Articles | Volume 12, issue 7
https://doi.org/10.5194/gmd-12-3119-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-3119-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
22 Jul 2019
Development and technical paper |
| 22 Jul 2019
| 22 Jul 2019
Comparison of different sequential assimilation algorithms for satellite-derived leaf area index using the Data Assimilation Research Testbed (version Lanai)
Xiao-Lu Ling
Institute for Climate and Global Change Research and School of
Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Joint International Research Laboratory of Atmospheric and Earth
System Sciences of Ministry of Education, Nanjing 210023, China
Department of Geological Sciences, John A. and Katherine G. Jackson School of Geosciences, University of Texas at Austin, Austin, TX 78705, USA
Cong-Bin Fu
CORRESPONDING AUTHOR
Institute for Climate and Global Change Research and School of
Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Joint International Research Laboratory of Atmospheric and Earth
System Sciences of Ministry of Education, Nanjing 210023, China
Department of Geological Sciences, John A. and Katherine G. Jackson School of Geosciences, University of Texas at Austin, Austin, TX 78705, USA
Wei-Dong Guo
Institute for Climate and Global Change Research and School of
Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Joint International Research Laboratory of Atmospheric and Earth
System Sciences of Ministry of Education, Nanjing 210023, China
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Cited
16 citations as recorded by crossref.
- Generating a Spatio-Temporal Complete 30 m Leaf Area Index from Field and Remote Sensing Data H. Zhou et al. 10.3390/rs12152394
- Within-season crop yield prediction by a multi-model ensemble with integrated data assimilation H. Zare et al. 10.1016/j.fcr.2024.109293
- A Comparison of Land Surface Phenology in the Northern Hemisphere Derived from Satellite Remote Sensing and the Community Land Model X. Li et al. 10.1175/JHM-D-21-0169.1
- High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, China C. Wang et al. 10.3390/rs16050764
- Assimilation of Remotely Sensed Leaf Area Index for Improving Land Surface Simulation Performance at a Global Scale X. Ling et al. 10.1109/JSTARS.2024.3388006
- Combining Crop Modeling with Remote Sensing Data Using a Particle Filtering Technique to Produce Real-Time Forecasts of Winter Wheat Yields under Uncertain Boundary Conditions H. Zare et al. 10.3390/rs14061360
- Simulating carbon and water fluxes using a coupled process-based terrestrial biosphere model and joint assimilation of leaf area index and surface soil moisture S. Li et al. 10.5194/hess-26-6311-2022
- Hydrological trends captured by assimilating GRACE total water storage data into the CLM5-BGC model H. Chi et al. 10.1016/j.jhydrol.2023.130527
- Assimilation of passive microwave vegetation optical depth in LDAS-Monde: a case study over the continental USA A. Mucia et al. 10.5194/bg-19-2557-2022
- Benefit of incorporating GLASS remote sensing vegetation products in improving Noah-MP land surface temperature simulations on the Tibetan Plateau Q. He et al. 10.1016/j.srs.2023.100115
- Quantifying and Reducing Uncertainty in Global Carbon Cycle Predictions: Lessons and Perspectives From 15 Years of Data Assimilation Studies With the ORCHIDEE Terrestrial Biosphere Model N. MacBean et al. 10.1029/2021GB007177
- Multiscale Assimilation of Sentinel and Landsat Data for Soil Moisture and Leaf Area Index Predictions Using an Ensemble-Kalman-Filter-Based Assimilation Approach in a Heterogeneous Ecosystem N. Montaldo et al. 10.3390/rs14143458
- Assimilation of NDVI data in a land surface – Vegetation model for leaf area index predictions in a tree-grass ecosystem N. Montaldo et al. 10.1080/17538947.2023.2259226
- An ensemble square root filter for the joint assimilation of surface soil moisture and leaf area index within the Land Data Assimilation System LDAS-Monde: application over the Euro-Mediterranean region B. Bonan et al. 10.5194/hess-24-325-2020
- Role of remotely sensed leaf area index assimilation in eco-hydrologic processes in different ecosystems over East Asia with Community Land Model version 4.5 – Biogeochemistry H. Seo & Y. Kim 10.1016/j.jhydrol.2021.125957
- Sub-Daily Natural CO2 Flux Simulation Based on Satellite Data: Diurnal and Seasonal Pattern Comparisons to Anthropogenic CO2 Emissions in the Greater Tokyo Area Q. Wang et al. 10.3390/rs13112037
15 citations as recorded by crossref.
