conditions. As shown in Figure 7, the apparent reflectance ofFigure GF‐1/WFV 6. Linear in bluerelationships band is ofvery blue sensitive band LSR to between the LSR MODIS when andthe AODdifferent is less WFV than cameras 3. How of ‐ Figure 6. Linear relationships of blue band LSR between MODIS and different WFV cameras of GF-1. ever,GF‐ 1.with the increase of AOD, the apparent reflectance grows slowly for all LSR condi‐ tions.4. Results Therefore, and Analysis an inaccurate LSR data can introduce a large uncertainty into the GF‐ 1/WFVHere, AOD ORT retrieval, LSR results and the were proper firstly estimation compared of with LSR the which MRT can LSR better data. capture Then, GF-the characteristics1/WFV AODs of over actual BTH surface from 2016condition to 2019 is very were important retrieved for based the onreal the‐time new observation algorithm of GF‐1/WFV AOD. mentioned in Section3. To assess the accuracy of our retrieved AOD_WORT, 550 nm AODs obtainedAccording from AERONET to the ORT ground-based method, it is measurements not difficult to and understand different satellite that ORT observations LSR is al‐ wayswere usedhigher for than the MRT validation. LSR. By comparing the monthly ORT LSR results over BHT region with the corresponding MRT ones, as shown in Figure 8, the difference of monthly LSR between4.1. Land MRT Surface and Reflectance ORT methods is in a range of −0.031 ± 0.014. BTHIn order vegetation to verify with the roleobvious of LSR zonation data in was the GF-1/WFVpredominated AOD with retrieval, deciduous a sensitive broad‐ leavedanalysis forest, was carriedand the out vegetation by simulating cover changes the apparent significantly reflectance with in the GF-1/WFV seasons. Due blue to band the growthunder different change of AOD land and surface LSR conditions. vegetation, As the shown land surface in Figure changes7, the apparent obviously reflectance in spring andof GF-1/WFV autumn. Additionally, in blue band the is land very surface sensitive covered to the by LSR snow when and the ice AODdiffers is significantly less than 3. fromHowever, that covered with the by increase bare soil of AOD,or wheat the in apparent winter. reflectanceTherefore, the grows minimum slowly forreflectance all LSR mayconditions. not be a Therefore, reasonable an representation inaccurate LSR of data the actual can introduce surface reflectance. a large uncertainty Although into there the areGF-1/WFV random AODerrors retrieval, in AOD andretrieval the proper by using estimation ORT LSR, of LSR these which errors can can better be reduced capture theby takingcharacteristics the average. of actual In summer, surface condition the difference is very between important MRT for theand real-time ORT LSRs observation is minimal of dueGF-1/WFV to the small AOD. change of underlying surface (Figure 8).

FigureFigure 7. TheThe change change of simulated apparent reﬂectancereflectance in in GF-1/WFV GF‐1/WFV blueblue band band with with AOD AOD and and LSR. LSR. According to the ORT method, it is not difﬁcult to understand that ORT LSR is always higher than MRT LSR. By comparing the monthly ORT LSR results over BHT region with

Remote Sens. 2021, 13, 280 12 of 19

Remote Sens. 2021, 13, x FOR PEER REVIEW 12 of 19 the corresponding MRT ones, as shown in Figure8, the difference of monthly LSR between MRT and ORT methods is in a range of −0.031 ± 0.014.

FigureFigure 8. 8. TheThe monthly monthly averaged averaged difference difference in in LSR LSR between between MRT and ORT.

