Abstract
Advances in Earth Observation (EO) technology, particularly over the last two decades, ... more Abstract
Advances in Earth Observation (EO) technology, particularly over the last two decades, have shown that soil moisture content (SMC) can be measured to some degree or other by all regions of the electromagnetic spectrum, and a variety of techniques have been proposed to facilitate this purpose. In this review we provide a synthesis of the efforts made during the last 20 years or so towards the estimation of surface SMC exploiting EO imagery, with a particular emphasis on retrievals from microwave sensors. Rather than replicating previous overview works, we provide a comprehensive and critical exploration of all the major approaches employed for retrieving SMC in a range of different global ecosystems. In this framework, we consider the newest techniques developed within optical and thermal infrared remote sensing, active and passive microwave domains, as well as assimilation or synergistic approaches. Future trends and prospects of EO for the accurate determination of SMC from space are subject to key challenges, some of which are identified and discussed within. It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments.
Given the importance of SMC in Earth’s land surface interactions and to a large range of applications, one can appreciate that its accurate estimation is critical in addressing key scientific and practical challenges in today’s world such as food security, sustainable planning and management of water resources. The launch of new, more sophisticated satellites strengthens the development of innovative research approaches and scientific inventions that will result in a range of pioneering and ground-breaking advancements in the retrievals of soil moisture from space.
Abstract
This study explored the capability of Support Vector Machines (SVMs) and regularised ke... more Abstract
This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher’s discriminant analysis (rkFDA) machine learning supervised
classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a
heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel) versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877). Both methods outperformed the standard Normalized Difference Water Index (NDWI) thresholding (OA = 94.63, K = 0.818) by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the “not flooded” class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models). This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study offers important information towards exploring the possibilities of the use of such data to map other significant flood events from space in an economically viable way.
Geoscientific Model Development Discussions, Mar 2015
Abstract
This paper describes the validation of the SimSphere SVAT model conducted at different ... more Abstract
This paper describes the validation of the SimSphere SVAT model conducted at different ecosystem types in the USA and Australia. Specific focus was given to examining the models’ ability in predicting Shortwave Incoming Solar Radiation (Rg), Net Radiation (Rnet), Latent Heat (LE), Sensible Heat (H), Air Temperature at 1.3m (Tair 1.3m) and Air Temperature at 50m (Tair 50m). Model predictions were compared against corresponding in situ measurements acquired for a total of 72 selected days of the year 2011 obtained from 8 sites belonging to the AmeriFlux (USA) and OzFlux (Australia) monitoring networks. Selected sites were representative of a variety of environmental, biome and climatic conditions, to allow for the inclusion of contrasting conditions in the model evaluation.
The application of the model confirmed its high capability in representing the multifarious and complex interactions of the Earth system. Comparisons showed a good agreement
between modelled and measured fluxes, especially for the days with smoothed daily flux trends. A good to excellent agreement between the model predictions and the in situ measurements was reported, particularly so for the LE, H, Tair 1.3m and Tair 50m parameters (RMSD= 39.47, 55.06Wm2, 3.23, 3.77 C respectively). A systematic underestimation of Rg and Rnet (RMSD=67.83, 58.69Wm2, MBE= 67.83, 58.69Wm2 respectively) was also found. Highest simulation accuracies were obtained for the open woodland savannah and mulga woodland sites for most of the compared parameters. Very high values of the Nash–Sutcliffe efficiency index were also reported for all parameters ranging from 0.720 to 0.998, suggesting a very good model representation of the observations.
To our knowledge, this study presents the first comprehensive validation of SimSphere, particularly so in USA and Australian ecosystem types. Findings are important and timely, given the rapidly expanding use of this model worldwide both as an educational and research tool. This includes ongoing research by different Space Agencies examining its synergistic use with Earth Observation data towards the development of global operational products.
Abstract
In this present study the ability of the SimSphere Soil Vegetation Atmosphere Transfer ... more Abstract
In this present study the ability of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) model in estimating key parameters characterising land surface interactions was evaluated. Specifically, SimSphere’s performance in predicting Net Radiation (Rnet), Latent Heat (LE), 5 Sensible Heat (H) and Air Temperature (Tair) at 1.3 and 50m was examined. Model simulations were validated by ground-based measurements of
the corresponding parameters for a total of 70 days of the year 2011 from 7 CarboEurope network sites. These included a variety of biomes, environmental and climatic conditions in the models evaluation.Overall, model performance can largely be described as satisfactory for most of the experimental sites and evaluated parameters. For all model parameters compared,
predicted H fluxes consistently obtained the highest agreement to the in-situ data in all ecosystems, with an average RMSD of 55.36Wm2. LE fluxes and Rnet also agreed well with the in-situ data with RSMDs of 62.75 and 64.65Wm2 respectively. A good
15 agreement between modelled and measured LE and H fluxes was found, especially for smoothed daily flux trends. For both Tair 1.3m and Tair 50m a mean RMSD of 4.14 and 3.54 C was reported respectively. This work presents the first all-inclusive evaluation of SimSphere, particularly so in
a European setting. Results of this study contribute decisively towards obtaining a better understanding of the model’s structure and its correspondence to the real world system. Findings also further establish the model’s capability as a useful teaching and research tool in modelling Earth’s land surface interactions. This is of considerable importance in the light of the rapidly expanding use of the model worldwide, including
ongoing research by various Space Agencies examining its synergistic use with Earth Observation data towards the development of operational products at a global scale.