- Generating a Spatio-Temporal Complete 30 m Leaf Area Index from Field and Remote Sensing Data H. Zhou et al. 10.3390/rs12152394
- Within-season crop yield prediction by a multi-model ensemble with integrated data assimilation H. Zare et al. 10.1016/j.fcr.2024.109293
- A Comparison of Land Surface Phenology in the Northern Hemisphere Derived from Satellite Remote Sensing and the Community Land Model X. Li et al. 10.1175/JHM-D-21-0169.1
- High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, China C. Wang et al. 10.3390/rs16050764
- Assimilation of Remotely Sensed Leaf Area Index for Improving Land Surface Simulation Performance at a Global Scale X. Ling et al. 10.1109/JSTARS.2024.3388006
- Combining Crop Modeling with Remote Sensing Data Using a Particle Filtering Technique to Produce Real-Time Forecasts of Winter Wheat Yields under Uncertain Boundary Conditions H. Zare et al. 10.3390/rs14061360
- Simulating carbon and water fluxes using a coupled process-based terrestrial biosphere model and joint assimilation of leaf area index and surface soil moisture S. Li et al. 10.5194/hess-26-6311-2022
- Hydrological trends captured by assimilating GRACE total water storage data into the CLM5-BGC model H. Chi et al. 10.1016/j.jhydrol.2023.130527
- Assimilation of passive microwave vegetation optical depth in LDAS-Monde: a case study over the continental USA A. Mucia et al. 10.5194/bg-19-2557-2022
- Benefit of incorporating GLASS remote sensing vegetation products in improving Noah-MP land surface temperature simulations on the Tibetan Plateau Q. He et al. 10.1016/j.srs.2023.100115
- Quantifying and Reducing Uncertainty in Global Carbon Cycle Predictions: Lessons and Perspectives From 15 Years of Data Assimilation Studies With the ORCHIDEE Terrestrial Biosphere Model N. MacBean et al. 10.1029/2021GB007177
- Multiscale Assimilation of Sentinel and Landsat Data for Soil Moisture and Leaf Area Index Predictions Using an Ensemble-Kalman-Filter-Based Assimilation Approach in a Heterogeneous Ecosystem N. Montaldo et al. 10.3390/rs14143458
- Assimilation of NDVI data in a land surface – Vegetation model for leaf area index predictions in a tree-grass ecosystem N. Montaldo et al. 10.1080/17538947.2023.2259226
- An ensemble square root filter for the joint assimilation of surface soil moisture and leaf area index within the Land Data Assimilation System LDAS-Monde: application over the Euro-Mediterranean region B. Bonan et al. 10.5194/hess-24-325-2020
- Role of remotely sensed leaf area index assimilation in eco-hydrologic processes in different ecosystems over East Asia with Community Land Model version 4.5 – Biogeochemistry H. Seo & Y. Kim 10.1016/j.jhydrol.2021.125957
Latest update: 19 Nov 2024
Short summary
Observation and simulation can provide the temporal and spatial variation of vegetation characteristics, while they are not satisfactory for understanding the mechanism of the exchange between ecosystems and atmosphere. Data assimilation (DA) can combine the observation and models via mathematical statistical analysis. Results show that the ensemble adjust Kalman filter (EAKF) is the optimal algorithm. In addition, models perform better when the DA accepts a higher proportion of observations.
Observation and simulation can provide the temporal and spatial variation of vegetation...
