An Algorithm Developed for Smallsats Accurately Retrieves Landsat Surface Reflectance Using Scene Statistics
Closed-form Method for Atmospheric Correction (CMAC) is software that overcomes radiative transfer method problems for smallsat surface reflectance retrieval: unknown sensor radiance responses because onboard monitors are omitted to conserve size/weight, and ancillary data availability that delays p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/23/12604 |
| _version_ | 1797400468825047040 |
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| author | David P. Groeneveld Timothy A. Ruggles |
| author_facet | David P. Groeneveld Timothy A. Ruggles |
| author_sort | David P. Groeneveld |
| collection | DOAJ |
| description | Closed-form Method for Atmospheric Correction (CMAC) is software that overcomes radiative transfer method problems for smallsat surface reflectance retrieval: unknown sensor radiance responses because onboard monitors are omitted to conserve size/weight, and ancillary data availability that delays processing by days. CMAC requires neither and retrieves surface reflectance in near real time, first mapping the atmospheric effect across the image as an index (Atm-I) from scene statistics, then reversing these effects with a closed-form linear model that has precedence in the literature. Five consistent-reflectance area-of-interest targets on thirty-one low-to-moderate Atm-I images were processed by CMAC and LaSRC. CMAC retrievals accurately matched LaSRC with nearly identical error profiles. CMAC and LaSRC output for paired images of low and high Atm-I were then compared for three additional consistent-reflectance area-of-interest targets. Three indices were calculated from the extracted reflectance: NDVI calculated with red (standard) and substitutions with blue and green. A null hypothesis for competent retrieval would show no difference. The pooled error for the three indices (<i>n</i> = 9) was 0–3% for CMAC, 6–20% for LaSRC, and 13–38% for uncorrected top-of-atmosphere results, thus demonstrating both the value of atmospheric correction and, especially, the stability of CMAC for machine analysis and AI application under increasing Atm-I from climate change-driven wildfires. |
| first_indexed | 2024-03-09T01:55:58Z |
| format | Article |
| id | doaj.art-aa056cbc654c4cb095aa82032ec88e72 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T01:55:58Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-aa056cbc654c4cb095aa82032ec88e722023-12-08T15:11:08ZengMDPI AGApplied Sciences2076-34172023-11-0113231260410.3390/app132312604An Algorithm Developed for Smallsats Accurately Retrieves Landsat Surface Reflectance Using Scene StatisticsDavid P. Groeneveld0Timothy A. Ruggles1Advanced Remote Sensing, Inc., Hartford, SD 57033, USAAdvanced Remote Sensing, Inc., Hartford, SD 57033, USAClosed-form Method for Atmospheric Correction (CMAC) is software that overcomes radiative transfer method problems for smallsat surface reflectance retrieval: unknown sensor radiance responses because onboard monitors are omitted to conserve size/weight, and ancillary data availability that delays processing by days. CMAC requires neither and retrieves surface reflectance in near real time, first mapping the atmospheric effect across the image as an index (Atm-I) from scene statistics, then reversing these effects with a closed-form linear model that has precedence in the literature. Five consistent-reflectance area-of-interest targets on thirty-one low-to-moderate Atm-I images were processed by CMAC and LaSRC. CMAC retrievals accurately matched LaSRC with nearly identical error profiles. CMAC and LaSRC output for paired images of low and high Atm-I were then compared for three additional consistent-reflectance area-of-interest targets. Three indices were calculated from the extracted reflectance: NDVI calculated with red (standard) and substitutions with blue and green. A null hypothesis for competent retrieval would show no difference. The pooled error for the three indices (<i>n</i> = 9) was 0–3% for CMAC, 6–20% for LaSRC, and 13–38% for uncorrected top-of-atmosphere results, thus demonstrating both the value of atmospheric correction and, especially, the stability of CMAC for machine analysis and AI application under increasing Atm-I from climate change-driven wildfires.https://www.mdpi.com/2076-3417/13/23/12604atmospheric correctionsmallsatharmonizationLandsatSentinel 2scene statistics |
| spellingShingle | David P. Groeneveld Timothy A. Ruggles An Algorithm Developed for Smallsats Accurately Retrieves Landsat Surface Reflectance Using Scene Statistics Applied Sciences atmospheric correction smallsat harmonization Landsat Sentinel 2 scene statistics |
| title | An Algorithm Developed for Smallsats Accurately Retrieves Landsat Surface Reflectance Using Scene Statistics |
| title_full | An Algorithm Developed for Smallsats Accurately Retrieves Landsat Surface Reflectance Using Scene Statistics |
| title_fullStr | An Algorithm Developed for Smallsats Accurately Retrieves Landsat Surface Reflectance Using Scene Statistics |
| title_full_unstemmed | An Algorithm Developed for Smallsats Accurately Retrieves Landsat Surface Reflectance Using Scene Statistics |
| title_short | An Algorithm Developed for Smallsats Accurately Retrieves Landsat Surface Reflectance Using Scene Statistics |
| title_sort | algorithm developed for smallsats accurately retrieves landsat surface reflectance using scene statistics |
| topic | atmospheric correction smallsat harmonization Landsat Sentinel 2 scene statistics |
| url | https://www.mdpi.com/2076-3417/13/23/12604 |
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