Abstract
In this study the relationships between vegetation regeneration dynamics to topography... more Abstract
In this study the relationships between vegetation regeneration dynamics to topography and burn severity for a Canadian landscape were investigated using freely available Earth Observation (EO) imagery from Landsat TM sensor. The Okanagan Mountain Park, located in the Montane Cordillera Ecozones of Western Canada at which a fire occurred in 2003, was used as a case study. First, vegetation regeneration dynamics were quantified for a period of 8 years following the fire event based on a chronosequence analysis of the Normalized Difference Vegetation Index (NDVI) and the Regeneration Index (RI). The spatio-temporal patterns of post-fire NDVI from each image date were statistically compared to
the pre-fire pattern to determine the extent to which the pre-fire spatial pattern was re-established and also the rate of recovery. Subsequently, the relationships of vegetation regrowth to both topography and burn severity was quantified using a series of additional statistical metrics. Burn severity was derived from the differenced Normalized Burn Ratio (dNBR) index computed from the Landsat TM images. Information
on topography properties of the region was obtained from the ASTER global operational product. NDVI and RI analysis indicated a moderate vegetation recovery to pre-fire patterns, with regeneration to over 60% of the pre-fire levels 8-years after the fire. Regression analysis of pre- and post-fire mean
NDVI exhibited significant re-growth in the first 3 years after the fire with a more gradual return in later years (an increase of 0.400 in R2 by 2006 compared to only an increase of 0.129 for the subsequent 5 years). Re-growth rates appeared to be somewhat higher in north-facing slopes in comparison to south
facing ones. As expected, NDVI decline due to fire was positively correlated with burn severity class, whereas negative correlation was found between damage and regeneration ability (recovery after 3 years = low severity 64%/high severity 58%, recovery after 8 years = low severity 72%/high severity 70%). To our knowledge, this study is one of the few attempting to explore the interrelationships of post-fire vegetation regrowth, topography and burn severity, especially in the case of a single large fire. RI based on control plots provides a valuable tool to quantify fire impact and subsequent vegetation regrowth. Furthermore, indication of burn severity is useful for strategically rehabilitating areas of slow or unsuitable
post-fire vegetation recovery. This study corroborates the significance of EO technology as a successful and cost-effective solution in providing information related to economic and environmental post-fire regeneration assessment.
Abstract
Evapotranspiration (ET) is an important variable in weather system and hydrometeorologi... more Abstract
Evapotranspiration (ET) is an important variable in weather system and hydrometeorological modelling, which is limited for ungauged catchments. In this study, an extensive validation was carried out on the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Evapotranspiration operational product, evaluating its accuracy at selected European sites. Validation was performed through comparisons to in-situ eddy covariance measurements belonging to the CarboEurope IP network. Comparisons were performed for selected cloud-free days with satisfactory Energy Balance Ratio (EBR) in 2011. In total nine sites covering six land covers were used in validating the products’ ET retrieval accuracy. A series of statistical metrics were computed to evaluate the agreement, exploring also the variability of site characteristics and influence of land cover on ET performances. In overall, a good agreement was reported between the satellite-derived ET estimates and the ground measurements (d-index = 0.755, Root Mean Square Deviation (RMSD) = 0.107 mm h-1). A minor negative bias of -0.015 mm h-1 suggested only slight underestimation of the in-situ data. In terms of land cover, highest agreement in ET was reported for the “olive orchards and “open shrubland” sites (d-index = 0.893/0.867, RMSD = 0.041/0.050 mm/h-1). A systematic ET underestimation by SEVIRI was found for all land cover types. Results of this study are largely comparable to previous analogous validation studies of the product. Our findings support the potential value of the SEVIRI ET product for regional to mesoscale studies and practical applications. The latter is of particular importance for water limiting environments such as those found in the Mediterranean basin, as accurate information on ET rates can provide tremendous support in sustainable water resource management as well as policy and decision making.
Abstract
Acquiring information on the spatio-temporal variability of soil moisture is of key imp... more Abstract
Acquiring information on the spatio-temporal variability of soil moisture is of key importance in extending our capability to understand the Earth system’s physical
processes, and is also required in many practical applications. Earth observation (EO) provides a promising avenue to observe the distribution of soil moisture at
different observational scales, with a number of products distributed at present operationally. Validation of such products at a range of climate and environmental conditions across continents is a fundamental step related to their practical use. Various in situ soil moisture ground observational networks have been established globally
providing suitable data for evaluating the accuracy of EO-based soil moisture products. This study aimed at evaluating the accuracy of soil moisture estimates provided from
the Soil Moisture and Ocean Salinity Mission (SMOS) global operational product at test sites from the REMEDHUS International Soil Moisture Network (ISMN) in Spain.
