METHOD FOR ESTIMATING PRECIPITATION DISTRIBUTION FOR A GEOGRAPHICAL REGION

Abstract

A method for estimating precipitation distribution for a geographical region comprising the steps of: providing precipitation data (S10) for the geographical region with a first spatial resolution for a predetermined period of time (t.sub.1, t.sub.2); providing first soil moisture data (S20) for the geographical region for a first point in time (t.sub.3) with a second spatial resolution, wherein the second spatial resolution is higher than the first spatial resolution, and wherein the first point in time (t.sub.3) is within the predetermined period of time (t.sub.1, t.sub.2); providing second soil moisture data (S30) for the geographical region for a second point in time (t.sub.4) with a third spatial resolution, wherein the third spatial resolution is higher than the first spatial resolution, and wherein the second point in time (t.sub.4) is within the predetermined period of time (t.sub.1, t.sub.2); calculating soil moisture difference data (S40) between the first soil moisture data and the second soil moisture data; calculating precipitation distribution data (S50) for the geographical region for the predetermined period of time (t.sub.1, t.sub.2) based on the precipitation data and the soil moisture difference data with a spatial resolution higher than the first spatial resolution.

Claims

1. A method for estimating precipitation distribution for a geographical region comprising: providing precipitation data (S10) for the geographical region with a first spatial resolution for a predetermined period of time (t.sub.1, t.sub.2); providing first soil moisture data (S20) for the geographical region for a first point in time (t.sub.3) with a second spatial resolution, wherein the second spatial resolution is higher than the first spatial resolution, and wherein the first point in time (t.sub.3) is within the predetermined period of time (t.sub.1, t.sub.2); providing second soil moisture data (S30) for the geographical region for a second point in time (t.sub.4) with a third spatial resolution, wherein the third spatial resolution is higher than the first spatial resolution, and wherein the second point in time (t.sub.4) is within the predetermined period of time (t.sub.1, t.sub.2); calculating soil moisture difference data (S40) between the first soil moisture data and the second soil moisture data; and calculating precipitation distribution data (S50) for the geographical region for the predetermined period of time (t.sub.1, t.sub.2) based on the precipitation data and the soil moisture difference data with a spatial resolution higher than the first spatial resolution.

2. The method according to claim 1, wherein the precipitation data are remotely sensed historical precipitation data or weather forecast data.

3. The method according to claim 1, wherein the precipitation data are calculated based on cloud distribution data and/or cloud surface temperature data.

4. The method according to claim 3, wherein the cloud distribution data and/or cloud surface temperature data are obtained by using geostationary satellites or stationary terrestrial weather radar data.

5. The method according to claim 1, wherein the second spatial resolution and the third spatial resolution are equal.

6. The method according to claim 1, wherein the ratio between the first spatial resolution and the second spatial resolution is between 1:15 and 1:5.

7. The method according to claim 1, wherein the first point in time (t.sub.3) is between 0 to 10 hours and/or the second point in time (t.sub.4) is between 20 to 30 hours.

8. The method according to claim 1, wherein the predetermined period of time is a 24-hour period.

9. The method according to claim 1, further comprising calculating one of the following models for at least a part of the geographical region: nutrient management model; navigation model; disease model; and pathogen model.

10. The method according to claim 1, further comprising determining one of the following agronomic management instructions for at least a part of the geographical region: nutrient management instructions; navigation instructions; disease treatment instructions; and pathogen treatment instructions.

11. The method according to claim 1 further comprising providing control data for at least one agricultural equipment.

12. Agricultural equipment configured to be controlled by control data at least based on precipitation distribution data calculated according to a method according to claim 1.

13. A system for estimating precipitation distribution for a geographical region, the system comprising: at least one processing unit configured to receive precipitation data (S10) for the geographical region with a first spatial resolution for a predetermined period of time (t.sub.1, t.sub.2); at least one processing unit configured to receive first soil moisture data (S20) for the geographical region for a first point in time (t.sub.3) with a second spatial resolution, wherein the second spatial resolution is higher than the first spatial resolution, and wherein the first point in time (t.sub.3) is within the predetermined period of time (t.sub.1, t.sub.2); at least one processing unit configured to receive second soil moisture data (S30) for the geographical region for a second point in time (t.sub.4) with a third spatial resolution, wherein the third spatial resolution is higher than the first spatial resolution, and wherein the second point in time (t.sub.4) is within the predetermined period of time (t.sub.1, t.sub.2); at least one processing unit configured to calculate soil moisture difference data (S40) between the first soil moisture data and the second soil moisture data; and at least one processing unit configured to calculate precipitation distribution data (S50) for the geographical region for the predetermined period of time (t.sub.1, t.sub.2) based on the precipitation data and the soil moisture difference data with a spatial resolution higher than the first spatial resolution.