4.2. SpatialBTH vegetationDistributions with of AODs obvious zonation was predominated with deciduous broad- leaved forest, and the vegetation cover changes significantly with the seasons. Due to the To illustrate the reliability of our retrieved GF‐1/WFV AOD_WORT in terms of spa‐ growth change of land surface vegetation, the land surface changes obviously in spring tial distribution, an inter‐comparison was performed based on GF‐1/WFV, Terra/MODIS, and autumn. Additionally, the land surface covered by snow and ice differs significantly Himawari‐8/AHI AODs derived from different retrieval algorithms. Figures 9 and 10 de‐ from that covered by bare soil or wheat in winter. Therefore, the minimum reflectance may pict the spatial distributions of AOD_WMRT (500 m), AOD_WORT (500 m), AOD_MODT not be a reasonable representation of the actual surface reflectance. Although there are (10 km), AOD_MODB (10 km), and AOD_AHI (0.05 deg) on 12 July 2017 and 1 January random errors in AOD retrieval by using ORT LSR, these errors can be reduced by taking 2018, respectively. The heavily polluted areas were mainly concentrated on the southeast the average. In summer, the difference between MRT and ORT LSRs is minimal due to the part of BTH region, which were obviously different from the northwest mountainous ar‐ small change of underlying surface (Figure8). eas. The spatial distribution of our AOD_WORT retrievals is generally consistent with those4.2. Spatial of other Distributions four satellite of AODs AODs. The high values of AOD (>0.8) are mainly distributed in theTo southeast illustrate of the BTH reliability region, ofand our zones retrieved with GF-1/WFVthe low AODs AOD_WORT are located in in terms the northwest of spatial parts.distribution, In the northwestern an inter-comparison BTH region was with performed lower AOD based values on GF-1/WFV, area, AOD_WORT, Terra/MODIS, and AOD_MODBHimawari-8/AHI are much AODs smoother derived fromthan differentother three retrieval AOD algorithms.results. It is Figuresnotable9 that and the10 de-in‐ validpict the value spatial ratio distributions of GF‐1/WFV of AOD_WMRT AODs is much (500 m), lower AOD_WORT than those (500 of m), Terra/MODIS, AOD_MODT Himawari(10 km), AOD_MODB‐8/AHI ones, (10 especially km), and for AOD_AHI AOD_MODT (0.05 deg)and AOD_AHI, on 12 July 2017 which and may 1 January be caused 2018, byrespectively. the difference The heavilyin spatial polluted resolution, areas overpassing were mainly time, concentrated and retrieval on the algorithm. southeast Such part ofa situationBTH region, of a which high invalid were obviously value ratio different was particularly from the northwest serious for mountainous the AOD_MODT areas. over The thespatial southeastern distribution BHT of our region AOD_WORT with air pollution, retrievals iswhich generally made consistent the status with over those this of region other cannotfour satellite be captured AODs. for The the high analysis. values And of AOD some (>0.8) pixels are covered mainly with distributed snow and in ice the in southeast Figures 9b,cof BTH and region, 10b,c were and also zones set with as invalid the low values AODs the are northwest located in mountains. the northwest However, parts. for In the retrievednorthwestern GF‐1/WFV BTH region AOD with with lower a spatial AOD resolution values area, of AOD_WORT,500 m, both AOD_WORT and AOD_MODB and AOD_WMRTare much smoother can provides than other more three abundant AOD results. details It than is notable MODIS that and the AHI invalid in the value spatial ratio distribution,of GF-1/WFV and AODs can more is much accurately lower than reflect those the of AOD Terra/MODIS, spatial variation Himawari-8/AHI over the polluted ones, region.especially for AOD_MODT and AOD_AHI, which may be caused by the difference in spatial resolution, overpassing time, and retrieval algorithm. Such a situation of a high invalid value ratio was particularly serious for the AOD_MODT over the southeastern BHT region with air pollution, which made the status over this region cannot be captured for the analysis. And some pixels covered with snow and ice in Figure9 b,c and Figure 10b,c were also set as invalid values the northwest mountains. However, for the retrieved GF-1/WFV AOD with a spatial resolution of 500 m, both AOD_WORT and AOD_WMRT can provides more abundant details than MODIS and AHI in the spatial distribution, and can more accurately reflect the AOD spatial variation over the polluted region.

(a) (b) (c)

Remote Sens. 2021, 13, x FOR PEER REVIEW 12 of 19

Figure 8. The monthly averaged difference in LSR between MRT and ORT.

