GaoFen-1 Wide Field View Research Articles

A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting

Clouds and cloud shadow cover cause missing data in some images captured by the Gaofen-1 Wide Field of View (GF-1 WFV) cameras, limiting the extraction and analysis of the image information and further applications. Therefore, this study proposes a methodology to fill GF-1 WFV images using the spatial autocorrelation and improved weighting (SAIW) method. Specifically, the search window size is adaptively determined using Getis-Ord Gi* as a metric. The spatial and spectral weights of the pixels are computed using the Chebyshev distance and spectral angle mapper to better filter the suitable similar pixels. Each missing pixel is predicted using linear regression with similar pixels on the reference image and the corresponding similar pixel located in the non-missing region of the cloudy image. Simulation experiments showed that the average correlation coefficient of the proposed method in this study is 0.966 in heterogeneous areas, 0.983 in homogeneous farmland, and 0.948 in complex urban areas. It suggests that SAIW can reduce the spread of errors in the gap-filling process to significantly improve the accuracy of the filling results and can produce satisfactory qualitative and quantitative fill results in a wide range of typical land cover types and has extensive application potential.

GF-1 WFV Surface Reflectance Quality Evaluation in Countries along “the Belt and Road”

The GaoFen-1 wide field of view (GF-1 WFV) has produced level 1 digital number data globally; however, most applications have focused on China, and data quality outside China has not been validated. This study presents a preliminary assessment of the 2020 GF-1 WFV surface reflectance data for Nepal, Azerbaijan, Kenya, and Sri Lanka along “the Belt and Road” route using Sentinel-2 Multi-Spectral Instrument (MSI), Landsat-8 Operational Land Image (OLI), and Moderate Resolution Imaging Spectroradiometer (MODIS) data. A method for obtaining the GF-1 WFV surface reflectance data was also proposed, with steps including atmospheric correction, cross-radiation calibration, and bidirectional reflectance distribution function correction. The results showed that WFV surface reflectance data was not significantly different from MSI, OLI, and MODIS surface reflectance data. In the visible and near-infrared bands, for most landcover types, the bias was less than 0.02, and the precision and root mean square error were less than 0.04. When the landcover types were forest and water, the MSI, OLI, and MODIS surface reflectance data were higher than that of WFV in the near-infrared band. The results of this study provide a basis for assessing the global application potential of GF-1 WFV.

Does the Rational Function Model’s Accuracy for GF1 and GF6 WFV Images Satisfy Practical Requirements?

The Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellites have acquired many GF-1 and GF-6 wide-field-view (WFV) images. These images have been made available for free use globally. The GF-1 WFV (GF-1) and GF-6 WFV (GF-6) images have rational polynomial coefficients (RPCs). In practical applications, RPC corrections of GF-1 and GF-6 images need to be completed using the rational function model (RFM). However, can the accuracy of the rational function model satisfy practical application requirements? To address this issue, a geometric accuracy method was proposed in this paper to evaluate the accuracy of the RFM of GF-1 and GF-6 images. First, RPC corrections were completed using the RFM and refined RFM, respectively. The RFM was constructed using the RPCs and Shuttle Radar Topography Mission (SRTM) 90 m DEM. The RFM was refined via affine transformation based on control points (CPs), which resulted in a refined RFM. Then, an automatic matching method was proposed to complete the automatic matching of GF-1/GF-6 images and reference images, which enabled us to obtain many uniformly distributed CPs. Finally, these CPs were used to evaluate the geometric accuracy of the RFM and refined RFM. The 14th-layer Google images of the corresponding area were used as reference images. In the experiments, the advantages and disadvantages of BRIEF, SIFT, and the proposed method were first compared. Then, the values of the root mean square error (RSME) of 10,561 Chinese, French, and Brazilian GF-1 and GF-6 images were calculated and statistically analyzed, and the local geometric distortions of the GF-1 and GF-6 images were evaluated; these were used to evaluate the accuracy of the RFM. Last, the accuracy of the refined RFM was evaluated using the eight GF-1 and GF-6 images. The experimental results indicate that the accuracy of the RFM for most GF-1 and GF-6 images cannot meet the actual use requirement of being better than 1.0 pixel, the accuracy of the refined RFM for GF-1 images cannot meet practical requirement of being better than 1.0 pixel, and the accuracy of the refined RFM for most GF-6 images meets the practical requirement of being better than 1.0 pixel. However, the RMSE values that meet the requirements are between 0.9 and 1.0, and the geometric accuracy can be further improved.

高分一号宽幅多光谱影像辐射定标偏差及其植被指数影响

PDF HTML阅读 XML下载 导出引用 引用提醒 高分一号宽幅多光谱影像辐射定标偏差及其植被指数影响 DOI: 10.5846/stxb202205241462 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划课题项目2019YFB2102004）；福建省自然科学基金项目（2021J011190）；厦门理工学院高层次人才项目（4010520004）；江西省水利科技项目（202224ZDKT11） Radiometric calibration bias of the Gaofen-1 WFV multispectral imagery and its influence on vegetation index Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:自2013年4月在轨运行以来，高分一号（GF-1）宽幅（WFV）多光谱相机已实现持续对地观测，为包括生态学在内的相关研究领域与行业应用提供了丰富的数据源。当前场地定标频次偏低以及定标参数更新、发布滞后的现状，一定程度上影响了GF-1 WFV多光谱数据的定量应用。然而，现有文献仅在数据预处理流程中谈及对GF-1 WFV影像的辐射定标处理，很少讨论定标参数选取不当甚至误用产生的可能影响。基于已公布的辐射定标参数（2014-2021年）和4景GF-1 WFV Level1A级影像产品数据，重点围绕辐射定标偏差及其对多光谱波段星上反射率和常用植被指数的影响等展开模型分析和讨论。结果显示：即使两个相邻年份间，在多数情况下误用辐射定标参数会导致不可忽视的波段星上反射率相对偏差；进而给不同类型植被指数的实际应用带来不同程度的挑战。因辐射定标偏差的影响，常用的两波段归一化型植被指数在监测稀疏植被覆盖区时会存在明显的误差；而对高植被覆盖区的监测时采用两波段简单比值型植被指数将面临更大的挑战。针对存档GF-1 WFV Level1A数据的应用需求，提出利用时间距离加权的线性内插法来修正基于公开定标参数的辐射定标结果，并通过案例分析表明了该处理方法的有效性。最后，希望研究结果能引起普通用户对卫星遥感影像辐射定标的关注和重视。 Abstract:Since its on-orbit operation in April 2013, the wide field of view (WFV) sensors onboard Gaofen-1 (GF-1), where "Gaofen" means high resolution in Chinese, have continuously collected multispectral imagery of the Earth's surface. The GF-1 WFV sensors have provided abundant data sources for many fields including ecology. The fact that insufficient site calibration (i.e. once a year) as well as the updates lag of calibration parameters might limit the quantitative application of the GF-1 WFV imagery to a certain extent. However, current literature has only mentioned radiometric calibration of the GF-1 WFV images as a pre-processing procedure, whereas has rarely discussed the possible impacts caused by improper selection or misuse of calibration parameters. Based on the radiometric calibration parameters published for the GF-1 WFV imagery (from 2014 to 2021) and four scenes of the Level1A data products, the issue on radiometric calibration bias was investigated. Furthermore, impacts of the radiometric calibration bias on the top of atmosphere (TOA) reflectance and on several vegetation indices commonly used were discussed accordingly. It showed that, generally, even the calibration difference between two neighboring years were considered, in most cases, improper selection of the calibration parameters could result in a significantly relative bias in the TOA reflectance. The bias in TOA reflectance further challenged different types of vegetation indices with varying degrees of patterns in practice. In particular, mainly due to the radiometric calibration biases, the obvious errors were more likely in the normalized difference vegetation index based on two bands, for monitoring sparse vegetation coverage area. Actually, the normalized difference vegetation index has been commonly used in assessing vegetation status as well as surface dynamics. At the same time, for monitoring high vegetation coverage area, the simple ratio vegetation index with two bands was possibly confronted with greater challenges. Consequently, to make full use of the GF-1 WFV Level1A products, it is critical to solve the radiometric calibration problems. In this study, a time weighted linear interpolation method was proposed. In the interpolation process for a specific GF-1 WFV imagery, both radiometric parameters obtained closely before and after the imagery acquisition were integrated. A case study suggested the effectiveness of the proposed processing to improve the radiometric calibration of the GF-1 WFV images in Level1A products, as compared against the results obtained merely based on public calibration parameters. Finally, general users should pay much attention to the radiometric calibration of satellite remotely sensed data (e.g., the GF-1 WFV multispectral imagery) for their quantitative applications, as discussed in this investigation. 参考文献 相似文献 引证文献