14. A non-transitory computer readable medium having instructions encoded thereon that when executed by a processor cause the processor to carry out the method according to claim 1.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In the following, the invention is described exemplarily with reference to the enclosed figure, in which

[0036] FIG. 1 is a schematic overview of a method for estimating precipitation distribution according to the preferred embodiment of the present invention;

[0037] FIG. 2 is an example for precipitation data for a geographical region provided with a 1-km-resolution fora predetermined period of time (t.sub.1, t.sub.2);

[0038] FIG. 3a is an example for first soil moisture data for the geographical region for a first point in time (t.sub.3) with a 100-m resolution;

[0039] FIG. 3b is an example for second soil moisture data for the geographical region for a second point in time (t.sub.4) with a 100-m resolution;

[0040] FIG. 4 is an example for soil moisture difference data;

[0041] FIG. 5 is an example for precipitation distribution data for the geographical region for the predetermined period of time (t.sub.1, t.sub.2) based on the precipitation data and the soil moisture difference data with a 100-m resolution;

[0042] FIG. 6 is a schematic overview of the different agronomic management models using precipitation distribution data; and

[0043] FIG. 7 is a schematic overview of the different agronomic management instructions using precipitation distribution data.

DETAILED DESCRIPTION OF EMBODIMENT

[0044] FIG. 1 is a schematic overview of a method for estimating precipitation distribution according to the preferred embodiment of the present invention. In the following, an exemplary order of the steps according to the preferred embodiment of the present invention is explained.

[0045] In a step S10, precipitation data for a geographical region with a low resolution for a predetermined period of time are provided, e.g. these precipitation data can be provided by weather forecast services, like the Global Forecast System (GFS) using atmospheric models to predict rain events. The precipitation data can also be provided as remotely sensed historical precipitation data. Alternatively or additionally, the precipitation data can also be calculated based on cloud distribution data and cloud surface temperature data. In this respect, it is preferred that the data of geostationary satellites or stationary terrestrial weather radar data is used.

[0046] The resolution of the precipitation data is preferably 1-kilometer square grid. The predetermined period of time is preferably a time window of the last 24 hours, i.e. t.sub.1 is 24 hours back and t.sub.2 is the current time. By such a choice of the time window, the present precipitation distribution in the geographical region can be determined with the preferred embodiment of the present invention. In this context, it should be noted that the present invention is not limited to the determination of the present precipitation distribution, but that a respective time window for which the weather forecast data are to be obtained also allows a future-oriented providing of the precipitation distribution, e.g. when t.sub.1 is the current time and t.sub.2 is 24 hours in the future. In FIG. 2, an example for such precipitation data for a geographical region for an area of one square kilometer for a time window of 24 hours is shown, wherein in this example a precipitation of 50 mm occurred.

[0047] In steps S20 and S30, first and second soil moisture data for the geographical region for two points in time are provided, wherein these soil moisture data are provided with higher resolutions compared to the resolution of the precipitation data. In this respect, it is preferred that the second spatial resolution and the third spatial resolution are equal and preferably a 100-meter square. Notably, the respective soil moisture values of the soil moisture data may be provided in any measuring unit, e.g. as shown in FIGS. 3a and 3b in cubicmeter water/fluid per cubicmeter soil. In FIGS. 3 shows examples of such soil moisture data, which were provided with a 100-meter resolution. In this example, FIG. 3a shows the first soil moisture data for a point in time t.sub.3 (e.g. the current time; t.sub.2−0 hours), wherein FIG. 3b shows the second soil moisture data for a point in time t.sub.4 (e.g. t.sub.2−24 hours). For instance, if the precipitation value in FIG. 2 represents the precipitation that fell from Apr. 5, 2020 (T00:00) to Apr. 6, 2020 (T00:00), FIGS. 3a and 3b preferably represent snapshots of the soil moisture at or near the beginning, i.e. t.sub.4=Apr. 5, 2020 (T00:00) and end, i.e. t.sub.3=Apr. 6, 2020 (T00:00) of the precipitation time period.

[0048] In a step S40, soil moisture difference data, i.e. residual data, between the first soil moisture data and the second soil moisture data is calculated. The latter can be done, for example, by calculating a difference between the second data and the first data. Based on this soil moisture difference data the precipitation distribution data for the geographical region can be calculated/estimated in a step S50. In an embodiment of the invention, the precipitation distribution data having a low resolution and the first and second soil moisture data having a high resolution can be fed to one or more trained machine-learning algorithm to distribute the total amount of precipitation and to provide precipitation distribution data with a high resolution. For example, such machine-learning algorithm can be trained on a specific geographical region and its specifics and then be used to distribute the total amount of precipitation in that geographical region, whereby it is then only necessary to input the precipitation data and the first and second soil moisture data into the machine-learning algorithm. In FIG. 4, an example for such soil moisture difference data is shown based on the first and second soil moisture data shown in FIGS. 3.

[0049] FIG. 5 is an example for precipitation distribution data for the geographical region for the predetermined period of time (t.sub.1 , t.sub.2 ) based on the precipitation data and the soil moisture difference data with a spatial resolution higher than the first unit of area. The amount of rainfall was distributed over the geographical region according to the distribution of soil moisture difference data.

[0050] FIG. 6 shows a schematic overview of the different agronomic management models using precipitation distribution data provided according to the above described method. FIG. 7 is a schematic overview of the different agronomic management instructions using precipitation distribution data provided according to the preferred embodiment of the present invention.

[0051] The present invention has been described in conjunction with a preferred embodiment as examples as well. However, other variations can be understood and effected by those persons skilled in the art and practicing the claimed invention, from the studies of the drawings, this disclosure and the claims. In the claims as well as in the description the word “comprising” does not exclude other elements or steps and the indefinite article “a” or “an” does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in the mutual different dependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation.