Correlation of thermal satellite image data to generate spatially high-resolution heat maps

The present invention relates to the determination of spatially high-resolution thermal maps using thermal satellite image data.

A land surface temperature (LST, Land Surface Temperature) determined as precisely as possible is an important component of many different applications, e.g. in the detection of forest fires, the measurement of local maxima in the urban temperature distribution (UHI, Urban Heat Island), in the determination of vegetation indices in agriculture or in modeling the local and global climate. The large-scale and precise measurement of the land surface temperature has only become possible with the use of satellites.

For example, the temperature is not measured directly, but derived from the radiance at the detector on board the satellite. The radiation density Rx is the radiation (energy) emitted by a surface in a given time in a given spectral range in a given direction and is given in watts per square meter steradian.

The detector itself records a gray value image in which the brightness of an individual pixel can be assigned to a measured radiance.

The assignment of gray values ​​DN to radiance R is done, for example, by a linear equation of the shape

R = a DN + b

, the constants a and b being determined by calibration of the detector.

With a known wavelength l, the temperature T can be deduced directly from Planck's law of radiation using the following relationship:

k = 2 hc 2

k 2 = hc / a B

Here h is Planck's quantum of action, c the speed of light in a vacuum and s B the Boltzmann constant.

The measured output variable from which the temperature can be derived can depend on the type of detector. With so-called micro bolometers, for example, either a temperature-dependent electrical resistance is measured or the temperature change of the absorber is measured directly by a thermometer. In the first case, the temperature is deduced from a previously determined functional relationship between resistance and temperature.

Mercury-cadmium-telluride (English MCT) detectors are semiconductor elements in which the amount of energy captured per pixel correlates linearly with its brightness to a good approximation. With a known field of vision of the pixel and exposure time, conclusions can be drawn about the radiance, which is related to the temperature as described above.

Another type of detector is the quantum well infrared photodetector (QWIP), which also releases charge carriers in the event of infrared (IR) light, which then generate a measurable current.

There are other types of detectors which usually have in common that the temperature is determined as a derived variable by changing easily measurable physical parameters (resistance, voltage, etc.).

A general challenge when measuring LST with infrared cameras from space is to achieve a high absolute radiometric accuracy, i.e. accuracy of the measured radiation temperature of the earth's surface. The measured temperature should reflect the actual radiation temperature of the measuring area on average as precisely as possible. Difficulties arise here, among other things, from temperature fluctuations of the detector, the passage of the IR radiation to be measured through the atmosphere, the length of the lenght and the viewing angle-dependent degree of emission of the earth's surface, different surface types (such as roads, fields, forests, buildings, etc.) with different degrees of emission in the measuring range, the reflected solar radiation and the physical interpretation of the results.

Many of the aforementioned applications require both high temporal and high spatial resolution at the same time, for example to record the daily cycle of UHIs or evapotranspiration. This requirement cannot be met by current satellite platforms or those planned for the next few years.

Geostationary satellites such as GOES or Meteosat have very high time repetition rates (5 minutes for MSG SEVIRI in Rapid Scanning Mode) and high radiometric precision, but limited spatial resolution with projected pixel sizes of several square kilometers.

Spatially high-resolution platforms such as Landsat-7 or 8 (ETM +, TI RS) and Terra (ASTER) offer spatial resolutions of around 60 - 120 m, but they only allow global coverage with a repetition rate of around two weeks.

In general, high-quality calibration methods for precise measurement of the surface temperature on board the satellite are very complex and can only be implemented within large satellites so far. These platforms have the disadvantage that with high spatial resolution (e.g. Landsat 7 and 8) in low orbits it takes a very long time before the same area can be observed again and using a constellation of a large number of such systems is extremely costly meant. More distant platforms (Meteosat, GEOS) suffer from the problem of poor spatial resolution, as described above.

There are therefore several approaches to generate radiometrically precise thermal data with high spatial and optionally also high temporal resolution. The approaches all have in common that they combine existing, precise thermal satellite data from large platforms such as Meteosat and Landsat, which have complex calibration methods on board, with spatial, higher-resolution auxiliary data. Such a downsampling of thermal data can be used to obtain thermal images with a higher spatial resolution and to use these, for example, in the context of a time series that is also highly temporally resolved.

There are currently two types of methods to achieve this spatial resolution increase. Thermal sharpening (TSP, Thermal SharPening) uses high-resolution physical environment models to infer temperatures in a rectangular subpixel network. These can be, for example, maps with precise information on emission coefficients or the local Al bedo. It is assumed that the recorded, coarse temperature pixel can be expressed as a linear combination of its subpixel constituents. These are divided into classes depending on the parameter set (for example "forest", "meadow", street "," building ") and correlated, for example, using multilinear regression or sequential Monte Carlo methods.

The emission coefficient e determines the relationship between the measured radiation temperature T s and the kinetic surface temperature T 0 of a body via the following relationship:

Ts e T 0

In remote sensing methods, the product of temperature and emission coefficient is usually measured. Since both quantities cannot be determined independently of one another at the same time in one volume, the problem is then underdetermined. Accordingly, further information is required. There are various ways of determining the emission coefficient.

On the one hand, there is a map of the observed area with detailed information on the type of landscape (CORINE is such a map for Europe, which is recreated about every 2 years). If the type of landscape is known, the corresponding emission coefficients (or, closely related to this, the reflection coefficients) can be read out and assigned from so-called spectral libraries (Gillespie et al. 1998).

Alternatively, when using several spectral bands (at least 3), emission coefficients and temperature can be separated. For areas covered with vegetation, normalized is also often used

Difference Vegetation Index (NDVI) inferred from the emission coefficient (Valor & Caselles 1996).

The use of TSP can lead to relatively precise results with moderate LST errors (RMSE, Root Mean Square Error) of about 2 - 3 K. The results are all the more accurate when the resolution is increased moderately (about 10) and the data available the fewer classes and sub-components are chosen. In real use on Meteosat data, the factor of the increase in resolution (downsampling) is around 40 - 100 depending on the application and there are many classes within a rough pixel (pixel size of SEVIRI at least 9.6 km 2 , over Central Europe around 15 km 2 ) . In the best case, the mean error is around 5 K, although individual outliers can differ by 20 K and more.