4.2. Spatial Distributions of AODs To illustrate the reliability of our retrieved GF-1/WFV AOD_WORT in terms of spatial distribution, an inter-comparison was performed based on GF-1/WFV, Terra/MODIS, Himawari-8/AHI AODs derived from different retrieval algorithms. Figures 9 and 10 depict the spatial distributions of AOD_WMRT (500 m), AOD_WORT (500 m), AOD_MODT (10 km), AOD_MODB (10 km), and AOD_AHI (0.05 deg) on 12 July 2017 and 1 January 2018, respectively. The heavily polluted areas were mainly concentrated on the southeast part of BTH region, which were obviously different from the northwest mountainous areas. The spatial distribution of our AOD_WORT retrievals is generally consistent with those of other four satellite AODs. The high values of AOD (>0.8) are mainly distributed in the southeast of BTH region, and zones with the low AODs are located in the northwest parts. In the northwestern BTH region with lower AOD values area, AOD_WORT, and AOD_MODB are much smoother than other three AOD results. It is notable that the invalid value ratio of GF-1/WFV AODs is much lower than those of Terra/MODIS, Himawari-8/AHI ones, especially for AOD_MODT and AOD_AHI, which may be caused by the difference in spatial resolution, overpassing time, and retrieval algorithm. Such a situation of a high invalid value ratio was particularly serious for the AOD_MODT over the southeastern BHT region with air pollution, which made the status over this region cannot be captured for the analysis. And some pixels covered with snow and ice in Figures 9b,c and 10b,c were also set as invalid values the northwest mountains. However, for the retrieved GF-1/WFV AOD with a spatial resolution of 500 m, both AOD_WORT and AOD_WMRT can provides more abundant Remote Sens. 2021, 13, 280 13 of 19 details than MODIS and AHI in the spatial distribution, and can more accurately reflect the AOD spatial variation over the polluted region.

Remote Sens. 2021, 13, x FOR PEER REVIEW 13 of 19

(a) (b) (c)

(d) (e) (f)

Figure 9. 9. AODAOD spatial spatial distribution distribution on on 12 12July July 2017. 2017. (a) Terra/MODIS (a) Terra/MODIS true color true colorimage, image, (b) GF-1/WFV (b) GF-1/WFV AOD_WORT, AOD_WORT, (c) GF- 1/WFV AOD_WMRT, (d) Terra/MODIS AOD_MODT, (e) Terra/MODIS AOD_MODB, and (f) Himawari-8/AHI (c) GF-1/WFV AOD_WMRT, (d) Terra/MODIS AOD_MODT, (e) Terra/MODIS AOD_MODB, and (f) Himawari-8/AHI AOD_AHI. AOD_AHI.

The reflectance in bright areas (mainly include urban, desert, bare land, and arid/semi- arid areas with sparse or little vegetation coverage) is more difficult to estimate accurately [14,15]. Central Beijing is a typically bright urban area with a dense population and extensive industrial activity. As shown in Figures9 and 10, the AOD distribution in Beijing is weakly affected by the bright surface. The bright surface of urban area does not cause high value distribution of retrieval results. The spatial distribution of AOD in urban and non-urban areas is relatively smooth, and the removal of surface information is more accurate. So, it is indicated that the high AODs were not caused by the bright surface of urban area in our retrieval algorithm. Because the LSR of the current month cannot be obtained for the near-real-time AOD retrieval in the operational application, in our study, the land surface reflectance constructed by the ORT method was a monthly dataset transformed from the MODIS LSR data during (a) (b) (c) the period from 2016 to 2018. In practice, the monthly LSR based on the MODIS LSRs of last three-year will be used to retrieve the near-real-time GF-1/WFV AOD of the current year, where the month of GF-1/WFV data is the same as that of monthly MODIS LSR. And the LSRs of the current month will be continuously updated into our monthly LSR dataset. According to the true color image on 28 September 2019 (Figure 11a), there was a serious pollution event in the southeast of the BTH region, which can be directly and significantly reflected in our retrieved GF-1/WFV AOD_ WORT result (Figure 11b). The GF-1/WFV AOD_WORT can also be used to strictly distinguish the heavy polluted regions on an urban-scale, which is more useful for the regional control and governance.

