Articles | Volume 15, issue 1
https://doi.org/10.5194/essd-15-345-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-345-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
19 Jan 2023
Data description paper |
| 19 Jan 2023
| 19 Jan 2023
AnisoVeg: anisotropy and nadir-normalized MODIS multi-angle implementation atmospheric correction (MAIAC) datasets for satellite vegetation studies in South America
Center for Tropical Research, Institute of the Environment and
Sustainability, University of California, Los Angeles, Los Angeles, CA 90095, USA
NASA Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São José dos Campos, SP, 12227-010, Brazil
RSATE – Remote Sensing Applied to Tropical Environments Group, Manchester, UK
Lênio Soares Galvão
Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São José dos Campos, SP, 12227-010, Brazil
Fabien Hubert Wagner
Center for Tropical Research, Institute of the Environment and
Sustainability, University of California, Los Angeles, Los Angeles, CA 90095, USA
NASA Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Yhasmin Mendes de Moura
RSATE – Remote Sensing Applied to Tropical Environments Group, Manchester, UK
Centre for Landscape and Climate Research, School of Geography, Geology, and the Environment, University of Leicester, Leicester, UK
Arcmor LLP, London, UK
Nathan Gonçalves
Department of Forestry, College of Agriculture
& Natural Resources, Michigan State University, East Lansing, MI, USA
Yujie Wang
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Joint Center for Earth Systems Technology, University of Maryland
Baltimore County, 1000 Hilltop Circle, Baltimore, MD, USA
Alexei Lyapustin
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Yan Yang
Center for Tropical Research, Institute of the Environment and
Sustainability, University of California, Los Angeles, Los Angeles, CA 90095, USA
Sassan Saatchi
Center for Tropical Research, Institute of the Environment and
Sustainability, University of California, Los Angeles, Los Angeles, CA 90095, USA
NASA Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Luiz Eduardo Oliveira Cruz Aragão
Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São José dos Campos, SP, 12227-010, Brazil
Geography, College of Life and Environmental Sciences, University of Exeter, Exeter EX44RJ, UK
Related authors
No articles found.
Lei Zhu, Philippe Ciais, Yitong Yao, Daniel Goll, Sebastiaan Luyssaert, Isabel Martínez Cano, Arthur Fendrich, Laurent Li, Hui Yang, Sassan Saatchi, Ricardo Dalagnol, and Wei Li
Geosci. Model Dev., 18, 4915–4933, https://doi.org/10.5194/gmd-18-4915-2025, https://doi.org/10.5194/gmd-18-4915-2025, 2025
Short summary
Short summary
This study enhances the accuracy of modeling the carbon dynamics of the Amazon rainforest by optimizing key model parameters based on satellite data. Using spatially varying parameters for tree mortality and photosynthesis, we improved predictions of biomass, productivity, and tree mortality. Our findings highlight the critical role of wood density and water availability in forest processes, offering insights to use in refining global carbon cycle models.
Xin Xi, Jun Wang, Zhendong Lu, Andrew M. Sayer, Jaehwa Lee, Robert C. Levy, Yujie Wang, Alexei Lyapustin, Hongqing Liu, Istvan Laszlo, Changwoo Ahn, Omar Torres, Sabur Abdullaev, James Limbacher, and Ralph A. Kahn
Atmos. Chem. Phys., 25, 7403–7429, https://doi.org/10.5194/acp-25-7403-2025, https://doi.org/10.5194/acp-25-7403-2025, 2025
Short summary
Short summary
The Aralkum Desert is challenging for aerosol retrieval due to its bright, heterogeneous, and dynamic surfaces and the lack of in situ constraints on aerosol properties. The performance and consistency of satellite algorithms in observing Aralkum-generated saline dust remain unknown. This study compares multisensor UVAI (ultraviolet aerosol index), AOD (aerosol optical depth), and ALH (aerosol layer height) products and reveals inconsistencies and potential biases over the Aral Sea basin.
Nathan B. Goncalves, Bruce W. Nelson, Adriana Simonetti, and Scott C. Stark
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 191–196, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-191-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-191-2024, 2024
Haihui Zhu, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Chi Li, Jun Meng, Christopher R. Oxford, Xuan Liu, Yanshun Li, Dandan Zhang, Inderjeet Singh, and Alexei Lyapustin
Atmos. Chem. Phys., 24, 11565–11584, https://doi.org/10.5194/acp-24-11565-2024, https://doi.org/10.5194/acp-24-11565-2024, 2024
Short summary
Short summary
Ambient fine particulate matter (PM2.5) contributes to 4 million deaths globally each year. Satellite remote sensing of aerosol optical depth (AOD), coupled with a simulated PM2.5–AOD relationship (η), can provide global PM2.5 estimations. This study aims to understand the spatial patterns and driving factors of η to guide future measurement and modeling efforts. We quantified η globally and regionally and found that its spatial variation is strongly influenced by aerosol composition.
