Estimating surface-soil moisture for retrieving maize leaf-area index from SAR data

2011 ◽  
Vol 37 (1) ◽  
pp. 136-150 ◽  
Author(s):  
Emilie Bériaux ◽  
Sébastien Lambot ◽  
Pierre Defourny
Keyword(s):  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Area Index ◽  
Maize Leaf ◽  
Sar Data

scholarly journals Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France

2014 ◽  
Vol 18 (1) ◽  
pp. 173-192 ◽  
Author(s):  
A. L. Barbu ◽  
J.-C. Calvet ◽  
J.-F. Mahfouf ◽  
S. Lafont
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Surface Soil ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Area Index ◽  
Land Data Assimilation ◽  
The Impact

Abstract. The land monitoring service of the European Copernicus programme has developed a set of satellite-based biogeophysical products, including surface soil moisture (SSM) and leaf area index (LAI). This study investigates the impact of joint assimilation of remotely sensed SSM derived from Advanced Scatterometer (ASCAT) backscatter data and the Copernicus Global Land GEOV1 satellite-based LAI product into the the vegetation growth version of the Interactions between Soil Biosphere Atmosphere (ISBA-A-gs) land surface model within the the externalised surface model (SURFEX) modelling platform of Météo-France. The ASCAT data were bias corrected with respect to the model climatology by using a seasonal-based CDF (Cumulative Distribution Function) matching technique. A multivariate multi-scale land data assimilation system (LDAS) based on the extended Kalman Filter (EKF) is used for monitoring the soil moisture, terrestrial vegetation, surface carbon and energy fluxes across the domain of France at a spatial resolution of 8 km. Each model grid box is divided into a number of land covers, each having its own set of prognostic variables. The filter algorithm is designed to provide a distinct analysis for each land cover while using one observation per grid box. The updated values are aggregated by computing a weighted average. In this study, it is demonstrated that the assimilation scheme works effectively within the ISBA-A-gs model over a four-year period (2008–2011). The EKF is able to extract useful information from the data signal at the grid scale and distribute the root-zone soil moisture and LAI increments throughout the mosaic structure of the model. The impact of the assimilation on the vegetation phenology and on the water and carbon fluxes varies from one season to another. The spring drought of 2011 is an interesting case study of the potential of the assimilation to improve drought monitoring. A comparison between simulated and in situ soil moisture gathered at the twelve SMOSMANIA (Soil Moisture Observing System–Meteorological Automatic Network Integrated Application) stations shows improved anomaly correlations for eight stations.

scholarly journals Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France

2013 ◽  
Vol 10 (7) ◽  
pp. 9057-9103 ◽  
Author(s):  
A. L. Barbu ◽  
J.-C. Calvet ◽  
J.-F. Mahfouf ◽  
S. Lafont
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Surface Soil ◽  
Cumulative Distribution ◽  
Surface Soil Moisture ◽  
Area Index ◽  
Land Data Assimilation ◽  
The Impact

Abstract. The land monitoring service of the European Copernicus programme has developed a set of satellite-based biogeophysical products, including surface soil moisture (SSM) and leaf area index (LAI). This study investigates the impact of joint assimilation of remotely sensed SSM derived from ASCAT backscatter data and the GEOV1 satellite-based LAI into the ISBA-A-gs land surface model within the SURFEX modelling platform of Meteo-France. The ASCAT data were bias corrected with respect to the model climatology by using a seasonal-based CDF (Cumulative Distribution Function) matching technique. A multivariate multi-scale land data assimilation system (LDAS) based on the Extended Kalman Filter (EKF) is used for monitoring the soil moisture, terrestrial vegetation, surface carbon and energy fluxes across the France domain at a spatial resolution of 8 km. Each model grid box is divided in a number of land covers, each having its own set of prognostic variables. The filter algorithm is designed to provide a distinct analysis for each land cover while using one observation per grid box. The updated values are aggregated by computing a weighted average. In this study, it is demonstrated that the assimilation scheme works effectively within the ISBA-A-gs model over a four-year period (2008–2011). The EKF is able to extract useful information from the data signal at the grid scale and to distribute the root-zone soil moisture and LAI increments among the mosaic structure of the model. The impact of the assimilation on the vegetation phenology and on the water and carbon fluxes varies from one season to another. The spring drought of 2011 is an interesting case study showing the potential of the assimilation to improve drought monitoring. A comparison between simulated and in situ soil moisture gathered at the twelve SMOSMANIA stations shows improved anomaly correlations for eight stations.

scholarly journals Assessment of Remotely Sensed Near-Surface Soil Moisture for Distributed Eco-Hydrological Model Implementation

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2613 ◽  
Author(s):  
Carlos Echeverría ◽  
Guiomar Ruiz-Pérez ◽  
Cristina Puertes ◽  
Luis Samaniego ◽  
Brian Barrett ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Surface Soil ◽  
Remotely Sensed ◽  
Validation Period ◽  
Surface Soil Moisture ◽  
Area Index ◽  
Spatio Temporal ◽  
Calibration Approach