(d) (e) (f) Figure 10. AOD spatial distribution on 1 January 2018. (a) Terra/MODIS true color image, (b) GF-1/WFV AOD_WORT, (c) GF-1/WFV AOD_WMRT, (d) Terra/MODIS AOD_MODT, (e) Terra/MODIS AOD_MODB, and (f) Himawari-8/AHI AOD_AHI.

The reflectance in bright areas (mainly include urban, desert, bare land, and arid/semi-arid areas with sparse or little vegetation coverage) is more difficult to estimate accurately [14,15]. Central Beijing is a typically bright urban area with a dense population and extensive industrial activity. As shown in Figures 9 and 10, the AOD distribution in Beijing is weakly affected by the bright surface. The bright surface of urban area does not cause high value distribution of retrieval results. The spatial distribution of AOD in urban and non-urban areas is relatively smooth, and the removal of surface information is more

Remote Sens. 2021, 13, x FOR PEER REVIEW 13 of 19

(d) (e) (f) Remote Sens. 2021, 13, 280 14 of 19 Figure 9. AOD spatial distribution on 12 July 2017. (a) Terra/MODIS true color image, (b) GF‐1/WFV AOD_WORT, (c) GF‐ 1/WFV AOD_WMRT, (d) Terra/MODIS AOD_MODT, (e) Terra/MODIS AOD_MODB, and (f) Himawari‐8/AHI AOD_AHI.

Remote Sens. 2021, 13, x FOR PEER REVIEW 14 of 19

(a) (b) (c)

accurate. So, it is indicated that the high AODs were not caused by the bright surface of urban area in our retrieval algorithm. Because the LSR of the current month cannot be obtained for the near‐real‐time AOD retrieval in the operational application, in our study, the land surface reflectance con‐ structed by the ORT method was a monthly dataset transformed from the MODIS LSR data during the period from 2016 to 2018. In practice, the monthly LSR based on the MODIS LSRs of last three‐year will be used to retrieve the near‐real‐time GF‐1/WFV AOD of the current year, where the month of GF‐1/WFV data is the same as that of monthly MODIS LSR. And the LSRs of the current month will be continuously updated into our monthly LSR dataset. According to the true color image on 28 September 2019 (Figure

(d) 11a), there was a serious pollution(e) event in the southeast of the(f) BTH region, which can be directly and significantly reflected in our retrieved GF‐1/WFV AOD_ WORT result (Fig‐ Figure 10. AODAOD spatial spatial distribution distributionure 11b). on on The 1 1 January January GF‐1/WFV 2018. 2018. ( aAOD_WORT ()a Terra/MODIS) Terra/MODIS can true true also color color be image, used image, ( bto) ( b GFstrictly) GF-1/WFV‐1/WFV distinguish AOD_WORT, AOD_WORT, the ( cheavy) GF‐1/WFV AOD_WMRT, (d) Terra/MODIS AOD_MODT, (e) Terra/MODIS AOD_MODB, and (f) Himawari‐8/AHI (c) GF-1/WFV AOD_WMRT,polluted (d) Terra/MODIS regions on AOD_MODT, an urban‐scale, (e) Terra/MODIS which is more AOD_MODB, useful for the and regional (f) Himawari-8/AHI control and gov‐ AOD_AHI. AOD_AHI. ernance. The reflectance in bright areas (mainly include urban, desert, bare land, and arid/semi‐arid areas with sparse or little vegetation coverage) is more difficult to estimate accurately [14,15]. Central Beijing is a typically bright urban area with a dense population and extensive industrial activity. As shown in Figures 9 and 10, the AOD distribution in Beijing is weakly affected by the bright surface. The bright surface of urban area does not cause high value distribution of retrieval results. The spatial distribution of AOD in urban and non‐urban areas is relatively smooth, and the removal of surface information is more

(a) (b)

FigureFigure 11. 11. ((aa)) Terra/MODIS Terra/MODIS true true color color image image and ( b) GF GF-1/WFV‐1/WFV AOD_WORT AOD_WORT AOD spatial distribu-distri‐ bution on 28 September 2019. tion on 28 September 2019.