Alternatively, attempts are made to use spectral unmixing (TUM, Temperature UnMixing) to identify irregularly shaped subpixel components. With this method, for example, several thermal bands are analyzed in parallel in order to increase the resolution of the output data. Alternatively, temporal image sequences of the same region, different viewing angles or different resolutions in further, synchronously recorded thermal bands can be used in order to improve the spatial resolution of a region. However, there are few satellite sensors that record regions from several angles, the required emission coefficient is difficult to determine when using several bands and time sequences are difficult to parameterize if the surface parameters are variable over time.

In contrast, less expensive small satellites with high spatial resolution are not able to record images with high radiometric precision. When using smaller satellite platforms, for example pico or nano satellites, approaches for creating high-spatial resolution thermal data are mostly based on the calibration of high-spatial resolution IR images together with reference objects of known temperature. By recognizing the reference objects within the IR image, this can then be calibrated accordingly. Alternatively, a second sensor is used, which supplies precise temperature information of an image section. The use of cooled detectors on small satellites is also used, but is usually not sufficient to determine the temperature to less than a few Kelvin.

There is therefore a need for simple, inexpensive solutions for the creation of spatially high-resolution thermal images with high radiometric precision, as well as for the creation of temporally high-resolution time series consisting of various spatially high-resolution thermal images of high radiometric precision.

The present application of property rights solves this problem in accordance with the independent claims. Advantageous further developments are also described in the dependent claims and this description.

The proposed solution consists in the use of small satellites to create spatially high-resolution heat maps of the earth's surface with simultaneously high radiometric precision. The principle presented is based on the compilation of 1) thermal data recorded with satellites with high absolute measurement accuracy but low spatial resolution with 2) co-registered in time and space, i.e. synchronously in time and from the same area, thermal data recorded by a second satellite platform with lower levels Measurement accuracy but significantly higher spatial resolution.

Messwerte sind hierbei beispielsweise die Grauwerte eines Photosensors, die einer physikalischen Strahldichte (und damit Strahlungstemperatur) zugeord net werden können, der elektrische Widerstand oder eine elektrische Span nung, der direkt einer Temperatur zugeordnet werden können oder weitere zur berührungslosen Bestimmung der Temperatur verwendeten physikali schen Messgrößen.

The method described here and the device combine the advantages of both measurements and thus result in both a high temperature precision and a high spatial resolution. The usual methods for spatial disaggregation of satellite-based LST can at least largely avoid the uncertainties that arise. Furthermore, a method for creating a time series of spatially high-resolution heat maps and a computer program product for performing the described method are described.

The method described is designed to determine the heat map of an area, preferably to determine a spatially high-resolution heat map with a high level of measurement accuracy, such as high temperature accuracy.

The method comprises receiving a first thermal image and receiving a second thermal image, the thermal images being made available by different platforms and therefore having different properties.

A thermal image is a graphic representation of a physical unit over a region and can represent different physical units, for example a radiance (W / m 2 sr) or a temperature (K).

The first thermal image, which is recorded by a recording device of a first satellite, has a high radiometric precision but a low spatial resolution. The first thermal image shows the recorded thermal radiation of a first landscape that includes the area, the first thermal image including pixels that are spatially assigned to the area and the first thermal image of the area pixel by pixel a measured first temperature value, radiance, or value of another radiometri associated measured variable.

The second thermal image, which is recorded by a recording device of a second satellite or, alternatively, a drone, a balloon or another manned or unmanned flying object, has a lower radiometric precision but a higher spatial resolution, compared to the first thermal image. The second thermal image shows the recorded thermal radiation of a second landscape that includes the area, the second thermal image including pixels that are spatially assigned to the area and the second thermal image assigns a measured second temperature value to the area pixel by pixel.

The "area" for which the heat map is to be determined can be a small part of the landscape, for example a single building or field, or a larger part of the landscape shown on the heat images, such as a village , a district or an entire city. The “area” can thus extend over various pixels of at least the second thermal image.

Different values ​​can be assigned to the individual pixels of the two thermal images. In addition, the spatial resolution of the second thermal image is higher than the spatial resolution of the first thermal image, so that a plurality of pixels of the second thermal image are spatially assigned to one pixel of the first thermal image.

The first and the second thermal image continued to be synchronized in time or with only a slight time offset below a specified one

Barrier of preferably a few minutes, for example 10 minutes. The time tolerance is given by the characteristic time span with which the temperature or the radiation properties in the area change significantly. This period of time can vary depending on the framework conditions, such as the weather conditions, and the intended application.

The high radiometric precision and thus temperature accuracy or, more generally, measurement accuracy of the first thermal image results, for example, from the recording device of the first satellite, which is preferably a large satellite, such as a weather satellite, and which has complex calibration technology, for example using specially temperature-controlled black bodies or sensors. Satellites that can record images with high measurement accuracy, such as temperature accuracy, are known from the prior art, but the necessary calibration technology is very costly and too large for small satellites such as pico, nano, or microsatellites. The first satellite can be a geo-stationary satellite, which further enables a high temporal repetition rate of the recorded thermal images. However, geostationary weather satellites are not able to take high-resolution images due to their great distance from Earth. The spatial resolution of the first thermal image is thus in the range of one to several square kilometers per pixel.

Alternatively, the first satellite can be a large satellite in low earth orbit, such as known from the Landsat satellites. A satellite of this type, which is also configured using calibration technology to record thermal images with high radiometric precision, enables a significantly better spatial resolution compared to a geo-stationary satellite. However, the spatial resolution is still in need of improvement for many applications. Furthermore, such large satellites in a low earth orbit can only record the same section of the earth's surface at an interval of several days to several weeks,

Satellite is possible. Since large satellites are very costly, it is financially very expensive to use the large number of large satellites necessary for better time accuracy in a low earth orbit (with a distance of less than 2000 km from earth).

