Sequential data assimilation to improve agricultural modeling

Patent metadata

Field	Value
Publication number	US-10853377-B2
Application number	US-201715814321-A
Country	US
Kind code	B2
Filing date	Nov 15, 2017
Priority date	Nov 15, 2017
Publication date	Dec 1, 2020
Grant date	Dec 1, 2020

Abstract

In an embodiment, a computer-implemented method of assimilation of soil moisture data is disclosed. The method comprises receiving, by a processor, a digital data model related to soil moisture with a plurality of parameters for a given geographical location. The method also comprises identifying a time-based or event-based trigger for a first of the plurality of parameters. The method further comprises receiving, by the processor, a plurality of values for the plurality of parameters measured from soil samples for a series of time points. Finally, the method comprises applying sequential data assimilation through the series of time points as soon as the plurality of measured values are received for each of the series of time points, by executing an optimization method to optimize values of the plurality of parameters with respect to the plurality of measured values for each of the time points, thereby obtaining a plurality of optimized values for the plurality of parameters for each of the time points, when the time-based or event-based trigger is satisfied for one of the series of time points, reducing the search space associated with the first parameter in executing the optimization method.

First claim

What is claimed is:

1. A computer-implemented method of assimilation of soil moisture data, comprising:
receiving, by a processor, a digital data model related to soil moisture with a plurality of parameters for a given geographical location, the digital data model comprising an electronic digitally stored set of executable instructions;
identifying, using the processor, a time-based or event-based trigger for a first parameter of the plurality of parameters of the digital model related to soil moisture for the given geographical location;
receiving, by the processor, a plurality of values of soil moisture measured from soil samples for a series of time points;
applying sequential data assimilation through the series of time points when the plurality of measured values are received for each time point of the series of time points, by executing an optimization method that optimizes an objective function to find best values for the plurality of parameters from a search space of values for the plurality of parameters of the digital model with respect to the plurality of measured values for each time point of the series of time points,
when the time-based or event-based trigger is satisfied for a time point of the series of time points, reducing the search space by reducing a number of values in the search space to be searched for the first parameter in executing the optimization method.

2. The computer-implemented method of claim 1 , the time-based or event-based trigger being a season where a historical rainfall average exceeds a certain threshold, a certain period after a heavy rainfall, or a certain period after a heavy rainfall through a certain soil layer.

3. The computer-implemented method of claim 1 , further comprising, upon applying the sequential data assimilation, causing display of a recommended increase of nitrate level in soil based on the optimized values of the plurality of parameters.

4. The computer-implemented method of claim 1 , further comprising
identifying a correlation between two of the plurality of parameters,
the applying being performed with a reduced search space for one of the two parameters based on the correlation.

5. The computer-implemented method of claim 1 , the optimization method being particle swarm optimization.

6. The computer-implemented method of claim 1 ,
the plurality of values of soil moisture being measured by in-soil sensors,
any two consecutive time points of the series of time points being up to 15 minutes apart.

7. The computer-implemented method of claim 1 , the plurality of parameters includes a hydraulic conductivity for saturated soil K sat , a saturated volumetric soil moisture content θ s , a residual volumetric soil moisture content θ r , a first shape parameter n, or a second shape parameter α of the of the Van Genuchten model.

8. The computer-implemented method of claim 1 , further comprising
defining a group of soil horizons for the geographical location,
the plurality of parameters including one set of parameters for each of a plurality of soil layers,
the group of soil horizons corresponding to a subset of the plurality of soil layers,
the applying being performed with a reduced search space based on the group of soil horizons.

9. The computer-implemented method of claim 8 , defining a group of soil horizons comprising:
classifying the geographical location vertically into a number of thin layers, each associated with a soil layer, and
merging two consecutive thin layers associated with identical or similar soil layers into a soil horizon.

10. The computer-implemented method of claim 8 , further comprising
identifying a correlation between two of the soil horizons for the geographical location for a second of the plurality of parameters,
the applying being performed with a reduced search space for the second parameter based on the correlation.

11. A non-transitory storage medium storing instructions which, when executed by one or more computing devices, cause performance of a method of assimilation of soil moisture data, the method comprising:
receiving, by a processor, a digital data model related to soil moisture with a plurality of parameters for a given geographical location, the digital data model comprising an electronic digitally stored set of executable instructions;
identifying, using the processor, a time-based or event-based trigger for a first parameter of the plurality of parameters of the digital model related to soil moisture for the given geographical location;
receiving, by the processor, a plurality of values of soil moisture from soil samples for a series of time points;
applying sequential data assimilation through the series of time points when the plurality of measured values are received for each time point of the series of time points, by executing an optimization method that optimizes an objective function to find best values for the plurality of parameters from a search space of values for the plurality of parameters of the digital model with respect to the plurality of measured values for each time point of the series of time points,
when the time-based or event-based trigger is satisfied for a time point of the series of time points, reducing the search space by reducing a number of values in the search space to be searched for the first parameter in executing the optimization method.

12. The non-transitory storage medium of claim 11 , the time-based or event-based trigger being a season where a historical rainfall average exceeds a certain threshold, a certain period after a heavy rainfall, or a certain period after a heavy rainfall through a certain soil layer.

13. The non-transitory storage medium of claim 11 , the method further comprising, upon applying the sequential data assimilation, causing display of a recommended increase of nitrate level in soil based on the optimized values of the plurality of parameters.

14. The non-transitory storage medium of claim 11 , the method further comprising identifying a correlation between two of the plurality of parameters, the applying being performed with a reduced search space for one of the two parameters based on the correlation.

15. The non-transitory storage medium of claim 11 , the optimization method being particle swarm optimization.

16. The non-transitory storage medium of claim 11 ,
the plurality of values of soil moisture being measured by in-soil sensors,
any two consecutive time points of the series of time points being up to 15 minutes apart.

17. The non-transitory storage medium of claim 11 , the plurality of parameters includes a hydraulic conductivity for saturated soil K sat , a saturated volumetric soil moisture content θ s , a residual volumetric soil moisture content θ r , a first shape parameter n, or a second shape parameter α of the of the Van Genuchten model.

18. The non-transitory storage medium of claim 11 , the method further comprising defining a group of soil horizons for the geographical location, the plurality of parameters including one set of parameters for each of a plurality of soil layers, the group of soil horizons corresponding to a subset of the plurality of soil layers, the applying being performed with a reduced search space based on the group of soil horizons.

19. The non-transitory storage medium of claim 18 , defining a group of soil horizons comprising: classifying the geographical location vertically into a number of thin layers, each associated with a soil layer, and merging two consecutive thin layers associated with identical or

Assignees

• Climate Corp

Inventors

• Zhan Wang
• Gates John

Classifications

• A01B79/02 Primary

  combined with other agricultural processing, e.g. fertilising, planting · CPC title

• A01G22/00

  Cultivation of specific crops or plants not otherwise provided for · CPC title

• G06F9/542

  Event management; Broadcasting; Multicasting; Notifications · CPC title

• G06F17/10

  Complex mathematical operations {(function generation by table look-up G06F1/03; evaluation of elementary functions by calculation G06F7/544)} · CPC title

• G06F16/24547

  Optimisations to support specific applications; Extensibility of optimisers · CPC title