4.3.4.3. AOD AOD Validation Validation Using Using AERONET AERONET Data Data GroundGround-based‐based AOD AOD measurements measurements of of five ﬁve AERONET AERONET sites sites (i.e., (i.e., Beijing, Beijing, XiangHe, XiangHe, BeijingBeijing-CAMS,‐CAMS, Beijing-PKU,Beijing‐PKU, andand Beijing-RADI Beijing‐RADI sites) sites) were were extracted extracted for for the the AOD AOD validation. valida‐ tion.The errorThe error statistics, statistics, including including the root the meanroot mean square square error error (RMSE) (RMSE) and the and mean the mean absolute ab‐ soluteerror (MAE), error were(MAE), veriﬁed were by comparingverified by the comparing satellite-retrieved the satellite AODs (AOD_WORT‐retrieved AODs and (AOD_WORTAOD_WMRT) and with AOD_WMRT) the AERONET with measurements the AERONET (AOD_AERO). measurements RMSE was(AOD_AERO). selected to RMSEmeasure was the selected differences to measure between the AOD differences retrievals between and ground-based AOD retrievals measurements, and ground‐ whichbased measurements, which was sensitive to both systematic and random errors. MAE could better reflect the actual situation of AOD retrievals error. RMSE and MAE can be written as follows, respectively:

1 𝑅𝑀𝑆𝐸 𝐴𝑂𝐷 𝐴𝑂𝐷 (10) 𝑛

1 𝑀𝐴𝐸 |𝐴𝑂𝐷 𝐴𝑂𝐷 | (11) 𝑛 Both of AOD_WMRT and AOD_WORT retrievals were matched with AERONET AOD measurements following the rule mentioned in Section 2.2. The validation results of AOD_WMRT and AOD_WORT from 2016 to 2018 were shown in Figure 12. Compared with AOD_WMRT (R = 0.79), AOD_WORT with a higher correlation coefficient (R = 0.88)

Remote Sens. 2021, 13, 280 15 of 19

was sensitive to both systematic and random errors. MAE could better reﬂect the actual situation of AOD retrievals error. RMSE and MAE can be written as follows, respectively:

s n 1 2 RMSE = ∑(AODRetrieval − AODAERONET) (10) n i=0

1 n MAE = ∑|AODRetrieval − AODAERONET| (11) n i=0 Both of AOD_WMRT and AOD_WORT retrievals were matched with AERONET Remote Sens. 2021, 13, x FOR PEER REVIEW 15 of 19 AOD measurements following the rule mentioned in Section 2.2. The validation results of AOD_WMRT and AOD_WORT from 2016 to 2018 were shown in Figure 12. Compared with AOD_WMRT (R = 0.79), AOD_WORT with a higher correlation coefficient (R = 0.88) showedshowed a a better better relationship relationship with with ground ground measurements. measurements. AOD_WMRT AOD_WMRT was was significantly significantly overestimatedoverestimated for for low low AOD AOD values, values, which which might might be be due due to to the the undervaluation undervaluation of of LSR. LSR. However,However, our our AOD_ AOD_ WORT WORT retrieval retrieval algorithm algorithm significantly significantly modified modified such such overestima overestima-‐ tiontion of of AOD_WMRT. AOD_WMRT. Especially, Especially, when the AOD is smaller than 0.2, a better agreement betweenbetween satellite satellite AOD AOD retrievals retrievals and and AERONET AERONET observations observations for for AOD_ AOD_ WORT WORT than that that forfor AOD_WMRT. AOD_WMRT. Separately, Separately, the AOD_WORT retrievals on Beijing, Beijing Beijing-CAMS,‐CAMS, and XiangHeXiangHe sites sites were were much better agreement with AERONET observations.

