360 research outputs found

    Linking glacier annual mass balance and glacier albedo retrieved from MODIS data

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    Albedo is one of the variables controlling the mass balance of temperate glaciers. Multispectral imagers, such as MODerate Imaging Spectroradiometer (MODIS) on board the TERRA and AQUA satellites, provide a means to monitor glacier surface albedo. In this study, different methods to retrieve broadband glacier surface albedo from MODIS data are compared. The effect of multiple reflections due to the rugged topography and of the anisotropic reflection of snow and ice are particularly investigated. The methods are tested on the Saint Sorlin Glacier (Grandes Rousses area, French Alps). The accuracy of the retrieved albedo is estimated using both field measurements, at two automatic weather stations located on the glacier, and albedo values derived from terrestrial photographs. For summers 2008 and 2009, the root mean square deviation (RMSD) between field measurements and the broadband albedo retrieved from MODIS data at 250 m spatial resolution was found to be 0.052 or about 10% relative error. The RMSD estimated for the MOD10 daily albedo product is about three times higher. One decade (2000โ€“2009) of MODIS data were then processed to create a time series of albedo maps of Saint Sorlin Glacier during the ablation season. The annual mass balance of Saint Sorlin Glacier was compared with the minimum albedo value (average over the whole glacier surface) observed with MODIS during the ablation season. A strong linear correlation exists between the two variables. Furthermore, the date when the average albedo of the whole glacier reaches a minimum closely corresponds to the period when the snow line is located at its highest elevation, thus when the snow line is a good indicator of the glacier equilibrium line. This indicates that this strong correlation results from the fact that the minimal average albedo values of the glacier contains considerable information regarding the relative share of areal surfaces between the ablation zone (i.e. ice with generally low albedo values) and the accumulation zone (i.e. snow with a relatively high albedo). As a consequence, the monitoring of the glacier surface albedo using MODIS data can provide a useful means to evaluate the interannual variability of the glacier mass balance. Finally, the albedo in the ablation area of Saint Sorlin Glacier does not exhibit any decreasing trend over the study period, contrasting with the results obtained on Morteratsch Glacier in the Swiss Alps

    Effects of atmospheric, topographic, and BRDF correction on imaging spectroscopy-derived data products

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    Surface reflectance is an important data product in imaging spectroscopy for obtaining surface information. The complex retrieval of surface reflectance, however, critically relies on accurate knowledge of atmospheric absorption and scattering, and the compensation of these effects. Furthermore, illumination and observation geometry in combination with surface reflectance anisotropy determine dynamics in retrieved surface reflectance not related to surface absorption properties. To the best of authorsโ€™ knowledge, no comprehensive assessment of the impact of atmospheric, topographic, and anisotropy effects on derived surface information is available so far.This study systematically evaluates the impact of these effects on reflectance, albedo, and vegetation products. Using three well-established processing schemes (ATCOR F., ATCOR R., and BREFCOR), high-resolution APEX imaging spectroscopy data, covering a large gradient of illumination and observation angles, are brought to several processing states, varyingly affected by mentioned effects. Pixel-wise differences of surface reflectance, albedo, and spectral indices of neighboring flight lines are quantitatively analyzed in their respective overlapping area. We found that compensation of atmospheric effects reveals actual anisotropy-related dynamics in surface reflectance and derived albedo, related to an increase in pixel-wise relative reflectance and albedo differences of more than 40%. Subsequent anisotropy compensation allows us to successfully reduce apparent relative reflectance and albedo differences by up to 20%. In contrast, spectral indices are less affected by atmospheric and anisotropy effects, showing relative differences of 3% to 10% in overlapping regions of flight lines.We recommend to base decisions on the use of appropriate processing schemes on individual use cases considering envisioned data products

    LANDSAT-D investigations in snow hydrology

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    Work undertaken during the contract and its results are described. Many of the results from this investigation are available in journal or conference proceedings literature - published, accepted for publication, or submitted for publication. For these the reference and the abstract are given. Those results that have not yet been submitted separately for publication are described in detail. Accomplishments during the contract period are summarized as follows: (1) analysis of the snow reflectance characteristics of the LANDSAT Thematic Mapper, including spectral suitability, dynamic range, and spectral resolution; (2) development of a variety of atmospheric models for use with LANDSAT Thematic Mapper data. These include a simple but fast two-stream approximation for inhomogeneous atmospheres over irregular surfaces, and a doubling model for calculation of the angular distribution of spectral radiance at any level in an plane-parallel atmosphere; (3) incorporation of digital elevation data into the atmospheric models and into the analysis of the satellite data; and (4) textural analysis of the spatial distribution of snow cover