The second thermal image can be recorded by a small satellite circling closer to the earth well below 1000 kg or below 500 kg, such as a picosatellite up to approx. 1 kg, a nanosatellite up to approx. 10-15 kg or a microsatellite up to approx. 100 kg . The proximity to the earth enables a higher spatial resolution. Such satellites orbit in a lower earth orbit at a distance of typically between 300 and 1000 km from the earth's surface, for example between 500 and 800 km from the earth's surface. In general, smaller satellites and satellites with shorter lifetimes orbit closer to Earth than larger satellites and / or satellites with longer lifespans. However, with the small satellites, which are several orders of magnitude cheaper (as of 2018, the costs for a geosatellite are in the range of approx. 1000 million euros, while a nanosatellite costs between 1 and 10 million euros), the technical possibilities and the space for Agile technology such as calibration technology on board is limited, so that the thermal images measured by a small satellite can under certain circumstances deviate significantly from the actual measured variables, such as temperatures or radiance. Deviations can arise, for example, from temperature fluctuations in the infrared sensor used for recording. Since small satellites orbit much closer to the earth than, for example, weather satellites, The thermal images can be recorded by a small satellite with a significantly higher resolution of edge lengths of the individual pixels under 100 m, preferably under 50 m, between 30 m and 50 m or under 30 m. When post-processing the recorded images, an even higher spatial resolution, for example less than 20 m or less than 10 m, is also possible, for example by using or superimposing several recorded images. The second satellite, with the recording device of which the second thermal image was recorded, is generally in a low earth orbit, in particular in a lower earth orbit than the first satellite, which means that a single satellite only has a lower repetition frequency of at least several hours preferably below 50 m, between 30 m and 50 m or below 30 m. When post-processing the recorded images, an even higher spatial resolution, for example less than 20 m or less than 10 m, is also possible, for example by using or superimposing several recorded images. The second satellite, with the recording device of which the second thermal image was recorded, is generally in a low earth orbit, in particular in a lower earth orbit than the first satellite, which means that a single satellite only has a lower repetition frequency of at least several hours preferably below 50 m, between 30 m and 50 m or below 30 m. When post-processing the recorded images, an even higher spatial resolution, for example less than 20 m or less than 10 m, is also possible, for example by using or superimposing several recorded images. The second satellite, with the recording device of which the second thermal image was recorded, is generally in a low earth orbit, in particular in a lower earth orbit than the first satellite, which means that a single satellite only has a lower repetition frequency of at least several hours For example, by using or superimposing several recorded images, an even higher spatial resolution, for example below 20 m or below 10 m, is possible. The second satellite, with the recording device of which the second thermal image was recorded, is generally in a low earth orbit, in particular in a lower earth orbit than the first satellite, which means that a single satellite only has a lower repetition frequency of at least several hours For example, by using or superimposing several recorded images, an even higher spatial resolution, for example below 20 m or below 10 m, is possible. The second satellite, with the recording device of which the second thermal image was recorded, is generally in a low earth orbit, in particular in a lower earth orbit than the first satellite, which means that a single satellite only has a lower repetition frequency of at least several hours

or days or, for example, 2-4 weeks at a relatively high resolution of, for example, about 100 m, for which images of an area are possible, please include. The repetition rate can be viewed as a function of the resolution. If spatially high-resolution images are recorded, a small area is necessarily recorded due to the limited detector size. Accordingly, it takes a relatively long time before this area is covered again (unless the orbit has been set in such a way that a certain area is overflown more frequently, at the expense of the total surface coverage). The revision time is therefore a function of the field of view of the detector and can therefore be directly linked to the resolution.

The method for determining the thermal map further comprises determining a measured variable offset, such as a temperature offset, of a first pixel group of the second thermal image spatially assigned to the area, the first pixel group comprising a plurality of pixels. As described above, the absolute measurement accuracy, for example temperature accuracy, of the second thermal image is low and can be, if the measured variable is, for example, a temperature, by several degrees, for example above 2 K, or, more often, above 5 K or even over 10-20 K, differ from reality. However, it can be assumed that the relative measured values, for example relative temperature values, of the second thermal image have sufficient accuracy,

By comparing a measured variable mean value, for example the temperature mean value, for the first pixel group with a value of the same measured variable of the reference pixels of the first thermal image, a measured variable offset for the first pixel group can be determined. For example, it is also possible to take into account neighboring pixels both with regard to the first thermal image (i.e. neighboring pixels of the at least one first pixel) and with regard to the second thermal image (i.e. neighboring pixels of the first pixel group) when determining the measured variable offset. The "coarser" reference pixels of the first thermal image can be expressed as a linear combination or weighted sum of the first pixel group. The pixels of the first pixel group are taken into account when calculating the mean with the weighting of their area share. This way, pixels of different sizes can also be taken into account. The measured values ​​can either be scalar measured variables for each pixel, or it is possible that several values ​​are recorded at the same time, so that the individual measured values ​​are then available in vector form.

The method further comprises the step of determining corrected absolute measured values ​​of the pixels of the first pixel group assigned to the area based on the second measured values ​​of the pixels of the first pixel group and the measured value offset.

The corrected absolute measured values ​​for the pixels of the first pixel group are preferably determined by adding the second measured values ​​and the previously determined measured value offset.

The method also includes a step for determining or generating the spatially high-resolution heat map with high radiometric precision on the basis of the corrected absolute measured values.

For example, the measured variable is a temperature, measured for example in Kelvin. In this case, the temperature offset between the two thermal images is determined. But it can also be a beam size, measured for example in watts per square meter and per stera diant, an electrical resistance measured in ohms, for example, or a voltage measured in volts, for example.

Thus, by combining two recorded thermal images with different properties, a significantly improved thermal map of the area can be created.