(a) (b) Figure 12. A comparison between ground‐measured AOD_AERO and GF‐1/WFV retrieved 550 nm AODs from 2016 to Figure 12. A comparison between ground-measured AOD_AERO and GF-1/WFV retrieved 550 nm AODs from 2016 to 2018. (a) AOD_WMRT and (b) AOD_WORT (red, green, blue, and black symbols represent Beijing‐CAMS, Beijing, 2018. (a) AOD_WMRT and (b) AOD_WORT (red, green, blue, and black symbols represent Beijing-CAMS, Beijing, XiangHe, XiangHe, and all sites, respectively). and all sites, respectively).

ToTo testify whetherwhether thethe LSR LSR dataset dataset based based on on 2016–2018 2016–2018 MODIS MODIS LSR LSR data data is properis proper for forretrieving retrieving the GF-1/WFVthe GF‐1/WFV AOD ofAOD 2019, of the 2019, same the validation same workvalidation for 2019 work AOD_WORT for 2019 AOD_WORT(Figure 13) was (Figure done as13) for was 2016–2018 done as AOD_WORTfor 2016–2018 (Figure AOD_WORT 12). Figures (Figure 13 and12). 14Figures indicate 13 anda better 14 indicate performance a better for performance our proposed for algorithmour proposed in this algorithm paper. Rin ofthis 2019 paper. AOD_WORT R of 2019 AOD_WORTreaches up to reaches 0.94, especially up to 0.94,R especiallyof the XiangHe R of the site XiangHe is the largest site is the one largest among one that among of all thatsites, of as all high sites, as as 0.98. high Both as 0.98. of RMSE Both and of RMSE MAE and for AOD_WORT MAE for AOD_WORT are much lower are much than lower those thanfor AOD_WMRT. those for AOD_WMRT. For R, the largestFor R, the one largest is 2019 one AOD_WORT, is 2019 AOD_WORT, and following and isfollowing 2016–2018 is 2016–2018AOD_WORT, AOD_WORT, and the smallest and the one smallest is 2016–2018 one is AOD_WMRT.2016–2018 AOD_WMRT. On the contrary, On the both con of‐ trary,RMSE both and of MAE RMSE for and 2019 MAE AOD_WORT for 2019 AOD_WORT are smaller than are thosesmaller of 2016–2018than those AOD_WORTof 2016–2018 AOD_WORTand AOD_WMRT. and AOD_WMRT. Overall, for all Overall, 5 AERONET for all sites5 AERONET over BTH sites region, over aBTH relatively region, high a relR‐ atively high R (R > 0.88) and low RMSE and MAE (RMSE < 0.13, MAE < 0.11) appear between AOD_ WORT and AOD_AERO. Overall, although there were random uncertainties in the dataset building of GF‐ 1/WFV surface reflectance led by the MODIS LSR errors, the proposed ORT method could reduce such uncertainties by dealing strictly with MODIS surface reflectance and taking the averaged LSR values. Our GF‐1/WFV AOD retrieval algorithm performed well in the improvement of the retrieval results, and the AOD inversions showed a good correlation and accuracy by comparing with multi‐source AOD products. Especially, the AOD_WORT is superior to AOD_WMRT for low AOD values. Whether the LSR dataset built by 2016–2018 MODIS LSR data was used to retrieve the GF‐1/WFV AOD of 2016– 2018 or 2019, both the AOD retrievals of the two periods were shown with high accuracy.

