IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217)
As spaceborne SAR is used for monitoring the snow cover during the spring melt period, temporal c... more As spaceborne SAR is used for monitoring the snow cover during the spring melt period, temporal changes in backscattering properties of forest cover disturb the quantitative estimation of snow covered area (SCA). This paper introduces an algorithm that eliminates the related disturbances in SCA estimation. The algorithm is developed based on the Helsinki University of Technology (HUT) forest backscattering model.
Geoscientific Instrumentation, Methods and Data Systems, 2016
During the last decade there has been considerable development in remote sensing techniques relat... more During the last decade there has been considerable development in remote sensing techniques relating to soil moisture retrievals over large areas. Within the framework of the European Space Agency's (ESA) Climate Change Initiative (CCI) a new soil moisture product has been generated, merging different satellite-based surface soil moisture based products. Such remotely sensed data need to be validated by means of in situ observations in different climatic regions. In that context, a comprehensive, distributed network of in situ measurement stations gathering information on soil moisture, as well as soil temperature, has been set up in recent years at the Finnish Meteorological Institute's (FMI) Sodankylä Arctic research station. The network forms a calibration and validation (CAL-VAL) reference site and is used as a tool to evaluate the validity of satellite retrievals of soil properties. In this paper we present the Sodankylä CAL-VAL reference site soil moisture observation network, its instrumentation as well as its areal representativeness over the study area and the region in general as a whole. As an example of data utilization, comparisons of spatially weighted average top-layer soil moisture observations between the years 2012 and 2014 against ESA CCI soil moisture data product estimates are presented and discussed. The comparisons were made against a single ESA CCI data product pixel encapsulating most of the Sodankylä CAL-VAL network sites. Comparisons are made with daily averaged and running weekly averaged soil moisture data as well as through application of an exponential soil moisture filter. The overall achieved correlation between the ESA CCI data product and in situ observations varies considerably (from 0.479 to 0.637) depending on the applied comparison perspective. Similarly, depending on the comparison perspective used, inter-annual correlation comparison results exhibit even more pronounced variation, ranging from 0.166 to 0.840.
Two field campaigns were used to demonstrate the usefulness of an atmospheric correction method s... more Two field campaigns were used to demonstrate the usefulness of an atmospheric correction method specifically developed for Case 2 waters. The correction method utilizes bio-optical reflectance modeling and principal component analysis. The MODTRAN radiative transfer code simulations were conducted to obtain a statistical database for varying atmospheric situations. Bio-optical reflectance model takes into account the water quality and it's influence on the detected reflectance. Both field campaigns represent high concentrations of optically active substances such as chlorophyll-a, total suspended sediment and humus. The first field campaign was held at a coastal site of Finland, on the Gulf of Finland, while the other field campaign represents a Finnish lake water area. On both campaigns MERIS satellite instrument and airborne spectrometer data were available.
Assimilation of ERS-2 SAR data to the operational hydrological runoff model using a nonlinear Bay... more Assimilation of ERS-2 SAR data to the operational hydrological runoff model using a nonlinear Bayesian techniques is demonstrated and tested. The developed assimilation technique combines SAR observations to runoff model by applying a constrained iteration procedure and forward modeling of SAR observations. The aim is to improve river discharge forecasts. The test site is located in the Northern Finland, in river Kemijoki drainage area. The satellite data consists of ERS-2 SAR images from four springs. The results show that inclusion of the satellite data can improve the performance of the discharge forecasting model during the snow melt period.
The European Space Agency (ESA) Data User Element (DUE) funded GlobSnow project aims at creating ... more The European Space Agency (ESA) Data User Element (DUE) funded GlobSnow project aims at creating a global database of snow parameters for climate research purposes. The main objective is to create a long term dataset on two essential snow parameters. The project will provide information concerning the areal extent of snow (SE) on a global scale and snow water equivalent
IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 2003
... Martti T. Hallikainen, Pekka Halme, Matias Takala, Jouni Pulliainen Laboratory of Space Techn... more ... Martti T. Hallikainen, Pekka Halme, Matias Takala, Jouni Pulliainen Laboratory of Space Technology Helsinki University of Technology 02015 HUT ... REFERENCES [1] MT Hallikainen and P. Jolma, Comparison of algorithms for retrieval of snow water equivalent from Nimbus-7 ...
