scholarly journals Calibrating an evapotranspiration model using radiometric surface temperature, vegetation cover fraction and near-surface soil moisture data

2018 ◽  
Vol 256-257 ◽  
pp. 104-115 ◽  
Author(s):  
Bouchra Ait Hssaine ◽  
Olivier Merlin ◽  
Zoubair Rafi ◽  
Jamal Ezzahar ◽  
Lionel Jarlan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Vegetation Cover ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Soil Moisture Data

scholarly journals An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data

2019 ◽  
Author(s):  
Bouchra Ait Hssaine ◽  
Olivier Merlin ◽  
Jamal Ezzahar ◽  
Nitu Ojha ◽  
Salah Er-raki ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Energy Balance ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Wide Range ◽  
The Mean

Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1 km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014–2018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014–2018). The field was seeded for the 2014–2015 (S1), 2016–2017 (S2) and 2017–2018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015–2016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated αPT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved αPT remains at a mostly constant value (∼ 0.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181 W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62 W/m2 for S1, S2, S3 and B1 respectively.

scholarly journals Influence of cracking clays on satellite estimated and model simulated soil moisture

2010 ◽  
Vol 14 (6) ◽  
pp. 979-990 ◽  
Author(s):  
Y. Y. Liu ◽  
J. P. Evans ◽  
M. F. McCabe ◽  
R. A. M. de Jeu ◽  
A. I. J. M. van Dijk ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Soil Properties ◽  
Surface Soil ◽  
Clay Soils ◽  
Soil Types ◽  
Soil Porosity ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Community Land Model

Abstract. Vertisols are clay soils that are common in the monsoonal and dry warm regions of the world. One of the characteristics of these soil types is to form deep cracks during periods of extended dry, resulting in significant variation of the soil and hydrologic properties. Understanding the influence of these varying soil properties on the hydrological behavior of the system is of considerable interest, particularly in the retrieval or simulation of soil moisture. In this study we compare surface soil moisture (θ in m3 m−3) retrievals from AMSR-E using the VUA-NASA (Vrije Universiteit Amsterdam in collaboration with NASA) algorithm with simulations from the Community Land Model (CLM) over vertisol regions of mainland Australia. For the three-year period examined here (2003–2005), both products display reasonable agreement during wet periods. During dry periods however, AMSR-E retrieved near surface soil moisture falls below values for surrounding non-clay soils, while CLM simulations are higher. CLM θ are also higher than AMSR-E and their difference keeps increasing throughout these dry periods. To identify the possible causes for these discrepancies, the impacts of land use, topography, soil properties and surface temperature used in the AMSR-E algorithm, together with vegetation density and rainfall patterns, were investigated. However these do not explain the observed θ responses. Qualitative analysis of the retrieval model suggests that the most likely reason for the low AMSR-E θ is the increase in soil porosity and surface roughness resulting from cracking of the soil. To quantitatively identify the role of each factor, more in situ measurements of soil properties that can represent different stages of cracking need to be collected. CLM does not simulate the behavior of cracking soils, including the additional loss of moisture from the soil continuum during drying and the infiltration into cracks during rainfall events, which results in overestimated θ when cracks are present. The hydrological influence of soil physical changes are expected to propagate through the modeled system, such that modeled infiltration, evaporation, surface temperature, surface runoff and groundwater recharge should be interpreted with caution over these soil types when cracks might be present. Introducing temporally dynamic roughness and soil porosity into retrieval algorithms and adding a "cracking clay" module into models are expected to improve the representation of vertisol hydrology.

scholarly journals Improving Near-Surface Short-Range Weather Forecasts Using Strongly Coupled Land–Atmosphere Data Assimilation with GSI-EnKF

2020 ◽  
Vol 148 (7) ◽  
pp. 2863-2888
Author(s):  
Liao-Fan Lin ◽  
Zhaoxia Pu
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Strongly Coupled ◽  
Soil Moisture Data ◽  
Control State

Abstract Strongly coupled land–atmosphere data assimilation has not yet been implemented into operational numerical weather prediction (NWP) systems. Up to now, upper-air measurements have been assimilated mainly in atmospheric analyses, while land and near-surface data have been assimilated mainly into land surface models. Thus, this study aims to explore the benefits of assimilating atmospheric and land surface observations within the framework of strongly coupled data assimilation. Specifically, we added soil moisture as a control state within the ensemble Kalman filter (EnKF)-based Gridpoint Statistical Interpolation (GSI) and conducted a series of numerical experiments through the assimilation of 2-m temperature/humidity and in situ surface soil moisture data along with conventional atmospheric measurements such as radiosondes into the Weather Research and Forecasting (WRF) Model with the Noah land surface model. The verification against in situ measurements and analyses show that compared to the assimilation of conventional data, adding soil moisture as a control state and assimilating 2-m humidity can bring additional benefits to analyses and forecasts. The impact of assimilating 2-m temperature (surface soil moisture) data is positive mainly on the temperature (soil moisture) analyses but on average marginal for other variables. On average, below 750 hPa, verification against the NCEP analysis indicates that the respective RMSE reduction in the forecasts of temperature and humidity is 5% and 2% for assimilating conventional data; 10% and 5% for including soil moisture as a control state; and 16% and 11% for simultaneously adding soil moisture as a control state and assimilating 2-m humidity data.