For this purpose, validated observations from in situ ground observations acquired nearly concurrent to SMOS overpass were utilized. Overall, results showed a generally reasonable agreement between the SMOS product and the in situ soil moisture measurements in the 0–5 cm soil moisture layer (root mean square error (RMSE) = 0.116 m3 m−3). An improvement in product accuracy for the overall comparison was shown when days of high radio frequency interference were filtered out (RMSE = 0.110 m3 m−3). Seasonal analysis showed highest agreement during
autumn, followed by summer, winter, and spring seasons. A systematic soil moisture underestimation was also found for the overall comparison and during the four seasons. Overall, the result provides supportive evidence of the potential value of this operational product for meso-scale studies and practical applications.
Abstract
Advances in Earth Observation (EO) technology, particularly over the last two decades, ... more Abstract
Advances in Earth Observation (EO) technology, particularly over the last two decades, have shown that soil moisture content (SMC) can be measured to some degree or other by all regions of the electromagnetic spectrum, and a variety of techniques have been proposed to facilitate this purpose. In this review we provide a synthesis of the efforts made during the last 20 years or so towards the estimation of surface SMC exploiting EO imagery, with a particular emphasis on retrievals from microwave sensors. Rather than replicating previous overview works, we provide a comprehensive and critical exploration of all the major approaches employed for retrieving SMC in a range of different global ecosystems. In this framework, we consider the newest techniques developed within optical and thermal infrared remote sensing, active and passive microwave domains, as well as assimilation or synergistic approaches. Future trends and prospects of EO for the accurate determination of SMC from space are subject to key challenges, some of which are identified and discussed within. It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments.
Given the importance of SMC in Earth’s land surface interactions and to a large range of applications, one can appreciate that its accurate estimation is critical in addressing key scientific and practical challenges in today’s world such as food security, sustainable planning and management of water resources. The launch of new, more sophisticated satellites strengthens the development of innovative research approaches and scientific inventions that will result in a range of pioneering and ground-breaking advancements in the retrievals of soil moisture from space.
Abstract
This study explored the capability of Support Vector Machines (SVMs) and regularised ke... more Abstract
This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher’s discriminant analysis (rkFDA) machine learning supervised
classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a
heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel) versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877). Both methods outperformed the standard Normalized Difference Water Index (NDWI) thresholding (OA = 94.63, K = 0.818) by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the “not flooded” class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models). This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study offers important information towards exploring the possibilities of the use of such data to map other significant flood events from space in an economically viable way.
Geoscientific Model Development Discussions, Mar 2015
Abstract
This paper describes the validation of the SimSphere SVAT model conducted at different ... more Abstract
This paper describes the validation of the SimSphere SVAT model conducted at different ecosystem types in the USA and Australia. Specific focus was given to examining the models’ ability in predicting Shortwave Incoming Solar Radiation (Rg), Net Radiation (Rnet), Latent Heat (LE), Sensible Heat (H), Air Temperature at 1.3m (Tair 1.3m) and Air Temperature at 50m (Tair 50m). Model predictions were compared against corresponding in situ measurements acquired for a total of 72 selected days of the year 2011 obtained from 8 sites belonging to the AmeriFlux (USA) and OzFlux (Australia) monitoring networks. Selected sites were representative of a variety of environmental, biome and climatic conditions, to allow for the inclusion of contrasting conditions in the model evaluation.
The application of the model confirmed its high capability in representing the multifarious and complex interactions of the Earth system. Comparisons showed a good agreement
between modelled and measured fluxes, especially for the days with smoothed daily flux trends. A good to excellent agreement between the model predictions and the in situ measurements was reported, particularly so for the LE, H, Tair 1.3m and Tair 50m parameters (RMSD= 39.47, 55.06Wm2, 3.23, 3.77 C respectively). A systematic underestimation of Rg and Rnet (RMSD=67.83, 58.69Wm2, MBE= 67.83, 58.69Wm2 respectively) was also found. Highest simulation accuracies were obtained for the open woodland savannah and mulga woodland sites for most of the compared parameters. Very high values of the Nash–Sutcliffe efficiency index were also reported for all parameters ranging from 0.720 to 0.998, suggesting a very good model representation of the observations.
To our knowledge, this study presents the first comprehensive validation of SimSphere, particularly so in USA and Australian ecosystem types. Findings are important and timely, given the rapidly expanding use of this model worldwide both as an educational and research tool. This includes ongoing research by different Space Agencies examining its synergistic use with Earth Observation data towards the development of global operational products.
Abstract
In this present study the ability of the SimSphere Soil Vegetation Atmosphere Transfer ... more Abstract
In this present study the ability of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) model in estimating key parameters characterising land surface interactions was evaluated. Specifically, SimSphere’s performance in predicting Net Radiation (Rnet), Latent Heat (LE), 5 Sensible Heat (H) and Air Temperature (Tair) at 1.3 and 50m was examined. Model simulations were validated by ground-based measurements of
the corresponding parameters for a total of 70 days of the year 2011 from 7 CarboEurope network sites. These included a variety of biomes, environmental and climatic conditions in the models evaluation.Overall, model performance can largely be described as satisfactory for most of the experimental sites and evaluated parameters. For all model parameters compared,
predicted H fluxes consistently obtained the highest agreement to the in-situ data in all ecosystems, with an average RMSD of 55.36Wm2. LE fluxes and Rnet also agreed well with the in-situ data with RSMDs of 62.75 and 64.65Wm2 respectively. A good
15 agreement between modelled and measured LE and H fluxes was found, especially for smoothed daily flux trends. For both Tair 1.3m and Tair 50m a mean RMSD of 4.14 and 3.54 C was reported respectively. This work presents the first all-inclusive evaluation of SimSphere, particularly so in
a European setting. Results of this study contribute decisively towards obtaining a better understanding of the model’s structure and its correspondence to the real world system. Findings also further establish the model’s capability as a useful teaching and research tool in modelling Earth’s land surface interactions. This is of considerable importance in the light of the rapidly expanding use of the model worldwide, including
ongoing research by various Space Agencies examining its synergistic use with Earth Observation data towards the development of operational products at a global scale.
