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 The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations

2011 ◽  
Vol 15 (10) ◽  
pp. 3135-3151 ◽  
Author(s):  
R. M. Parinussa ◽  
T. R. H. Holmes ◽  
M. T. Yilmaz ◽  
W. T. Crow
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Ka Band ◽  
L Band ◽  
Evaluation Techniques ◽  
Soil Moisture Anomaly

Abstract. For several years passive microwave observations have been used to retrieve soil moisture from the Earth's surface. Low frequency observations have the most sensitivity to soil moisture, therefore the current Soil Moisture and Ocean Salinity (SMOS) and future Soil Moisture Active and Passive (SMAP) satellite missions observe the Earth's surface in the L-band frequency. In the past, several satellite sensors such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and WindSat have been used to retrieve surface soil moisture using multi-channel observations obtained at higher microwave frequencies. While AMSR-E and WindSat lack an L-band channel, they are able to leverage multi-channel microwave observations to estimate additional land surface parameters. In particular, the availability of Ka-band observations allows AMSR-E and WindSat to obtain coincident surface temperature estimates required for the retrieval of surface soil moisture. In contrast, SMOS and SMAP carry only a single frequency radiometer and therefore lack an instrument suited to estimate the physical temperature of the Earth. Instead, soil moisture algorithms from these new generation satellites rely on ancillary sources of surface temperature (e.g. re-analysis or near real time data from weather prediction centres). A consequence of relying on such ancillary data is the need for temporal and spatial interpolation, which may introduce uncertainties. Here, two newly-developed, large-scale soil moisture evaluation techniques, the triple collocation (TC) approach and the Rvalue data assimilation approach, are applied to quantify the global-scale impact of replacing Ka-band based surface temperature retrievals with Modern Era Retrospective-analysis for Research and Applications (MERRA) surface temperature output on the accuracy of WindSat and AMSR-E based surface soil moisture retrievals. Results demonstrate that under sparsely vegetated conditions, the use of MERRA land surface temperature instead of Ka-band radiometric land surface temperature leads to a relative decrease in skill (on average 9.7%) of soil moisture anomaly estimates. However the situation is reversed for highly vegetated conditions where soil moisture anomaly estimates show a relative increase in skill (on average 13.7%) when using MERRA land surface temperature. In addition, a pre-processing technique to shift phase of the modelled surface temperature is shown to generally enhance the value of MERRA surface temperature estimates for soil moisture retrieval. Finally, a very high correlation (R2 = 0.95) and consistency between the two evaluation techniques lends further credibility to the obtained results.

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

2020 ◽  
Vol 24 (4) ◽  
pp. 1781-1803
Author(s):  
Bouchra Ait Hssaine ◽  
Olivier Merlin ◽  
Jamal Ezzahar ◽  
Nitu Ojha ◽  
Salah Er-Raki ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Energy Balance ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Surface Soil ◽  
Heat Fluxes ◽  
Sensible Heat ◽  
Surface Soil Moisture

Abstract. Thermal-based two-source energy balance modeling is essential to estimate the land evapotranspiration (ET) in a wide range of spatial and temporal scales. However, the use of thermal-derived land surface temperature (LST) is not sufficient to simultaneously constrain both soil and vegetation flux components. Therefore, assumptions (about either soil or vegetation fluxes) are commonly required. To avoid such assumptions, an energy balance model, TSEB-SM, was recently developed by Ait Hssaine et al. (2018b) in order to consider the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc) normally used. While TSEB-SM has been successfully tested using in situ measurements, this paper represents its first evaluation in real life using 1 km resolution satellite data, comprised of MODIS (MODerate resolution Imaging Spectroradiometer) for LST and fc data and 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations. The approach is applied during a 4-year period (2014–2018) over a rainfed wheat field in the Tensift basin, central Morocco. The field used was seeded for the 2014–2015 (S1), 2016–2017 (S2) and 2017–2018 (S3) agricultural seasons, while it remained unploughed (as bare soil) during the 2015–2016 (B1) agricultural season. The classical TSEB model, which is driven only by LST and fc data, significantly overestimates latent heat fluxes (LE) and underestimates sensible heat fluxes (H) for the four seasons. The overall mean bias values are 119, 94, 128 and 181 W m−2 for LE and −104, −71, −128 and −181 W m−2 for H, for S1, S2, S3 and B1, respectively. Meanwhile, when using TSEB-SM (SM and LST combined data), these errors are significantly reduced, resulting in mean bias values estimated as 39, 4, 7 and 62 W m−2 for LE and −10, 24, 7, and −59 W m−2 for H, for S1, S2, S3 and B1, respectively. Consequently, this finding confirms again the robustness of the TSEB-SM in estimating latent/sensible heat fluxes at a large scale by using readily available satellite data. In addition, the TSEB-SM approach has the original feature to allow for calibration of its main parameters (soil resistance and Priestley–Taylor coefficient) from satellite data uniquely, without relying either on in situ measurements or on a priori parameter values.