Winter wheat leaf area index inversion by the genetic algorithms neural network model based on SAR data

ABSTRACT The leaf area index (LAI) is an important agroecological physiological parameter affecting vegetation growth. To apply the genetic algorithms neural network model (GANNM) to the remote sensing inversion of winter wheat LAI throughout the growth cycle and based on GaoFen-3 Synthetic aperture radar (GF-3 SAR) images and GaoFen-1 Wide Field of View (GF-1 WFV) images, the Xiangfu District in the east of Kaifeng City, Henan Province, was selected as the testing region. Winter wheat LAI data from five growth stages were combined, and optical and microwave polarization decomposition vegetation index models were used. The backscattering coefficient was extracted by modified water cloud model (MWCM), and the LAI was obtained by MWCM inversion as input factors to construct GANNM to invert LAI. The root mean square error (RMSE) and determination coefficient (R 2) were used as evaluation indicators of the model. The fitting accuracy of winter wheat LAI in five growth stages by GANNM inversion was better than that of the BP neural network model; the R 2 was higher than 0.8, and RMSE was lower than 0.3, indicating that the model could accurately invert the growth status of winter wheat in five growth stages .

Red tide detection based on high spatial resolution broad band optical satellite data

Red tide, one of the major ecological disasters in the world, exerts great impact on the marine environment. The ocean color satellite data with low spatial resolution and high spectral resolution is often used for red tide detection, but is insufficient for the fine-scale red tide detection due to its coarse spatial resolution. To address this problem, a new red tide detection method based on pseudo hue angle (PHA-RI) for high spatial resolution broad band satellite data is proposed in this paper. Different from the standard International Commission on Illumination (CIE) system, the false-color bands of near infrared (NIR), red and green are used to calculate the CIE tristimulus X, Y, Z, instead of the true-color bands of red, green and blue. With this method, the red tide can be easily differentiated from non-red tide water, and the distinction between red tide and non-red tide water is doubled compared to that of true-color bands composite. Experiment results show that the PHA-RI method can effectively detect red tide, with an averaged overall accuracy, F1-score and precision of 92%, 0.92 and 0.92 respectively. Compared with the traditional machine learning method support vector machine (SVM) and spectral index algorithm Gaofen-1 (GF-1) red tide detection index (GF-RI), the proposed algorithm has obvious advantages in red tide detection, especially for the strip distributed red tide. Besides, the proposed algorithm has good applicability, and it is proved to be suitable for the detection of red tides with different dominant species including Noctiluca scintillans, Skeletonema costatum and Heterosigma akashiide. Also, it is applicable to different broad band sensors, such as Chinese satellites GF-1 Wide Field of View (WFV), Haiyang 1D (HY-1D) Coastal Zone Imager (CZI), Sentinel-2 MultiSpectral Instrument (MSI) and Landsat 8 Operational Land Imager (OLI). The application experiment indicates that the proposed algorithm is effective in red tide detection, and it can successfully detect fine-scale red tide events, which have not been detected by Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua due to its coarse spatial resolution. The results also shows the advantages of high spatial resolution satellites in fine-scale red tide detection. This work provides a method for red tide detection using high spatial resolution broad band satellite data.

Semi-Automatic Extraction and Mapping of Farmlands Based on High-Resolution Remote Sensing Images

Extraction of agricultural parcels from high-resolution satellite imagery is an important task in precision agriculture. Here, we present a semi-automatic approach for agricultural parcel detection that achieves high accuracy and efficiency. Unlike the techniques presented in previous literatures, this method is pixel based, and it exploits the properties of a spectral angle mapper (SAM) to develop customized operators to accurately derive the parcels. The main steps of the method are sample selection, textural analysis, spectral homogenization, SAM, thresholding, and region growth. We have systematically evaluated the algorithm proposed on a variety of images from Gaofen-1 wide field of view (GF-1 WFV), Resource 1-02C (ZY1-02C), and Gaofen-2 (GF-2) to aerial image; the accuracies are 99.09% of GF-1 WFV, 84.42% of ZY1-02C, 96.51% and 92.18% of GF-2, and close to 100% of aerial image; these results demonstrated its accuracy and robustness.

Ocean Chlorophyll-a retrieval using GF1-WFV data-a case study of the central Bohai Sea

Chlorophyll-a (Chl-a) concentration is an important indicator of phytoplankton bloom and water eutrophication. It is also a basic parameter of water quality. Remote sensing technology has been widely used for water quality monitoring in the past. In this study, the Chinese GF1 (Gaofen-1) WFV (Wide Field of View) images acquired on May 19th, 2016 were used to retrieve Chl-a concentration of central Bohai sea in order to assess the recovery condition of marine ecosystem which has been seriously polluted by the Penglai 19-3 platform oil spill accident in 2011. The remote sensing model of Chl-a concentration was developed using the linear regression method. The leave-one-out cross-validation (LOOCV) method showed that the average relative error and root mean square error (RMSE) between retrieved Chl-a concentration and in situ measurement was 11.67% and 0.1808, respectively. It indicated that the remote sensing model could correctly reflect the Chl-a concentration and its spatial distribution of the study area. The average Chl-a concentration in the buffer regions with the distance of 2km, 4km, 6km to the Penglai 19-3 platform was 0.32μg/L, 0.37μg/L and 0.40μg/L, respectively. They were in normal range and lower than that of the coastal waters. The spatial distribution of the result was similar to previous studies of Bohai sea in spring. These results indicated that Chl-a concentration of central Bohai sea was returned to normal after oil spill accident occurred for 5 years. The growth of phytoplankton in surface seawater has been returned to normal. The major causes for Chl-a concentration in the Bohai Sea was the land-sourced pollutant during the study period.

Estimation of Aboveground Vegetation Nitrogen Contents in the Yellow River Estuary Wetland Using GaoFen-1 Remote Sensing Data

Wang, J.B.; Ren, G.B.; Lin, Z.Y.; Wang, A.D.; Hu, Y.B.; Li, X.M.; Wu, P.Q.; Ma, Y., and Zhang, J., 2020. Estimation and analysis of nitrogen contents in the yellow river estuary wetland using Gaofen-1 remote sensing data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 1-10. Coconut Creek (Florida), ISSN 0749-0208.Nitrogen storage is a key indicator of vegetation growth, and the spatial distribution of nitrogen storage provides important information about the ecological status of wetland ecosystems. In this study, the aboveground vegetation nitrogen content (AVNC) in the Yellow River estuary wetland is estimated based on GaoFen-1 wide field of view (GF-1 WFV) image. Based on the spatial distribution of vegetation and the fractional vegetation coverage (FVC), the characteristics of AVNC are analyzed. On the basis of this study, the best model for estimating AVNC based on vegetation indices is the sample rate index (SRI), whose determination coefficient (R2) is 0.72 and root mean square error (RMSE) is 1.5 g/m2. Per the results of this model, the total AVNC of the study area is obtained, which is 620.6 tons.The total ANVC for freshwater Phragmites australis (FPA), Tamarix chinesis shrub (TCS), Spartina alterniflora (SA), Suaeda salsa (SS), and tidal flat Phragmites australis (TFPA) communities are 296.2 t, 115.3 t, 97.1 t, 86.3 t and 25.7 t, respectively. The average AVNC per unit area for SA, FPA, TCS, TFPA, and SS communities are 4.2 g/m2, 4.8 g/m2, 3.1 g/m2, 1.7 g/m2 and 1.4 g/m2, respectively. The correlation between FVC and AVNC is also analyzed, from which, the spatial distribution of AVNC and FVC is found to be in good agreement. Results of this study thus provide a valuable reference for determining AVNC in coastal wetland ecosystems with implications for ecological health and other ecological characteristics.