The timing of springtime end of snowmelt (snowoff date) in northern Eurasia in version 5.4 of the... more The timing of springtime end of snowmelt (snowoff date) in northern Eurasia in version 5.4 of the ECHAM5 atmospheric general circulation model (GCM) is evaluated through comparison with a snow-off date data set based on space-borne microwave radiometer measurements and with Russian snow course data. ECHAM5 reproduces well the observed gross geographical pattern of snow-off dates, with earliest snow-off (in March) in the Baltic region and latest snow-off (in June) in the Taymyr Peninsula and in northeastern parts of the Russian Far East. The primary biases are (1) a delayed snow-off in southeastern Siberia (associated with too low springtime temperature and too high surface albedo, in part due to insufficient shielding by canopy); and (2) an early bias in the western and northern parts of northern Eurasia. Several sensitivity experiments were conducted, where biases in simulated atmospheric circulation were corrected through nudging and/or the treatment of surface albedo was modified. While this alleviated some of the model biases in snow-off dates, 2 m temperature and surface albedo, especially the early bias in snow-off in the western parts of northern Eurasia proved very robust and was actually larger in the nudged runs. A key issue underlying the snow-off biases in ECHAM5 is that snowmelt occurs at too low temperatures. Very likely, this is related to the treatment of the surface energy budget. On one hand, the surface temperature T s is not computed separately for the snow-covered and snow-free parts of the grid cells, which prevents T s from rising above 0 • C before all snow has vanished. Consequently, too much of the surface net radiation is consumed in melting snow and too little in heating the air. On the other hand, ECHAM5 does not include a canopy layer. Thus, while the albedo reduction due to canopy is accounted for, the shielding of snow on ground by the overlying canopy is not considered, which leaves too much solar radiation available for melting snow.
Four satellite-based snow products are evaluated over the Tibetan Plateau for the 2007-2010 snow ... more Four satellite-based snow products are evaluated over the Tibetan Plateau for the 2007-2010 snow seasons. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow cover daily L3 Global 500 m grid products (MOD10A1 and MYD10A1), the National Oceanic and Atmospheric Administration (NOAA) Interactive Multisensor Snow and Ice Mapping System (IMS) daily Northern Hemisphere snow cover product and the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) Daily Snow Water Equivalent (SWE) were validated against Thematic Mapper (TM) snow cover maps of Landsat-5 and meteorological station snow depth observations. The overall accuracy of MOD10A1, MYD10A1 and IMS is higher than 91% against stations observations and than 79% against Landsat TM images. In general, the daily MODIS snow cover products show better performance than the multi-sensor IMS product. However, the IMS snow cover product, is suitable for larger scale (~ 4km) analysis and applications, with the advantage over MODIS to allow for mitigation for cloud cover. The accuracy of the three products decreases with decreasing snow depth. Overestimation errors are most common over forested regions; the IMS and AMSR-E SWE products also show poorer performance that the MODIS products over grassland. By identifying weaknesses in the satellite products, this study provides a focus for the improvement of snow products over the Tibetan plateau. The quantitative evaluation of the products proposed here can also be used to assess their relative weight in data assimilation, against other data sources, such as modeling and in situ measurement networks.
Monitoring of environment requires several sources of information to be combined: "General knowle... more Monitoring of environment requires several sources of information to be combined: "General knowledge" of the monitored area, measurement information on it and naturally previously made assessments of the same area. To use earth observation in monitoring of for example lake water quality, traditional images have limited value: How could they be used for example when one wishes to draw a graph of time series from several lakes in order to compare them? Indicator values stored in a database would be required. To solve these data use problems in water quality monitoring, U L A P P A s y s t e m w a s d e v e l o p e d u s i n g t a r g e t a r e a approach. It starts from the fact that usually the areas to be monitored are clearly defined: Lakes, fields etc. have more or less well-defined locations and boundaries. They have distinctive a priori characteristics as individual entities or according to their type, in situ measurements are made on them, they are being observed by several types of satellites and all this data is combined using several different algorithms, possibly with several versions of algorithm parameters. Sometimes model assimilation requires access to uninterpreted, usable satellite data. Management of usable earth observation data is critical for optical data, as clouds often obscure the target area. All these needs can be integrated according to user needs using the target area approach.
ABSTRACT Assimilation of ERS-2 SAR data to the operational hydrological runoff model using a non-... more ABSTRACT Assimilation of ERS-2 SAR data to the operational hydrological runoff model using a non-linear Bayesian tech- niques is demonstrated and tested. The developed assimilation method combines SAR observations to runoff model by applying a constrained iteration procedure and forward modeling of SAR observations. The aim is to improve river discharge forecasts. The test site is located in the Northern Finland, in river Kemijoki drainage area. The satellite data consists of ERS-2 SAR images from five springs. The results show that inclusion of the satellite data can improve the performance of the discharge forecasting model during the snow melt period.
Novel methods are developed to directly assimilate remote sensing data to (a) discrete in situ mo... more Novel methods are developed to directly assimilate remote sensing data to (a) discrete in situ monitoring data and (b) environmental models. The objective is to investigate the feasibility of the developed methods as they are applied to the operative and semi-operative systems employed in Finland by the Finnish Environment Institute. The development work is focused on (a) water quality monitoring and prediction and (b) hydrological monitoring and forecasting. The investigated applications include the monitoring and prediction of snowmelt, runoff and the level of soil moisture/evapotranspiration. The developed assimilation procedures take into account the accuracy characteristics of physical models (or those of in situ data), the accuracy of remote sensing data and models, and additionally, the propagation of errors with time. The first test results of satellite data assimilation to dynamical environmental models were obtained in snowmelt monitoring/discharge forecasting. These results indicate that the inclusion of satellite data improves the performance of runoff forecasting models during the critical snow melt period. As well, encouraging results were obtained in the in the case of snow depth mapping when assimilating remote sensing data to discrete in situ observations (to snow depth values interpolated from discrete observation data).
In this paper empirical algorithms for determining the Secchi disk depth (SDD) are developed and ... more In this paper empirical algorithms for determining the Secchi disk depth (SDD) are developed and employed using optical (e.g., Landsat TM) and microwave (e.g., ERS-2 SAR) remote sensing data from the Gulf of Finland and the Archipelago Sea. The SDD is an important optical measure of water quality in the study area, where the coastal water considerably attenuates light because of the presence of phytoplankton, suspended matter and yellow substance. The results show that the accuracy of SDD estimation using a neural network-based method is much higher than that of a semiempirical or multivariate approach. On the other hand, the additional use of SAR data only slightly improved SDD estimation when compared with the use of TM data only. Although the improvement is marginal, the results suggest that there may be some SAR backscattering signatures correlated to SDD measurements in the area. However, such a small improvement is not very helpful for the practical estimation of SDD. In the future, the technique of using combined optical and microwave data still needs to be refi ned using, e.g., MERIS and ASAR data.
The European Space Agency's Data User Element (DUE) project GlobSnow was established to create a ... more The European Space Agency's Data User Element (DUE) project GlobSnow was established to create a global database of Snow Extent and Snow Water Equivalent. The Snow Extent (SE) product portfolio provided within ESA DUE GlobSnow (2008-2014) is introduced and described, with a special focus on the Daily Fractional Snow Cover (DFSC) of the SE version 2.0 and its successor 2.1 released in 2013-2014. The fractional snow retrieval uses the SCAmod method designed ecpecially to enable accurate snow mapping including forests. The basics of the methodology are presented, as well as the cloud screening method applied in SE production. Considerations for future validations together with discussion on some current issues and potential inaccuracies are presented. One focus of the investigation is on the representativeness of reference FSC generated from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data, with a particular interest in forested areas. Two methods for reference data generation are investigated. When comparing the GlobSnow Daily Fractional Snow Cover to these reference data, we try to identify how the comparison reflects the possible inaccuracies of the DFSC and to define the conditions where the reference data are not representative. It is obvious that the evaluation result strongly depends on the quality of the reference data, and that the two methods investigated cannot provide representative reference data for dense forests. For fully snow-covered dense conifer forest area in Finland, a Root Mean Squared Error of 20-30% was obtained from comparisons although DFSC indicated full snow cover correctly. These first evaluations would indicate a good performance of GlobSnow SE products in forests; however, this does not necessarily show up in validations due to the non-representativeness of the reference data. It is also concluded that GlobSnow SE products are sensitive to the representativeness of the applied SCAmod parameters and that FSC overestimations may occur in dense forests. GlobSnow SE products are available at www.globsnow.info/se/.
2010 IEEE International Geoscience and Remote Sensing Symposium, 2010
Snow Water Equivalent (SWE) is a measure which describes the amount of snow. The authors have dev... more Snow Water Equivalent (SWE) is a measure which describes the amount of snow. The authors have developed algorithm to estimate SWE assimilating spaceborne and ground based observations. The authors have also developed algorithm to detect the snow clearance date. In this work those algorithms are combined to produce a single snow product. Two series of figures are presented and the improvements discussed.
