Mapping Arctic Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer
Sea-ice surface roughness (SIR) is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer-melt pond extent, and being related to ice age and thickness. High-resolution roughness estimates from ai...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/24/6249 |
| _version_ | 1797455430323011584 |
|---|---|
| author | Thomas Johnson Michel Tsamados Jan-Peter Muller Julienne Stroeve |
| author_facet | Thomas Johnson Michel Tsamados Jan-Peter Muller Julienne Stroeve |
| author_sort | Thomas Johnson |
| collection | DOAJ |
| description | Sea-ice surface roughness (SIR) is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer-melt pond extent, and being related to ice age and thickness. High-resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness does not extend over multi-decadal timescales. Launched on the Terra satellite in 1999, the NASA Multi-angle Imaging SpectroRadiometer (MISR) instrument acquires optical imagery from nine near-simultaneous camera view zenith angles. Extending on previous work to model surface roughness from specular anisotropy, a training dataset of cloud-free angular reflectance signatures and surface roughness, defined as the standard deviation of the within-pixel lidar elevations, from near-coincident operation IceBridge (OIB) airborne laser data is generated and is modelled using support vector regression (SVR) with a radial basis function (RBF) kernel selected. Blocked k-fold cross-validation is implemented to tune hyperparameters using grid optimisation and to assess model performance, with an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> (coefficient of determination) of 0.43 and MAE (mean absolute error) of 0.041 m. Product performance is assessed through independent validation by comparison with unseen similarly generated surface-roughness characterisations from pre-IceBridge missions (Pearson’s r averaged over six scenes, <i>r</i> = 0.58, <i>p</i> < 0.005), and with AWI CS2-SMOS sea-ice thickness (Spearman’s rank, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>r</mi><mi>s</mi></msup></semantics></math></inline-formula> = 0.66, <i>p</i> < 0.001), a known roughness proxy. We present a derived sea-ice roughness product at 1.1 km resolution (2000–2020) over the seasonal period of OIB operation and a corresponding time-series analysis. Both our instantaneous swaths and pan-Arctic monthly mosaics show considerable potential in detecting surface-ice characteristics such as deformed rough ice, thin refrozen leads, and polynyas. |
| first_indexed | 2024-03-09T15:54:17Z |
| format | Article |
| id | doaj.art-3992119ccd614886bfd7cefd71f56671 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T15:54:17Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3992119ccd614886bfd7cefd71f566712023-11-24T17:46:33ZengMDPI AGRemote Sensing2072-42922022-12-011424624910.3390/rs14246249Mapping Arctic Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometerThomas Johnson0Michel Tsamados1Jan-Peter Muller2Julienne Stroeve3Earth Sciences Department, University College London, Gower Street, London WC1E 6BT, UKEarth Sciences Department, University College London, Gower Street, London WC1E 6BT, UKMullard Space Science Laboratory (MSSL), Department of Space & Climate Physics, University College London, Holmbury St. Mary, Surrey RH5 6NT, UKEarth Sciences Department, University College London, Gower Street, London WC1E 6BT, UKSea-ice surface roughness (SIR) is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer-melt pond extent, and being related to ice age and thickness. High-resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness does not extend over multi-decadal timescales. Launched on the Terra satellite in 1999, the NASA Multi-angle Imaging SpectroRadiometer (MISR) instrument acquires optical imagery from nine near-simultaneous camera view zenith angles. Extending on previous work to model surface roughness from specular anisotropy, a training dataset of cloud-free angular reflectance signatures and surface roughness, defined as the standard deviation of the within-pixel lidar elevations, from near-coincident operation IceBridge (OIB) airborne laser data is generated and is modelled using support vector regression (SVR) with a radial basis function (RBF) kernel selected. Blocked k-fold cross-validation is implemented to tune hyperparameters using grid optimisation and to assess model performance, with an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> (coefficient of determination) of 0.43 and MAE (mean absolute error) of 0.041 m. Product performance is assessed through independent validation by comparison with unseen similarly generated surface-roughness characterisations from pre-IceBridge missions (Pearson’s r averaged over six scenes, <i>r</i> = 0.58, <i>p</i> < 0.005), and with AWI CS2-SMOS sea-ice thickness (Spearman’s rank, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>r</mi><mi>s</mi></msup></semantics></math></inline-formula> = 0.66, <i>p</i> < 0.001), a known roughness proxy. We present a derived sea-ice roughness product at 1.1 km resolution (2000–2020) over the seasonal period of OIB operation and a corresponding time-series analysis. Both our instantaneous swaths and pan-Arctic monthly mosaics show considerable potential in detecting surface-ice characteristics such as deformed rough ice, thin refrozen leads, and polynyas.https://www.mdpi.com/2072-4292/14/24/6249surface roughnesssea icesupport vector regressionmulti-angle imaging spectroradiometericebridge |
| spellingShingle | Thomas Johnson Michel Tsamados Jan-Peter Muller Julienne Stroeve Mapping Arctic Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer Remote Sensing surface roughness sea ice support vector regression multi-angle imaging spectroradiometer icebridge |
| title | Mapping Arctic Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer |
| title_full | Mapping Arctic Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer |
| title_fullStr | Mapping Arctic Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer |
| title_full_unstemmed | Mapping Arctic Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer |
| title_short | Mapping Arctic Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer |
| title_sort | mapping arctic sea ice surface roughness with multi angle imaging spectroradiometer |
| topic | surface roughness sea ice support vector regression multi-angle imaging spectroradiometer icebridge |
| url | https://www.mdpi.com/2072-4292/14/24/6249 |
| work_keys_str_mv | AT thomasjohnson mappingarcticseaicesurfaceroughnesswithmultiangleimagingspectroradiometer AT micheltsamados mappingarcticseaicesurfaceroughnesswithmultiangleimagingspectroradiometer AT janpetermuller mappingarcticseaicesurfaceroughnesswithmultiangleimagingspectroradiometer AT juliennestroeve mappingarcticseaicesurfaceroughnesswithmultiangleimagingspectroradiometer |