In particular, the method determines the radiation temperature of the land surface, for example by means of infrared. Depending on the application, the land surface temperature can also be precisely determined from the radiation temperature given by the specific heat map with the aid of an emissivity of the respective surface, depending on the type of the respective surface.

It should be noted that infrared sensors first measure specific intensities or radiance, which are integrated over the respective surfaces that are as congruent as possible in order to compare the measured radiances of the first and second thermal images. A radiation temperature can then be determined from the measured values, which can then be averaged for the respective areas.

For this purpose, a pixel is illuminated, for example, with a certain amount of energy in the IR and thus has a certain brightness. Due to the known pixel response (e.g. measured in the laboratory before the start), which is normally almost linear in the area to be considered, physical units can now be deduced from the pixel brightness, e.g. by means of a compensation function or a conversion table. In this case it is the spectral radiance (W / (m 2 sr pm)). The (radiation) temperature then results directly from Planck's law of radiation. The surface temperature can then be determined from this and an emission coefficient.

Due to the high spatial resolution and high radiometric precision of the thermal images generated by the method described, the determination of the surface temperature is possible with significantly reduced effort compared to the methods known in the prior art.

Preferably, precisely one pixel of the first thermal image is spatially assigned to the first pixel group of the second thermal image. In this case, the measured value offset of the first pixel group of the second thermal image is preferably determined by a possibly weighted sum of relative measured values ​​of the first pixel group of the second thermal image compared to the first measured value of the pixel of the first thermal image spatially assigned to the first pixel group of the second thermal image .

This calculation is carried out on the assumption that a mean value of the measured values ​​of the pixels of the first pixel group of the second thermal image is known from the value of the first pixel of the first thermal image measured with high measurement accuracy. The measured value offset DG can then be calculated out. This is given by the difference between the mean values ​​of the measured value of the first pixel of the first thermal image, on the one hand, and the measured values ​​of the pixels of the first pixel group of the second thermal image on the other.

The following applies

T A ( . X, y) is the recorded measured variable of the first thermal image of the pixel (x, y) and T B (i, j) that of the measurement of the second thermal image in all pixels (i, j) within the first pixel group where w (j, j) represents the respective area portion of the pixel (i) in the total area spatially assigned to the first pixel. Thus w (j, j) = 1 if the pixel (i, /) is completely contained in the total area of ​​the first pixel.

If the temperature is selected as the measured value, then this linear approach applies as a good approximation for an average temperature range around 300 K found on earth, as can normally be found on a single thermal image with a difference between the maximum and minimum recorded Temperature of a few tens of Kelvin, e.g. 20 K or 30 K.

If, instead, the radiance is chosen as the measured variable, the mathematical formulation corresponds to the law of conservation of energy and it applies exactly.

It is furthermore preferably possible for the method, after calculating the corrected absolute temperature values ​​of the pixels of the first pixel group, to include an optional post-processing of the pixels of the first pixel group using land surface models. With such land surface models, for example using emission coefficients associated with the respective surfaces, precise surface temperatures can be calculated from corrected absolute temperature values, which for example indicate a radiation temperature.

The present application also comprises a device for determining the heat map of an area, the device comprising at least one receiving unit and one determining unit.

The receiving unit is configured to receive a first thermal image of a first landscape encompassing the area, the first thermal image being recorded by a recording device of a first satellite, the first thermal image including pixels that are spatially assigned to the area and the thermal image to the Area assigns a first recorded measured value pixel by pixel.

The at least one receiving unit is further configured to receive a second thermal image of a second landscape comprising the area, the second thermal image being recorded by a recording device of a second satellite, the second thermal image comprising pixels that are spatially assigned to the area and the second thermal image assigns a second recorded measured value to the area pixel by pixel.

Between the recording of the first thermal image and the recording of the second thermal image, there is only a slight time lag below a specified limit of preferably a few minutes, for example recorded less than 30 minutes or less than 10 minutes.

Furthermore, as already described in relation to the method, a ra diometric precision of the first thermal image is higher than a radiometric precision of the second thermal image, and a spatial resolution of the second thermal image is higher than a spatial resolution of the first thermal image.

The determination unit of the device for determining the thermal map of the area is configured to determine a measured value offset of a first pixel group of the second thermal image spatially assigned to the area, the first pixel group comprising a plurality of pixels by a sum or linear combination of relative measured values ​​of the pixels first pixel group compared to the first recorded measured value of the least one first pixel of the first thermal image, the at least one first pixel of the first thermal image being at least partially spatially assigned to the first pixel group of the second thermal image.

If the measured values ​​are temperature values, a temperature offset can be determined, for example, using formulas (1) and (2).

It should be noted here that, when recording radiation data, the calculation of the radiation temperature can be done either before or after the offset is determined. It is therefore possible, as described in formulas (1) and (2), for the sensor data to be converted into temperature values ​​and then a temperature offset to be determined by comparing the temperature values ​​of the first and second thermal image. Alternatively, however, a measured value offset can also take place on the basis of another measured or derived variable and the conversion into temperature values ​​only take place afterwards.

The determination unit is further configured to determine corrected absolute measured values ​​of the pixels of the first pixel group based on the second recorded measured values ​​of the pixels of the first pixel group and the measured value offset.

Optional, advantageous embodiments for the method and the device for determining the heat map are described below.

Preferably, both the first and the second thermal image are each infrared images that were recorded by infrared sensors of the first and the second satellite, respectively.

For normal LSTs in the range of large-area temperatures occurring on earth, eg -40 to 60 ° C, infrared waves in the range of 8-14 pm, often only 10.8 and 12 pm, are used, for example. For forest fires, the shorter-wave infrared range is more interesting, for example between 3 - 5 pm. Most weather satellites therefore have multiple bands that cover the range 0.5-13 mih. The recorded measured values ​​can therefore be in vector form.

