Adjacency Effect Correction Research Articles

Adjacency effect correction of the GF-1 image in coastal waters of Hangzhou Bay and Taihu Lake, China

For coastal waters, the radiation signals received by the sensor are affected by the high reflectance of the coastal land, inducing reduced image contrast and significant adjacency effect. Eliminate the atmospheric attenuation and adjacency effect, and obtain the accurate remote sensing reflectance above water surface is an important prerequisite for the quantitative water color remote sensing. Based on the in situ spectral data of the coastal Hangzhou Bay, China on July 23, 2017 and the Taihu Lake, China on April 29, 2016, as well as the synchronous Gaofen-1 wide field-of-view camera (GF-1/ WFV) images, the 6S model was used to eliminate the atmospheric attenuation, and a point spread kernel function was used to correct the adjacency effect. The experimental results show that the images after adjacency effect correction are clearer, with increased image contrast and more abundant information of the water body. Compared to the atmospheric correction results only after the 6S model, the average relative errors of three in situ synchronous samples in Hangzhou Bay after adjacency effect correction are reduced by 8.39%, 10.81% and 7.29%, and that in Taihu Lake after adjacency effect correction are reduced by 10.8%, 5.24% and 0.39%, respectively. The adjacency effect correction improves the radiation detection accuracy of the remote sensing reflectance above water surface and can be a reference for quantitative water color remote sensing in coastal waters.

An adaptive atmospheric correction algorithm for the effective adjacency effect correction of submeter-scale spatial resolution optical satellite images: Application to a WorldView-3 panchromatic image

The adjacency effect can blur and reduce the contrast of satellite images. For low visibility, the adjacency effect has a crucial impact on submeter-scale spatial resolution optical (SM) satellite images. Therefore, SM satellite images are preprocessed with an atmospheric correction before performing visual interpretation and quantitative research. This atmospheric correction includes the adjacency effect (expressed by the average background reflectance, ABR) correction and atmospheric intrinsic reflectance correction. ABR is related to the spatial distance from the central pixel to its surrounding pixels (SDCS) and is affected by the reflectance difference between the central pixel and its surrounding pixels (RDCS). Existing methods to calculate ABR do not include the effect of RDCS. Therefore, in this study, we use equivalent average background reflectance (EABR) instead of ABR used in the 6S model. EABR considers the effects of the strengths of the aerosol optical depth (AOD), SDCS, and RDCS on the adjacency effect. Compared with ABR, EABR can better represent the adjacency effect in an SM satellite image. We develop an adaptive atmospheric correction algorithm (called adaptive-AC) based on EABR. The adaptive characteristic of adaptive-AC is that it can adjust the contribution factor (or weighted value) of the background pixel to the adjacency effect according to the SDCS and RDCS. The application of adaptive-AC to a panchromatic band image of WorldView-3 (WV-3) reveals that adaptive-AC can significantly improve image quality, with the reflectance of pixels in the corrected image processed by adaptive-AC being close to the actual reflectance. Comparison with the results of the atmospheric correction algorithm in the 6S model (6S-AC) indicates that adaptive-AC is more suitable for SM satellite image preprocessing than 6S-AC approaches. There is no clear theory or formula available to calculate the exact value of the adjacency effect range (Rad). Therefore, different Rad values are used in applying the adaptive-AC and 6S-AC. The results show that the larger is the Rad used to construct the adjacency effect model, the more satisfactory is the atmospheric correction.

Adjacency effect correction of optical satellite image with sub-meter spatial resolution