Abstract
In this study the relationships between vegetation regeneration dynamics to topography... more Abstract
In this study the relationships between vegetation regeneration dynamics to topography and burn severity for a Canadian landscape were investigated using freely available Earth Observation (EO) imagery from Landsat TM sensor. The Okanagan Mountain Park, located in the Montane Cordillera Ecozones of Western Canada at which a fire occurred in 2003, was used as a case study. First, vegetation regeneration dynamics were quantified for a period of 8 years following the fire event based on a chronosequence analysis of the Normalized Difference Vegetation Index (NDVI) and the Regeneration Index (RI). The spatio-temporal patterns of post-fire NDVI from each image date were statistically compared to
the pre-fire pattern to determine the extent to which the pre-fire spatial pattern was re-established and also the rate of recovery. Subsequently, the relationships of vegetation regrowth to both topography and burn severity was quantified using a series of additional statistical metrics. Burn severity was derived from the differenced Normalized Burn Ratio (dNBR) index computed from the Landsat TM images. Information
on topography properties of the region was obtained from the ASTER global operational product. NDVI and RI analysis indicated a moderate vegetation recovery to pre-fire patterns, with regeneration to over 60% of the pre-fire levels 8-years after the fire. Regression analysis of pre- and post-fire mean
NDVI exhibited significant re-growth in the first 3 years after the fire with a more gradual return in later years (an increase of 0.400 in R2 by 2006 compared to only an increase of 0.129 for the subsequent 5 years). Re-growth rates appeared to be somewhat higher in north-facing slopes in comparison to south
facing ones. As expected, NDVI decline due to fire was positively correlated with burn severity class, whereas negative correlation was found between damage and regeneration ability (recovery after 3 years = low severity 64%/high severity 58%, recovery after 8 years = low severity 72%/high severity 70%). To our knowledge, this study is one of the few attempting to explore the interrelationships of post-fire vegetation regrowth, topography and burn severity, especially in the case of a single large fire. RI based on control plots provides a valuable tool to quantify fire impact and subsequent vegetation regrowth. Furthermore, indication of burn severity is useful for strategically rehabilitating areas of slow or unsuitable
post-fire vegetation recovery. This study corroborates the significance of EO technology as a successful and cost-effective solution in providing information related to economic and environmental post-fire regeneration assessment.
Abstract
Evapotranspiration (ET) is an important variable in weather system and hydrometeorologi... more Abstract
Evapotranspiration (ET) is an important variable in weather system and hydrometeorological modelling, which is limited for ungauged catchments. In this study, an extensive validation was carried out on the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Evapotranspiration operational product, evaluating its accuracy at selected European sites. Validation was performed through comparisons to in-situ eddy covariance measurements belonging to the CarboEurope IP network. Comparisons were performed for selected cloud-free days with satisfactory Energy Balance Ratio (EBR) in 2011. In total nine sites covering six land covers were used in validating the products’ ET retrieval accuracy. A series of statistical metrics were computed to evaluate the agreement, exploring also the variability of site characteristics and influence of land cover on ET performances. In overall, a good agreement was reported between the satellite-derived ET estimates and the ground measurements (d-index = 0.755, Root Mean Square Deviation (RMSD) = 0.107 mm h-1). A minor negative bias of -0.015 mm h-1 suggested only slight underestimation of the in-situ data. In terms of land cover, highest agreement in ET was reported for the “olive orchards and “open shrubland” sites (d-index = 0.893/0.867, RMSD = 0.041/0.050 mm/h-1). A systematic ET underestimation by SEVIRI was found for all land cover types. Results of this study are largely comparable to previous analogous validation studies of the product. Our findings support the potential value of the SEVIRI ET product for regional to mesoscale studies and practical applications. The latter is of particular importance for water limiting environments such as those found in the Mediterranean basin, as accurate information on ET rates can provide tremendous support in sustainable water resource management as well as policy and decision making.
Abstract
Acquiring information on the spatio-temporal variability of soil moisture is of key imp... more Abstract
Acquiring information on the spatio-temporal variability of soil moisture is of key importance in extending our capability to understand the Earth system’s physical
processes, and is also required in many practical applications. Earth observation (EO) provides a promising avenue to observe the distribution of soil moisture at
different observational scales, with a number of products distributed at present operationally. Validation of such products at a range of climate and environmental conditions across continents is a fundamental step related to their practical use. Various in situ soil moisture ground observational networks have been established globally
providing suitable data for evaluating the accuracy of EO-based soil moisture products. This study aimed at evaluating the accuracy of soil moisture estimates provided from
the Soil Moisture and Ocean Salinity Mission (SMOS) global operational product at test sites from the REMEDHUS International Soil Moisture Network (ISMN) in Spain.
