YL
Y. Lu
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1
Surface heat fluxes are vital to hydrological and environmental studies, but mapping them accurately over a large area remains a problem. In this study, brightness temperature (TB) observations or soil moisture retrievals from the NASA Soil Moisture Active Passive (SMAP) mission and land surface temperature (LST) product from the Geostationary Operational Environmental Satellite (GOES) are assimilated together into a coupled water and heat transfer model to improve surface heat flux estimates. A particle filter is used to assimilate SMAP data, while a particle smoothing method is adopted to assimilate GOES LST time series, correcting for both systematic biases via parameter updating and for short-term error via state updating. One experiment assimilates SMAP TB at horizontal polarization and GOES LST, a second experiment assimilates SMAP TB at vertical polarization and GOES LST, and a third experiment assimilates SMAP soil moisture retrievals along with GOES LST. The aim is to examine if the assimilation of physically consistent TB and LST observations could yield improved surface heat flux estimates. It is demonstrated that all three assimilation experiments improved flux estimates compared to a no-assimilation case. Assimilating TB data tends to produce smaller bias in soil moisture estimates compared to assimilating soil moisture retrievals, but the estimates are influenced by the respective bias correction approaches. Despite the differences in soil moisture estimates, the flux estimates from different assimilation experiments are in general very similar.
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Surface heat fluxes are vital to hydrological and environmental studies, but mapping them accurately over a large area remains a problem. In this study, brightness temperature (TB) observations or soil moisture retrievals from the NASA Soil Moisture Active Passive (SMAP) mission and land surface temperature (LST) product from the Geostationary Operational Environmental Satellite (GOES) are assimilated together into a coupled water and heat transfer model to improve surface heat flux estimates. A particle filter is used to assimilate SMAP data, while a particle smoothing method is adopted to assimilate GOES LST time series, correcting for both systematic biases via parameter updating and for short-term error via state updating. One experiment assimilates SMAP TB at horizontal polarization and GOES LST, a second experiment assimilates SMAP TB at vertical polarization and GOES LST, and a third experiment assimilates SMAP soil moisture retrievals along with GOES LST. The aim is to examine if the assimilation of physically consistent TB and LST observations could yield improved surface heat flux estimates. It is demonstrated that all three assimilation experiments improved flux estimates compared to a no-assimilation case. Assimilating TB data tends to produce smaller bias in soil moisture estimates compared to assimilating soil moisture retrievals, but the estimates are influenced by the respective bias correction approaches. Despite the differences in soil moisture estimates, the flux estimates from different assimilation experiments are in general very similar.
Impact of soil moisture data resolution on soil moisture and surface heat flux estimates through data assimilation
A case study in the Southern Great Plains
The spatial heterogeneity and temporal variation of soil moisture and surface heat fluxes are key to many geophysical and environmental studies. It has been demonstrated that they can be mapped by assimilating soil thermal and wetness information into surface energy balance models. The aim of this work is to determine whether enhancing the spatial resolution or temporal sampling frequency of soil moisture data could improve soil moisture or surface heat flux estimates. Two experiments are conducted in an area mainly covered by grassland, and land surface temperature (LST) observations from the Geostationary Operational Environmental Satellite (GOES) mission are assimilated together with either an enhanced L-band passive soil moisture product (9 km, 2-3 days) from the Soil Moisture Active Passive (SMAP) mission or a merged product (36 km, quasi-daily) from the SMAP and the Soil Moisture Ocean Salinity (SMOS) mission. The results suggest that the availability of soil moisture observations is increased by 41% after merging data from the SMAP and the SMOS missions. A comparison with results from a previous study that assimilated a coarser SMAP soil moisture product (36 km, 2-3 days) suggests that enhancing the temporal sampling frequency of soil moisture observations leads to improved soil moisture estimates at both the surface and root zone, and the largest improvement is seen in the bias metric (0.008 and 0.007m 3 m -3 on average at the surface and root zone, respectively). Enhancing the spatial resolution, however, does not significantly improve soil moisture estimates, particularly at the surface. Surface heat flux estimates from assimilating soil moisture data of different spatial or temporal resolutions are very similar.