Myungje Choi, Alexei Lyapustin, Gregory L. Schuster, Sujung Go, Yujie Wang, Sergey Korkin, Ralph Kahn, Jeffrey S. Reid, Edward J. Hyer, Thomas F. Eck, Mian Chin, David J. Diner, Olga Kalashnikova, Oleg Dubovik, Jhoon Kim, and Hans Moosmüller
Atmos. Chem. Phys., 24, 10543–10565, https://doi.org/10.5194/acp-24-10543-2024, https://doi.org/10.5194/acp-24-10543-2024, 2024
Short summary
Short summary
This paper introduces a retrieval algorithm to estimate two key absorbing components in smoke (black carbon and brown carbon) using DSCOVR EPIC measurements. Our analysis reveals distinct smoke properties, including spectral absorption, layer height, and black carbon and brown carbon, over North America and central Africa. The retrieved smoke properties offer valuable observational constraints for modeling radiative forcing and informing health-related studies.
Peter Joyce, Cristina Ruiz Villena, Yahui Huang, Alex Webb, Manuel Gloor, Fabien H. Wagner, Martyn P. Chipperfield, Rocío Barrio Guilló, Chris Wilson, and Hartmut Boesch
Atmos. Meas. Tech., 16, 2627–2640, https://doi.org/10.5194/amt-16-2627-2023, https://doi.org/10.5194/amt-16-2627-2023, 2023
Short summary
Short summary
Methane emissions are responsible for a lot of the warming caused by the greenhouse effect, much of which comes from a small number of point sources. We can identify methane point sources by analysing satellite data, but it requires a lot of time invested by experts and is prone to very high errors. Here, we produce a neural network that can automatically identify methane point sources and estimate the mass of methane that is being released per hour and are able to do so with far smaller errors.
Xavier Ceamanos, Bruno Six, Suman Moparthy, Dominique Carrer, Adèle Georgeot, Josef Gasteiger, Jérôme Riedi, Jean-Luc Attié, Alexei Lyapustin, and Iosif Katsev
Atmos. Meas. Tech., 16, 2575–2599, https://doi.org/10.5194/amt-16-2575-2023, https://doi.org/10.5194/amt-16-2575-2023, 2023
Short summary
Short summary
A new algorithm to retrieve the diurnal evolution of aerosol optical depth over land and ocean from geostationary meteorological satellites is proposed and successfully evaluated with reference ground-based and satellite data. The high-temporal-resolution aerosol observations that are obtained from the EUMETSAT Meteosat Second Generation mission are unprecedented and open the door to studies that cannot be conducted with the once-a-day observations available from low-Earth-orbit satellites.
Shengli Tao, Zurui Ao, Jean-Pierre Wigneron, Sassan Saatchi, Philippe Ciais, Jérôme Chave, Thuy Le Toan, Pierre-Louis Frison, Xiaomei Hu, Chi Chen, Lei Fan, Mengjia Wang, Jiangling Zhu, Xia Zhao, Xiaojun Li, Xiangzhuo Liu, Yanjun Su, Tianyu Hu, Qinghua Guo, Zhiheng Wang, Zhiyao Tang, Yi Y. Liu, and Jingyun Fang
Earth Syst. Sci. Data, 15, 1577–1596, https://doi.org/10.5194/essd-15-1577-2023, https://doi.org/10.5194/essd-15-1577-2023, 2023
Short summary
Short summary
We provide the first long-term (since 1992), high-resolution (8.9 km) satellite radar backscatter data set (LHScat) with a C-band (5.3 GHz) signal dynamic for global lands. LHScat was created by fusing signals from ERS (1992–2001; C-band), QSCAT (1999–2009; Ku-band), and ASCAT (since 2007; C-band). LHScat has been validated against independent ERS-2 signals. It could be used in a variety of studies, such as vegetation monitoring and hydrological modelling.
Gonzalo A. Ferrada, Meng Zhou, Jun Wang, Alexei Lyapustin, Yujie Wang, Saulo R. Freitas, and Gregory R. Carmichael
Geosci. Model Dev., 15, 8085–8109, https://doi.org/10.5194/gmd-15-8085-2022, https://doi.org/10.5194/gmd-15-8085-2022, 2022
Short summary
Short summary
The smoke from fires is composed of different compounds that interact with the atmosphere and can create poor air-quality episodes. Here, we present a new fire inventory based on satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS). We named this inventory the VIIRS-based Fire Emission Inventory (VFEI). Advantages of VFEI are its high resolution (~500 m) and that it provides information for many species. VFEI is publicly available and has provided data since 2012.