Remote Sens. 2021, 13, 280 16 of 19

(R > 0.88) and low RMSE and MAE (RMSE < 0.13, MAE < 0.11) appear between AOD_ WORT and AOD_AERO. Overall, although there were random uncertainties in the dataset building of GF- 1/WFV surface reﬂectance led by the MODIS LSR errors, the proposed ORT method could reduce such uncertainties by dealing strictly with MODIS surface reﬂectance and taking the averaged LSR values. Our GF-1/WFV AOD retrieval algorithm performed well in the improvement of the retrieval results, and the AOD inversions showed a good correlation and accuracy by comparing with multi-source AOD products. Especially, the AOD_WORT is superior to AOD_WMRT for low AOD values. Whether the LSR dataset built by 2016– RemoteRemote Sens.Sens. 20212021,, 1313,, xx FORFOR PEERPEER REVIEWREVIEW 1616 ofof 1919 2018 MODIS LSR data was used to retrieve the GF-1/WFV AOD of 2016–2018 or 2019, both the AOD retrievals of the two periods were shown with high accuracy.

FigureFigure 13.13. AAA comparisoncomparison between between ground ground-measuredground‐‐measuredmeasured AOD_AEROAOD_AERO andand GF-1/WFVGFGF‐‐1/WFV1/WFV retrievedretrieved AOD_WORTAOD_WORT in in 2019. 2019. (Red, (Red, green,green, blue, blue, orange orange and and black black symbols symbols represent represent Beijing Beijing-CAMS,Beijing‐‐CAMS,CAMS, Beijing Beijing (&Beijing‐RADI), XiangHe, Beijing‐PKU, and all sites, respectively). (&Beijing-RADI),(&Beijing‐RADI), XiangHe, Beijing-PKU,Beijing‐PKU, and all sites,sites, respectively).respectively).

Figure 14. Error statistics (RMSE and MAE) and correlation coefﬁcient (R) for AOD_WMRT (2016– FigureFigure 14.14. ErrorError statisticsstatistics (RMSE(RMSE andand MAE)MAE) andand correlationcorrelation coefficientcoefficient (R)(R) forfor AOD_WMRTAOD_WMRT (2016–(2016– 2018),2018), AOD_WORTAOD_WORT (2016–2018), (2016–2018), and and AOD_WORT AOD_WORT (2019),(2019), respectively.respectively. 5. Conclusions 5.5. ConclusionsConclusions In this study, a new DB AOD retrieval algorithm was developed for GF-1/WFV, In this study, a new DB AOD retrieval algorithm was developed for GF‐1/WFV, in‐ involvingIn this a study, cloud screeninga new DB method AOD retrieval based on algorithm the only was four developed bands of GF-1/WFV for GF‐1/WFV, and in an‐ volving a cloud screening method based on the only four bands of GF‐1/WFV and an op‐ volvingoptimal a reﬂectivity cloud screening technique method method based for on building the only afour monthly bands LSRof GF dataset‐1/WFV transmitted and an op‐ timal reflectivity technique method for building a monthly LSR dataset transmitted from timalfrom MODISreflectivity LSR. technique GF-1/WFV method AOD_WORT for building from a 2016 monthly to 2019 LSR was dataset retrieved transmitted and validated from MODISMODIS LSR.LSR. GFGF‐‐1/WFV1/WFV AOD_WORTAOD_WORT fromfrom 20162016 toto 20192019 waswas retrievedretrieved andand validatedvalidated byby multimulti‐‐sourcesource satellitesatellite AODAOD productsproducts andand AERONETAERONET groundground‐‐basedbased measurements.measurements. DeDe‐‐ tailedtailed validationvalidation andand comparisoncomparison resultsresults werewere shownshown asas follows:follows: 1.1. InIn termsterms ofof spatialspatial distribution,distribution, ourour AOD_WORTAOD_WORT resultsresults werewere roughlyroughly consistentconsistent withwith Terra/MODISTerra/MODIS andand HimawariHimawari‐‐8/AHI8/AHI AODAOD retrievalsretrievals basedbased onon differentdifferent AODAOD rere‐‐ trievaltrieval algorithms.algorithms. GFGF‐‐1/WFV1/WFV AODAOD withwith aa higherhigher spatialspatial resolutionresolution ofof 500500 mm cancan propro‐‐ videvide moremore abundantabundant detailsdetails ofof spatialspatial variationvariation thanthan Terra/MODISTerra/MODIS andand HimawariHimawari‐‐ 8/AHI.8/AHI. 2.2. TheThe validationvalidation resultsresults showedshowed thatthat thethe AOD_WORTAOD_WORT hadhad aa goodgood correlationcorrelation andand acac‐‐ curacycuracy withwith AOD_AERO.AOD_AERO. Especially,Especially, ourour AOD_WORTAOD_WORT retrievalretrieval algorithmalgorithm couldcould efef‐‐ fectivelyfectively reducereduce thethe overvaluationovervaluation ofof AOD_WMRTAOD_WMRT atat lowlow levelslevels ofof AOD.AOD. Generally,Generally, RRORTORT (0.88)(0.88) >> RRMRTMRT (0.79),(0.79), RMSERMSEORTORT (0.13)(0.13) << RMSERMSEMRTMRT (0.19),(0.19), andand MAEMAEORTORT (0.1)(0.1) << MAEMAEMRTMRT (0.15).(0.15).