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The spatial heterogeneity and temporal variation of soil moisture and surface heat fluxes are key to many geophysical and environmental studies. It has been demonstrated that they can be mapped by assimilating soil thermal and wetness information into surface energy balance models. The aim of this work is to determine whether enhancing the spatial resolution or temporal sampling frequency of soil moisture data could improve soil moisture or surface heat flux estimates. Two experiments are conducted in an area mainly covered by grassland, and land surface temperature (LST) observations from the Geostationary Operational Environmental Satellite (GOES) mission are assimilated together with either an enhanced L-band passive soil moisture product (9 km, 2-3 days) from the Soil Moisture Active Passive (SMAP) mission or a merged product (36 km, quasi-daily) from the SMAP and the Soil Moisture Ocean Salinity (SMOS) mission. The results suggest that the availability of soil moisture observations is increased by 41% after merging data from the SMAP and the SMOS missions. A comparison with results from a previous study that assimilated a coarser SMAP soil moisture product (36 km, 2-3 days) suggests that enhancing the temporal sampling frequency of soil moisture observations leads to improved soil moisture estimates at both the surface and root zone, and the largest improvement is seen in the bias metric (0.008 and 0.007m 3 m -3 on average at the surface and root zone, respectively). Enhancing the spatial resolution, however, does not significantly improve soil moisture estimates, particularly at the surface. Surface heat flux estimates from assimilating soil moisture data of different spatial or temporal resolutions are very similar.
Estimating Surface Heat Fluxes Using Temperature and Wetness Information
A Particle Data Assimilation Framework
Surface heat fluxes (latent and sensible heat over the land surface) play a key role in the land-atmosphere interaction, and their spatial pattern as well as temporal evolution are vital to the terrestrial water cycle and surface energy balance. Ideally, we want to have accurate estimates of spatially distributed and temporally continuous fluxes. However, this cannot be achieved through interpolation of point measurements because of the limited number of flux stations and the high heterogeneity of fluxes, nor can this be done using large scale monitoring platforms such as remote sensing, since fluxes lack a unique signature that can be detected by satellites. Given the fact that surface heat fluxes are closely related to the thermal and wetness condition of the land surface, which are available from remote sensing instruments, this PhD research proposes a methodology to improve flux estimates by assimilating land surface temperature (LST) and soil wetness information into a coupled water and heat transfer model. The goal is to acquire accurate flux estimates over a large area using a simple model and a small suite of input data...
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Surface heat fluxes (latent and sensible heat over the land surface) play a key role in the land-atmosphere interaction, and their spatial pattern as well as temporal evolution are vital to the terrestrial water cycle and surface energy balance. Ideally, we want to have accurate estimates of spatially distributed and temporally continuous fluxes. However, this cannot be achieved through interpolation of point measurements because of the limited number of flux stations and the high heterogeneity of fluxes, nor can this be done using large scale monitoring platforms such as remote sensing, since fluxes lack a unique signature that can be detected by satellites. Given the fact that surface heat fluxes are closely related to the thermal and wetness condition of the land surface, which are available from remote sensing instruments, this PhD research proposes a methodology to improve flux estimates by assimilating land surface temperature (LST) and soil wetness information into a coupled water and heat transfer model. The goal is to acquire accurate flux estimates over a large area using a simple model and a small suite of input data...
Surface heat fluxes play a crucial role in the surface energy and water balance. In situ measurements are costly and difficult, and large-scale flux mapping is hindered by surface heterogeneity. Previous studies have demonstrated that surface heat fluxes can be estimated by assimilating land surface temperature (LST) and soil moisture to determine two key parameters: a neutral bulk heat transfer coefficient (CHN) and an evaporative fraction (EF). Here a methodology is proposed to estimate surface heat fluxes by assimilating Soil Moisture Active Passive (SMAP) soil moisture data and Geostationary Operational Environmental Satellite (GOES) LST data into a dual-source (DS) model using a hybrid particle assimilation strategy. SMAP soil moisture data are assimilated using a particle filter (PF), and GOES LST data are assimilated using an adaptive particle batch smoother (APBS) to account for the large gap in the spatial and temporal resolution. The methodology is implemented in an area in the U.S. Southern Great Plains. Assessment against in situ observations suggests that soil moisture and LST estimates are in better agreement with observations after assimilation. The RMSD for 30 min (daytime) flux estimates is reduced by 6.3% (8.7%) and 31.6% (37%) for H and LE on average. Comparison against a LST-only and a soil moisture-only assimilation case suggests that despite the coarse resolution, assimilating SMAP soil moisture data is not only beneficial but also crucial for successful and robust flux estimation, particularly when the uncertainties in the model estimates are large.