Yan Yang, A. Anthony Bloom, Shuang Ma, Paul Levine, Alexander Norton, Nicholas C. Parazoo, John T. Reager, John Worden, Gregory R. Quetin, T. Luke Smallman, Mathew Williams, Liang Xu, and Sassan Saatchi
Geosci. Model Dev., 15, 1789–1802, https://doi.org/10.5194/gmd-15-1789-2022, https://doi.org/10.5194/gmd-15-1789-2022, 2022
Short summary
Short summary
Global carbon and water have large uncertainties that are hard to quantify in current regional and global models. Field observations provide opportunities for better calibration and validation of current modeling of carbon and water. With the unique structure of CARDAMOM, we have utilized the data assimilation capability and designed the benchmarking framework by using field observations in modeling. Results show that data assimilation improves model performance in different aspects.
Sujung Go, Alexei Lyapustin, Gregory L. Schuster, Myungje Choi, Paul Ginoux, Mian Chin, Olga Kalashnikova, Oleg Dubovik, Jhoon Kim, Arlindo da Silva, Brent Holben, and Jeffrey S. Reid
Atmos. Chem. Phys., 22, 1395–1423, https://doi.org/10.5194/acp-22-1395-2022, https://doi.org/10.5194/acp-22-1395-2022, 2022
Short summary
Short summary
This paper presents a retrieval algorithm of iron-oxide species (hematite, goethite) content in the atmosphere from DSCOVR EPIC observations. Our results display variations within the published range of hematite and goethite over the main dust-source regions but show significant seasonal and spatial variability. This implies a single-viewing satellite instrument with UV–visible channels may provide essential information on shortwave dust direct radiative effects for climate modeling.
Xinxin Ye, Pargoal Arab, Ravan Ahmadov, Eric James, Georg A. Grell, Bradley Pierce, Aditya Kumar, Paul Makar, Jack Chen, Didier Davignon, Greg R. Carmichael, Gonzalo Ferrada, Jeff McQueen, Jianping Huang, Rajesh Kumar, Louisa Emmons, Farren L. Herron-Thorpe, Mark Parrington, Richard Engelen, Vincent-Henri Peuch, Arlindo da Silva, Amber Soja, Emily Gargulinski, Elizabeth Wiggins, Johnathan W. Hair, Marta Fenn, Taylor Shingler, Shobha Kondragunta, Alexei Lyapustin, Yujie Wang, Brent Holben, David M. Giles, and Pablo E. Saide
Atmos. Chem. Phys., 21, 14427–14469, https://doi.org/10.5194/acp-21-14427-2021, https://doi.org/10.5194/acp-21-14427-2021, 2021
Short summary
Short summary
Wildfire smoke has crucial impacts on air quality, while uncertainties in the numerical forecasts remain significant. We present an evaluation of 12 real-time forecasting systems. Comparison of predicted smoke emissions suggests a large spread in magnitudes, with temporal patterns deviating from satellite detections. The performance for AOD and surface PM2.5 and their discrepancies highlighted the role of accurately represented spatiotemporal emission profiles in improving smoke forecasts.
Caroline A. Famiglietti, T. Luke Smallman, Paul A. Levine, Sophie Flack-Prain, Gregory R. Quetin, Victoria Meyer, Nicholas C. Parazoo, Stephanie G. Stettz, Yan Yang, Damien Bonal, A. Anthony Bloom, Mathew Williams, and Alexandra G. Konings
Biogeosciences, 18, 2727–2754, https://doi.org/10.5194/bg-18-2727-2021, https://doi.org/10.5194/bg-18-2727-2021, 2021
Short summary
Short summary
Model uncertainty dominates the spread in terrestrial carbon cycle predictions. Efforts to reduce it typically involve adding processes, thereby increasing model complexity. However, if and how model performance scales with complexity is unclear. Using a suite of 16 structurally distinct carbon cycle models, we find that increased complexity only improves skill if parameters are adequately informed. Otherwise, it can degrade skill, and an intermediate-complexity model is optimal.