For this purpose, validated observations from in situ ground observations acquired nearly concurrent to SMOS overpass were utilized. Overall, results showed a generally reasonable agreement between the SMOS product and the in situ soil moisture measurements in the 0–5 cm soil moisture layer (root mean square error (RMSE) = 0.116 m3 m−3). An improvement in product accuracy for the overall comparison was shown when days of high radio frequency interference were filtered out (RMSE = 0.110 m3 m−3). Seasonal analysis showed highest agreement during
autumn, followed by summer, winter, and spring seasons. A systematic soil moisture underestimation was also found for the overall comparison and during the four seasons. Overall, the result provides supportive evidence of the potential value of this operational product for meso-scale studies and practical applications.
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Advances in Earth Observation (EO) technology, particularly over the last two decades, have shown that soil moisture content (SMC) can be measured to some degree or other by all regions of the electromagnetic spectrum, and a variety of techniques have been proposed to facilitate this purpose. In this review we provide a synthesis of the efforts made during the last 20 years or so towards the estimation of surface SMC exploiting EO imagery, with a particular emphasis on retrievals from microwave sensors. Rather than replicating previous overview works, we provide a comprehensive and critical exploration of all the major approaches employed for retrieving SMC in a range of different global ecosystems. In this framework, we consider the newest techniques developed within optical and thermal infrared remote sensing, active and passive microwave domains, as well as assimilation or synergistic approaches. Future trends and prospects of EO for the accurate determination of SMC from space are subject to key challenges, some of which are identified and discussed within. It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments.
Given the importance of SMC in Earth’s land surface interactions and to a large range of applications, one can appreciate that its accurate estimation is critical in addressing key scientific and practical challenges in today’s world such as food security, sustainable planning and management of water resources. The launch of new, more sophisticated satellites strengthens the development of innovative research approaches and scientific inventions that will result in a range of pioneering and ground-breaking advancements in the retrievals of soil moisture from space.
This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher’s discriminant analysis (rkFDA) machine learning supervised
classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a
heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel) versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877). Both methods outperformed the standard Normalized Difference Water Index (NDWI) thresholding (OA = 94.63, K = 0.818) by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the “not flooded” class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models). This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study offers important information towards exploring the possibilities of the use of such data to map other significant flood events from space in an economically viable way.
This paper describes the validation of the SimSphere SVAT model conducted at different ecosystem types in the USA and Australia. Specific focus was given to examining the models’ ability in predicting Shortwave Incoming Solar Radiation (Rg), Net Radiation (Rnet), Latent Heat (LE), Sensible Heat (H), Air Temperature at 1.3m (Tair 1.3m) and Air Temperature at 50m (Tair 50m). Model predictions were compared against corresponding in situ measurements acquired for a total of 72 selected days of the year 2011 obtained from 8 sites belonging to the AmeriFlux (USA) and OzFlux (Australia) monitoring networks. Selected sites were representative of a variety of environmental, biome and climatic conditions, to allow for the inclusion of contrasting conditions in the model evaluation.
The application of the model confirmed its high capability in representing the multifarious and complex interactions of the Earth system. Comparisons showed a good agreement
between modelled and measured fluxes, especially for the days with smoothed daily flux trends. A good to excellent agreement between the model predictions and the in situ measurements was reported, particularly so for the LE, H, Tair 1.3m and Tair 50m parameters (RMSD= 39.47, 55.06Wm2, 3.23, 3.77 C respectively). A systematic underestimation of Rg and Rnet (RMSD=67.83, 58.69Wm2, MBE= 67.83, 58.69Wm2 respectively) was also found. Highest simulation accuracies were obtained for the open woodland savannah and mulga woodland sites for most of the compared parameters. Very high values of the Nash–Sutcliffe efficiency index were also reported for all parameters ranging from 0.720 to 0.998, suggesting a very good model representation of the observations.
To our knowledge, this study presents the first comprehensive validation of SimSphere, particularly so in USA and Australian ecosystem types. Findings are important and timely, given the rapidly expanding use of this model worldwide both as an educational and research tool. This includes ongoing research by different Space Agencies examining its synergistic use with Earth Observation data towards the development of global operational products.
In this present study the ability of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) model in estimating key parameters characterising land surface interactions was evaluated. Specifically, SimSphere’s performance in predicting Net Radiation (Rnet), Latent Heat (LE), 5 Sensible Heat (H) and Air Temperature (Tair) at 1.3 and 50m was examined. Model simulations were validated by ground-based measurements of
the corresponding parameters for a total of 70 days of the year 2011 from 7 CarboEurope network sites. These included a variety of biomes, environmental and climatic conditions in the models evaluation.Overall, model performance can largely be described as satisfactory for most of the experimental sites and evaluated parameters. For all model parameters compared,
predicted H fluxes consistently obtained the highest agreement to the in-situ data in all ecosystems, with an average RMSD of 55.36Wm2. LE fluxes and Rnet also agreed well with the in-situ data with RSMDs of 62.75 and 64.65Wm2 respectively. A good
15 agreement between modelled and measured LE and H fluxes was found, especially for smoothed daily flux trends. For both Tair 1.3m and Tair 50m a mean RMSD of 4.14 and 3.54 C was reported respectively. This work presents the first all-inclusive evaluation of SimSphere, particularly so in
a European setting. Results of this study contribute decisively towards obtaining a better understanding of the model’s structure and its correspondence to the real world system. Findings also further establish the model’s capability as a useful teaching and research tool in modelling Earth’s land surface interactions. This is of considerable importance in the light of the rapidly expanding use of the model worldwide, including
ongoing research by various Space Agencies examining its synergistic use with Earth Observation data towards the development of operational products at a global scale.