Remote Sens. 2021, 13, 280 17 of 19

by multi-source satellite AOD products and AERONET ground-based measurements. Detailed validation and comparison results were shown as follows: 1. In terms of spatial distribution, our AOD_WORT results were roughly consistent with Terra/MODIS and Himawari-8/AHI AOD retrievals based on different AOD retrieval algorithms. GF-1/WFV AOD with a higher spatial resolution of 500 m can provide more abundant details of spatial variation than Terra/MODIS and Himawari-8/AHI. 2. The validation results showed that the AOD_WORT had a good correlation and accuracy with AOD_AERO. Especially, our AOD_WORT retrieval algorithm could effectively reduce the overvaluation of AOD_WMRT at low levels of AOD. Generally, RORT (0.88) > RMRT (0.79), RMSEORT (0.13) < RMSEMRT (0.19), and MAEORT (0.1) < MAEMRT (0.15). 3. The near-real-time inversion of AOD based on the LSR dataset of last three years also performed excellently both in terms of the spatial distribution and the quantitative comparison with AERONET observations. The high correlation with AERONET AOD (RORT = 0.94), the low RMSE (RMSEORT = 0.11) and the low MAE (MAEORT = 0.09) could certificate the reliability of our AOD_WORT retrieval algorithm. 4. Although there was no detailed comparison of inversion effects between rural and urban backgrounds, the smoothing effect on the spatial distribution could confirm that the presence of urban bright surfaces would not cause high values or noises. Our GF-1/WFV AOD inversion study can lay a foundation for other similar sensors aboard Gaofen satellite series (such as GF-1B, GF-1C, GF-2, and GF-6), in terms of the solution of the inversion problem, the optimization of the inversion algorithm, and the processing and comparison of the inversion verification.

Author Contributions: This work was conducted in collaboration by all authors. Data curation, F.Y.; Funding acquisition, M.F.; Methodology, F.Y., M.F. and J.T.; Validation, F.Y.; Writing—original draft, F.Y.; Writing—review & editing, M.F. and J.T. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Natural Science Foundation of China (Grant No. 41830109, 41871254, 91644216) and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19010403 and XDA19040201). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The authors gratefully acknowledge the Japan Aerospace Exploration Agency (JAXA) for the AHI data (http://www.eorc.jaxa.jp/ptree/terms.html). The authors would like to thank the National Aeronautics and Space Administration (NASA) for the MODIS data (https://ladsweb.modaps.eosdis.nasa.gov). Restrictions apply to the availability of GF-1 data, and it is available from the funders by request. The authors would like to thank the principal investigators of the AERONET sites used in this work for maintaining their sites (https://aeronet.gsfc.nasa.gov). Conﬂicts of Interest: The authors declare no conﬂict of interest.

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