Cheng Chen, Oleg Dubovik, David Fuertes, Pavel Litvinov, Tatyana Lapyonok, Anton Lopatin, Fabrice Ducos, Yevgeny Derimian, Maurice Herman, Didier Tanré, Lorraine A. Remer, Alexei Lyapustin, Andrew M. Sayer, Robert C. Levy, N. Christina Hsu, Jacques Descloitres, Lei Li, Benjamin Torres, Yana Karol, Milagros Herrera, Marcos Herreras, Michael Aspetsberger, Moritz Wanzenboeck, Lukas Bindreiter, Daniel Marth, Andreas Hangler, and Christian Federspiel
Earth Syst. Sci. Data, 12, 3573–3620, https://doi.org/10.5194/essd-12-3573-2020, https://doi.org/10.5194/essd-12-3573-2020, 2020
Short summary
Short summary
Aerosol products obtained from POLDER/PARASOL processed by the GRASP algorithm have been released. The entire archive of PARASOL/GRASP aerosol products is evaluated against AERONET and compared with MODIS (DT, DB and MAIAC), as well as PARASOL/Operational products. PARASOL/GRASP aerosol products provide spectral 443–1020 nm AOD correlating well with AERONET with a maximum bias of 0.02. Finally, GRASP shows capability to derive detailed spectral properties, including aerosol absorption.
A. Anthony Bloom, Kevin W. Bowman, Junjie Liu, Alexandra G. Konings, John R. Worden, Nicholas C. Parazoo, Victoria Meyer, John T. Reager, Helen M. Worden, Zhe Jiang, Gregory R. Quetin, T. Luke Smallman, Jean-François Exbrayat, Yi Yin, Sassan S. Saatchi, Mathew Williams, and David S. Schimel
Biogeosciences, 17, 6393–6422, https://doi.org/10.5194/bg-17-6393-2020, https://doi.org/10.5194/bg-17-6393-2020, 2020
Short summary
Short summary
We use a model of the 2001–2015 tropical land carbon cycle, with satellite measurements of land and atmospheric carbon, to disentangle lagged and concurrent effects (due to past and concurrent meteorological events, respectively) on annual land–atmosphere carbon exchanges. The variability of lagged effects explains most 2001–2015 inter-annual carbon flux variations. We conclude that concurrent and lagged effects need to be accurately resolved to better predict the world's land carbon sink.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone Alin, Luiz E. O. C. Aragão, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp, Selma Bultan, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Wiley Evans, Liesbeth Florentie, Piers M. Forster, Thomas Gasser, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Luke Gregor, Nicolas Gruber, Ian Harris, Kerstin Hartung, Vanessa Haverd, Richard A. Houghton, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Koji Kadono, Etsushi Kato, Vassilis Kitidis, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Gregg Marland, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Adam J. P. Smith, Adrienne J. Sutton, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Guido van der Werf, Nicolas Vuichard, Anthony P. Walker, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Xu Yue, and Sönke Zaehle
Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
Short summary
Short summary
The Global Carbon Budget 2020 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
J. Doblas, A. Carneiro, Y. Shimabukuro, S. Sant’Anna, and L. Aragão
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W12-2020, 493–498, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-493-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-493-2020, 2020
Nick Schutgens, Andrew M. Sayer, Andreas Heckel, Christina Hsu, Hiren Jethva, Gerrit de Leeuw, Peter J. T. Leonard, Robert C. Levy, Antti Lipponen, Alexei Lyapustin, Peter North, Thomas Popp, Caroline Poulsen, Virginia Sawyer, Larisa Sogacheva, Gareth Thomas, Omar Torres, Yujie Wang, Stefan Kinne, Michael Schulz, and Philip Stier
Atmos. Chem. Phys., 20, 12431–12457, https://doi.org/10.5194/acp-20-12431-2020, https://doi.org/10.5194/acp-20-12431-2020, 2020
Short summary
Short summary
We intercompare 14 different datasets of satellite observations of aerosol. Such measurements are challenging but also provide the best opportunity to globally observe an atmospheric component strongly related to air pollution and climate change. Our study shows that most datasets perform similarly well on a global scale but that locally errors can be quite different. We develop a technique to estimate satellite errors everywhere, even in the absence of surface reference data.
Cited articles
Anderson, L. O., Ribeiro Neto, G., Cunha, A. P., Fonseca, M. G., Mendes de
Moura, Y., Dalagnol, R., Wagner, F. H., and Aragão, L.: Vulnerability of
Amazonian forests to repeated droughts, Philos. T.
Roy. Soc. B, 373, 20170411,
https://doi.org/10.1098/rstb.2017.0411, 2018.
Bhandari, S., Phinn, S., and Gill, T.: Assessing viewing and illumination
geometry effects on the MODIS vegetation index (MOD13Q1) time series:
implications for monitoring phenology and disturbances in forest communities
in Queensland, Australia, Int. J. Remote Sens., 32,
7513–7538, https://doi.org/10.1080/01431161.2010.524675, 2011.