Discriminative Feature Learning Constrained Unsupervised Network for Cloud Detection in Remote Sensing Imagery

Cloud detection is a significant preprocessing step for increasing the exploitability of remote sensing imagery that faces various levels of difficulty due to the complexity of underlying surfaces, insufficient training data, and redundant information in high-dimensional data. To solve these problems, we propose an unsupervised network for cloud detection (UNCD) on multispectral (MS) and hyperspectral (HS) remote sensing images. The UNCD method enforces discriminative feature learning to obtain the residual error between the original input and the background in deep latent space, which is based on the observation that clouds are sparse and modeled as sparse outliers in remote sensing imagery. The UNCD enforces discriminative feature learning to obtain the residual error between the original input and the background in deep latent space, which is based on the observation that clouds are sparse and modeled as sparse outliers in remote sensing imagery. First, a compact representation of the original imagery is obtained by a latent adversarial learning constrained encoder. Meanwhile, the majority class with sufficient samples (i.e., background pixels) is more accurately reconstructed than the clouds with limited samples by the decoder. An image discriminator is used to prevent the generalization of out-of-class features caused by latent adversarial learning. To further highlight the background information in the deep latent space, a multivariate Gaussian distribution is introduced. In particular, the residual error with clouds highlighted and background samples suppressed is applied in the cloud detection in deep latent space. To evaluate the performance of the proposed UNCD method, experiments were conducted on both MS and HS datasets that were captured by various sensors over various scenes, and the results demonstrate its state-of-the-art performance. The sensors that captured the datasets include Landsat 8, GaoFen-1 (GF-1), and GaoFen-5 (GF-5). Landsat 8 was launched at Vandenberg Air Force Base in California on 11 February 2013, in a mission that was initially known as the Landsat Data Continuity Mission (LDCM). China launched the GF-1 satellite. The GF-5 satellite captures hyperspectral observations in the Chinese Key Projects of High-Resolution Earth Observation System. The overall accuracy (OA) values for Images I and II from the Landsat 8 dataset were 0.9526 and 0.9536, respectively, and the OA values for Images III and IV from the GF-1 wide field of view (WFV) dataset were 0.9957 and 0.9934, respectively. Hence, the proposed method outperformed the other considered methods.

Monitoring Temporal Change of River Islands in the Yangtze River by Remotely Sensed Data

The spatial extent and area of river islands are always changing due to the impact of hydrodynamic conditions, sediment supply and human activities. A catastrophic flood disaster was driven by sustained and heavy rainfall around the middle and lower Yangtze River in 18 June to 21 July 2016. The flood resulted in the most serious social-economic loss since 1954 and caused a larger-scale inundation for a short time. It is essential to continuously monitor the dynamics changes of river islands because this can avoid frequent field measurements in river islands before and after flood disasters, which are helpful for flood warning. This paper focuses on the temporal change of three river islands called Fenghuangzhou, Changshazhou, and one uninhabited island in the Yangtze River in 2016. In this study, GF-1 (GaoFen-1) WFV (wide field view) data was used for our study owing to its fine spatial and temporal resolution. A simple NDWI (Normalized Difference Water Index) method was used for the river island mapping. Human checking was then performed to ensure mapping accuracy. We estimated the relationship between the area of river islands and measured water levels using four models. Furthermore, we mapped the spatial pattern of inundation risk of river islands. The results indicate a good ability of the GF-1 WFV data with a 16-m spatial resolution to characterize the variation of river islands and to study the association between flood disaster and river islands. A significantly negative but nonlinear relationship between the water level and the area of the river island was observed. We also found that the cubic function fits best among three models (R2 > 0.8, P < 0.001). The maximum of the inundated area at the river island appeared in the rainy season on 8 July 2016 and the minimum occurred in the dry season on 28 December 2016, which is consistent with the water level measured by the hydrological station. Our results derived from GF-1 data can provide a useful reference for decision-making of flood warning, disaster assessment, and post-disaster reconstruction.

