Above-Ground Biomass Estimation Based on Multi-Angular <italic>L</italic>-Band Measurements of Brightness Temperatures
There is growing interest in using passive microwave observations and vegetation optical depth (VOD) to study the above-ground biomass (AGB) and carbon stocks evolution. <italic>L</italic>-band observations, in particular, have been shown to be very sensitive to AGB. Here, thanks to the...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10149509/ |
| _version_ | 1797356390591758336 |
|---|---|
| author | Julio Cesar Salazar-Neira Arnaud Mialon Philippe Richaume Stephane Mermoz Yann H. Kerr Alexandre Bouvet Thuy Le Toan Simon Boitard Nemesio J. Rodriguez-Fernandez |
| author_facet | Julio Cesar Salazar-Neira Arnaud Mialon Philippe Richaume Stephane Mermoz Yann H. Kerr Alexandre Bouvet Thuy Le Toan Simon Boitard Nemesio J. Rodriguez-Fernandez |
| author_sort | Julio Cesar Salazar-Neira |
| collection | DOAJ |
| description | There is growing interest in using passive microwave observations and vegetation optical depth (VOD) to study the above-ground biomass (AGB) and carbon stocks evolution. <italic>L</italic>-band observations, in particular, have been shown to be very sensitive to AGB. Here, thanks to the multiangle capabilities of the soil moisture and ocean salinity mission, a new approach to estimate AGB directly from multiangular <italic>L</italic>-band brightness temperatures (TBs) is proposed, thus surpassing the use of intermediate variables such as VOD. The European Space Agency (ESA) Climate Change Initiative (CCI) Biomass maps for the years 2010, 2017, and 2018 are used as the AGB reference. AGB estimates from artificial neural networks (ANN) using a purely data-driven approach explained up to 88% of AGB variability globally; even so, a decrease in retrieval performance was observed when models are applied to data from years different than the year used for their training. A new training methodology based on multiyear training sets is presented, leading to results showing more stability for temporal analyses. The best set of predictors and an optimal learning dataset configuration are proposed based on an assessment of the accuracy of the estimates. The ANN methodology using TBs is a promising alternative with respect to the common method of using a parametric function to estimate AGB from VOD. ANNs AGB estimates showed a higher correlation with CCI AGB maps (<inline-formula><tex-math notation="LaTeX">$R$</tex-math></inline-formula><sup>2</sup> <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>0.87 instead of <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>0.84) and presented a stronger agreement with their spatial structure and less differences in residual maps. |
| first_indexed | 2024-03-08T14:26:57Z |
| format | Article |
| id | doaj.art-3a54fcdfe11548ee8d5839fb3a9aef71 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-08T14:26:57Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-3a54fcdfe11548ee8d5839fb3a9aef712024-01-13T00:00:37ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01165813582710.1109/JSTARS.2023.328528810149509Above-Ground Biomass Estimation Based on Multi-Angular <italic>L</italic>-Band Measurements of Brightness TemperaturesJulio Cesar Salazar-Neira0https://orcid.org/0000-0002-6662-5367Arnaud Mialon1https://orcid.org/0000-0001-7970-0701Philippe Richaume2https://orcid.org/0000-0002-2945-0262Stephane Mermoz3https://orcid.org/0000-0002-3166-7583Yann H. Kerr4https://orcid.org/0000-0001-6352-1717Alexandre Bouvet5https://orcid.org/0000-0002-7428-4339Thuy Le Toan6https://orcid.org/0000-0003-4843-5962Simon Boitard7https://orcid.org/0009-0001-3751-0202Nemesio J. Rodriguez-Fernandez8https://orcid.org/0000-0003-3796-149XCESBIO (Université de Toulouse, CNES, CNRS, IRD, INRAE), Toulouse, FranceCESBIO (Université de Toulouse, CNES, CNRS, IRD, INRAE), Toulouse, FranceCESBIO (Université de Toulouse, CNES, CNRS, IRD, INRAE), Toulouse, FranceGlobEO-Global Earth Observation, Toulouse, FranceCESBIO (Université de Toulouse, CNES, CNRS, IRD, INRAE), Toulouse, FranceCESBIO (Université de Toulouse, CNES, CNRS, IRD, INRAE), Toulouse, FranceCESBIO (Université de Toulouse, CNES, CNRS, IRD, INRAE), Toulouse, FranceCESBIO, Toulouse, FranceCESBIO (Université de Toulouse, CNES, CNRS, IRD, INRAE), Toulouse, FranceThere is growing interest in using passive microwave observations and vegetation optical depth (VOD) to study the above-ground biomass (AGB) and carbon stocks evolution. <italic>L</italic>-band observations, in particular, have been shown to