Bi, J., Knyazikhin, Y., Choi, S., Park, T., Barichivich, J., Ciais, P., Fu, R., Ganguly, S., Hall, F., Hilker, T., Huete, A., Jones, M., Kimball, J., Lyapustin, A. I., Mõttus, M., Nemani, R. R., Piao, S., Poulter, B., Saleska, S. R., Saatchi, S. S., Xu, L., Zhou, L., and Myneni, R. B.: Sunlight mediated seasonality
in canopy structure and photosynthetic activity of Amazonian rainforests,
Environ. Res. Lett., 10, 064014,
https://doi.org/10.1088/1748-9326/10/6/064014, 2015.
Bontempo, E., Dalagnol, R., Ponzoni, F., and Valeriano, D.: Adjustments to
SIF aid the interpretation of drought responses at the caatinga of Northeast
Brazil, Remote Sens., 12, 1–29, https://doi.org/10.3390/rs12193264,
2020.
Chen, J. M., Liu, J., Leblanc, S. G., Lacaze, R., and Roujean, J. L.:
Multi-angular optical remote sensing for assessing vegetation structure and
carbon absorption, Remote Sens. Environ., 84, 516–525,
https://doi.org/10.1016/S0034-4257(02)00150-5, 2003.
Chen, J. M., Menges, C. H., and Leblanc, S. G.: Global mapping of foliage
clumping index using multi-angular satellite data, Remote Sens.
Environ., 97, 447–457, https://doi.org/10.1016/j.rse.2005.05.003,
2005.
Chen, W. and Cao, C.: Topographic correction-based retrieval of leaf area
index in mountain areas, J. Mountain Sci., 9, 166–174,
https://doi.org/10.1007/s11629-012-2248-2, 2012.
Dalagnol, R.: Back scattering data of AnisoVeg: Anisotropy and
Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in
South America, Zenodo [data set], https://doi.org/10.5281/zenodo.6040300,
2022a.
Dalagnol, R.: Forward scattering data of AnisoVeg: Anisotropy and
Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in
South America, Zenodo, https://doi.org/10.5281/zenodo.6048785 [data set],
2022b.
Dalagnol, R.: EVI Nadir layer from the AnisoVeg dataset, https://code.earthengine.google.com/?asset=projects/anisoveg/assets/evi_nadir (last access: 16 January 2023), 2022c.
Dalagnol, R.: EVI Anisotropy layer from the AnisoVeg dataset, https://code.earthengine.google.com/?asset=projects/anisoveg/assets/evi_anisotropy (last access: 16 January 2023), 2022d.
Dalagnol, R. and Wagner, F. H.: maiac_processing: Script and
functions to process daily MODIS MAIAC data to BRDF-corrected 16-day and
monthly mosaic composites (Version 1.0), Zenodo [code],
https://doi.org/10.5281/zenodo.6561350, 2022.
Dalagnol, R., Wagner, F. H., Galvão, L. S., Nelson, B. W., and Aragão, L. E. O. E. C. D.: Life cycle of bamboo in the southwestern Amazon and its relation to fire events, Biogeosciences, 15, 6087–6104, https://doi.org/10.5194/bg-15-6087-2018, 2018.
Dalagnol, R., Galvão, L. S., Wagner, F. H., Moura, Y. M., Gonçalves,
N., Wang, Y., Lyapustin, A., Yang, Y., Saatchi, S., and Aragão, L. E. O. C.:
AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite
vegetation studies in South America (Version v1),
Zenodo [data set], https://doi.org/10.5281/zenodo.3878879, 2022.
de Moura, Y. M., Hilker, T., Lyapustin, A. I., Galvão, L. S., dos
Santos, J. R., Anderson, L. O., de Sousa, C. H. R., and Arai, E.:
Seasonality and drought effects of Amazonian forests observed from
multi-angle satellite data, Remote Sens. Environ., 171, 278–290,
https://doi.org/10.1016/j.rse.2015.10.015, 2015.
de Moura, Y. M., Hilker, T., Gonçalves, F. G., Galvão, L. S., dos
Santos, J. R., Lyapustin, A., Maeda, E. E., and de Jesus Silva, C. V.:
Scaling estimates of vegetation structure in Amazonian tropical forests
using multi-angle MODIS observations, Int. J. Appl. Earth
Obs., 52, 580–590,
https://doi.org/10.1016/j.jag.2016.07.017, 2016.
de Sousa, C. H. R., Hilker, T., Waring, R., de Moura, Y. M., and Lyapustin,
A.: Progress in remote sensing of photosynthetic activity over the amazon
basin, Remote Sens., 9, 1–23, https://doi.org/10.3390/rs9010048, 2017.