The spatial resolution of the first thermal image is generally clear, at least one or even two or three orders of magnitude coarser than the spatial resolution of the second thermal image. For example, the pixels of the first thermal image can have an edge length of at least approx. 1 km, so that the area covered by a pixel in the first thermal image is approximately 1 km 2 or even more than 1 km 2 or more than 3 km 2lies. In contrast, the edge length of a pixel of the second thermal image is preferably less than 100 m, or even less than 70 m or less than 50 m. Ideally, the edge length of a pixel of the second thermal image is in the range of only 10 m, so that the area covered by a pixel of the second thermal image is imaged, is only a few 100 m 2 .

Alternatively, the method described here can also be used if the spatial resolution of the first thermal image is less than an order of magnitude, for example only by a factor of 2 or 3, coarser than the spatial resolution of the second thermal image. The method can be applied to any image pair in which the spatial resolution of the second thermal image is higher or less coarse than the spatial resolution of the first thermal image.

In addition, the radiometric precision of the first thermal image is higher than the radiometric precision of the second thermal image. This property of the thermal images is preferably achieved in that the recording device of the first satellite has appropriate calibration technology, for example through the use of black bodies or specially cooled or temperature-controlled sensors, which have a high radiometric accuracy, preferably below 2 K or particularly preferred, even below 1 K, ideally guaranteed per pixel or also on the average of the recorded thermal images. To increase the ability to assess the accuracy, the satellite recording devices can continue to deliver data that, preferably per recorded pixel specify a measurement accuracy estimated by sensors of the recording device. The radiometric precision of the second thermal image is lower, for example because the second thermal image was recorded by the recording device of an inexpensive small satellite or another manned or unmanned aircraft that does not have a correspondingly precise calibration technique.

Furthermore, the present application comprises a method for determining the change in a measured variable within an area over a period of time, such as, for example, within an hour, a day or also within a week, a month or a year. By determining the temporal change in the measured variable of an area as precisely as possible, urban heat islands, for example, can be recorded and their causes analyzed. Furthermore, such measured variable time series provide knowledge for urban planning, for example the effect of green spaces, various types of roofs or bodies of water on the urban climate.

The described method for determining the change in the measured variable within an area initially comprises determining at least two, preferably a plurality of more than 10 or more than 100, as precise as possible heat map of the area at different times, the two heat maps according to the above Procedures were determined.

In particular, the first thermal map was determined using a first and a second received thermal image of the area, the first and second thermal images being recorded synchronously or approximately synchronously at a first point in time and, of the first and second thermal images, the first thermal image being a higher radiometric image Precision and the second thermal image has a higher spatial resolution.

Furthermore, the second thermal map was determined using a third and a fourth received thermal image of the area, the third and fourth thermal images being recorded synchronously or approximately synchronously at a second point in time and, of the third and fourth thermal images, the third thermal image being a higher radiometric image Precision and the fourth thermal image has a higher spatial resolution.

A time series of the measured variable within the area can then be created from the first and second heat map of the area.

It is optionally also possible in this way to determine heat maps of a multiplicity of points in time for an area and to create a time series of the measured variable within the area based on the multiplicity of heat maps of the area.

The first and second points in time are preferably within a specified period of time, for example within a day, or within a few hours or even within less than an hour or half an hour, in order to ensure a high temporal resolution of the time series of the measured variable within the area, for example around the Record changes in the measured variable within a day.

In order to achieve such a high temporal resolution, it is possible, for example, that the first and third thermal images are each recorded from a large satellite platform with appropriate calibration technology to ensure high radiometric precision, such as a geostationary weather satellite. When using a first and third thermal image which are recorded by geostationary satellites, both the first and the third thermal image are preferably recorded by the recording device of the same geostationary satellite. Alternatively, the first and third thermal images can also come from different geostationary satellites with similar coverage.

Furthermore, it is alternatively also possible that the first and third thermal image are recorded by the recording devices of different large but not geostationary satellites in a low earth orbit, the recording devices each being equipped with calibration technology for high measurement accuracy. If the first and third thermal image come from different non-geostationary satellite platforms, this is only possible if these two satellite platforms have been over the area within the specified time period. Since large, non-geostationary satellites, such as Landsats, have a repetition rate of several days to several weeks in relation to a given area,

How many pairs of images are necessary to create a time series, as well as the tolerable time intervals between the pairs of images, is generally very dependent on the application. For some applications in which a temperature development is to be observed, for example over the year, one or two images per day may be sufficient, while for other applications in which, for example, a heat development is to be examined during the day, a temporal resolution of only a few minutes is desirable worth, for example, under 10 or under 30 minutes. To create a longer time series consisting of over 10 or over 100 pairs of images, it is helpful to

In order to achieve a high spatial resolution at the same time, the second thermal image and the fourth thermal image are each recorded by recording devices from small satellites or other manned or unmanned flying objects, which can provide a high spatial resolution of the recorded thermal images due to their proximity to earth.

These small satellites just described generally orbit the earth in low earth orbit. To achieve a high temporal resolution, the thermal images of several different small satellites, such as pico, nano or microsatellites, can therefore preferably be used, so that the second thermal image was recorded by the recording device of a first small satellite while the fourth thermal image was recorded by the recording device of a second small satellite was recorded. Since small satellites, such as CubeSats, are cheaper to manufacture compared to large geostationary satellites, it is possible to use various small satellites to generate a time series with a high temporal resolution of the measured variable within an area.

The present description further comprises a computer program product for calculating a thermal image, the computer program product containing instructions which, when executed on a computer, carry out the method described above. The instructions can optionally be instructions for determining a spatially highly resolved and at the same time precise thermal image of an area, or the instructions can be instructions for determining a time series of the measured variable within the area. In any case, the instructions are based on thermal images from different satellite platforms, which are received by a corresponding receiving unit on the computer.

The computer can be a stationary PC or a mobile computer. The computer can also be part of a distributed system or a

be a cloud-based service suitable for executing program instructions.

The features described above with reference to the method or the device for creating a heat map can also be used in relation to the method, the device or the computer program product.