Desert vegetation-habitat complexes mapping using Gaofen-1 WFV (wide field of view) time series images in Minqin County, China

Feedbacks between vegetation and associated habitats play a crucial role in the arid and semi-arid dryland ecosystem for maintaining the stability and sustainability. Due to the high spatial and temporal heterogeneous sparse vegetation with the associated habitats in dryland system, it is a challenge to map their interactions and feedbacks for environment management and policy decision. This paper attempted to develop an algorithm using endmembers (EMs) fraction series unmixed from Gaofen-1 (GF-1) WFV (wide field of view) finer time series images for mapping desert vegetation-habitat complexes as vegetation function groups with associated habitat. The time series of EMs, including green vegetation (GV), sand land (SL), saline land (SA), dark surface (DA) at 16 m subpixel level, derived from Multiple Endmember Spectral Mixture Analysis (MESMA), were combined to obtain classification knowledge describing the interactions and feedbacks between vegetation and habitat, and organized with decision tree (DT). According to the similarity of the interactions and feedbacks in the desert vegetation-habitat complexes, this paper further identified their potential of assessing the status of ecosystems (i.e., land degradation). The results show that the finer time series of EMs with satisfied spatial resolution can discern the sparse vegetation and the associated habitats with an overall accuracy of 83.91%, and help understanding degradation processes (i.e., sandification and local salinization) in the study area.

Satellite-based time series calibration of GF-1 WFV sensors for large view zenith angle observations

ABSTRACTA novel satellite cross-calibration method is proposed using image pairs with large view zenith angle observations. Previous studies on cross calibration have focused primarily on image pairs with nadir or small view zenith angles, while this study introduces a technique for large view zenith angle observations using bidirectional reflectance distribution function (BRDF) correction. This technique was applied to images from the wide field of view (WFV) onboard the GAO FEN-1 (GF-1) satellite and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors as it transited over the Dunhuang site in China. A total of 62 image pairs, collected from 2013 to 2016, were used in the cross-calibration process. However, only 12 of 62 image pairs included a small view zenith angle difference (less than 10°), while the other image pairs featured a large view zenith angle difference. An experimental time series calibration was conducted using the proposed technique as well as conventional cross-calibration methods for comparison purposes. Time series calibration results also showed an attenuation trend for GF-1 WFV sensor and produced a calibration equation for day since launch (DSL). Then, the calibration equation by DSL was compared with four vicarious calibration results carried out over the Dunhuang test site from 2013 to 2016 to demonstrate its consistency. The calibration coefficients using the proposed method were also compared with the coefficients from other cross-calibration methods, taking the vicarious calibration coefficient as the reference. The comparison results showed that the calibration coefficients using the proposed method were with higher calibration accuracy and higher calibration frequency than other cross-calibration methods. The cross-calibration uncertainties caused by difference influence factors were analysed quantitatively. The results showed that the calibration uncertainties were decreased from 35.03–44.60% to 5.95–8.26% after BRDF correction, which demonstrated the proposed method to be superior for processing large view zenith angle images. These results suggest the proposed technique could be an effective new tool for future time series satellite calibration.

Improving Geometric Performance for Imagery Captured by Non-Cartographic Optical Satellite: A Case Study of GF-1 WFV Imagery

Numerous countries have established their own Earth observing systems (EOSs) for global change research. Data acquisition efforts are generally only concerned with the completion of the mission regardless of the potential to expand into other areas, which reduces the application effectiveness of Earth observation data. This paper explores the cartographic possibility of images being not initially intended for surveying and mapping, and a novel method is proposed to improve the geometric performance. First, the rigorous sensor model (RSM) is recovered from the rational function model (RFM), and then the system errors of the non-cartographic satellite’s imagery are compensated by using the conventional geometric calibration method based on RSM; finally, a new and improved RFM is generated. The advantage of the method over traditional ones is that it divides the errors into static errors and non-static errors for each image during the improvement process. Experiments using images collected with the Gaofen-1 (GF-1) wide-field view (WFV) camera demonstrate that the orientation accuracy of the proposed method is within 1 pixel for both calibration and validation images, and the obvious high-order system errors are eliminated. Moreover, a block adjustment test shows that the vertical accuracy is improved from 21 m to 11 m with four ground control points (GCPs) after compensation, which can fulfill requirements for 1:100,000 stereo mapping in mountainous areas. Generally, the proposed method can effectively improve the geometric potential for images captured by non-cartographic satellite.