In this study the relationships between vegetation regeneration dynamics to topography and burn severity for a Canadian landscape were investigated using freely available Earth Observation (EO) imagery from Landsat TM sensor. The Okanagan Mountain Park, located in the Montane Cordillera Ecozones of Western Canada at which a fire occurred in 2003, was used as a case study. First, vegetation regeneration dynamics were quantified for a period of 8 years following the fire event based on a chronosequence analysis of the Normalized Difference Vegetation Index (NDVI) and the Regeneration Index (RI). The spatio-temporal patterns of post-fire NDVI from each image date were statistically compared to
the pre-fire pattern to determine the extent to which the pre-fire spatial pattern was re-established and also the rate of recovery. Subsequently, the relationships of vegetation regrowth to both topography and burn severity was quantified using a series of additional statistical metrics. Burn severity was derived from the differenced Normalized Burn Ratio (dNBR) index computed from the Landsat TM images. Information
on topography properties of the region was obtained from the ASTER global operational product. NDVI and RI analysis indicated a moderate vegetation recovery to pre-fire patterns, with regeneration to over 60% of the pre-fire levels 8-years after the fire. Regression analysis of pre- and post-fire mean
NDVI exhibited significant re-growth in the first 3 years after the fire with a more gradual return in later years (an increase of 0.400 in R2 by 2006 compared to only an increase of 0.129 for the subsequent 5 years). Re-growth rates appeared to be somewhat higher in north-facing slopes in comparison to south
facing ones. As expected, NDVI decline due to fire was positively correlated with burn severity class, whereas negative correlation was found between damage and regeneration ability (recovery after 3 years = low severity 64%/high severity 58%, recovery after 8 years = low severity 72%/high severity 70%). To our knowledge, this study is one of the few attempting to explore the interrelationships of post-fire vegetation regrowth, topography and burn severity, especially in the case of a single large fire. RI based on control plots provides a valuable tool to quantify fire impact and subsequent vegetation regrowth. Furthermore, indication of burn severity is useful for strategically rehabilitating areas of slow or unsuitable
post-fire vegetation recovery. This study corroborates the significance of EO technology as a successful and cost-effective solution in providing information related to economic and environmental post-fire regeneration assessment.
Evapotranspiration (ET) is an important variable in weather system and hydrometeorological modelling, which is limited for ungauged catchments. In this study, an extensive validation was carried out on the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Evapotranspiration operational product, evaluating its accuracy at selected European sites. Validation was performed through comparisons to in-situ eddy covariance measurements belonging to the CarboEurope IP network. Comparisons were performed for selected cloud-free days with satisfactory Energy Balance Ratio (EBR) in 2011. In total nine sites covering six land covers were used in validating the products’ ET retrieval accuracy. A series of statistical metrics were computed to evaluate the agreement, exploring also the variability of site characteristics and influence of land cover on ET performances. In overall, a good agreement was reported between the satellite-derived ET estimates and the ground measurements (d-index = 0.755, Root Mean Square Deviation (RMSD) = 0.107 mm h-1). A minor negative bias of -0.015 mm h-1 suggested only slight underestimation of the in-situ data. In terms of land cover, highest agreement in ET was reported for the “olive orchards and “open shrubland” sites (d-index = 0.893/0.867, RMSD = 0.041/0.050 mm/h-1). A systematic ET underestimation by SEVIRI was found for all land cover types. Results of this study are largely comparable to previous analogous validation studies of the product. Our findings support the potential value of the SEVIRI ET product for regional to mesoscale studies and practical applications. The latter is of particular importance for water limiting environments such as those found in the Mediterranean basin, as accurate information on ET rates can provide tremendous support in sustainable water resource management as well as policy and decision making.
Acquiring information on the spatio-temporal variability of soil moisture is of key importance in extending our capability to understand the Earth system’s physical
processes, and is also required in many practical applications. Earth observation (EO) provides a promising avenue to observe the distribution of soil moisture at
different observational scales, with a number of products distributed at present operationally. Validation of such products at a range of climate and environmental conditions across continents is a fundamental step related to their practical use. Various in situ soil moisture ground observational networks have been established globally
providing suitable data for evaluating the accuracy of EO-based soil moisture products. This study aimed at evaluating the accuracy of soil moisture estimates provided from
the Soil Moisture and Ocean Salinity Mission (SMOS) global operational product at test sites from the REMEDHUS International Soil Moisture Network (ISMN) in Spain.