Further advantageous embodiments are described below with reference to the figures. In this case, the temperature was chosen as the measured variable because it is easy to interpret. Alternatively, however, the measured values ​​can also include other radiometric data such as radiance or other data recorded by the sensors of the satellites used or derived from the recorded measured values. Show it

1 shows a schematic representation of a device for determining a heat map of an area;

2 shows a schematic representation of a sequence of the method for determining a heat map of an area;

3A shows a schematic representation of a thermal image of the earth's surface recorded with a first satellite A;

3B shows a schematic representation of a thermal image of the earth's surface recorded with a second satellite B in synchronism with FIG. 3B;

FIG. 4A shows a schematic representation of an individual image point of the thermal image recorded by satellite A according to FIG. 3A; FIG.

4B shows a schematic representation of the measurement of satellite B of the same region as shown in FIG. 4A;

Fig. 4C Schematic representation of the specific heat map based on Fig. 4B and a specific temperature offset.

Figure 1 shows a device for determining a heat map of a Ge area. The device 101 comprises a receiving unit 102, which can receive signals from various satellites 104 and 105 orbiting the earth 106. The signals from the satellites 104, 105 are mostly received indirectly, that is, not directly from the satellite, but via an intermediate device 107 which receives the signals from the satellite (s) 104, 105 and then forward them or make them available for download. The receiving unit receives, in particular, thermal image data of the earth's surface that were recorded by recording devices of the satellites 104 and 105. To obtain a detailed heat map of an area with high spatial resolution in the range of a few meters to less than 10 meters and high radiometric measurement accuracy,

Here, the first satellite 104 is a large satellite with high temperature accuracy, such as a geostationary weather satellite or a large satellite orbiting in a low earth orbit, such as a Landsat.

In order to achieve a temperature accuracy of preferably below 2 K, the first satellite 104 has built-in calibration technology. In the case of a geostationary first satellite, this first satellite 104 only has a very coarse spatial resolution due to its great distance from the earth, so that the recorded pixels have a size of several square kilometers. Alternatively, if a large satellite in a low earth orbit, such as a Landsat, is used as the first satellite 104, the spatial resolution is already better than the previously described geostationary satellites, but there is still potential for improvement considering spatial Higher resolution thermal images from a second satellite platform are possible. For example, Landsat 8 currently has a resolution of 100 m. By using a small satellite as an auxiliary satellite, this resolution can be improved to well below 60 m. With post-processing of the data, even lower resolutions, for example less than 30 m, are possible.

The second satellite 105 is a small satellite or CubeSat, which has a higher spatial resolution compared to the first satellite, but only a reduced measurement accuracy compared to the first satellite.

The receiving unit 102 of the described device receives temporally and synchronously recorded first and second thermal images of spatially co-registered landscapes of the two satellites 104 and 105, ie the subsections of the earth's surface recorded at least partially overlap. In order to create a precise thermal map of an area that is mapped on both thermal images, the device 101 further comprises a determination unit 103.

The determination unit 103 is configured to spatially assign a pixel of the first thermal image of the first satellite to a group of pixels of the second thermal image of the second satellite and to calculate a measured variable offset or temperature offset of the group of pixels of the second thermal image. This calculation is based on the assumption that the temperature deviation or measured variable deviation of the pixels of the second thermal image is at least locally constant, ie that the deviation of one pixel does not differ, or only very slightly, from the deviation of neighboring pixels. Furthermore, the assumption is made that the (measured with high precision) measured variable or temperature of the pixel of the first thermal image can be represented as a (weighted) sum or linear combination of the assigned pixels of the second thermal image. The measured variable offset for the group of image points of the second thermal image can thus be determined from the relative measured variable differences between the image points of the group of the second thermal image and the recorded measured variable of the assigned image point of the first thermal image. The determination unit 103 is further configured to create a precise heat map of the area from the measured variable offset and the recorded measured variables of the second thermal image. The measured variable offset for the group of image points of the second thermal image can thus be determined from the relative measured variable differences between the image points of the group of the second thermal image and the recorded measured variable of the assigned image point of the first thermal image. The determination unit 103 is further configured to create a precise heat map of the area from the measured variable offset and the recorded measured variables of the second thermal image. The measured variable offset for the group of image points of the second thermal image can thus be determined from the relative measured variable differences between the image points of the group of the second thermal image and the recorded measured variable of the assigned image point of the first thermal image. The determination unit 103 is further configured to create a precise heat map of the area from the measured variable offset and the recorded measured variables of the second thermal image.

FIG. 2 schematically shows a method for determining a precise heat map of an area, the method being able to be implemented, for example, by the arrangement described above or by a computer program product which includes corresponding instructions for carrying out the method.

The procedure described here for determining the precise thermal map of an area involves the following steps:

S201: Receiving a first thermal image of a first landscape that encompasses the area, which was recorded by a recording device of a first satellite, the first thermal image including pixels that are spatially assigned to the area and the thermal image of the area is recorded pixel by pixel Assigns measured value.

S202: Receiving a second thermal image of a second landscape encompassing the area, which was recorded by a recording device of a second satellite, the second thermal image including pixels that are spatially assigned to the area and the thermal image of the area one pixel at a time assigns the second recorded measurement value.

It should be noted that steps S201 and S202 can be carried out in any order or also simultaneously.

The first and second thermal images received must be used to perform the

Method also meet the following requirements: between the recording of the first satellite image and the recording of the second satellite image, there may only be a time offset below a specified limit, preferably below 10 minutes or even below 5 minutes, since the correlation of the thermal measured values ​​that is given in the two thermal images, if there is a greater time interval between the recordings of the two images, this is impossible or only possible with difficulty. The time limit can be selected depending on the prevailing framework conditions. If the weather conditions remain the same, longer distances between the two recordings can be tolerated under certain circumstances than in changing weather conditions.

Furthermore, a radiometric precision of the first thermal image, ie the accuracy of the recorded measured variable, is higher than a radiometric precision of the second thermal image, whereas a spatial resolution of the second thermal image is higher than a spatial resolution of the first thermal image.