An advanced approach for estimating the sub-pixel fraction of snow covered area for boreal forest... more An advanced approach for estimating the sub-pixel fraction of snow covered area for boreal forest and tundra belt from optical data is presented. The method named SCAmod by the Finnish Environment Institute (SYKE) is based on a forward semi-empirical model where the at-satellite observed reflectance is expressed as a function of the fractional snow cover (FSC). The effective forest transmissivity determined for each target unit-area and the generally applicable reflectances of three major contributors (wet snow, forest canopy and snow-free ground) serve as model parameters. The forest transmissivity describes the visibility of the ground through the forest canopy from above, and can be determined from visible reflectance data acquired at full snow cover conditions. SCAmod can be applied to data from various sensors operating at optical and near-infrared region. We apply the method to Envisat/AATSR and Terra/MODIS data and validate the resulting FSC against ground truth data over Finland, with considerations of feasibility of these ground truth data in a scale of the MODIS/AATSR pixel. In comparison, also NASA MOD10_L2 fractional snow product is validated. The results indicate that SCAmod performs better than MOD10_L2 particularly in forested areas; an RMSE of 0.11 for the fraction of snow covered area (range is 0-1) is achieved. For large-scale snow mapping, using reflectance data for transmissivity determination would evidently be very time-consuming. Here we present also a new method for transmissivity generation using global land cover data and demonstrate its use for snow cover mapping in continental scale. The resulting FSC data are compared against FSC from highresolution Landsat TM/ETM + data for selected areas in Northern and Eastern Europe. The comparison indicates that SCAmod is feasible also in continental-scale snow mapping and is superior in identifying snow in dense forests.
The spatial resolution of passive microwave observations from space is of the order of tens of ki... more The spatial resolution of passive microwave observations from space is of the order of tens of kilometers with currently available instruments, such as the Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer (AMSR-E). The large field of view of these instruments dictates that the observed brightness temperature can originate from heterogeneous land cover, with different vegetation and surface properties. In this study, we assess the influence of freshwater lakes on the observed brightness temperature of AMSR-E in winter conditions. The study focuses on the geographic region of Finland, where lakes account for 10% of the total terrestrial area. We present a method to mitigate for the influence of lakes through forward modeling of snow covered lakes, as a part of a microwave emission simulation scheme of space-borne observations. We apply a forward model to predict brightness temperatures of snow covered sceneries over several winter seasons, using available data on snow cover, vegetation and lake ice cover to set the forward model input parameters. Comparison of model estimates with space-borne observations shows that the modeling accuracy improves in the majority of examined cases when lakes are accounted for, with respect to the case where lakes are not included in the simulation. Moreover, we present a method for applying the correction to the retrieval of Snow Water Equivalent (SWE) in lake-rich areas, using a numerical inversion method of the forward model. In a comparison to available independent validation data on SWE, also the retrieval accuracy is seen to improve when applying the influence of snow covered lakes in the emission model.
The retrieval of snow water equivalent (SWE) and snow depth is performed by inverting Special Sen... more The retrieval of snow water equivalent (SWE) and snow depth is performed by inverting Special Sensor Microwave Imager (SSM/I) brightness temperatures at 19 and 37 GHz using artificial neural network ANN-based techniques. The SSM/I used data, which consist of Pathfinder Daily EASE-Grid brightness temperatures, were supplied by the National Snow and Ice Data Centre (NSIDC). They were gathered during the period of time included between the beginning of 1996 and the end of 1999 all over Finland. A ground snow data set based on observations of the Finnish Environment Institute (SYKE) and the Finnish Meteorological Institute (FMI) was used to estimate the performances of the technique. The ANN results were confronted with those obtained using the spectral polarization difference (SPD) algorithm, the HUT model-based iterative inversion and the Chang algorithm, by comparing the RMSE, the R2, and the regression coefficients. In general, it was observed that the results obtained through ANN-based technique are better than, or comparable to, those obtained through other approaches, when trained with simulated data. Performances were very good when the ANN were trained with experimental data.
The effects of an atmospheric correction method for water quality estimation have been studied an... more The effects of an atmospheric correction method for water quality estimation have been studied and validated for Airborne Imaging Spectrometer for Applications (AISA) data. This novel approach uses atmospheric input parameters from a numerical weather prediction model: HIRLAM (High Resolution Limited Area Model). The atmospheric correction method developed by de Haan and Kokke (1996) corrects the spectrometer data according to the coefficients calculated using Moderate Resolution Transmittance Code (MODTRAN) radiative transfer code simulations. The airborne campaigns were carried out at lake and coastal Case 2 type water areas between 1996 and 1998. The water quality interpretation was made using the MERIS satellite instrument wavelengths. The correction improved most of the water quality (turbidity, total suspended solids, and Secchi disk depth) estimates when data from several flight campaigns were used jointly. The atmospheric correction reduced the standard deviation of the measurements conducted on different days. The highest improvement was obtained in the estimation of turbidity.
IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217)
As spaceborne SAR is used for monitoring the snow cover during the spring melt period, temporal c... more As spaceborne SAR is used for monitoring the snow cover during the spring melt period, temporal changes in backscattering properties of forest cover disturb the quantitative estimation of snow covered area (SCA). This paper introduces an algorithm that eliminates the related disturbances in SCA estimation. The algorithm is developed based on the Helsinki University of Technology (HUT) forest backscattering model.
Geoscientific Instrumentation, Methods and Data Systems, 2016
During the last decade there has been considerable development in remote sensing techniques relat... more During the last decade there has been considerable development in remote sensing techniques relating to soil moisture retrievals over large areas. Within the framework of the European Space Agency's (ESA) Climate Change Initiative (CCI) a new soil moisture product has been generated, merging different satellite-based surface soil moisture based products. Such remotely sensed data need to be validated by means of in situ observations in different climatic regions. In that context, a comprehensive, distributed network of in situ measurement stations gathering information on soil moisture, as well as soil temperature, has been set up in recent years at the Finnish Meteorological Institute's (FMI) Sodankylä Arctic research station. The network forms a calibration and validation (CAL-VAL) reference site and is used as a tool to evaluate the validity of satellite retrievals of soil properties. In this paper we present the Sodankylä CAL-VAL reference site soil moisture observation network, its instrumentation as well as its areal representativeness over the study area and the region in general as a whole. As an example of data utilization, comparisons of spatially weighted average top-layer soil moisture observations between the years 2012 and 2014 against ESA CCI soil moisture data product estimates are presented and discussed. The comparisons were made against a single ESA CCI data product pixel encapsulating most of the Sodankylä CAL-VAL network sites. Comparisons are made with daily averaged and running weekly averaged soil moisture data as well as through application of an exponential soil moisture filter. The overall achieved correlation between the ESA CCI data product and in situ observations varies considerably (from 0.479 to 0.637) depending on the applied comparison perspective. Similarly, depending on the comparison perspective used, inter-annual correlation comparison results exhibit even more pronounced variation, ranging from 0.166 to 0.840.
Two field campaigns were used to demonstrate the usefulness of an atmospheric correction method s... more Two field campaigns were used to demonstrate the usefulness of an atmospheric correction method specifically developed for Case 2 waters. The correction method utilizes bio-optical reflectance modeling and principal component analysis. The MODTRAN radiative transfer code simulations were conducted to obtain a statistical database for varying atmospheric situations. Bio-optical reflectance model takes into account the water quality and it's influence on the detected reflectance. Both field campaigns represent high concentrations of optically active substances such as chlorophyll-a, total suspended sediment and humus. The first field campaign was held at a coastal site of Finland, on the Gulf of Finland, while the other field campaign represents a Finnish lake water area. On both campaigns MERIS satellite instrument and airborne spectrometer data were available.
Assimilation of ERS-2 SAR data to the operational hydrological runoff model using a nonlinear Bay... more Assimilation of ERS-2 SAR data to the operational hydrological runoff model using a nonlinear Bayesian techniques is demonstrated and tested. The developed assimilation technique combines SAR observations to runoff model by applying a constrained iteration procedure and forward modeling of SAR observations. The aim is to improve river discharge forecasts. The test site is located in the Northern Finland, in river Kemijoki drainage area. The satellite data consists of ERS-2 SAR images from four springs. The results show that inclusion of the satellite data can improve the performance of the discharge forecasting model during the snow melt period.
The European Space Agency (ESA) Data User Element (DUE) funded GlobSnow project aims at creating ... more The European Space Agency (ESA) Data User Element (DUE) funded GlobSnow project aims at creating a global database of snow parameters for climate research purposes. The main objective is to create a long term dataset on two essential snow parameters. The project will provide information concerning the areal extent of snow (SE) on a global scale and snow water equivalent
IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 2003
... Martti T. Hallikainen, Pekka Halme, Matias Takala, Jouni Pulliainen Laboratory of Space Techn... more ... Martti T. Hallikainen, Pekka Halme, Matias Takala, Jouni Pulliainen Laboratory of Space Technology Helsinki University of Technology 02015 HUT ... REFERENCES [1] MT Hallikainen and P. Jolma, Comparison of algorithms for retrieval of snow water equivalent from Nimbus-7 ...
