Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

Ren, Kai; Sun, Weiwei; Meng, Xiangchao; Yang, Gang; Du, Qian

doi:10.3390/rs12050882

Cited by 46 publications

(36 citation statements)

References 56 publications

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“…In these bordering low-reflectance scenes, the greatly augmented spatial stray light could mimic a blooming effect caused by the spectral smile, thus leading to higher DI around strong, deep spectral transitions that may exceed the imposed threshold. In the OMI data sampling the high-contrast scenes, the spatial stray-light effects induce wavelength shifts that affect trace-gas retrievals (Richter et al, 2020). Some of the above-threshold DIs in the global maps (midlatitudes to high latitudes, open-water scenes; see below) could be triggered by the high-contrast scenario.…”

Section: Saturation Over Lakes and Oceansmentioning

confidence: 99%

Detection of anomalies in the UV–vis reflectances from the Ozone Monitoring Instrument

et al. 2021

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Abstract. Various instrumental or geophysical artifacts, such as saturation, stray light or obstruction of light (either coming from the instrument or related to solar eclipses), negatively impact satellite measured ultraviolet and visible Earthshine radiance spectra and downstream retrievals of atmospheric and surface properties derived from these spectra. In addition, excessive noise such as from cosmic-ray impacts, prevalent within the South Atlantic Anomaly, can also degrade satellite radiance measurements. Saturation specifically pertains to observations of very bright surfaces such as sunglint over open water or thick clouds. When saturation occurs, additional photoelectric charge generated at the saturated pixel may overflow to pixels adjacent to a saturated area and be reflected as a distorted image in the final sensor output. When these effects cannot be corrected to an acceptable level for science-quality retrievals, flagging of the affected pixels is indicated. Here, we introduce a straightforward detection method that is based on the correlation, r, between the observed Earthshine radiance and solar irradiance spectra over a 10 nm spectral range; our decorrelation index (DI for brevity) is simply defined as a DI of 1−r. DI increases with anomalous additive effects or excessive noise in either radiances, the most likely cause in data from the Ozone Monitoring Instrument (OMI), or irradiances. DI is relatively straightforward to use and interpret and can be computed for different wavelength intervals. We developed a set of DIs for two spectral channels of the OMI, a hyperspectral pushbroom imaging spectrometer. For each OMI spatial measurement, we define 14 wavelength-dependent DIs within the OMI visible channel (350–498 nm) and six DIs in its ultraviolet 2 (UV2) channel (310–370 nm). As defined, DIs reflect a continuous range of deviations of observed spectra from the reference irradiance spectrum that are complementary to the binary saturation possibility warning (SPW) flags currently provided for each individual spectral or spatial pixel in the OMI radiance data set. Smaller values of DI are also caused by a number of geophysical factors; this allows one to obtain interesting physical results on the global distribution of spectral variations.

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Section: Saturation Over Lakes and Oceansmentioning

confidence: 99%

Detection of anomalies in the UV–vis reflectances from the Ozone Monitoring Instrument

et al. 2021

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“…It is important to consider the increasing availability of hyperspectral satellite data: (a) Italy launched the PRecursore IperSpettrale della Missione Applicativa (PRISMA) Earth Observation satellite in 2019 [21]; (b) China launched the GaoFen-5 (GF-5) in 2018 [22]; (c) Japan launched the Hyperspectral Imager Suite (HISUI) hyperspectral satellite sensor in 2019 [23]; (d) India launched the ISRO's Hyperspectral Imaging Satellite (HYSIS) hyperspectral satellite in 2018 [24]; and (e) Germany launched the DLR Earth Sensing Imaging Spectrometer (DESIS) hyperspectral instrument in 2018 on board the International Space Station (ISS) and is planning to launch the Environmental Mapping and Analysis Program (EnMAP) hyperspectral satellite in 2022 [25,26]. Moreover, NASA and ESA are cooperating for two new hyperspectral missions, i.e., SBG-NASA and CHIME-ESA [27].…”

