Study on Surface Reflectance Sampling Method and Uncertainty Based on Airborne Hyperspectral Images
"> Figure 1
<p>Experimental areas: (<b>a</b>) the xiong’an experimental area; (<b>b</b>) the yan’qing experimental area.</p> "> Figure 1 Cont.
<p>Experimental areas: (<b>a</b>) the xiong’an experimental area; (<b>b</b>) the yan’qing experimental area.</p> "> Figure 2
<p>Hyspex hyperspectral camera.</p> "> Figure 3
<p>Remote sensing image of the experimental area (site size: 30 m × 30 m). (<b>a</b>) Bare soil, a non-vegetation experimental area; (<b>b</b>) wheat, a dense vegetation area; (<b>c</b>) soybean, a low, sparse-vegetation experimental area; (<b>d</b>) corn1, a medium-height, dense-vegetation experimental area; (<b>e</b>) is corn2, a medium-height, sparse-vegetation experimental area.</p> "> Figure 4
<p>Spatial heterogeneity of five sample areas.</p> "> Figure 5
<p>Stitched images of hyperspectral flight experiment: (<b>a</b>) a stitched image of the corn area—horizontally stitched together from four flight strips; (<b>b</b>) a stitched image of the soybean area—vertically stitched together from three flight strips.</p> "> Figure 6
<p>Comparison of images before and after stitching of hyperspectral flight experiment.</p> "> Figure 7
<p>Schematic diagram of ESU.</p> "> Figure 8
<p>Schematic diagram of spectrometer measurement.</p> "> Figure 9
<p>The simulated ground spectral measurement mask.</p> "> Figure 10
<p>Ground truth spectra at satellite pixel scale in experimental area based on UAV hyperspectral images.</p> "> Figure 11
<p>Ground and UAV endmember spectral validation results. (<b>a</b>) A photograph of the vegetation endmember; (<b>b</b>) a photograph of the bare soil endmember; (<b>c</b>) the vegetation endmember spectrum; (<b>d</b>) the bare soil endmember spectrum; (<b>e</b>) the comparison of the ground measurement spectrum and the Hyspex spectrum of vegetation endmembers; (<b>f</b>) the comparison of the ground measurement spectrum and the Hyspex spectrum of bare soil endmembers; (<b>g</b>) the correlation coefficient between the ground measurement reflectance and Hyspex reflectance of vegetation endmembers; (<b>h</b>) the correlation coefficient between ground measurement reflectance and Hyspex reflectance of bare soil endmembers.</p> "> Figure 11 Cont.
<p>Ground and UAV endmember spectral validation results. (<b>a</b>) A photograph of the vegetation endmember; (<b>b</b>) a photograph of the bare soil endmember; (<b>c</b>) the vegetation endmember spectrum; (<b>d</b>) the bare soil endmember spectrum; (<b>e</b>) the comparison of the ground measurement spectrum and the Hyspex spectrum of vegetation endmembers; (<b>f</b>) the comparison of the ground measurement spectrum and the Hyspex spectrum of bare soil endmembers; (<b>g</b>) the correlation coefficient between the ground measurement reflectance and Hyspex reflectance of vegetation endmembers; (<b>h</b>) the correlation coefficient between ground measurement reflectance and Hyspex reflectance of bare soil endmembers.</p> "> Figure 12
<p>Schematic of the simulated spectrum of the ESU based on UAV hyperspectral images.</p> "> Figure 13
<p>The simulated point-scale and pixel-scale measurement spectra: (<b>a</b>) the point-scale measurement spectra of the wheat field of the five-ESU method, the five spectra of different colors correspond to the average spectra of five ESUs; (<b>b</b>) the pixel-scale measurement spectra of the wheat field of the five-ESU method.</p> "> Figure 14
<p>Uncertainty of point-to-pixel-scale conversion in different sample areas under the 5-ESU method.</p> ">
Abstract
:1. Introduction
2. Study Area and Data
2.1. Introduction of Study Area
2.2. Data
3. Methods
3.1. Data Preprocessing
3.1.1. Hyperspectral Image Preprocessing
- Radiometric calibration of hyperspectral images is performed to convert hyperspectral images from a gray value into apparent radiance.