scholarly journals A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

Climate ◽  
2016 ◽  
Vol 4 (4) ◽  
pp. 50 ◽  
Author(s):  
Robert Parinussa ◽  
Richard de Jeu ◽  
Robin van der Schalie ◽  
Wade Crow ◽  
Fangni Lei ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Global Approach ◽  
Surface Soil Moisture

scholarly journals The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations

2011 ◽  
Vol 8 (4) ◽  
pp. 6683-6719 ◽  
Author(s):  
R. M. Parinussa ◽  
T. R. H. Holmes ◽  
W. T. Crow
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Passive Microwave ◽  
Surface Soil Moisture ◽  
Ka Band ◽  
L Band ◽  
Soil Moisture Anomaly

Abstract. For several years passive microwave observations have been used to retrieve soil moisture from the Earth's surface. Low frequency observations have the most sensitivity to soil moisture, therefore the modern Soil Moisture and Ocean Salinity (SMOS) and future Soil Moisture Active and Passive (SMAP) satellite missions observe the Earth's surface in the L-band frequency. In the past, several satellite sensors such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and Windsat have been used to retrieve surface soil moisture using multi-channel observations obtained at higher microwave frequencies. While AMSR-E and Windsat lack an L-band channel, they are able to leverage multi-channel microwave observations to estimate additional land surface parameters. In particular, the availability of Ka-band observations allows AMSR-E and Windsat to obtain surface temperature estimates required for the retrieval of surface soil moisture. In contrast, SMOS and SMAP carry only a single frequency radiometer. Because of this, ancillary – and potentially less accurate – sources of surface temperature information (e.g. re-analysis data from operational weather prediction centers) must be sought to produce surface soil moisture retrievals. Here, two newly-developed, large-scale soil moisture evaluation techniques, the triple collocation (TC) approach and the R value data assimilation approach, are applied to quantify the global-scale impact of replacing Ka-band based surface temperature retrievals with Modern Era Retrospective-analysis for Research and Applications (MERRA) surface temperature predictions on the accuracy of Windsat and AMSR-E surface soil moisture retrievals. Results demonstrate that under sparsely vegetated conditions, the use of Ka-band radiometric land surface temperature leads to better soil moisture anomaly estimates compared to those retrieved using MERRA land surface temperature predictions. However the situation is reversed for highly vegetated conditions where soil moisture anomaly estimates retrieved using MERRA land surface temperature are superior. In addition, the surface temperature phase shifting approach is shown to generally enhance the value of MERRA surface temperature estimates for soil moisture retrieval. Finally, a high degree of consistency is noted between evaluation results produced by the TC and Rvalue soil moisture verification approaches.

Retrieving China’s surface soil moisture and land surface temperature using AMSR-E brightness temperatures

2014 ◽  
Vol 5 (7) ◽  
pp. 662-671 ◽  
Author(s):  
Xiuzhi Chen ◽  
Yongxian Su ◽  
Yong Li ◽  
Liusheng Han ◽  
Jishan Liao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Brightness Temperatures

scholarly journals Evapotranspiration and evaporation/transpiration partitioning with dual source energy balance models in agricultural lands

2018 ◽  
Vol 380 ◽  
pp. 17-22 ◽  
Author(s):  
Gilles Boulet ◽  
Emilie Delogu ◽  
Sameh Saadi ◽  
Wafa Chebbi ◽  
Albert Olioso ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Stress ◽  
Energy Balance ◽  
Surface Temperature ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Stress Levels ◽  
Dual Source ◽  
Energy Balance Models

Abstract. EvapoTranspiration (ET) is an important component of the water cycle, especially in semi-arid lands. Its quantification is crucial for a sustainable management of scarce water resources. A way to quantify ET is to exploit the available surface temperature data from remote sensing as a signature of the surface energy balance, including the latent heat flux. Remotely sensed energy balance models enable to estimate stress levels and, in turn, the water status of most continental surfaces. The evaporation and transpiration components of ET are also just as important in agricultural water management and ecosystem health monitoring. Single temperatures can be used with dual source energy balance models but rely on specific assumptions on raw levels of plant water stress to get both components out of a single source of information. Additional information from remote sensing data are thus required, either something specifically related to evaporation (such as surface water content) or transpiration (such as PRI or fluorescence). This works evaluates the SPARSE dual source energy balance model ability to compute not only total ET, but also water stress and transpiration/evaporation components. First, the theoretical limits of the ET component retrieval are assessed through a simulation experiment using both retrieval and prescribed modes of SPARSE with the sole surface temperature. A similar work is performed with an additional constraint, the topsoil surface soil moisture level, showing the significant improvement on the retrieval. Then, a flux dataset acquired over rainfed wheat is used to check the robustness of both stress levels and ET retrievals. In particular, retrieval of the evaporation and transpiration components is assessed in both conditions (forcing by the sole temperature or the combination of temperature and soil moisture). In our example, there is no significant difference in the performance of the total ET retrieval, since the evaporation rate retrieved from the sole surface temperature is already fairly close to the one we can reconstruct from observed surface soil moisture time series, but current work is underway to test it over other plots.