After receiving the two thermal images, the following additional process steps can be carried out:

S203: determining a measured variable offset of a first pixel group of the second thermal image spatially assigned to the area, the first pixel group comprising a plurality of pixels, by means of a weighted sum of relative measured values ​​of the pixels of the first pixel group of the second thermal image compared to the first recorded measured value of the at least one pixel of the first thermal image which is at least partially spatially assigned to the first pixel group of the second thermal image. In this case, for example, the mean value of the measured values ​​of the first pixel group is determined in order to then determine the offset, for example as a difference, to the assigned measured value or values ​​of the first area of ​​the first thermal image.

S204: Determination of corrected absolute measured values ​​of the pixels of the first pixel group assigned to the area based on the second measured values ​​of the pixels of the first pixel group and the measured value offset.

S205: Creation of the precise heat map of the area based on the corrected absolute measured values.

The method just described for determining spatially high-resolution heat maps with high measurement accuracy is also described with reference to FIGS. 3A, 3B, 4A, 4B and 4C. The method presented here combines the radiometric accuracy of large satellite platforms with the spatial resolution of a second satellite platform, e.g. a small satellite or CubeSat. The method is based on the assumption that the specific intensity (radiance) of a pixel results from the linear combination of its subpixel components (linear mixing model). The ideal large satellite platform is one with a very high radiometric precision (Sentinel-3) and at the same time high temporal resolution (Meteosat Second and Third Generation).

The only requirement for the additional, smaller satellite is that it has the option of synchronous recording, i.e. recording IR images of the same region at the same time. The recorded images are shown schematically for both satellites in FIGS. 3A and 3B, in which case the temperature was selected as the relevant measured variable. Satellite A (FIG. 3A) denotes the platform with high radiometric precision and satellite B (FIG. 3B) denotes the platform with high spatial resolution.

The grid shown shows which areas of the image from satellite B are to be assigned to which image point from satellite A. The data from satellite B have an unknown temperature offset DG when measuring the radiation temperature, which does not allow an exact absolute temperature determination for satellite B alone. Relative temperature differences, for example between adjacent pixels, are correctly measured by satellite B (ensured by a previous calibration of the instrument).

By recording satellite A, the mean value of each grid cell drawn in from the image of satellite B is known. The as yet unknown temperature offset DG can thus be calculated out. This is given by the difference between the mean values ​​of each cell in both images,

The following applies

^ O ^ y) is the measured temperature of satellite A in cell (x, y) and T B (i, j) of the temperature measurement of satellite B in all image points (i, /) that are within this cell (x, y ) of satellite A with an area portion of w (i, j) each.

The measurement method is shown schematically for a single cell in FIGS. 4A, 4B and 4C: The mean value of all the pixels shown from satellite B (FIG. 4B) does not yet correspond to the value of the corresponding pixel from satellite A (FIG. 4A). By subtracting a temperature offset resulting from the difference, the entire cell of the image from satellite B is calibrated and now has the same mean value as recorded by satellite A (FIG. 4C).

There are several advantages over existing solutions:

The presented method combines the advantages of both measurements, high radiometric precision on the one hand and high spatial resolution on the other hand, without having to access other data sources. Resolutions of the order of a few meters can thus be achieved, which is particularly important for applications in the field of UHI and satellite-supported agriculture.

Due to the high spatial resolution of satellite B, there is no or only very limited downsampling and the number of surface classes per pixel is significantly reduced with a high resolution, which increases the stability and accuracy of the land surface temperature, which, taking into account land surface models and known emission levels the radiation temperature given by the specific heat map

ture can be determined.

Furthermore, the use of the unstable correlation between visual and IR images is avoided and the effect of temperature homogenization due to the selection of too few surface classes is significantly attenuated. In addition, errors due to a large resolution gap between the ground resolution of the satellite sensor and the resolution of the auxiliary data are avoided.

Thermal data with high radiometric precision already exist that can be used as a calibration standard for the concept. Copernicus offers temperature data with <0.2 K accuracy via Sentinel-3 at a resolution of 1 km 2 . These data are freely and openly accessible through the Copernicus program. Meteosat-9 offers a resolution of around 9 - 15 km 2 with a temperature accuracy of around 2 K and a temporal resolution of a few minutes with extensive coverage.

With a temporal repetition rate of around 10-15, or up to 5 minutes over Europe through Meteosat-9, the two data streams can be easily synchronized in terms of time and space.

The concept is therefore also suitable for measurements with high repetition rates over time when using several (small) satellites in a suitable constellation. A time series of the measured variable within an area can thus be created, which enables an analysis and / or visualization of the measured variable change of an area in the course of a certain period of time, for example in the course of a day.

Here, previously determined heat maps of an area are combined into a time series, the recording times of two consecutive heat maps of the time series each being within a specified time span. The time of recording the first and second thermal images assigned to this thermal map can be considered as the recording time of a heat map or, if the two synchronous recordings of the thermal images are slightly offset by the two satellites used, also an average time or a pair of the at both times when the thermal images were taken. The specified period of time can vary depending on the application, but is in many cases within a few hours, under an hour or even in the range of around 10 minutes.

The requirements for the IR detector of satellite B are relatively low, so that the approach is suitable for use in small satellites or CubeSats, which are much cheaper than traditional satellite missions. It is thus possible to improve the previously available spatial resolution by several orders of magnitude at a fraction of the cost of a traditional satellite mission. The maximum achievable spatial resolution is virtually only limited by the detector and the optics of the satellite.

An expensive and complex absolute calibration of the detector on board of satellite B is not necessary, without the accuracy of the resulting land surface temperature deteriorating. The solution does not require tracking or detection of objects in the image and no ground measurements.

The significant increase in spatial resolution without the introduction of sources of error generated by downsampling enables a multitude of new or improved applications, e.g. in the localized forecast of traffic conditions (e.g. wet or busy roads), in environmental research (e.g. movement of gravel deposits on glaciers Forest fires), in agriculture (vegetation health via evapotranspiration), in medicine (e.g. health risk from overheating) and, closely related to this, in urban planning (e.g. energy efficiency determination of buildings or measurement of the urban microclimate).