Diner, D. J., Braswell, B. H., Davies, R., Gobron, N., Hu, J., Jin, Y., Kahn, R. A., Knyazikhin, Y., Loeb, N., Muller, J. P., Nolin, A. W., Pinty, B., Schaaf, C. B., Seiz, G., and Stroeve, J.: The value of multiangle measurements for retrieving structurally and radiatively consistent properties of clouds, aerosols, and surfaces, Remote Sens. Environ., 97, 495–518, https://doi.org/10.1016/j.rse.2005.06.006, 2005.
Durieux, L., Toledo Machado, L. A., and Laurent, H.: The impact of
deforestation on cloud cover over the Amazon arc of deforestation, Remote
Sens. Environ., 86, 132–140,
https://doi.org/10.1016/S0034-4257(03)00095-6, 2003.
Fonseca, L. D. M., Dalagnol, R., Malhi, Y., Rifai, S. W., Costa, G. B.,
Silva, T. S. F., Da Rocha, H. R., Tavares, I. B., and Borma, L. S.:
Phenology and Seasonal Ecosystem Productivity in an Amazonian Floodplain
Forest, Remote Sens., 11, 1530, https://doi.org/10.3390/rs11131530,
2019.
Foody, G. M. and Curran, P. J.: Estimation of Tropical Forest Extent and
Regenerative Stage Using Remotely Sensed Data, J. Biogeogr.,
21, 223, https://doi.org/10.2307/2845527, 1994.
Galvão, L. S., Ponzoni, F. J., Epiphanio, J. C. N., Rudorff, B. F. T., and Formaggio, A. R.: Sun and view angle effects on NDVI determination of land cover types in the Brazilian Amazon region with hyperspectral data, Int. J. Remote Sen., 25, 1861–1879, https://doi.org/10.1080/01431160310001598908, 2004.
Galvão, L. S., dos Santos, J. R., Roberts, D. A., Breunig, F. M.,
Toomey, M., and de Moura, Y. M.: On intra-annual EVI variability in the dry
season of tropical forest: A case study with MODIS and hyperspectral data,
Remote Sens. Environ., 115, 2350–2359,
https://doi.org/10.1016/j.rse.2011.04.035, 2011.
Galvão, L. S., Breunig, F. M., Santos, J. R. dos, and de Moura, Y. M.:
View-illumination effects on hyperspectral vegetation indices in the
Amazonian tropical forest, Int. J. Appl. Earth
Obs., 21, 291–300,
https://doi.org/10.1016/j.jag.2012.07.005, 2013.
Galvão, L. S., Breunig, F. M., Teles, T. S., Gaida, W., and Balbinot,
R.: Investigation of terrain illumination effects on vegetation indices and
VI-derived phenological metrics in subtropical deciduous forests, GIScience
and Remote Sensing, 53, 360–381,
https://doi.org/10.1080/15481603.2015.1134140, 2016.
Gao, F., Schaaf, C. B., Strahler, A. H., Jin, Y., and Li, X.: Detecting
vegetation structure using a kernel-based BRDF model, Remote Sens.
Environ., 86, 198–205, https://doi.org/10.1016/S0034-4257(03)00100-7,
2003.
Gobron, N., Pinty, B., Verstraete, M. M., Widlowski, J. L., and Diner, D. J.: Uniqueness of multiangular measurements – Part II: Joint retrieval of vegetation structure and photosynthetic activity from MISR, IEEE T. Geosci. Remote, 40, 1574–1592, https://doi.org/10.1109/TGRS.2002.801147, 2002.
Gonçalves, N. B., Lopes, A. P., Dalagnol, R., Wu, J., Pinho, D. M., and
Nelson, B. W.: Both near-surface and satellite remote sensing confirm
drought legacy effect on tropical forest leaf phenology after 2015/2016 ENSO
drought, Remote Sens. Environ., 237, 111489,
https://doi.org/10.1016/j.rse.2019.111489, 2020.
Hilker, T., Lyapustin, A. I., Tucker, C. J., Sellers, P. J., Hall, F. G.,
and Wang, Y.: Remote sensing of tropical ecosystems: Atmospheric correction
and cloud masking matter, Remote Sens. Environ., 127, 370–384,
https://doi.org/10.1016/j.rse.2012.08.035, 2012.
Hilker, T., Lyapustin, A. I., Tucker, C. J., Hall, F. G., Myneni, R. B.,
Wang, Y., Bi, J., De Moura, Y. M., and Sellers, P. J.: Vegetation dynamics
and rainfall sensitivity of the Amazon, P. Natl. Acad. Sci. USA, 111, 16041–16046,
https://doi.org/10.1073/pnas.1404870111, 2014.