The adjacency effect, the contribution of the neighboring pixels to the radiance of the line of sight pixel, is caused by the Rayleigh scattering of atmospheric molecules and Mie scattering of aerosol particles. The adjacency effect will cause the reflectance of each pixel in the apparent reflectance satellite image to be between the real reflectance and the average background reflectance, reducing the accuracy of the surface reflectance inversion. Therefore, it is very important to remove the adjacency effect to improve the accuracy of retrieving the surface reflectance from satellite images. The most critical issue of the adjacency effect is to accurately calculate the weight of the contribution of each background pixel to the adjacency effect. The weight value of the contribution of each background pixel to the adjacency effect mainly depends on the spatial distance between the target pixel and the background pixel, the difference in reflectance between the target pixel and the background pixel, and the optical thickness of atmospheric molecules and the optical thickness of aerosol. At present, the commonly used weight function for calculating the weight value considers only the influence of optical thickness and spatial distance on the weight value. These weight functions are applied to a relatively uniform surface. However, when these weight functions are applied to an inhomogeneous surface, they will greatly reduce the accuracy of the adjacency effect correction. The combination of ground features in satellite images with the sub-meter spatial resolution is complex, so the influence of the difference in reflectance between the target pixel and the background pixel on the adjacency effect must be considered. The adaptive atmospheric correction algorithm proposed in this paper can adjust the weight value of the contribution of background pixels to the adjacency effect according to the spatial distance between the target pixel and the background pixel, the difference in reflectance between the target pixel and the background pixel, and the difference between the atmospheric molecules’ optical thickness and aerosol optical thickness. The adaptive atmospheric correction algorithm is used to correct the adjacency effect on GF-2 panchromatic satellite images. The results show that the adaptive atmospheric correction algorithm can effectively remove the adjacency effect in sub-meter spatial resolution optical satellite images, improve both the accuracy of quantitative study and the satellite image quality.

Influence of adjacency effect on high-spatial-resolution thermal infrared imagery: Implication for radiative transfer simulation and land surface temperature retrieval

The influence of adjacency effect on high-spatial-resolution satellite imagery in the visible and near infrared region has been studies since the 1980s. However, the adjacency effect is usually neglected in the thermal infrared (TIR) region. The conventional TIR radiative transfer equation (RTE) only takes into account radiance from target pixel and neglects radiance contribution from surrounding background pixels. In this study, two TIR RTEs taking into account the adjacency effect for uniform and non-uniform Lambertian surfaces were deduced based on the radiative transfer theory. In terms of the TIR RTEs, we analyze the dominating influence factors of the adjacency effect and quantify the magnitude of the adjacency effect in the TIR region using simulation and actual satellite data. Two different definitions of the adjacency effect are presented in this study: (1) the effect due to radiance from background pixels scattered into sensor's instantaneous field of view, and (2) the effect due to apparent thermal contrast between target and background pixels. The results show that atmospheric visibility, water vapor content, sensor spectral band, and background pixel land surface temperature (LST) have significant influence on the adjacency effect. For a specific simulation scene, the magnitude of the first definition of the adjacency effect is larger than 0.5 K when atmospheric visibility is lower than 23 km. This magnitude is as much as 2 K under haze weather conditions with atmospheric visibility of 5 km. The results indicate that the adjacency effect should be taken into account in the radiative transfer simulation. The magnitude of the second definition of the adjacency effect is less than 0.3 K, even at atmospheric visibility of 5 km. The results reveal that the accuracy of the TIR RTE for a uniform Lambertian surface is enough for the development of LST retrieval algorithm under relatively haze weather conditions. In situ LST measurements collected at the Hailar, Urad Front Banner, and Wuhai sites in China were used to validate the accuracies of the LST retrieved by the RTE-based single channel algorithm with/without adjacency effect correction. There are nearly no discrepancies between the LST retrieved with/without adjacency effect correction when aerosol optical depth (AOD) is low than 0.3. However, the RMSE of the differences between the retrieved LST and the in situ LST decreases from approximately 1.4 K for the LST retrieved without adjacency effect correction to approximately 0.6 K for the LST retrieved with adjacency effect correction when AOD is high than 0.3.

Ground Reflectance Retrieval on Horizontal and Inclined Terrains Using the Software Package REFLECT

This paper presents the software package REFLECT for the retrieval of ground reflectance from high and very-high resolution multispectral satellite images. The computation of atmospheric parameters is based on the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) routines. Aerosol optical properties are calculated using the OPAC (Optical Properties of Aerosols and Clouds) model, while aerosol optical depth is estimated using the dark target method. A new approach is proposed for adjacency effect correction. Topographic effects were also taken into account, and a new model was developed for forest canopies. Validation has shown that ground reflectance estimation with REFLECT is performed with an accuracy of approximately ±0.01 in reflectance units (for the visible, near-infrared, and mid-infrared spectral bands), even for surfaces with varying topography. The validation of the software was performed through many tests. These tests involve the correction of the effects that are associated with sensor calibration, irradiance, and viewing conditions, atmospheric conditions (aerosol optical depth AOD and water vapour), adjacency, and topographic conditions.

Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes

Satellite data provide the only viable means for extensive monitoring of remote and large freshwater systems, such as the Amazon floodplain lakes. However, an accurate atmospheric correction is required to retrieve water constituents based on surface water reflectance ( R W ). In this paper, we assessed three atmospheric correction methods (Second Simulation of a Satellite Signal in the Solar Spectrum (6SV), ACOLITE and Sen2Cor) applied to an image acquired by the MultiSpectral Instrument (MSI) on-board of the European Space Agency’s Sentinel-2A platform using concurrent in-situ measurements over four Amazon floodplain lakes in Brazil. In addition, we evaluated the correction of forest adjacency effects based on the linear spectral unmixing model, and performed a temporal evaluation of atmospheric constituents from Multi-Angle Implementation of Atmospheric Correction (MAIAC) products. The validation of MAIAC aerosol optical depth (AOD) indicated satisfactory retrievals over the Amazon region, with a correlation coefficient (R) of ~0.7 and 0.85 for Terra and Aqua products, respectively. The seasonal distribution of the cloud cover and AOD revealed a contrast between the first and second half of the year in the study area. Furthermore, simulation of top-of-atmosphere (TOA) reflectance showed a critical contribution of atmospheric effects (>50%) to all spectral bands, especially the deep blue (92%–96%) and blue (84%–92%) bands. The atmospheric correction results of the visible bands illustrate the limitation of the methods over dark lakes ( R W < 1%), and better match of the R W shape compared with in-situ measurements over turbid lakes, although the accuracy varied depending on the spectral bands and methods. Particularly above 705 nm, R W was highly affected by Amazon forest adjacency, and the proposed adjacency effect correction minimized the spectral distortions in R W (RMSE < 0.006). Finally, an extensive validation of the methods is required for distinct inland water types and atmospheric conditions.

An Operational Radiometric Landsat Preprocessing Framework for Large-Area Time Series Applications

We developed a large-area preprocessing framework for multisensor Landsat data, capable of processing large data volumes. Cloud and cloud shadow detection is performed by a modified Fmask code. Surface reflectance is inferred from Tanre's formulation of the radiative transfer, including adjacency effect correction. A precompiled MODIS water vapor database provides daily or climatological fallback estimates. Aerosol optical depth (AOD) is estimated over dark objects (DOs) that are identified in a combined database and image-based approach, where information on their temporal persistency is utilized. AOD is inferred with consideration of the actual target reflectance and background contamination effect. In case of absent DOs in bright scenes, a fallback approach with a modeled AOD climatology is used instead. Topographic normalization is performed by a modified C-correction. The data are projected into a single coordinate system and are organized in a gridded data structure for simplified pixel-based access. We based the assessment of the produced data set on an exhaustive analysis of overlapping pixels: 98.8% of the redundant overlaps are in the range of the expected ±2.5% overall radiometric algorithm accuracy. AOD is in very good agreement with Aerosol Robotic Network sunphotometer data ( $R^{2}$ : 0.72 to 0.79, low intercepts, and slopes near unity). The uncertainty in using the water vapor fallback climatology is approximately ±2.8% for the TM SWIR1 band in the wet season. The topographic correction was considered successful by an investigation of the nonrelationship between the illumination angle and the corrected radiance.

Efficient correction of adjacency effects for high-resolution imagery: integral equations, analytic continuation, and Padé approximants

This paper deals with the inverse problem of determining surface reflectances from top of atmosphere reflectance measurements when adjacency effects are significant, as is the case with high-resolution imagery. The problem is rigorously formulated as an integral equation of the second kind for the unknown surface reflectance. It is solved by summing the Neumann series associated with the integral equation. Conditions for the convergence of the series are established and, in cases where the Neumann series diverges, analytic continuation by Pade approximants is used to extend the range of applicability of the method. Pade approximants provide very efficient algorithms for the reconstruction of the unknown surface reflectance. In cases where the Neumann series converges Pade approximants yield accurate results at a fraction of the computational cost of the Neumann series. The efficiency of the method is further enhanced by fast FFT-based evaluation of local reflectance averages. The efficiency and versatility of the method are demonstrated on Landsat-based examples.

Atmospheric correction of high-spatial-resolution satellite images with adjacency effects: application to EO-1 ALI data

Adjacency effects are an interesting physical phenomenon caused by multiple scattering between the atmosphere and the surface. It is necessary to remove adjacency effects in the surface reflectance retrieved from satellite data at a high spatial resolution. In this study, we propose an atmospheric correction method with adjacency effect correction to derive surface reflectance from Earth Observing-1 (EO-1) Advanced Land Imager (ALI) data. Adjacency effects are corrected using an atmospheric point spread function. An analytical expression of the atmospheric point spread function is presented based on a single scattering approximation. This method was applied to ALI imagery acquired through Watershed Airborne Telemetry Experimental Research (WATER) on 20 May 2008. Compared with the surface reflectance before the adjacency effects were corrected for, the surface reflectance after correction exhibited increased between-pixel contrast. Furthermore, the discrepancies between the surface reflectance before and after corrections decreased from the blue band to the shortwave infrared band.