The timing of springtime end of snowmelt (snowoff date) in northern Eurasia in version 5.4 of the... more The timing of springtime end of snowmelt (snowoff date) in northern Eurasia in version 5.4 of the ECHAM5 atmospheric general circulation model (GCM) is evaluated through comparison with a snow-off date data set based on space-borne microwave radiometer measurements and with Russian snow course data. ECHAM5 reproduces well the observed gross geographical pattern of snow-off dates, with earliest snow-off (in March) in the Baltic region and latest snow-off (in June) in the Taymyr Peninsula and in northeastern parts of the Russian Far East. The primary biases are (1) a delayed snow-off in southeastern Siberia (associated with too low springtime temperature and too high surface albedo, in part due to insufficient shielding by canopy); and (2) an early bias in the western and northern parts of northern Eurasia. Several sensitivity experiments were conducted, where biases in simulated atmospheric circulation were corrected through nudging and/or the treatment of surface albedo was modified. While this alleviated some of the model biases in snow-off dates, 2 m temperature and surface albedo, especially the early bias in snow-off in the western parts of northern Eurasia proved very robust and was actually larger in the nudged runs. A key issue underlying the snow-off biases in ECHAM5 is that snowmelt occurs at too low temperatures. Very likely, this is related to the treatment of the surface energy budget. On one hand, the surface temperature T s is not computed separately for the snow-covered and snow-free parts of the grid cells, which prevents T s from rising above 0 • C before all snow has vanished. Consequently, too much of the surface net radiation is consumed in melting snow and too little in heating the air. On the other hand, ECHAM5 does not include a canopy layer. Thus, while the albedo reduction due to canopy is accounted for, the shielding of snow on ground by the overlying canopy is not considered, which leaves too much solar radiation available for melting snow.
Four satellite-based snow products are evaluated over the Tibetan Plateau for the 2007-2010 snow ... more Four satellite-based snow products are evaluated over the Tibetan Plateau for the 2007-2010 snow seasons. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow cover daily L3 Global 500 m grid products (MOD10A1 and MYD10A1), the National Oceanic and Atmospheric Administration (NOAA) Interactive Multisensor Snow and Ice Mapping System (IMS) daily Northern Hemisphere snow cover product and the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) Daily Snow Water Equivalent (SWE) were validated against Thematic Mapper (TM) snow cover maps of Landsat-5 and meteorological station snow depth observations. The overall accuracy of MOD10A1, MYD10A1 and IMS is higher than 91% against stations observations and than 79% against Landsat TM images. In general, the daily MODIS snow cover products show better performance than the multi-sensor IMS product. However, the IMS snow cover product, is suitable for larger scale (~ 4km) analysis and applications, with the advantage over MODIS to allow for mitigation for cloud cover. The accuracy of the three products decreases with decreasing snow depth. Overestimation errors are most common over forested regions; the IMS and AMSR-E SWE products also show poorer performance that the MODIS products over grassland. By identifying weaknesses in the satellite products, this study provides a focus for the improvement of snow products over the Tibetan plateau. The quantitative evaluation of the products proposed here can also be used to assess their relative weight in data assimilation, against other data sources, such as modeling and in situ measurement networks.
Monitoring of environment requires several sources of information to be combined: "General knowle... more Monitoring of environment requires several sources of information to be combined: "General knowledge" of the monitored area, measurement information on it and naturally previously made assessments of the same area. To use earth observation in monitoring of for example lake water quality, traditional images have limited value: How could they be used for example when one wishes to draw a graph of time series from several lakes in order to compare them? Indicator values stored in a database would be required. To solve these data use problems in water quality monitoring, U L A P P A s y s t e m w a s d e v e l o p e d u s i n g t a r g e t a r e a approach. It starts from the fact that usually the areas to be monitored are clearly defined: Lakes, fields etc. have more or less well-defined locations and boundaries. They have distinctive a priori characteristics as individual entities or according to their type, in situ measurements are made on them, they are being observed by several types of satellites and all this data is combined using several different algorithms, possibly with several versions of algorithm parameters. Sometimes model assimilation requires access to uninterpreted, usable satellite data. Management of usable earth observation data is critical for optical data, as clouds often obscure the target area. All these needs can be integrated according to user needs using the target area approach.
ABSTRACT Assimilation of ERS-2 SAR data to the operational hydrological runoff model using a non-... more ABSTRACT Assimilation of ERS-2 SAR data to the operational hydrological runoff model using a non-linear Bayesian tech- niques is demonstrated and tested. The developed assimilation method combines SAR observations to runoff model by applying a constrained iteration procedure and forward modeling of SAR observations. The aim is to improve river discharge forecasts. The test site is located in the Northern Finland, in river Kemijoki drainage area. The satellite data consists of ERS-2 SAR images from five springs. The results show that inclusion of the satellite data can improve the performance of the discharge forecasting model during the snow melt period.