Hilker, T., Galvão, L. S., Aragão, L. E. O. C., de Moura, Y. M., do
Amaral, C. H., Lyapustin, A. I., Wu, J., Albert, L. P., Ferreira, M. J.,
Anderson, L. O., dos Santos, V. A. H. F., Prohaska, N., Tribuzy, E., Barbosa
Ceron, J. V., Saleska, S. R., Wang, Y., de Carvalho Gonçalves, J. F., de
Oliveira Junior, R. C., Cardoso Rodrigues, J. V. F., and Garcia, M. N.:
Vegetation chlorophyll estimates in the Amazon from multi-angle MODIS
observations and canopy reflectance model, Int. J. Appl.
Earth Obs., 58, 278–287,
https://doi.org/10.1016/j.jag.2017.01.014, 2017.
Huang, W., Zhang, L., Furumi, S., Muramatsu, K., Daigo, M., and Li, P.: Topographic effects on estimating net primary productivity of green coniferous forest in complex terrain using Landsat data: a case study of Yoshino Mountain, Japan, Int. J. Remote Sens., 31, 2941–2957, https://doi.org/10.1080/01431160903140829, 2010.
Huete, A., Didan, K., Miura, T., Rodriguez, E., Gao, X., and Ferreira, L.:
Overview of the radiometric and biophysical performance of the MODIS
vegetation indices, Remote Sens. Environ., 83, 195–213,
https://doi.org/10.1016/S0034-4257(02)00096-2, 2002.
Lacaze, R., Chen, J. M., Roujean, J. L., and Leblanc, S. G.: Retrieval of
vegetation clumping index using hot spot signatures measured by POLDER
instrument, Remote Sens. Environ., 79, 84–95,
https://doi.org/10.1016/S0034-4257(01)00241-3, 2002.
Liesenberg, V., Galvão, L. S., and Ponzoni, F. J.: Variations in
reflectance with seasonality and viewing geometry: Implications for
classification of Brazilian savanna physiognomies with MISR/Terra data,
Remote Sens. Environ., 107, 276–286,
https://doi.org/10.1016/j.rse.2006.03.018, 2007.
Lyapustin, A. and Wang, Y.: MCD19A1 MODIS/Terra+Aqua Land Surface BRF
Daily L2G Global 500m, 1km and 5km SIN Grid V006, NASA EOSDIS
Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD19A1.006, 2018.
Lyapustin, A., Martonchik, J., Wang, Y., Laszlo, I., and Korkin, S.:
Multiangle implementation of atmospheric correction (MAIAC): 1. Radiative
transfer basis and look-up tables, J. Geophys. Res.-Atmos., 116, D03210, https://doi.org/10.1029/2010JD014985, 2011.
Lyapustin, A., Wang, Y., Korkin, S., and Huang, D.: MODIS Collection 6 MAIAC algorithm, Atmos. Meas. Tech., 11, 5741–5765, https://doi.org/10.5194/amt-11-5741-2018, 2018.
Lyapustin, A., Zhao, F., and Wang, Y.: A Comparison of Multi-Angle
Implementation of Atmospheric Correction and MOD09 Daily Surface Reflectance
Products From MODIS, Front. Remote Sens., 2, 1–15,
https://doi.org/10.3389/frsen.2021.712093, 2021.
Lyapustin, A. I., Wang, Y., Laszlo, I., Hilker, T., Hall, G. F., Sellers, P.
J., Tucker, C. J., and Korkin, S. V.: Multi-angle implementation of
atmospheric correction for MODIS (MAIAC): 3. Atmospheric correction, Remote
Sens. Environ., 127, 385–393,
https://doi.org/10.1016/j.rse.2012.09.002, 2012.
Morton, D. C., Nagol, J., Carabajal, C. C., Rosette, J., Palace, M., Cook,
B. D., Vermote, E. F., Harding, D. J., and North, P. R. J.: Amazon forests
maintain consistent canopy structure and greenness during the dry season,
Nature, 506, 221–224, https://doi.org/10.1038/nature13006, 2014.
Pocewicz, A., Vierling, L. A., Lentile, L. B., and Smith, R.: View angle
effects on relationships between MISR vegetation indices and leaf area index
in a recently burned ponderosa pine forest, Remote Sens. Environ.,
107, 322–333, https://doi.org/10.1016/j.rse.2006.06.019, 2007.
R Core Team: R: A Language and Environment for Statistical Computing
(v3.3.1), Vol. 1, Issue C, R Foundation for Statistical Computing,
https://www.r-project.org/ (last access: 16 January 2023), 2016.
Rouse, J. W., Hass, R. H., Schell, J. A., and Deering, D. W.: Monitoring
Vegetation Systems in the Great Plains with ERTS, Third Earth Resources
Technology Satellite-1 Symposium, 1, 301–317, 1974.