Validation of MERIS spectral inversion processors using reflectance, IOP and water quality measurements in boreal lakes

We evaluated MERIS spectral inversion processors in the estimation of chlorophyll a (Chl-a), total suspended matter (TSM), absorption of colored dissolved organic matter (aCDOM at 443nm) and Secchi disk transparency (ZSD) in four lakes located in southern Finland. The lakes represent oligotrophic, mesotrophic and humic (high aCDOM, low TSM) lake types. Our validation data was extensive, consisting of remote sensing reflectance (Rrs) measured in the field, detailed measurements of (specific) inherent optical properties ((S)IOP) and water quality measurements. Validation measurements were collected at stations and as spatially intensive transects. The tested processors were Boreal lakes (BOR) and Free University of Berlin WeW WATER processor (FUB). In the case of BOR we included two versions, the original one available in 2008 and the most recent version. The processing chains of these versions differed in terms of MERIS archive reprocessing (2nd reprocessing (RP2) and 3rd reprocessing (RP3) were used), version of adjacency effect correction (ICOL) and rectification method. However, all these had only minor effect on the estimated Rrs when compared to the effect caused by the differences in the atmospheric correction versions. The use of ICOL improved the estimation of Rrs and therefore all IOP and water quality validations were made with ICOL pre-processed data. Rrs was best estimated with RP2 BOR, although it somewhat overestimated Rrs in blue in the humic lake. RP3 BOR greatly overestimated the Rrs in blue. FUB was outside its aCDOM training range in most of the studied lakes. RP2 BOR estimated water quality with the best accuracy in mesotrophic lakes, but failed in humic and clear water lakes. BOR overestimated Chl-a, particularly with RP3. The best TSM correlation was for RP2 BOR. All processors underestimated aCDOM. The SIOP measurements showed that overestimation of Chl-a was not due to incorrect conversion factors, and that underestimation of TSM was to some extent explained by lower average conversion factor than that measured in the validation lakes. The inaccuracies in Chl-a and aCDOM(443) estimation were related to the partition of atot to phytoplankton and CDOM absorption, which emphasizes the importance of including IOP measurements in the validation studies. ZSD was best estimated on the basis of atot and btot, which were separately calculated from BOR output and the bio-optical model assumed in the processor. For the needs of transparency estimation the processors should produce atot and btot for all MERIS bands as standard output. Atmospheric correction over humic lakes is another problem to be solved in future versions of the processors. The 2008 version of BOR provided better atmospheric correction results in humic lakes and is thus more applicable in the boreal zone where these lakes are common. This suggests that before a new processor version is published it should be validated with existing in situ data.

Detection and correction of adjacency effects in hyperspectral airborne data of coastal and inland waters: the use of the near infrared similarity spectrum

A method for the detection and correction of water pixels affected by adjacency effects is presented. The approach is based on the comparison of spectra with the near infrared (NIR) similarity spectrum. Pixels affected by adjacency effects have a water-leaving reflectance spectrum with a different shape to the reference spectrum. This deviation from the similarity spectrum is used as a measure for the adjacency effect. Secondly, the correspondence with the NIR similarity spectrum is used to quantify and to correct for the contribution of the background radiance during atmospheric correction. The advantage of the approach is that it requires no a priori assumptions on the sediment load or related reflectance values in the NIR and can therefore be applied to turbid waters. The approach is tested on hyperspectral airborne data (Compact Airborne Spectrographic Imager (CASI), Airborne Hyperspectral Scanner (AHS)) acquired above coastal and inland waters at different flight altitudes and under varying atmospheric conditions. As the NIR similarity spectrum forms the basis of the approach, the method will fail for water bodies for which this similarity spectrum is no longer valid.