Section: Introductionmentioning

confidence: 99%

Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

et al. 2021

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Novel approaches and algorithms to estimate crop physiological processes from Earth Observation (EO) data are essential to develop more sustainable management practices in agricultural systems. Within this context, this paper presents the results of different research activities carried out within the ESA-MOST Dragon 4 programme. The paper encompasses two research avenues: (a) the retrieval of biophysical variables of crops and yield prediction; and (b) food security related to different crop management strategies. Concerning the retrieval of variables, results show that LAI, derived by radiative transfer model (RTM) inversion, when assimilated into a crop growth model (i.e., SAFY) provides a way to assess yields with a higher accuracy with respect to open loop model runs: 1.14 t·ha−1 vs 4.42 t·ha−1 RMSE for assimilation and open loop, respectively. Concerning food security, results show that different pathogens could be detected by remote sensing satellite data. A k coefficient higher than 0.84 was achieved for yellow rust, thus assuring a monitoring accuracy, and for the diseased samples k was higher than 0.87. Concerning permanent crops, neural network (NN) algorithms allow classification of the Pseudomonas syringae pathogen on kiwi orchards with an overall accuracy higher than 91%.

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“…7 shows the synthetic hyperspectral image, Fig. 8 and Table II show the quantitative results by using SAM [25], spectral information divergence (SID) [26] and correlation coefficient (CC) [27], and our method is compared with Vertex Component Analysis (VCA) [28], N-FINDR [29], Simplex Identification via Split Augmented Lagrangian (SISAL) [30], Non-negative Matrix Factorization (NMF) (NMF) [31]. III show the abundance evaluation results of various methods, measured from CC [27], Abundance Information Divergence (AID) [32] and Abundance Angle Distance (AAD) [33].…”

Section: A Simulated Experimental Resultsmentioning

confidence: 99%

“…We compared the spectral curves of ground objects sampled on the spot with the extracted endmembers, and used SAM [25], CC [27] and SID [26] indicators for quantitative evaluation. Fig.…”

Section: B Real Experimental Resultsmentioning

confidence: 99%

Collaborative Coupled Hyperspectral Unmixing Based Subpixel Change Detection for Analyzing Coastal Wetlands

Chang

Meng

Sun

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Owing to the complicated and heterogeneous distribution characteristics of wetland features, the existing hyperspectral technology is difficult to investigate the inner-pixel subtle changes. In this paper, we present a sub-pixel change detection method based on collaborative coupled unmixing (SCDUM) for monitoring coastal wetlands. A novel multitemporal and spatial scale collaborative endmember extraction method based on joint spatial and spectral information is proposed. In the proposed method, the multitemporal hyperspectral images are firstly jointly clustered and segmented based on multi-feature fusion of spectral features, texture features, and shape features. Then a different spatial scale non-negative matrix factorization based on original and down-sampled multitemporal hyperspectral images is proposed to accurately extract the pure endmembers of each segmented images. Finally, the global abundance of the multitemporal image is effectively estimated for change detection. In addition, in order to verify the accuracy of the change detection results without reference, an accuracy verification strategy by using high spatial resolution Sentinel-2A image as auxiliary data is implemented. The Yellow River Estuary coastal wetlands was selected as the research area, and the Gaofen (GF)-5 and ZY-1 02D hyperspectral images were used as the research data. In particular, the proposed method not only provides the overall change information, but also obtains the component of change direction and intensity of each kind of endmember, and the experimental results shows that the SCDUM gives more accurate detection results, with closer to the endmember spectral curves of real objects, compared with other state-of-the-art methods.

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Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

Cited by 46 publications

References 56 publications

Detection of anomalies in the UV–vis reflectances from the Ozone Monitoring Instrument

Detection of anomalies in the UV–vis reflectances from the Ozone Monitoring Instrument

Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

Collaborative Coupled Hyperspectral Unmixing Based Subpixel Change Detection for Analyzing Coastal Wetlands

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