- Geometric correction of hyperspectral images is performed to eliminate geometric distortions caused by the hyperspectral camera or the surrounding environment, thereby improving the plane positioning accuracy of the images.
- Atmospheric correction of hyperspectral images is performed to convert hyperspectral images from apparent radiance into surface reflectance. The atmospheric correction method is based on the MODTRAN radiative transfer model, and the atmospheric parameters are input to achieve the inversion of surface reflectance. Among them, based on the imaging area and imaging date of the images, the aerosol model selects the rural type, and the atmospheric model selects the mid-latitude summer mode. The ground altitude is determined based on the experimental area’s elevation; the flying altitude is 100 m.
- The hyperspectral image is geometrically stitched to obtain a hyperspectral image covering the entire experimental site. The width of the site in the Xiong’an area is about 40 m, and there is only one flight strip in the whole sample area, which does not require image stitching. In the Yan’qing area, there are 4 flight strips for corn and 3 flight strips for soybeans, requiring the stitching of the hyperspectral images.
- By clipping the images, five hyperspectral images with a size of 30 m × 30 m (corresponding to the number of pixels 1111 × 1111) can be obtained. The spatial resolution is 2.7 cm, the number of bands is 200, and the spectral range is 400–1000 nm (as shown in Figure 3).
3.1.2. Ground Object Spectrometer Data Processing
3.2. Construction of Ground Spectral Sampling Model
3.3. Uncertainty Analysis
4. Results
4.1. Results and Evaluation of Surface Reflectance Retrieval from Airborne Hyperspectral Image
4.2. Ground Point-Scale and Area-Scale Spectral Simulation
4.3. Calculation Results of Ground Measurement Spectral Uncertainty
5. Discussions
5.1. The Influence of Sampling Methods on the Uncertainty of Scale Conversion
5.2. The Effect of Measurement Height on Scale Conversion Uncertainty
5.3. The Effect of Number of Spectra on Scale Conversion Uncertainty
6. Conclusions and Outlook
- We used the nine-ESU system sampling method, a 1 m measurement height, and five spectra in the ESU to simulate the point-to-pixel-scale conversion uncertainty of different ground objects. Among them, the point-to-pixel-scale conversion uncertainties in bare soil, wheat, soybean, corn1 (denser) and corn2 (sparser) were 0.337%, 0.467%, 0.27%, 0.336%, and 0.573%, respectively. All were less than 0.6%.
- We optimized the sampling method according to the heterogeneity of the sample area. For the bare soil sample area, we recommend the four-ESU method, with an uncertainty of 0.301%. For the wheat sample area, we recommend the five-ESU method, with an uncertainty of 0.137%. For the soybean sample area, we recommend the nine-ESU method, with an uncertainty of 0.27%. For the denser corn sample area, we recommend the four-ESU method, with an uncertainty of 0.226%. For the relatively sparse corn sample area, we recommend the 16-ESU method, with an uncertainty of 0.043%. It should be noted that the above quantitative results are only applicable to the sample areas selected in this study, but this conclusion can provide a certain reference for similar ground objects or sample areas with similar scenes.