be very sensitive to AGB. Here, thanks to the multiangle capabilities of the soil moisture and ocean salinity mission, a new approach to estimate AGB directly from multiangular <italic>L</italic>-band brightness temperatures (TBs) is proposed, thus surpassing the use of intermediate variables such as VOD. The European Space Agency (ESA) Climate Change Initiative (CCI) Biomass maps for the years 2010, 2017, and 2018 are used as the AGB reference. AGB estimates from artificial neural networks (ANN) using a purely data-driven approach explained up to 88% of AGB variability globally; even so, a decrease in retrieval performance was observed when models are applied to data from years different than the year used for their training. A new training methodology based on multiyear training sets is presented, leading to results showing more stability for temporal analyses. The best set of predictors and an optimal learning dataset configuration are proposed based on an assessment of the accuracy of the estimates. The ANN methodology using TBs is a promising alternative with respect to the common method of using a parametric function to estimate AGB from VOD. ANNs AGB estimates showed a higher correlation with CCI AGB maps (<inline-formula><tex-math notation="LaTeX">$R$</tex-math></inline-formula><sup>2</sup> <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>0.87 instead of <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>0.84) and presented a stronger agreement with their spatial structure and less differences in residual maps.https://ieeexplore.ieee.org/document/10149509/Above-ground biomass (AGB)forest biomass<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$L$</tex-math> </inline-formula> </named-content>-bandmachine learningneural networkspassive microwaves (PMWs) |
| spellingShingle | Julio Cesar Salazar-Neira Arnaud Mialon Philippe Richaume Stephane Mermoz Yann H. Kerr Alexandre Bouvet Thuy Le Toan Simon Boitard Nemesio J. Rodriguez-Fernandez Above-Ground Biomass Estimation Based on Multi-Angular <italic>L</italic>-Band Measurements of Brightness Temperatures IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Above-ground biomass (AGB) forest biomass <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$L$</tex-math> </inline-formula> </named-content>-band machine learning neural networks passive microwaves (PMWs) |
| title | Above-Ground Biomass Estimation Based on Multi-Angular <italic>L</italic>-Band Measurements of Brightness Temperatures |
| title_full | Above-Ground Biomass Estimation Based on Multi-Angular <italic>L</italic>-Band Measurements of Brightness Temperatures |
| title_fullStr | Above-Ground Biomass Estimation Based on Multi-Angular <italic>L</italic>-Band Measurements of Brightness Temperatures |
| title_full_unstemmed | Above-Ground Biomass Estimation Based on Multi-Angular <italic>L</italic>-Band Measurements of Brightness Temperatures |
| title_short | Above-Ground Biomass Estimation Based on Multi-Angular <italic>L</italic>-Band Measurements of Brightness Temperatures |
| title_sort | above ground biomass estimation based on multi angular italic l italic band measurements of brightness temperatures |
| topic | Above-ground biomass (AGB) forest biomass <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$L$</tex-math> </inline-formula> </named-content>-band machine learning neural networks passive microwaves (PMWs) |
| url | https://ieeexplore.ieee.org/document/10149509/ |
| work_keys_str_mv | AT juliocesarsalazarneira abovegroundbiomassestimationbasedonmultiangularitaliclitalicbandmeasurementsofbrightnesstemperatures AT arnaudmialon abovegroundbiomassestimationbasedonmultiangularitaliclitalicbandmeasurementsofbrightnesstemperatures AT philipperichaume abovegroundbiomassestimationbasedonmultiangularitaliclitalicbandmeasurementsofbrightnesstemperatures AT stephanemermoz abovegroundbiomassestimationbasedonmultiangularitaliclitalicbandmeasurementsofbrightnesstemperatures AT yannhkerr abovegroundbiomassestimationbasedonmultiangularitaliclitalicbandmeasurementsofbrightnesstemperatures AT alexandrebouvet abovegroundbiomassestimationbasedonmultiangularitaliclitalicbandmeasurementsofbrightnesstemperatures AT thuyletoan abovegroundbiomassestimationbasedonmultiangularitaliclitalicbandmeasurementsofbrightnesstemperatures AT simonboitard abovegroundbiomassestimationbasedonmultiangularitaliclitalicbandmeasurementsofbrightnesstemperatures AT nemesiojrodriguezfernandez abovegroundbiomassestimationbasedonmultiangularitaliclitalicbandmeasurementsofbrightnesstemperatures |