Roy, S.: samapriya/geeup: geeup: Simple CLI for Earth Engine Uploads (0.5.8), Zenodo [code], https://doi.org/10.5281/zenodo.7047124, 2022.
Saatchi, S., Buermann, W., ter Steege, H., Mori, S., and Smith, T. B.: Modeling distribution of Amazonian tree species and diversity using remote sensing measurements, Remote Sens. Environ., 112, 2000–2017, https://doi.org/10.1016/j.rse.2008.01.008, 2008.
Saleska, S. R., Wu, J., Guan, K., Araujo, A. C., Huete, A., Nobre, A. D.,
and Restrepo-Coupe, N.: Dry-season greening of Amazon forests, Nature,
531, E4–E5, https://doi.org/10.1038/nature16457, 2016.
Sandmeier, S., Müller, C., Hosgood, B., and Andreoli, G.: Physical
mechanisms in hyperspectral BRDF data of grass and watercress, Remote
Sens. Environ., 66, 222–233,
https://doi.org/10.1016/S0034-4257(98)00060-1, 1998.
Santoro, M. and Cartus, O.: ESA Biomass Climate Change Initiative
(Biomass_cci): Global datasets of forest above-ground biomass
for the years 2010, 2017 and 2018 (No. 2), Centre for Environmental Data
Analysis [data set], https://doi.org/10.5285/84403d09cef3485883158f4df2989b0c, 2021.
Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T., Strugnell, N. C., Zhang, X., Jin, Y., Muller, J.-P., Lewis, P., Barnsley, M., Hobson, P., Disney, M., Roberts, G., Dunderdale, M., Doll, C., Robert, P., Hu, B. L., Shunlin, P., Jeffrey, L., and Roy, D.: First operational BRDF, albedo nadir
reflectance products from MODIS, Remote Sens. Environ., 83, 135–148,
2002.
Sharma, R. C.: Vegetation structure index (Vsi): Retrieving vegetation structural information from multi-angular satellite remote sensing, J. Imaging, 7, 84, https://doi.org/10.3390/jimaging7050084, 2021.
Sims, D. A., Rahman, A. F., Vermote, E. F., and Jiang, Z.: Seasonal and inter-annual variation in view angle effects on MODIS vegetation indices at three forest sites, Remote Sens. Environ., 115, 3112–3120, https://doi.org/10.1016/j.rse.2011.06.018, 2011.
Wagner, F. H., Hérault, B., Rossi, V., Hilker, T., Maeda, E. E.,
Sanchez, A., Lyapustin, A. I., Galvão, L. S., Wang, Y., and Aragão,
L. E. O. C.: Climate drivers of the Amazon forest greening, PLoS ONE, 12,
1–15, https://doi.org/10.1371/journal.pone.0180932, 2017.
Wanner, W., Li, X., and Strahler, H.: On the derivation of kernels for
kernel-driven models of bidirectional reflectance, J. Geophys.
Res., 100, 21077, https://doi.org/10.1029/95JD02371, 1995.
Wu, J., Kobayashi, H., Stark, S. C., Meng, R., Guan, K., Tran, N. N., Gao,
S., Yang, W., Restrepo-Coupe, N., Miura, T., Oliviera, R. C., Rogers, A.,
Dye, D. G., Nelson, B. W., Serbin, S. P., Huete, A. R., and Saleska, S. R.:
Biological processes dominate seasonality of remotely sensed canopy
greenness in an Amazon evergreen forest, New Phytologist, 217,
1507–1520, https://doi.org/10.1111/nph.14939, 2018.
Xu, L., Saatchi, S. S., Yang, Y., Myneni, R. B., Frankenberg, C., Chowdhury,
D., and Bi, J.: Satellite observation of tropical forest seasonality:
Spatial patterns of carbon exchange in Amazonia, Environ. Res.
Lett., 10, 084005, https://doi.org/10.1088/1748-9326/10/8/084005, 2015.
Zhang, H., Hagan, D. F. T., Dalagnol, R., and Liu, Y.: Forest Canopy Changes
in the Southern Amazon during the 2019 Fire Season Based on Passive
Microwave and Optical Satellite Observations, Remote Sens., 13, 2238,
https://doi.org/10.3390/rs13122238, 2021.
Short summary
The AnisoVeg dataset brings 22 years of monthly satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for South America at 1 km resolution aimed at vegetation applications. It has nadir-normalized data, which is the most traditional approach to correct satellite data but also unique anisotropy data with strong biophysical meaning, explaining 55 % of Amazon forest height. We expect this dataset to help large-scale estimates of vegetation biomass and carbon.
The AnisoVeg dataset brings 22 years of monthly satellite data from the Moderate Resolution...
Altmetrics
Final-revised paper
Preprint