Wide Swath Stereo Mapping from Gaofen-1 Wide-Field-View (WFV) Images Using Calibration.

The development of Earth observation systems has changed the nature of survey and mapping products, as well as the methods for updating maps. Among optical satellite mapping methods, the multiline array stereo and agile stereo modes are the most common methods for acquiring stereo images. However, differences in temporal resolution and spatial coverage limit their application. In terms of this issue, our study takes advantage of the wide spatial coverage and high revisit frequencies of wide swath images and aims at verifying the feasibility of stereo mapping with the wide swath stereo mode and reaching a reliable stereo accuracy level using calibration. In contrast with classic stereo modes, the wide swath stereo mode is characterized by both a wide spatial coverage and high-temporal resolution and is capable of obtaining a wide range of stereo images over a short period. In this study, Gaofen-1 (GF-1) wide-field-view (WFV) images, with total imaging widths of 800 km, multispectral resolutions of 16 m and revisit periods of four days, are used for wide swath stereo mapping. To acquire a high-accuracy digital surface model (DSM), the nonlinear system distortion in the GF-1 WFV images is detected and compensated for in advance. The elevation accuracy of the wide swath stereo mode of the GF-1 WFV images can be improved from 103 m to 30 m for a DSM with proper calibration, meeting the demands for 1:250,000 scale mapping and rapid topographic map updates and showing improved efficacy for satellite imaging.

On-Orbit Radiometric Calibration for a Space-Borne Multi-Camera Mosaic Imaging Sensor

As the core and foundational technology, on-orbit radiometric calibration of a space-borne sensor is of great importance for quantitative remote sensing applications. As for the space-borne multi-camera mosaic imaging sensor, however, the currently available on-orbit radiometric calibration method cannot carry out the integrated processing of on-orbit absolute radiometric calibration and relative radiometric correction simultaneously between cameras, influencing the accuracy of quantitative applications. Therefore, taking the GaoFen-1 (GF-1) wide-field-of-view (WFV) sensor as an example in this research, an innovative on-orbit radiometric calibration method is proposed to overcome this bottleneck. Firstly, according to the principle of the cross-calibration approach, we retrieve valid MODIS and GF-1 WFV image pairs over the Dunhuang radiometric calibration sites (DRCS) in China by using a set of criteria and extract the radiometric control points (RCPs) connecting in both images. Secondly, the DEM-aided block adjustment of the rational function model is applied to eliminate the geometrical misalignment of GF-1 WFV images at the same orbit. Then, the average digital numbers of spectral and spatial homogeneous surfaces are calculated and chosen as the radiometric tie points (RTPs) extracted from the overlapping region of the adjacent WFV cameras. Thirdly, the radiometric block adjustment (RBA) algorithm is introduced into on-orbit radiometric calibration of the space-borne multi-camera mosaic imaging sensor. Finally, the radiometric calibration coefficients are solved by the least square method. The validation results indicate that our proposed method can acquire high absolute radiometric calibration accuracy and achieve relative radiometric correction between cameras. Compared with the results using the cross-calibration method to calibrate each WFV camera independently, the advantages of RBA are presented. In addition, the uncertainties caused by RCPs’ distribution are discussed, which is beneficial to further optimize the calibration program.