- When the measurement height was changed from 1 m to 0.5 m and 0.2 m, the scale conversion uncertainty increased by about 20% and 50%, respectively. When the five spectra in the ESU were reduced to one to calculate the point-scale spectrum of the ESU, except for under the 25-ESU method, the average uncertainty of other sampling methods increased, with an average increase of about 40%.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Origo, N.; Gorrono, J.; Ryder, J.; Nightingale, J.; Bialek, A. Fiducial Reference Measurements for validation of Sentinel-2 and Proba-V surface reflectance products. Remote Sens. Environ. 2020, 241, 111690. [Google Scholar] [CrossRef]
- Justice, C.; Belward, A.; Morisette, J.; Lewis, P.; Privette, J.; Baret, F. Developments in the ‘validation’ of satellite sensor products for the study of the land surface. Int. J. Remote Sens. 2000, 21, 3383–3390. [Google Scholar] [CrossRef]
- Morisette, J.T.; Privette, J.L.; Justice, C.O. A framework for the validation of MODIS Land products. Remote Sens. Environ. 2002, 83, 77–96. [Google Scholar] [CrossRef]
- Roman, M.O.; Schaaf, C.B.; Woodcock, C.E.; Strahler, A.H.; Yang, X.; Braswell, R.H.; Curtis, P.S.; Davis, K.J.; Dragoni, D.; Goulden, M.L.; et al. The MODIS (Collection V005) BRDF/albedo product: Assessment of spatial representativeness over forested landscapes. Remote Sens. Environ. 2009, 113, 2476–2498. [Google Scholar] [CrossRef]
- Cescatti, A.; Marcolla, B.; Vannan, S.K.S.; Pan, J.Y.; Roman, M.O.; Yang, X.; Ciais, P.; Cook, R.B.; Law, B.E.; Matteucci, G.; et al. Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network. Remote Sens. Environ. 2012, 121, 323–334. [Google Scholar] [CrossRef]
- Hufkens, K.; Bogaert, J.; Dong, Q.H.; Lu, L.; Huang, C.L.; Ma, M.G.; Che, T.; Li, X.; Veroustraete, F.; Ceulemans, R. Impacts and uncertainties of upscaling of remote-sensing data validation for a semi-arid woodland. J. Arid Environ. 2008, 72, 1490–1505. [Google Scholar] [CrossRef]
- Xiaowen, L.I. Review of the Project of Quantitative Remote Sensing of Major Factors for Spatial-Temporal Heterogeneity on the Land Surface. Adv. Earth Sci. 2006, 21, 771–780. [Google Scholar]
- Hu, X.; Liu, J.; Sun, L.; Rong, Z.; Li, Y.; Zhang, Y.; Zheng, Z.; Wu, R.; Zhang, L.; Gu, X. Characterization of CRCS Dunhuang test site and vicarious calibration utilization for Fengyun (FY) series sensors. Can. J. Remote Sens. 2010, 36, 566–582. [Google Scholar] [CrossRef]
- Gao, H.L.; Gu, X.F.; Yu, T.; Gong, H.; Li, J.G.; Li, X.Y. HJ-1A HSI on-orbit radiometric calibration and validation research. Sci. China Technol. Sci. 2010, 53, 3119–3128. [Google Scholar] [CrossRef]
- Li, X.; Liu, S.; Ma, M.; Xiao, Q.; Liu, Q.; Jin, R.; Che, T.; Wang, W.; Qi, Y.; Li, H.; et al. HiWATER: An Integrated Remote Sensing Experiment on Hydrological and Ecological Processes in the Heihe River Basin. Adv. Earth Sci. 2012, 27, 481–498. [Google Scholar]
- Wu, X.; Wen, J.; Xiao, Q.; You, D.; Wang, J.; Ma, D.; Lin, X. A Multiscale Nested Sampling Method for Representative Albedo Observations at Various Pixel Scales. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 8193–8207. [Google Scholar] [CrossRef]