Novel methods are developed to directly assimilate remote sensing data to (a) discrete in situ mo... more Novel methods are developed to directly assimilate remote sensing data to (a) discrete in situ monitoring data and (b) environmental models. The objective is to investigate the feasibility of the developed methods as they are applied to the operative and semi-operative systems employed in Finland by the Finnish Environment Institute. The development work is focused on (a) water quality monitoring and prediction and (b) hydrological monitoring and forecasting. The investigated applications include the monitoring and prediction of snowmelt, runoff and the level of soil moisture/evapotranspiration. The developed assimilation procedures take into account the accuracy characteristics of physical models (or those of in situ data), the accuracy of remote sensing data and models, and additionally, the propagation of errors with time. The first test results of satellite data assimilation to dynamical environmental models were obtained in snowmelt monitoring/discharge forecasting. These results indicate that the inclusion of satellite data improves the performance of runoff forecasting models during the critical snow melt period. As well, encouraging results were obtained in the in the case of snow depth mapping when assimilating remote sensing data to discrete in situ observations (to snow depth values interpolated from discrete observation data).
In this paper empirical algorithms for determining the Secchi disk depth (SDD) are developed and ... more In this paper empirical algorithms for determining the Secchi disk depth (SDD) are developed and employed using optical (e.g., Landsat TM) and microwave (e.g., ERS-2 SAR) remote sensing data from the Gulf of Finland and the Archipelago Sea. The SDD is an important optical measure of water quality in the study area, where the coastal water considerably attenuates light because of the presence of phytoplankton, suspended matter and yellow substance. The results show that the accuracy of SDD estimation using a neural network-based method is much higher than that of a semiempirical or multivariate approach. On the other hand, the additional use of SAR data only slightly improved SDD estimation when compared with the use of TM data only. Although the improvement is marginal, the results suggest that there may be some SAR backscattering signatures correlated to SDD measurements in the area. However, such a small improvement is not very helpful for the practical estimation of SDD. In the future, the technique of using combined optical and microwave data still needs to be refi ned using, e.g., MERIS and ASAR data.
The European Space Agency's Data User Element (DUE) project GlobSnow was established to create a ... more The European Space Agency's Data User Element (DUE) project GlobSnow was established to create a global database of Snow Extent and Snow Water Equivalent. The Snow Extent (SE) product portfolio provided within ESA DUE GlobSnow (2008-2014) is introduced and described, with a special focus on the Daily Fractional Snow Cover (DFSC) of the SE version 2.0 and its successor 2.1 released in 2013-2014. The fractional snow retrieval uses the SCAmod method designed ecpecially to enable accurate snow mapping including forests. The basics of the methodology are presented, as well as the cloud screening method applied in SE production. Considerations for future validations together with discussion on some current issues and potential inaccuracies are presented. One focus of the investigation is on the representativeness of reference FSC generated from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data, with a particular interest in forested areas. Two methods for reference data generation are investigated. When comparing the GlobSnow Daily Fractional Snow Cover to these reference data, we try to identify how the comparison reflects the possible inaccuracies of the DFSC and to define the conditions where the reference data are not representative. It is obvious that the evaluation result strongly depends on the quality of the reference data, and that the two methods investigated cannot provide representative reference data for dense forests. For fully snow-covered dense conifer forest area in Finland, a Root Mean Squared Error of 20-30% was obtained from comparisons although DFSC indicated full snow cover correctly. These first evaluations would indicate a good performance of GlobSnow SE products in forests; however, this does not necessarily show up in validations due to the non-representativeness of the reference data. It is also concluded that GlobSnow SE products are sensitive to the representativeness of the applied SCAmod parameters and that FSC overestimations may occur in dense forests. GlobSnow SE products are available at www.globsnow.info/se/.
2010 IEEE International Geoscience and Remote Sensing Symposium, 2010
Snow Water Equivalent (SWE) is a measure which describes the amount of snow. The authors have dev... more Snow Water Equivalent (SWE) is a measure which describes the amount of snow. The authors have developed algorithm to estimate SWE assimilating spaceborne and ground based observations. The authors have also developed algorithm to detect the snow clearance date. In this work those algorithms are combined to produce a single snow product. Two series of figures are presented and the improvements discussed.