Auto-calibration of GF-1 WFV images using flat terrain

Four wide field view (WFV) cameras with 16-m multispectral medium-resolution and a combined swath of 800 km are onboard the Gaofen-1 (GF-1) satellite, which can increase the revisit frequency to less than 4 days and enable large-scale land monitoring. The detection and elimination of WFV camera distortions is key for subsequent applications. Due to the wide swath of WFV images, geometric calibration using either conventional methods based on the ground control field (GCF) or GCF independent methods is problematic. This is predominantly because current GCFs in China fail to cover the whole WFV image and most GCF independent methods are used for close-range photogrammetry or computer vision fields. This study proposes an auto-calibration method using flat terrain to detect nonlinear distortions of GF-1 WFV images. First, a classic geometric calibration model is built for the GF1 WFV camera, and at least two images with an overlap area that cover flat terrain are collected, then the elevation residuals between the real elevation and that calculated by forward intersection are used to solve nonlinear distortion parameters in WFV images. Experiments demonstrate that the orientation accuracy of the proposed method evaluated by GCF CPs is within 0.6 pixel, and residual errors manifest as random errors. Validation using Google Earth CPs further proves the effectiveness of auto-calibration, and the whole scene is undistorted compared to not using calibration parameters. The orientation accuracy of the proposed method and the GCF method is compared. The maximum difference is approximately 0.3 pixel, and the factors behind this discrepancy are analyzed. Generally, this method can effectively compensate for distortions in the GF-1 WFV camera.

Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery

The wide field of view (WFV) imaging system onboard the Chinese GaoFen-1 (GF-1) optical satellite has a 16-m resolution and four-day revisit cycle for large-scale Earth observation. The advantages of the high temporal-spatial resolution and the wide field of view make the GF-1 WFV imagery very popular. However, cloud cover is an inevitable problem in GF-1 WFV imagery, which influences its precise application. Accurate cloud and cloud shadow detection in GF-1 WFV imagery is quite difficult due to the fact that there are only three visible bands and one near-infrared band. In this paper, an automatic multi-feature combined (MFC) method is proposed for cloud and cloud shadow detection in GF-1 WFV imagery. The MFC algorithm first implements threshold segmentation based on the spectral features and mask refinement based on guided filtering to generate a preliminary cloud mask. The geometric features are then used in combination with the texture features to improve the cloud detection results and produce the final cloud mask. Finally, the cloud shadow mask can be acquired by means of the cloud and shadow matching and follow-up correction process. The method was validated using 108 globally distributed scenes. The results indicate that MFC performs well under most conditions, and the average overall accuracy of MFC cloud detection is as high as 96.8%. In the contrastive analysis with the official provided cloud fractions, MFC shows a significant improvement in cloud fraction estimation, and achieves a high accuracy for the cloud and cloud shadow detection in the GF-1 WFV imagery with fewer spectral bands. The proposed method could be used as a preprocessing step in the future to monitor land-cover change, and it could also be easily extended to other optical satellite imagery which has a similar spectral setting. The global validation dataset and the software tool used in this study have been made available online (http://sendimage.whu.edu.cn/en/mfc/).

High Resolution Aerosol Optical Depth Retrieval Using Gaofen-1 WFV Camera Data

Aerosol Optical Depth (AOD) is crucial for urban air quality assessment. However, the frequently used moderate-resolution imaging spectroradiometer (MODIS) AOD product at 10 km resolution is too coarse to be applied in a regional-scale study. Gaofen-1 (GF-1) wide-field-of-view (WFV) camera data, with high spatial and temporal resolution, has great potential in estimation of AOD. Due to the lack of shortwave infrared (SWIR) band and complex surface reflectivity brought from high spatial resolution, it is difficult to retrieve AOD from GF-1 WFV data with traditional methods. In this paper, we propose an improved AOD retrieval algorithm for GF-1 WFV data. The retrieved AOD has a spatial resolution of 160 m and covers all land surface types. Significant improvements in the algorithm include: (1) adopting an improved clear sky composite method by using the MODIS AOD product to identify the clearest days and correct the background atmospheric effect; and (2) obtaining local aerosol models from long-term CIMEL sun-photometer measurements. Validation against MODIS AOD and ground measurements showed that the GF-1 WFV AOD has a good relationship with MODIS AOD (R2 = 0.66; RMSE = 0.27) and ground measurements (R2 = 0.80; RMSE = 0.25). Nevertheless, the proposed algorithm was found to overestimate AOD in some cases, which will need to be improved upon in future research.