    Active microwave remote sensing of earth/land, chapter 2

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    Geoscience applications of active microwave remote sensing systems are examined. Major application areas for the system include: (1) exploration of petroleum, mineral, and ground water resources, (2) mapping surface and structural features, (3) terrain analysis, both morphometric and genetic, (4) application in civil works, and (5) application in the areas of earthquake prediction and crustal movements. Although the success of radar surveys has not been widely publicized, they have been used as a prime reconnaissance data base for mineral exploration and land-use evaluation in areas where photography cannot be obtained

    Anisotropy parameterization development and evaluation for glacier surface albedo retrieval from satellite observations.

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    Glacier albedo determines the net shortwave radiation absorbed at the glacier surface and plays a crucial role in glacier energy and mass balance. Remote sensing techniques are efficient means to retrieve glacier surface albedo over large and inaccessible areas and to study its variability. However, corrections of anisotropic reflectance of glacier surface have been established for specific shortwave bands only, such as Landsat 5 Thematic Mapper (L5/TM) band 2 and band 4, which is a major limitation of current retrievals of glacier broadband albedo. In this study, we calibrated and evaluated four anisotropy correction models for glacier snow and ice, applicable to visible, near-infrared and shortwave-infrared wavelengths using airborne datasets of Bidirectional Reflectance Distribution Function (BRDF). We then tested the ability of the best-performing anisotropy correction model, referred to from here on as the โ€˜updated modelโ€™, to retrieve albedo from L5/TM, Landsat 8 Operational Land Imager (L8/OLI) and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, and evaluated these results with field measurements collected on eight glaciers around the world. Our results show that the updated model: (1) can accurately estimate anisotropic factors of reflectance for snow and ice surfaces; (2) generally performs better than prior approaches for L8/OLI albedo retrieval but is not appropriate for L5/TM; (3) generally retrieves MODIS albedo better than the MODIS standard albedo product (MCD43A3) in both absolute values and glacier albedo temporal evolution, i.e., exhibiting both fewer gaps and better agreement with field observations. As the updated model enables anisotropy correction of a maximum of 10 multispectral bands and is implemented in Google Earth Engine (GEE), it is promising for observing and analyzing glacier albedo at large spatial scales