The aim of this study was to implement an eco-hydrological distributed model using only remotely sensed information (soil moisture and leaf area index) during the calibration phase. Four soil moisture-based metrics were assessed, and the best alternative was chosen, which was a metric based on the similarity between the principal components that explained at least 95% of the soil moisture variation and the Nash-Sutcliffe Efficiency (NSE) index between simulated and observed surface soil moisture. The selected alternative was compared with a streamflow-based calibration approach. The results showed that the streamflow-based calibration approach, even presenting satisfactory results in the calibration period (NSE = 0.91), performed poorly in the validation period (NSE = 0.47) and Leaf Area Index (LAI) and soil moisture were neither sensitive to the spatio-temporal pattern nor to the spatial correlation in both calibration and validation periods. Hence, the selected soil moisture-based approach showed an acceptable performance in terms of discharges, presenting a negligible decrease in the validation period (ΔNSE = 0.1) and greater sensitivity to the spatio-temporal variables’ spatial representation.

scholarly journals The effect of satellite-derived surface soil moisture and leaf area index land data assimilation on streamflow simulations over France

2017 ◽  
Vol 21 (4) ◽  
pp. 2015-2033 ◽  
Author(s):  
David Fairbairn ◽  
Alina Lavinia Barbu ◽  
Adrien Napoly ◽  
Clément Albergel ◽  
Jean-François Mahfouf ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Land Surface ◽  
River Discharge ◽  
Surface Soil ◽  
Land Surface Model ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Area Index

Abstract. This study evaluates the impact of assimilating surface soil moisture (SSM) and leaf area index (LAI) observations into a land surface model using the SAFRAN–ISBA–MODCOU (SIM) hydrological suite. SIM consists of three stages: (1) an atmospheric reanalysis (SAFRAN) over France, which forces (2) the three-layer ISBA land surface model, which then provides drainage and runoff inputs to (3) the MODCOU hydro-geological model. The drainage and runoff outputs from ISBA are validated by comparing the simulated river discharge from MODCOU with over 500 river-gauge observations over France and with a subset of stations with low-anthropogenic influence, over several years. This study makes use of the A-gs version of ISBA that allows for physiological processes. The atmospheric forcing for the ISBA-A-gs model underestimates direct shortwave and long-wave radiation by approximately 5 % averaged over France. The ISBA-A-gs model also substantially underestimates the grassland LAI compared with satellite retrievals during winter dormancy. These differences result in an underestimation (overestimation) of evapotranspiration (drainage and runoff). The excess runoff flowing into the rivers and aquifers contributes to an overestimation of the SIM river discharge. Two experiments attempted to resolve these problems: (i) a correction of the minimum LAI model parameter for grasslands and (ii) a bias-correction of the model radiative forcing. Two data assimilation experiments were also performed, which are designed to correct random errors in the initial conditions: (iii) the assimilation of LAI observations and (iv) the assimilation of SSM and LAI observations. The data assimilation for (iii) and (iv) was done with a simplified extended Kalman filter (SEKF), which uses finite differences in the observation operator Jacobians to relate the observations to the model variables. Experiments (i) and (ii) improved the median SIM Nash scores by about 9 % and 18 % respectively. Experiment (iii) reduced the LAI phase errors in ISBA-A-gs but had little impact on the discharge Nash efficiency of SIM. In contrast, experiment (iv) resulted in spurious increases in drainage and runoff, which degraded the median discharge Nash efficiency by about 7 %. The poor performance of the SEKF originates from the observation operator Jacobians. These Jacobians are dampened when the soil is saturated and when the vegetation is dormant, which leads to positive biases in drainage and/or runoff and to insufficient corrections during winter, respectively. Possible ways to improve the model are discussed, including a new multi-layer diffusion model and a more realistic response of photosynthesis to temperature in mountainous regions. The data assimilation should be advanced by accounting for model and forcing uncertainties.

Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data

2019 ◽  
Vol 35 (8) ◽  
pp. 905-915 ◽  
Author(s):  
Thota Sivasankar ◽  
Dheeraj Kumar ◽  
Hari Shanker Srivastava ◽  
Parul Patel
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Wheat Leaf ◽  
Sar Data

scholarly journals Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

2020 ◽  
Vol 12 (13) ◽  
pp. 2148 ◽  
Author(s):  
Adnan Rajib ◽  
I Luk Kim ◽  
Heather E. Golden ◽  
Charles R. Lane ◽  
Sujay V. Kumar ◽  
...  
Keyword(s):  
Water Quality ◽  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Dynamics ◽  
Watershed Modeling ◽  
Remotely Sensed ◽  
Area Index ◽  
Daily Streamflow ◽  
Modis Lai

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’s LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

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