The latter is particularly interesting as an application to investigate the effects of urban heat islands and corresponding countermeasures. Increasing the resolution from square kilometers to the size of a building or building block provides important information on energy efficiency, temperature development and the influence of green spaces, whether green or green

reflective roofs or open bodies of water on urban climates. So far, corresponding surveys have been based on a patchwork of simulated models together with isolated soil measurements and data that were generated by one of the downsampling methods described above. A method with high temporal and spatial resolution can help

Planning the cities of the future to be healthier and more environmentally friendly.

FRAUNHOFER-GESELLSCHAFT ... eV

197PCT 1297

Claims

1. A method for creating a heat map of an area, comprising the following steps:

Receiving a first thermal image of a first landscape encompassing the area, which was recorded by a recording device of a first satellite (104), the first thermal image comprising pixels that are spatially assigned to the area and the thermal image of the area each pixel by a first measured value assigns (S201);

Receiving a second thermal image of a second landscape comprising the area, which was recorded by a recording device of a second satellite (105), wherein the second thermal image comprises pixels that are spatially assigned to the area and the thermal image of the area is a second pixel by pixel Assigns measurement value (S202);

wherein there is a time offset between the recording of the first thermal image and the recording of the second thermal image below a defined limit; and a radiometric precision of the first thermal image is higher than a radiometric precision of the second thermal image; and a spatial resolution of the second thermal image is higher than a spatial resolution of the first thermal image;

Determining a measured value offset of a first pixel group of the second thermal image spatially assigned to the area, the first pixel group comprising a plurality of pixels, by comparing relative measured values ​​of the pixels of the first pixel group of the second thermal image to the first measured value of the at least one pixel of the first Thermal image that is at least partially spatially assigned to the first pixel group of the second thermal image (S203),

Determining corrected absolute measured values ​​of the pixels of the first pixel group assigned to the area based on the second measured values ​​of the pixels of the first pixel group and the measured value offset (S204); and

Creation of the precise heat map of the area based on the corrected absolute measured values ​​(S205).

2. The method according to claim 1, wherein the method comprises in particular: the determination of the measured value offset of the first pixel group of the second thermal image by comparing relative measured values ​​of the first pixel group of the second thermal image to the first measured value of the pixel spatially assigned to the first pixel group of the second thermal image of the first thermal image.

3. The method according to any one of the preceding claims, further comprising determining land surface temperatures of the area based on the precise heat map and land surface models and / or emission models.

4. The method according to any one of the preceding claims, wherein the thermal images are infrared images.

5. The method according to any one of the preceding claims, wherein the spatial resolution of the second thermal image is at least one or two orders of magnitude higher than the spatial resolution of the two th thermal image.

6. The method or arrangement according to one of the preceding claims, wherein the measured values ​​are temperature values ​​and a radiometric deviation of the first thermal image per pixel is below 2K and the radiometric deviation of the second thermal image is on average over 2K.

7. The method according to any one of the preceding claims, wherein the first thermal image was recorded by a recording device of the first satellite calibrated to increase the radiometric precision.

8. The method according to any one of the preceding claims, wherein the first thermal image was recorded by the recording device of a large satellite platform, for example a geostationary satellite in a high earth orbit or a large, non-geostationary satellite in a low earth orbit.

9. The method according to any one of the preceding claims, wherein the second thermal image was recorded by the recording device of a small satellite.

10. A method for determining a change in temperatures of an area over a period of time, including the following steps:

Determining a first heat map of the area at a first point in time according to the method according to any one of claims 1 to 9 using a first and second received heat image;

Determining a second thermal map of the area at a second point in time according to the method according to one of claims 1 to 9 using a third and fourth received thermal image,

Create a time series of the temperature of the area based on the first and second heat map.

11. The method according to claim 10, wherein the first and the second point in time are within a specified period of time and the first and the third thermal image each have a high radiometric precision and were each taken from a first, large satellite platform, the second thermal image has high spatial resolution and was recorded by the recording device of a second satellite and the fourth thermal image has a high spatial resolution and was recorded by the recording device of a third satellite, the second and third satellites being different small satellites.

12. The method according to claim 10 or 11, further comprising the following steps:

Determine a plurality of additional heat maps of the Ge area at additional points in time, two consecutive points in time of the additional points in time each being within a specified time period, and

Creation of a time series of the temperature of the area based on the first heat map, the second heat map and the additional heat maps.

13. Computer program product for determining a heat map of an area or method for determining a change in temperatures of an area, the computer program product comprising instructions which, when executed on a computer, execute the method according to one of claims 1 to 12.

14. Device (101) for creating a heat map of an area

at least one receiving unit (102) configured for

Receiving a first thermal image of a first landscape encompassing the area, which was recorded by a recording device of a first satellite (104), the first thermal image comprising pixels that are spatially assigned to the area and the thermal image of the area each pixel by a first measured value assigns

Receiving a second thermal image of a second landscape comprising the area, which was recorded by a recording device of a second satellite (105), wherein the second thermal image comprises pixels that are spatially assigned to the area and the second thermal image to the area a second pixel by pixel Assigns measured value,

wherein there is a time offset between the recording of the first thermal image and the recording of the second thermal image below a defined limit; and a radiometric precision of the first thermal image is higher than a radiometric precision of the second thermal image; and a spatial resolution of the second thermal image is higher than a spatial resolution of the first thermal image;

Determination unit (103) configured for

Determining a measured value offset of a first pixel group of the second thermal image spatially assigned to the area, the first pixel group comprising a plurality of pixels, by comparing relative measured values ​​of the pixels of the first pixel group with the first measured value of the at least one pixel of the first thermal image, is at least partially spatially assigned to the first pixel group of the second thermal image;

Determining corrected absolute measured values ​​of the pixels of the first pixel group based on the second measured temperature values ​​of the pixels of the first pixel group and the measured value offset;

Creation of the heat map of the area based on the corrected absolute measured values.