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Surface heat fluxes play a crucial role in the surface energy and water balance. In situ measurements are costly and difficult, and large-scale flux mapping is hindered by surface heterogeneity. Previous studies have demonstrated that surface heat fluxes can be estimated by assimilating land surface temperature (LST) and soil moisture to determine two key parameters: a neutral bulk heat transfer coefficient (CHN) and an evaporative fraction (EF). Here a methodology is proposed to estimate surface heat fluxes by assimilating Soil Moisture Active Passive (SMAP) soil moisture data and Geostationary Operational Environmental Satellite (GOES) LST data into a dual-source (DS) model using a hybrid particle assimilation strategy. SMAP soil moisture data are assimilated using a particle filter (PF), and GOES LST data are assimilated using an adaptive particle batch smoother (APBS) to account for the large gap in the spatial and temporal resolution. The methodology is implemented in an area in the U.S. Southern Great Plains. Assessment against in situ observations suggests that soil moisture and LST estimates are in better agreement with observations after assimilation. The RMSD for 30 min (daytime) flux estimates is reduced by 6.3% (8.7%) and 31.6% (37%) for H and LE on average. Comparison against a LST-only and a soil moisture-only assimilation case suggests that despite the coarse resolution, assimilating SMAP soil moisture data is not only beneficial but also crucial for successful and robust flux estimation, particularly when the uncertainties in the model estimates are large.
Surface heat fluxes interact with the overlying atmosphere and play a crucial role in meteorology, hydrology, and climate change studies, but in situ observations are costly and difficult. It has been demonstrated that surface heat fluxes can be estimated from assimilation of land surface temperature (LST). One approach is to estimate a neutral bulk heat transfer coefficient (CHN) to scale the sum of turbulent heat fluxes, and an evaporative fraction (EF) that represents the partitioning between fluxes. Here the newly developed particle batch smoother (PBS) is implemented. The PBS makes no assumptions about the prior distributions and is therefore well-suited for non-Gaussian processes. It is also particularly advantageous for parameter estimation by tracking the entire prior distribution of parameters using Monte Carlo sampling. To improve the flux estimation on wet or densely vegetated surfaces, a simple soil moisture scheme is introduced to further constrain EF, and soil moisture observations are assimilated simultaneously. This methodology is implemented with the FIFE 1987 and 1988 data sets. Validation against observed fluxes indicates that assimilating LST using the PBS significantly improves the flux estimates at both daily and half-hourly timescales. When soil moisture is assimilated, the estimated EFs become more accurate, particularly when the surface heat flux partitioning is energy-limited. The feasibility of extending the methodology to use remote sensing observations is tested by limiting the number of LST observations. Results show that flux estimates are greatly improved after assimilating soil moisture, particularly when LST observations are sparse.
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Surface heat fluxes interact with the overlying atmosphere and play a crucial role in meteorology, hydrology, and climate change studies, but in situ observations are costly and difficult. It has been demonstrated that surface heat fluxes can be estimated from assimilation of land surface temperature (LST). One approach is to estimate a neutral bulk heat transfer coefficient (CHN) to scale the sum of turbulent heat fluxes, and an evaporative fraction (EF) that represents the partitioning between fluxes. Here the newly developed particle batch smoother (PBS) is implemented. The PBS makes no assumptions about the prior distributions and is therefore well-suited for non-Gaussian processes. It is also particularly advantageous for parameter estimation by tracking the entire prior distribution of parameters using Monte Carlo sampling. To improve the flux estimation on wet or densely vegetated surfaces, a simple soil moisture scheme is introduced to further constrain EF, and soil moisture observations are assimilated simultaneously. This methodology is implemented with the FIFE 1987 and 1988 data sets. Validation against observed fluxes indicates that assimilating LST using the PBS significantly improves the flux estimates at both daily and half-hourly timescales. When soil moisture is assimilated, the estimated EFs become more accurate, particularly when the surface heat flux partitioning is energy-limited. The feasibility of extending the methodology to use remote sensing observations is tested by limiting the number of LST observations. Results show that flux estimates are greatly improved after assimilating soil moisture, particularly when LST observations are sparse.