- Wu, X.; Wen, J.; Xiao, Q.; Youe, D. Upscaling of Single-Site-Based Measurements for Validation of Long-Term Coarse-Pixel Albedo Products. IEEE Trans. Geosci. Remote Sens. 2020, 58, 3411–3425. [Google Scholar] [CrossRef]
- Wu, X.; Wen, J.; Tang, R.; Wang, J.; Zeng, Q.; Li, Z.; You, D.; Lin, X.; Gong, B.; Xiao, Q. Quantification of the uncertainty in multiscale validation of coarse-resolution satellite albedo products: A study based on airborne CASI data. Remote Sens. Environ. 2023, 287, 113465. [Google Scholar] [CrossRef]
- Jing, X.; Uprety, S.; Liu, T.C.; Zhang, B.; Shao, X. Evaluation of SNPP and NOAA-20 VIIRS Datasets Using RadCalNet and Landsat 8/OLI Data. Remote Sens. 2022, 14, 3913. [Google Scholar] [CrossRef]
- Kim, K.; Lee, K. A Validation Experiment of the Reflectance Products of KOMPSAT-3A Based on RadCalNet Data and Its Applicability to Vegetation Indexing. Remote Sens. 2020, 12, 3971. [Google Scholar] [CrossRef]
- Wenny, B.N.; Thome, K.; Czapla-Myers, J. Evaluation of vicarious calibration for airborne sensors using RadCalNet. J. Appl. Remote Sens. 2021, 15, 034501. [Google Scholar] [CrossRef]
- Visschers, R.; Finke, P.A.; de Gruijter, J.J. A soil sampling program for the Netherlands. Geoderma 2007, 139, 60–72. [Google Scholar] [CrossRef]
- Sankey, J.B.; Brown, D.J.; Bernard, M.L.; Lawrence, R.L. Comparing local vs. global visible and near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) calibrations for the prediction of soil clay, organic C and inorganic C. Geoderma 2008, 148, 149–158. [Google Scholar] [CrossRef]
- Zhu, Z.Y.; Stein, M.L. Spatial sampling design for prediction with estimated parameters. J. Agric. Biol. Environ. Stat. 2006, 11, 24–44. [Google Scholar] [CrossRef]
- Vasat, R.; Heuvelink, G.B.M.; Boruvka, L. Sampling design optimization for multivariate soil mapping. Geoderma 2010, 155, 147–153. [Google Scholar] [CrossRef]
- Li, R.; Zhou, X.; Lyu, T.; Tao, Z.; Wang, J.; Xie, F. Optimal sampling strategy for authenticity test in heterogeneous vegetated areas. Trans. Chin. Soc. Agric. Eng. 2021, 37, 177–186. [Google Scholar]
- Wu, X.; Xiao, Q.; Wen, J.; Liu, Q.; Peng, J.; Li, X. Advances in uncertainty analysis for the validation of remote sensing products: Take leaf area index for example. J. Remote Sens. 2014, 18, 1011–1023. [Google Scholar]
- Li, X.; Liu, S.; Yang, X.; Ma, Y.; He, X.; Xu, Z.; Xu, T.; Song, L.; Zhang, Y.; Hu, X.; et al. Upscaling Evapotranspiration from a Single-Site to Satellite Pixel Scale. Remote Sens. 2021, 13, 4072. [Google Scholar] [CrossRef]
Parameter | Value |
---|---|
Spectral range | 400–1000 nm |
Number of bands | 200 |
Full width at half maximum | 3.5 nm |
Spatial resolution | 2.7 cm |
Field of view | 20° |
Digital quantization level | 16 bits |
Weight | 3.5 kg |
Vegetation | Bare Soil | |
---|---|---|
Correlation coefficient | 0.9911 | 0.9901 |
Average absolute difference | 1.18% | 1.41% |
Sample Area | Bare Soil | Wheat | Soybean | Corn1 | Corn2 |
---|---|---|---|---|---|