An advanced approach for estimating the sub-pixel fraction of snow covered area for boreal forest... more An advanced approach for estimating the sub-pixel fraction of snow covered area for boreal forest and tundra belt from optical data is presented. The method named SCAmod by the Finnish Environment Institute (SYKE) is based on a forward semi-empirical model where the at-satellite observed reflectance is expressed as a function of the fractional snow cover (FSC). The effective forest transmissivity determined for each target unit-area and the generally applicable reflectances of three major contributors (wet snow, forest canopy and snow-free ground) serve as model parameters. The forest transmissivity describes the visibility of the ground through the forest canopy from above, and can be determined from visible reflectance data acquired at full snow cover conditions. SCAmod can be applied to data from various sensors operating at optical and near-infrared region. We apply the method to Envisat/AATSR and Terra/MODIS data and validate the resulting FSC against ground truth data over Finland, with considerations of feasibility of these ground truth data in a scale of the MODIS/AATSR pixel. In comparison, also NASA MOD10_L2 fractional snow product is validated. The results indicate that SCAmod performs better than MOD10_L2 particularly in forested areas; an RMSE of 0.11 for the fraction of snow covered area (range is 0-1) is achieved. For large-scale snow mapping, using reflectance data for transmissivity determination would evidently be very time-consuming. Here we present also a new method for transmissivity generation using global land cover data and demonstrate its use for snow cover mapping in continental scale. The resulting FSC data are compared against FSC from highresolution Landsat TM/ETM + data for selected areas in Northern and Eastern Europe. The comparison indicates that SCAmod is feasible also in continental-scale snow mapping and is superior in identifying snow in dense forests.
The spatial resolution of passive microwave observations from space is of the order of tens of ki... more The spatial resolution of passive microwave observations from space is of the order of tens of kilometers with currently available instruments, such as the Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer (AMSR-E). The large field of view of these instruments dictates that the observed brightness temperature can originate from heterogeneous land cover, with different vegetation and surface properties. In this study, we assess the influence of freshwater lakes on the observed brightness temperature of AMSR-E in winter conditions. The study focuses on the geographic region of Finland, where lakes account for 10% of the total terrestrial area. We present a method to mitigate for the influence of lakes through forward modeling of snow covered lakes, as a part of a microwave emission simulation scheme of space-borne observations. We apply a forward model to predict brightness temperatures of snow covered sceneries over several winter seasons, using available data on snow cover, vegetation and lake ice cover to set the forward model input parameters. Comparison of model estimates with space-borne observations shows that the modeling accuracy improves in the majority of examined cases when lakes are accounted for, with respect to the case where lakes are not included in the simulation. Moreover, we present a method for applying the correction to the retrieval of Snow Water Equivalent (SWE) in lake-rich areas, using a numerical inversion method of the forward model. In a comparison to available independent validation data on SWE, also the retrieval accuracy is seen to improve when applying the influence of snow covered lakes in the emission model.
The retrieval of snow water equivalent (SWE) and snow depth is performed by inverting Special Sen... more The retrieval of snow water equivalent (SWE) and snow depth is performed by inverting Special Sensor Microwave Imager (SSM/I) brightness temperatures at 19 and 37 GHz using artificial neural network ANN-based techniques. The SSM/I used data, which consist of Pathfinder Daily EASE-Grid brightness temperatures, were supplied by the National Snow and Ice Data Centre (NSIDC). They were gathered during the period of time included between the beginning of 1996 and the end of 1999 all over Finland. A ground snow data set based on observations of the Finnish Environment Institute (SYKE) and the Finnish Meteorological Institute (FMI) was used to estimate the performances of the technique. The ANN results were confronted with those obtained using the spectral polarization difference (SPD) algorithm, the HUT model-based iterative inversion and the Chang algorithm, by comparing the RMSE, the R2, and the regression coefficients. In general, it was observed that the results obtained through ANN-based technique are better than, or comparable to, those obtained through other approaches, when trained with simulated data. Performances were very good when the ANN were trained with experimental data.
The effects of an atmospheric correction method for water quality estimation have been studied an... more The effects of an atmospheric correction method for water quality estimation have been studied and validated for Airborne Imaging Spectrometer for Applications (AISA) data. This novel approach uses atmospheric input parameters from a numerical weather prediction model: HIRLAM (High Resolution Limited Area Model). The atmospheric correction method developed by de Haan and Kokke (1996) corrects the spectrometer data according to the coefficients calculated using Moderate Resolution Transmittance Code (MODTRAN) radiative transfer code simulations. The airborne campaigns were carried out at lake and coastal Case 2 type water areas between 1996 and 1998. The water quality interpretation was made using the MERIS satellite instrument wavelengths. The correction improved most of the water quality (turbidity, total suspended solids, and Secchi disk depth) estimates when data from several flight campaigns were used jointly. The atmospheric correction reduced the standard deviation of the measurements conducted on different days. The highest improvement was obtained in the estimation of turbidity.
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Papers by J. Pulliainen