For this purpose, validated observations from in situ ground observations acquired nearly concurrent to SMOS overpass were utilized. Overall, results showed a generally reasonable agreement between the SMOS product and the in situ soil moisture measurements in the 0–5 cm soil moisture layer (root mean square error (RMSE) = 0.116 m3 m−3). An improvement in product accuracy for the overall comparison was shown when days of high radio frequency interference were filtered out (RMSE = 0.110 m3 m−3). Seasonal analysis showed highest agreement during
autumn, followed by summer, winter, and spring seasons. A systematic soil moisture underestimation was also found for the overall comparison and during the four seasons. Overall, the result provides supportive evidence of the potential value of this operational product for meso-scale studies and practical applications.
Advances in Earth Observation (EO) technology, particularly over the last two decades, have shown that soil moisture content (SMC) can be measured to some degree or other by all regions of the electromagnetic spectrum, and a variety of techniques have been proposed to facilitate this purpose. In this review we provide a synthesis of the efforts made during the last 20 years or so towards the estimation of surface SMC exploiting EO imagery, with a particular emphasis on retrievals from microwave sensors. Rather than replicating previous overview works, we provide a comprehensive and critical exploration of all the major approaches employed for retrieving SMC in a range of different global ecosystems. In this framework, we consider the newest techniques developed within optical and thermal infrared remote sensing, active and passive microwave domains, as well as assimilation or synergistic approaches. Future trends and prospects of EO for the accurate determination of SMC from space are subject to key challenges, some of which are identified and discussed within. It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments.
Given the importance of SMC in Earth’s land surface interactions and to a large range of applications, one can appreciate that its accurate estimation is critical in addressing key scientific and practical challenges in today’s world such as food security, sustainable planning and management of water resources. The launch of new, more sophisticated satellites strengthens the development of innovative research approaches and scientific inventions that will result in a range of pioneering and ground-breaking advancements in the retrievals of soil moisture from space.
This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher’s discriminant analysis (rkFDA) machine learning supervised
classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a
heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel) versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877). Both methods outperformed the standard Normalized Difference Water Index (NDWI) thresholding (OA = 94.63, K = 0.818) by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the “not flooded” class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models). This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study offers important information towards exploring the possibilities of the use of such data to map other significant flood events from space in an economically viable way.
This paper describes the validation of the SimSphere SVAT model conducted at different ecosystem types in the USA and Australia. Specific focus was given to examining the models’ ability in predicting Shortwave Incoming Solar Radiation (Rg), Net Radiation (Rnet), Latent Heat (LE), Sensible Heat (H), Air Temperature at 1.3m (Tair 1.3m) and Air Temperature at 50m (Tair 50m). Model predictions were compared against corresponding in situ measurements acquired for a total of 72 selected days of the year 2011 obtained from 8 sites belonging to the AmeriFlux (USA) and OzFlux (Australia) monitoring networks. Selected sites were representative of a variety of environmental, biome and climatic conditions, to allow for the inclusion of contrasting conditions in the model evaluation.
The application of the model confirmed its high capability in representing the multifarious and complex interactions of the Earth system. Comparisons showed a good agreement
between modelled and measured fluxes, especially for the days with smoothed daily flux trends. A good to excellent agreement between the model predictions and the in situ measurements was reported, particularly so for the LE, H, Tair 1.3m and Tair 50m parameters (RMSD= 39.47, 55.06Wm2, 3.23, 3.77 C respectively). A systematic underestimation of Rg and Rnet (RMSD=67.83, 58.69Wm2, MBE= 67.83, 58.69Wm2 respectively) was also found. Highest simulation accuracies were obtained for the open woodland savannah and mulga woodland sites for most of the compared parameters. Very high values of the Nash–Sutcliffe efficiency index were also reported for all parameters ranging from 0.720 to 0.998, suggesting a very good model representation of the observations.
To our knowledge, this study presents the first comprehensive validation of SimSphere, particularly so in USA and Australian ecosystem types. Findings are important and timely, given the rapidly expanding use of this model worldwide both as an educational and research tool. This includes ongoing research by different Space Agencies examining its synergistic use with Earth Observation data towards the development of global operational products.
In this present study the ability of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) model in estimating key parameters characterising land surface interactions was evaluated. Specifically, SimSphere’s performance in predicting Net Radiation (Rnet), Latent Heat (LE), 5 Sensible Heat (H) and Air Temperature (Tair) at 1.3 and 50m was examined. Model simulations were validated by ground-based measurements of
the corresponding parameters for a total of 70 days of the year 2011 from 7 CarboEurope network sites. These included a variety of biomes, environmental and climatic conditions in the models evaluation.Overall, model performance can largely be described as satisfactory for most of the experimental sites and evaluated parameters. For all model parameters compared,
predicted H fluxes consistently obtained the highest agreement to the in-situ data in all ecosystems, with an average RMSD of 55.36Wm2. LE fluxes and Rnet also agreed well with the in-situ data with RSMDs of 62.75 and 64.65Wm2 respectively. A good
15 agreement between modelled and measured LE and H fluxes was found, especially for smoothed daily flux trends. For both Tair 1.3m and Tair 50m a mean RMSD of 4.14 and 3.54 C was reported respectively. This work presents the first all-inclusive evaluation of SimSphere, particularly so in
a European setting. Results of this study contribute decisively towards obtaining a better understanding of the model’s structure and its correspondence to the real world system. Findings also further establish the model’s capability as a useful teaching and research tool in modelling Earth’s land surface interactions. This is of considerable importance in the light of the rapidly expanding use of the model worldwide, including
ongoing research by various Space Agencies examining its synergistic use with Earth Observation data towards the development of operational products at a global scale.