5-ESU method | 0.366 | 0.137 | 0.855 | 0.534 | 0.472 |
Sample Area | Bare Soil | Wheat | Soybean | Corn1 | Corn2 | Average |
---|---|---|---|---|---|---|
Single-ESU method | 0.409 | 0.592 | 4.926 | 1.300 | 1.156 | 1.677 |
4ESU method | 0.301 | 1.016 | 0.955 | 0.226 | 0.757 | 0.651 |
5-ESU method | 0.366 | 0.137 | 0.885 | 0.534 | 0.427 | 0.470 |
9-ESU method | 0.337 | 0.467 | 0.270 | 0.336 | 0.573 | 0.397 |
16-ESU method | 0.231 | 0.414 | 0.518 | 0.297 | 0.043 | 0.301 |
25-ESU method | 0.251 | 0.195 | 0.741 | 0.520 | 0.061 | 0.354 |
Average | 0.316 | 0.470 | 1.383 | 0.536 | 0.503 |
Sample Area | Bare Soil | Wheat | Soybean | Corn1 | Corn2 | Average |
---|---|---|---|---|---|---|
Single-ESU method | 0.678 | 0.667 | 5.590 | 2.125 | 0.481 | 1.908 |
4-ESU method | 0.264 | 0.799 | 0.732 | 0.761 | 1.203 | 0.752 |
5-ESU method | 0.388 | 0.138 | 1.784 | 0.571 | 0.115 | 0.599 |
9-ESU method | 0.331 | 0.281 | 0.667 | 0.655 | 0.595 | 0.506 |
16-ESU method | 0.218 | 0.498 | 0.718 | 0.331 | 0.197 | 0.392 |
25-ESU method | 0.231 | 0.268 | 0.995 | 0.634 | 0.149 | 0.455 |
Average | 0.352 | 0.442 | 1.748 | 0.846 | 0.457 |
Sample Area | Bare Soil | Wheat | Soybean | Corn1 | Corn2 | Average |
---|---|---|---|---|---|---|
Single-ESU method | 1.099 | 0.698 | 5.840 | 3.232 | 0.863 | 2.346 |
4-ESU method | 0.277 | 0.552 | 0.474 | 1.023 | 1.093 | 0.684 |
5-ESU method | 0.379 | 0.243 | 2.565 | 1.038 | 0.275 | 0.900 |
9-ESU method | 0.332 | 0.111 | 1.221 | 1.032 | 0.333 | 0.606 |
16-ESU method | 0.203 | 0.617 | 0.969 | 0.190 | 0.249 | 0.446 |
25-ESU method | 0.211 | 0.407 | 1.286 | 0.637 | 0.296 | 0.567 |
Average | 0.417 | 0.438 | 2.059 | 1.192 | 0.518 |
Sample Area | Bare Soil | Wheat | Soybean | Corn1 | Corn2 | Average |
---|---|---|---|---|---|---|
Single-ESU method | 0.588 | 0.146 | 5.896 | 1.916 | 1.710 | 2.051 |
4-ESU method | 0.320 | 0.245 | 1.641 | 1.992 | 0.652 | 0.970 |
5-ESU method | 0.401 | 0.263 | 1.371 | 1.112 | 0.724 | 0.774 |
9-ESU method | 0.365 | 0.238 | 0.260 | 1.503 | 0.692 | 0.612 |
16-ESU method | 0.225 | 0.515 | 0.383 | 0.321 | 0.547 | 0.398 |
25-ESU method | 0.246 | 0.203 | 0.740 | 0.308 | 0.205 | 0.340 |
Average | 0.358 | 0.268 | 1.715 | 1.192 | 0.755 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Gao, H.; Wang, Q.; Gu, X.; Yang, J.; Liu, Q.; Tao, Z.; Qiu, X.; Zhang, W.; Shi, X.; Zhao, X. Study on Surface Reflectance Sampling Method and Uncertainty Based on Airborne Hyperspectral Images. Remote Sens. 2023, 15, 5090. https://doi.org/10.3390/rs15215090
Gao H, Wang Q, Gu X, Yang J, Liu Q, Tao Z, Qiu X, Zhang W, Shi X, Zhao X. Study on Surface Reflectance Sampling Method and Uncertainty Based on Airborne Hyperspectral Images. Remote Sensing. 2023; 15(21):5090. https://doi.org/10.3390/rs15215090
Chicago/Turabian StyleGao, Hailiang, Qianqian Wang, Xingfa Gu, Jian Yang, Qiyue Liu, Zui Tao, Xingchen Qiu, Wei Zhang, Xinda Shi, and Xiaofei Zhao. 2023. "Study on Surface Reflectance Sampling Method and Uncertainty Based on Airborne Hyperspectral Images" Remote Sensing 15, no. 21: 5090. https://doi.org/10.3390/rs15215090