Adjacency Effect Correction Study of ASTER Image Based on the Synchronized Measured Spectral Data

In July,2008,during the large-scale synchronous remote sensing experiment of WATER:Watershed Airborne Telemetry Experiment Research in Zhangye district,Gansu province,we did the adjacency effect measurement synchronous with satellite at Lanzhou University grass site,with the device designed by ourselves.The adjacency effect correction coefficient was computed using backward least square algorithm.Then it was imported into the actual reflectance transformation equation to correct the adjacency effect of the synchronous ASTER image.The quality of the result image was improved.Moreover,the result image was analyzed compared with that the SHDOM equation.The result showed that,both the reflectance and normalized difference vegetation index of the result image corrected with the adjacency effect correction coefficient had greater differences,meanwhile the spatial autocorrelation between pixels decreased obviously.

Improvement of MERIS level 2 products in Baltic Sea coastal areas by applying the Improved Contrast between Ocean and Land processor (ICOL) - data analysis and validation

In this paper we compare the following MERIS processors against sea-truthing data: the standard MERIS processor (MEGS 7.4.1), the Case 2 Regional processor (C2R) of the German Institute for Coastal Research (GKSS), and the Case 2 Water Properties processor developed at the Freie Universitat Berlin (FUB). Furthermore, the Improved Contrast between Ocean and Land processor (ICOL), a prototype processor for the correction of adjacency effects from land, was tested on all three processors, and the retrieval of level 2 data was evaluated against sea-truthing data, before and after ICOL processing. The results show that by using ICOL the retrieval of spectral reflectance in the open sea was improved for all processors. After ICOL processing, the FUB showed rather small errors in the blue, but underestimated in the red -34% Mean Normalised Bias (MNB) and 37% Root Mean Square (R,MS). For MEGS the reflectance in the red was underestimated by about -20% MNB and 23% RMS, whereas the reflectance in the other channels was well predicted, even without any ICOL processing. The C2R, underestimated the red with about -27% MNB and 29% RMS and at 412 nm it overestimated the reflectance with about 23% MNB and 29% RMS. At the outer open sea stations ICOL processing did not have a strong effect: the effect of the processor diminishes progressively up to 30 km from land. At the open sea stations the ICOL processor improved chlorophyll retrieval using MEGS from -74% to about 34% MU; and TSM retrieval from -63% to about 22% MNB. Using FUB in combination with ICOL gave even better results for both chlorophyll (25% MNB and 45% RMS) and TSM (-4% MNB and 36% EMS) in the open Baltic Sea. All three processors predicted TSM rather well, but the standard processor gave the best results (-12% MNB and 17% RMS). The C2R had a very low MNB for TSM (1%), but a rather high RMS (54%). The FUB was intermediate with -16% MNB and 31% RMS. In coastal waters, the spectral diffuse attenuation coefficient K-d (490) was well predicted using PUB or MEGS in combination with ICOL (MNB about 12% for FUB and 0.4% for MEGS). Chlorophyll was rather well predicted in the open Baltic Sea using FUB with ICOL (MNB 25%) and even without ICOL processing (MNB about 15%). ICOL-processed MEGS data also gave rather good retrieval of chlorophyll in the coastal areas (MNB of 19% and RMS of 28%). In the open Baltic Sea chlorophyll retrieval gave a MNB of 34% and RMS of 70%, which may be due to the considerable patchiness caused by cyanobacterial blooms. The results presented here indicate that with the MERIS mission, ESA and co-workers are in the process of solving some of the main issues regarding the remote sensing of coastal waters: spatial resolution; land-water adjacency effects; improved level 2 product retrieval in the Baltic Sea, i.e. the retrieval of spectral reflectance and of the water quality products TSM and chlorophyll.

基于SHDOM经验方程和基于同步实测光谱数据的遥感影像邻近效应校正算法对比研究

基于SHDOM经验方程和基于同步实测光谱数据的遥感影像邻近效应校正算法对比研究

Chlorophyll retrieval with MERIS Case-2-Regional in perialpine lakes

Semi-analytical remote sensing applications for eutrophic waters are not applicable to oligo- and mesotrophic lakes in the perialpine area, since they are insensitive to chlorophyll concentration variations between 1 and 10 mg/m 3. The neural network based Case-2-Regional algorithm for MERIS was developed to fill this gap, along with the ICOL adjacency effect correction algorithm. The algorithms are applied to a collection of 239 satellite images from 2003–2008, and the results are compared to experimental and official water quality data collected in six perialpine lakes in the same period. It is shown that remote sensing estimates can provide an adequate supplementary data source to in situ data series of the top 5 m water layer, provided that a sufficient number of matchups for a site specific maximum temporal offset are available.