scholarly journals Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

2017 ◽  
Author(s):  
Peter J. Shellito ◽  
Eric E. Small
Keyword(s):  
Soil Moisture ◽  
Soil Texture ◽  
Vegetation Cover ◽  
Land Surface ◽  
Surface Soil ◽  
Surface Model ◽  
Soil Drying ◽  
Potential Evaporation ◽  
Surface Soil Moisture ◽  
Drying Rates

Abstract. Drydown periods that follow precipitation events provide an opportunity to assess the mechanisms by which soil moisture dissipates from the land surface. We use SMAP (Soil Moisture Active Passive) observations and Noah simulations from drydown periods to quantify the role of soil moisture, potential evaporation, vegetation cover, and soil texture on soil drying rates. Rates are determined using finite differences over intervals of 1 to 3 days. In the Noah model, the drying rates are a good approximation of direct soil evaporation rates. Data cover the domain of the North American Land Data Assimilation System phase 2 and span the first 1.8 years of SMAP's operation. Drying of surface soil moisture observed by SMAP is faster than that simulated by Noah. SMAP drying is fastest when surface soil moisture levels are high, potential evaporation is high, and when vegetation cover is low. Soil texture plays a minor role in SMAP drying rates. Noah simulations show similar responses to soil moisture and potential evaporation, but vegetation has a minimal effect and soil texture has a much larger effect compared to SMAP. When drying rates are normalized by potential evaporation, SMAP observations and Noah simulations both show that increases in vegetation cover lead to decreases in evaporative efficiency from the surface soil. However, the magnitude of this effect simulated by Noah is much weaker than that determined from SMAP observations.

scholarly journals Spatiotemporal Impacts of Urban Land Use/Land Cover Changes on Land Surface Temperature: A Comparative Study of Damascus and Aleppo (Syria)

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1037
Author(s):  
Mohamed Ali Mohamed
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Land Use Land Cover ◽  
Lulc Changes ◽  
Increasing Trend ◽  
The Mean

Monitoring the impact of changes in land use/land cover (LULC) and land surface temperature (LST) is of great importance in environmental and urban studies. In this context, this study aimed to analyze the dynamics of LULC and its impact on the spatiotemporal variation of the LST in the two largest urban cities in Syria, Damascus, and Aleppo. To achieve this, LULC changes, normalized difference vegetation index (NDVI), and LST were calculated from multi-temporal Landsat data for the period 2010 to 2018. The study revealed significant changes in LULC, which were represented by a decrease in agricultural land and green areas and an increase in bare areas in both cities. In addition, built-up areas decreased in Aleppo and increased in Damascus during the study period. The temporal and spatial variation of the LST and its distribution pattern was closely related to the effect of changes in LULC as well as to land use conditions in each city. This effect was greater in Aleppo than in Damascus, where Aleppo recorded a higher increase in the mean LST, by about 2 °C, than in Damascus, where it was associated with greater degradation and loss of vegetation cover. In general, there was an increasing trend in the minimum and maximum LST as well as an increasing trend in the mean LST in both cities. The negative linear relationship between LST and NDVI confirms that vegetation cover can help reduce LST in both cities. This study can draw the attention of relevant departments to pay more attention to mitigating the negative impact of LULC changes in order to limit the increase in LST.

scholarly journals Estimate soil moisture using trapezoidal relationship between remotely sensed land surface temperature and vegetation index

2010 ◽  
Vol 7 (6) ◽  
pp. 8703-8740 ◽  
Author(s):  
W. Wang ◽  
D. Huang ◽  
X.-G. Wang ◽  
Y.-R. Liu ◽  
F. Zhou
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Surface Soil ◽  
Remotely Sensed ◽  
Surface Soil Moisture ◽  
Antecedent Precipitation ◽  
Walnut Gulch ◽  
Semi Arid Region

Abstract. The trapezoidal relationship between surface temperature (Ts) and vegetation index (VI) was used to estimate soil moisture in the present study. An iterative algorithm is proposed to estimate the vertices of the Ts~VI trapezoid theoretically for each grid, and then WDI is calculated for each grid using MODIS remotely sensed measurements of surface temperature and enhanced vegetation index (EVI). The capability of using WDI based on Ts~VI trapezoid to estimate soil moisture is evaluated using soil moisture observations and antecedent precipitation in the Walnut Gulch Experimental Watershed (WGEW) in Arizona, USA. The result shows that, Ts~VI trapezoid based WDI can well capture temporal variation in surface soil moisture, but the capability of detecting spatial variation is poor for such a semi-arid region as WGEW.

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