In this study the relationships between vegetation regeneration dynamics to topography and burn severity for a Canadian landscape were investigated using freely available Earth Observation (EO) imagery from Landsat TM sensor. The Okanagan Mountain Park, located in the Montane Cordillera Ecozones of Western Canada at which a fire occurred in 2003, was used as a case study. First, vegetation regeneration dynamics were quantified for a period of 8 years following the fire event based on a chronosequence analysis of the Normalized Difference Vegetation Index (NDVI) and the Regeneration Index (RI). The spatio-temporal patterns of post-fire NDVI from each image date were statistically compared to
the pre-fire pattern to determine the extent to which the pre-fire spatial pattern was re-established and also the rate of recovery. Subsequently, the relationships of vegetation regrowth to both topography and burn severity was quantified using a series of additional statistical metrics. Burn severity was derived from the differenced Normalized Burn Ratio (dNBR) index computed from the Landsat TM images. Information
on topography properties of the region was obtained from the ASTER global operational product. NDVI and RI analysis indicated a moderate vegetation recovery to pre-fire patterns, with regeneration to over 60% of the pre-fire levels 8-years after the fire. Regression analysis of pre- and post-fire mean
NDVI exhibited significant re-growth in the first 3 years after the fire with a more gradual return in later years (an increase of 0.400 in R2 by 2006 compared to only an increase of 0.129 for the subsequent 5 years). Re-growth rates appeared to be somewhat higher in north-facing slopes in comparison to south
facing ones. As expected, NDVI decline due to fire was positively correlated with burn severity class, whereas negative correlation was found between damage and regeneration ability (recovery after 3 years = low severity 64%/high severity 58%, recovery after 8 years = low severity 72%/high severity 70%). To our knowledge, this study is one of the few attempting to explore the interrelationships of post-fire vegetation regrowth, topography and burn severity, especially in the case of a single large fire. RI based on control plots provides a valuable tool to quantify fire impact and subsequent vegetation regrowth. Furthermore, indication of burn severity is useful for strategically rehabilitating areas of slow or unsuitable
post-fire vegetation recovery. This study corroborates the significance of EO technology as a successful and cost-effective solution in providing information related to economic and environmental post-fire regeneration assessment.
Evapotranspiration (ET) is an important variable in weather system and hydrometeorological modelling, which is limited for ungauged catchments. In this study, an extensive validation was carried out on the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Evapotranspiration operational product, evaluating its accuracy at selected European sites. Validation was performed through comparisons to in-situ eddy covariance measurements belonging to the CarboEurope IP network. Comparisons were performed for selected cloud-free days with satisfactory Energy Balance Ratio (EBR) in 2011. In total nine sites covering six land covers were used in validating the products’ ET retrieval accuracy. A series of statistical metrics were computed to evaluate the agreement, exploring also the variability of site characteristics and influence of land cover on ET performances. In overall, a good agreement was reported between the satellite-derived ET estimates and the ground measurements (d-index = 0.755, Root Mean Square Deviation (RMSD) = 0.107 mm h-1). A minor negative bias of -0.015 mm h-1 suggested only slight underestimation of the in-situ data. In terms of land cover, highest agreement in ET was reported for the “olive orchards and “open shrubland” sites (d-index = 0.893/0.867, RMSD = 0.041/0.050 mm/h-1). A systematic ET underestimation by SEVIRI was found for all land cover types. Results of this study are largely comparable to previous analogous validation studies of the product. Our findings support the potential value of the SEVIRI ET product for regional to mesoscale studies and practical applications. The latter is of particular importance for water limiting environments such as those found in the Mediterranean basin, as accurate information on ET rates can provide tremendous support in sustainable water resource management as well as policy and decision making.
Acquiring information on the spatio-temporal variability of soil moisture is of key importance in extending our capability to understand the Earth system’s physical
processes, and is also required in many practical applications. Earth observation (EO) provides a promising avenue to observe the distribution of soil moisture at
different observational scales, with a number of products distributed at present operationally. Validation of such products at a range of climate and environmental conditions across continents is a fundamental step related to their practical use. Various in situ soil moisture ground observational networks have been established globally
providing suitable data for evaluating the accuracy of EO-based soil moisture products. This study aimed at evaluating the accuracy of soil moisture estimates provided from
the Soil Moisture and Ocean Salinity Mission (SMOS) global operational product at test sites from the REMEDHUS International Soil Moisture Network (ISMN) in Spain.
For this purpose, validated observations from in situ ground observations acquired nearly concurrent to SMOS overpass were utilized. Overall, results showed a generally reasonable agreement between the SMOS product and the in situ soil moisture measurements in the 0–5 cm soil moisture layer (root mean square error (RMSE) = 0.116 m3 m−3). An improvement in product accuracy for the overall comparison was shown when days of high radio frequency interference were filtered out (RMSE = 0.110 m3 m−3). Seasonal analysis showed highest agreement during
autumn, followed by summer, winter, and spring seasons. A systematic soil moisture underestimation was also found for the overall comparison and during the four seasons. Overall, the result provides supportive evidence of the potential value of this operational product for meso-scale studies and practical applications.