    ๋“œ๋ก ์„ ํ™œ์šฉํ•œ ์œ„์„ฑ ์ง€ํ‘œ๋ฐ˜์‚ฌ๋„ ์‚ฐ์ถœ๋ฌผ ๊ณต๊ฐ„ ํŒจํ„ด ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ์ƒํƒœ์กฐ๊ฒฝยท์ง€์—ญ์‹œ์Šคํ…œ๊ณตํ•™๋ถ€(์ƒํƒœ์กฐ๊ฒฝํ•™), 2021.8. ์กฐ๋Œ€์†”.High-resolution satellites are assigned to monitor land surface in detail. The reliable surface reflectance (SR) is the fundamental in terrestrial ecosystem modeling so the temporal and spatial validation is essential. Usually based on multiple ground control points (GCPs), field spectroscopy guarantees the temporal continuity. Due to limited sampling, however, it hardly illustrates the spatial pattern. As a map, the pixelwise spatial variability of SR products is not well-documented. In this study, we introduced drone-based hyperspectral image (HSI) as a reference and compared the map with Sentinel 2 and Landsat 8 SR products on a heterogeneous rice paddy landscape. First, HSI was validated by field spectroscopy and swath overlapping, which assured qualitative radiometric accuracy within the viewing geometry. Second, HSI was matched to the satellite SRs. It involves spectral and spatial aggregation, co-registration and nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance (NBAR) conversion. Then, we 1) quantified the spatial variability of the satellite SRs and the vegetation indices (VIs) including NDVI and NIRv by APU matrix, 2) qualified them pixelwise by theoretical error budget and 3) examined the improvement by BRDF normalization. Sentinel 2 SR exhibits overall good agreement with drone HSI while the two NIRs are biased up to 10%. Despite the bias in NIR, the NDVI shows a good match on vegetated areas and the NIRv only displays the discrepancy on built-in areas. Landsat 8 SR was biased over the VIS bands (-9 ~ -7.6%). BRDF normalization just contributed to a minor improvement. Our results demonstrate the potential of drone HSI to replace in-situ observation and evaluate SR or atmospheric correction algorithms over the flat terrain. Future researches should replicate the results over the complex terrain and canopy structure (i.e. forest).์›๊ฒฉํƒ์‚ฌ์—์„œ ์ง€ํ‘œ ๋ฐ˜์‚ฌ๋„(SR)๋Š” ์ง€ํ‘œ์ •๋ณด๋ฅผ ๋น„ํŒŒ๊ดด์ ์ด๊ณ  ์ฆ‰๊ฐ์ ์ธ ๋ฐฉ๋ฒ•์œผ๋กœ ์ „๋‹ฌํ•ด์ฃผ๋Š” ๋งค๊ฐœ์ฒด ์—ญํ• ์„ ํ•œ๋‹ค. ์‹ ๋ขฐํ•  ์ˆ˜ ์žˆ๋Š” SR์€ ์œก์ƒ ์ƒํƒœ๊ณ„ ๋ชจ๋ธ๋ง์˜ ๊ธฐ๋ณธ์ด๊ณ , ์ด์— ๋”ฐ๋ผ SR์˜ ์‹œ๊ณต๊ฐ„์  ๊ฒ€์ฆ์ด ์š”๊ตฌ๋œ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ SR์€ ์—ฌ๋Ÿฌ ์ง€์ƒ ๊ธฐ์ค€์ (GCP)์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ํ˜„์žฅ ๋ถ„๊ด‘๋ฒ•์„ ํ†ตํ•ด์„œ ์‹œ๊ฐ„์  ์—ฐ์†์„ฑ์ด ๋ณด์žฅ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ˜„์žฅ ๋ถ„๊ด‘๋ฒ•์€ ์ œํ•œ์ ์ธ ์ƒ˜ํ”Œ๋ง์œผ๋กœ ๊ณต๊ฐ„ ํŒจํ„ด์„ ๊ฑฐ์˜ ๋ณด์—ฌ์ฃผ์ง€ ์•Š์•„, ์œ„์„ฑ SR์˜ ํ”ฝ์…€ ๋ณ„ ๊ณต๊ฐ„ ๋ณ€๋™์„ฑ์€ ์ž˜ ๋ถ„์„๋˜์ง€ ์•Š์•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋“œ๋ก  ๊ธฐ๋ฐ˜์˜ ์ดˆ๋ถ„๊ด‘ ์˜์ƒ(HSI)์„ ์ฐธ๊ณ ์ž๋ฃŒ๋กœ ๋„์ž…ํ•˜์—ฌ, ์ด๋ฅผ ์ด์งˆ์ ์ธ ๋…ผ ๊ฒฝ๊ด€์—์„œ Sentinel 2 ๋ฐ Landsat 8 SR๊ณผ ๋น„๊ตํ•˜์˜€๋‹ค. ์šฐ์„ , ๋“œ๋ก  HSI๋Š” ํ˜„์žฅ ๋ถ„๊ด‘๋ฒ• ๋ฐ ๊ฒฝ๋กœ ์ค‘์ฒฉ์„ ํ†ตํ•ด์„œ ๊ด€์ธก๊ฐ๋„ ๋ฒ”์œ„ ๋‚ด์—์„œ ์ •์„ฑ์ ์ธ ๋ฐฉ์‚ฌ ์ธก์ •์„ ๋ณด์žฅํ•œ๋‹ค๊ณ  ๊ฒ€์ฆ๋˜์—ˆ๋‹ค. ์ดํ›„, ๋“œ๋ก  HSI๋Š” ์œ„์„ฑ SR์˜ ๋ถ„๊ด‘๋ฐ˜์‘ํŠน์„ฑ, ๊ณต๊ฐ„ํ•ด์ƒ๋„ ๋ฐ ์ขŒํ‘œ๊ณ„๋ฅผ ๊ธฐ์ค€์œผ๋กœ ๋งž์ถฐ์กŒ๊ณ , ๊ด€์ธก ๊ธฐํ•˜๋ฅผ ํ†ต์ผํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋“œ๋ก  HIS์™€ ์œ„์„ฑ SR์€ ๊ฐ๊ฐ ์–‘๋ฐฉํ–ฅ๋ฐ˜์‚ฌ์œจ๋ถ„ํฌํ•จ์ˆ˜ (BRDF) ์ •๊ทœํ™” ๋ฐ˜์‚ฌ๋„ (NBAR)๋กœ ๋ณ€ํ™˜๋˜์—ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, 1) APU ํ–‰๋ ฌ์œผ๋กœ ์œ„์„ฑ SR๊ณผ NDVI, NIRv๋ฅผ ํฌํ•จํ•˜๋Š” ์‹์ƒ์ง€์ˆ˜(VI)์˜ ๊ณต๊ฐ„๋ณ€๋™์„ฑ์„ ์ •๋Ÿ‰ํ™” ํ–ˆ๊ณ , 2) ๋Œ€๊ธฐ๋ณด์ •์˜ ์ด๋ก ์  ์˜ค์ฐจ๋ฅผ ๊ธฐ์ค€์œผ๋กœ SR๊ณผ VI๋ฅผ ํ”ฝ์…€๋ณ„๋กœ ํ‰๊ฐ€ํ–ˆ๊ณ , 3) BRDF ์ •๊ทœํ™”๋ฅผ ํ†ตํ•œ ๊ฐœ์„  ์‚ฌํ•ญ์„ ๊ฒ€ํ† ํ–ˆ๋‹ค. Sentinel 2 SR์€ ๋“œ๋ก  HSI์™€ ์ „๋ฐ˜์ ์œผ๋กœ ์ข‹์€ ์ผ์น˜๋ฅผ ๋ณด์ด๋‚˜, ๋‘ NIR ์ฑ„๋„์€ ์ตœ๋Œ€ 10% ํŽธํ–ฅ๋˜์—ˆ๋‹ค. NIR์˜ ํŽธํ–ฅ์€ ์‹์ƒ์ง€์ˆ˜์—์„œ ํ† ์ง€ ํ”ผ๋ณต์— ๋”ฐ๋ผ ๋‹ค๋ฅธ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. NDVI๋Š” ์‹์ƒ์—์„œ๋Š” ๋‚ฎ์€ ํŽธํ–ฅ์„ ๋ณด์—ฌ์คฌ๊ณ , NIRv๋Š” ๋„์‹œ์‹œ์„ค๋ฌผ ์˜์—ญ์—์„œ๋งŒ ๋†’์€ ํŽธํ–ฅ์„ ๋ณด์˜€๋‹ค. Landsat 8 SR์€ VIS ์ฑ„๋„์— ๋Œ€ํ•ด ํŽธํ–ฅ๋˜์—ˆ๋‹ค (-9 ~ -7.6%). BRDF ์ •๊ทœํ™”๋Š” ์œ„์„ฑ SR์˜ ํ’ˆ์งˆ์„ ๊ฐœ์„ ํ–ˆ์ง€๋งŒ, ๊ทธ ์˜ํ–ฅ์€ ๋ถ€์ˆ˜์ ์ด์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ‰ํƒ„ํ•œ ์ง€ํ˜•์—์„œ ๋“œ๋ก  HSI๊ฐ€ ํ˜„์žฅ ๊ด€์ธก์„ ๋Œ€์ฒดํ•  ์ˆ˜ ์žˆ๊ณ , ๋”ฐ๋ผ์„œ ์œ„์„ฑ SR์ด๋‚˜ ๋Œ€๊ธฐ๋ณด์ • ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ‰๊ฐ€ํ•˜๋Š”๋ฐ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์˜€๋‹ค. ํ–ฅํ›„ ์—ฐ๊ตฌ์—์„œ๋Š” ์‚ฐ๋ฆผ์œผ๋กœ ๋Œ€์ƒ์ง€๋ฅผ ํ™•๋Œ€ํ•˜์—ฌ, ์ง€ํ˜•๊ณผ ์บ๋…ธํ”ผ ๊ตฌ์กฐ๊ฐ€ ๋“œ๋ก  HSI ๋ฐ ์œ„์„ฑ SR์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค.Chapter 1. Introduction 1 1.1 Background 1 Chapter 2. Method 3 2.1 Study Site 3 2.2 Drone campaign 4 2.3 Data processing 4 2.3.1 Sensor calibration 5 2.3.2 Bidirectional reflectance factor (BRF) calculation 7 2.3.3 BRDF correction 7 2.3.4 Orthorectification 8 2.3.5 Spatial Aggregation 9 2.3.6 Co-registration 10 2.4 Satellite dataset 10 2.4.2 Landsat 8 12 Chapter 3. Result and Discussion 12 3.1 Drone BRF map quality assessment 12 3.1.1 Radiometric accuracy 12 3.1.2 BRDF effect 15 3.2 Spatial variability in satellite surface reflectance product 16 3.2.1 Sentinel 2B (10m) 17 3.2.2 Sentinel 2B (20m) 22 3.2.3 Landsat 8 26 Chapter 4. Conclusion 28 Supplemental Materials 30 Bibliography 34 Abstract in Korean 43์„
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