scholarly journals Analysis of spatial variability of near-surface soil moisture to increase rainfall-runoff modelling accuracy in SW Hungary

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
P. Hegedüs ◽  
S. Czigány ◽  
E. Pirkhoffer ◽  
L. Balatonyi ◽  
R. Hickey
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Watershed Scale ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Moisture Sensor ◽  
Soil Moisture Sensor ◽  
Soil Moisture Data ◽  
Data Points ◽  
Monitoring Stations

AbstractBetween September 5, 2008 and September 5, 2009, near-surface soil moisture time series were collected in the northern part of a 1.7 km2 watershed in SWHungary at 14 monitoring locations using a portable TDR-300 soil moisture sensor. The objectives of this study are to increase the accuracy of soil moisture measurement at watershed scale, to improve flood forecasting accuracy, and to optimize soil moisture sensor density.According to our results, in 10 of 13 cases, a strong correlation exists between the measured soil moisture data of Station 5 and all other monitoring stations; Station 5 is considered representative for the entire watershed. Logically, the selection of the location of the representative measurement point(s) is essential for obtaining representative and accurate soil moisture values for the given watershed. This could be done by (i) employing monitoring stations of higher number at the exploratory phase of the monitoring, (ii) mapping soil physical properties at watershed scale, and (iii) running cross-relational statistical analyses on the obtained data.Our findings indicate that increasing the number of soil moisture data points available for interpolation increases the accuracy of watershed-scale soil moisture estimation. The data set used for interpolation (and estimation of mean antecedent soil moisture values) could be improved (thus, having a higher number of data points) by selecting points of similar properties to the measurement points from the DEM and soil databases. By using a higher number of data points for interpolation, both interpolation accuracy and spatial resolution have increased for the measured soil moisture values for the Pósa Valley.

scholarly journals An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

Sensors ◽  
2017 ◽  
Vol 17 (6) ◽  
pp. 1390 ◽  
Author(s):  
Jialin Zhang ◽  
Xiuhong Li ◽  
Rongjin Yang ◽  
Qiang Liu ◽  
Long Zhao ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Soil Moisture ◽  
Sensor Networks ◽  
Surface Soil ◽  
Wireless Sensor ◽  
Kriging Method ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Soil Moisture Data

scholarly journals Characteristics of Background Error Covariance of Soil Moisture and Atmospheric States in Strongly Coupled Land–Atmosphere Data Assimilation

2018 ◽  
Vol 57 (11) ◽  
pp. 2507-2529 ◽  
Author(s):  
Liao-Fan Lin ◽  
Zhaoxia Pu
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Surface Soil ◽  
Forecast Errors ◽  
Surface Soil Moisture ◽  
Background Error ◽  
Near Surface ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Temperature And Humidity

AbstractThis study characterizes the spatial and temporal variability of the background error covariance between the land surface soil moisture and atmospheric states for a better understanding of the potentials of assimilating satellite soil moisture data under a framework of strongly coupled land–atmosphere data assimilation. The study uses the Noah land surface model coupled with the Weather Research and Forecasting (WRF) Model and the National Meteorological Center (NMC) method for computing the land–atmosphere background error covariance from 2015 to 2017 over the contiguous United States. The results show that the forecast errors in top-10-cm soil moisture and near-surface air potential temperature and specific humidity are correlated and relatively large during the daytime in the summer. The magnitude of the error correlation between these three states is comparable. For example, in July, the error correlation averaged over all day- and nighttime samples is −0.13 for near-surface temperature and humidity, −0.20 for surface soil moisture and near-surface temperature, and 0.15 for surface soil moisture and near-surface humidity. During the summer, the forecast errors in surface soil moisture are correlated with those of atmospheric states up to the sigma pressure level of 0.9 (approximately 900 hPa for a sea level location) with domain-mean correlations of −0.15 and 0.1 for temperature and humidity, respectively. The results suggest that assimilation of satellite soil moisture data could provide cross-variable impacts comparable to those assimilating conventional near-surface temperature and humidity data. The forecast errors of soil moisture are only marginally correlated with those of the winds.

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