Portable apparatus for soil chemical characterization

Patent metadata

Field	Value
Publication number	US-10107770-B2
Application number	US-201515319816-A
Country	US
Kind code	B2
Filing date	Jun 18, 2015
Priority date	Jun 18, 2014
Publication date	Oct 23, 2018
Grant date	Oct 23, 2018

Abstract

The present invention determines one or more properties of a soil sample by scanning a soil sample using a visible near infrared diffuse reflectance (VisNIR) spectroradiometer, scanning the soil sample using a x-ray fluorescence (PXRF) spectrometer, receiving a diffuse reflectance spectra from the VisNIR spectroradiometer and an elemental data from the PXRF spectrometer, determining one or more properties of the soil sample using one or more processors and a predictive model that relates the diffuse reflectance spectra and the elemental data to the one or more properties, and providing the one or more properties of the soil sample to one or more input/output interface.

First claim

The invention claimed is:

1. A computerized method for determining one or more properties of a soil sample comprising the steps of:
providing a visible near infrared diffuse reflectance (VisNIR) spectroradiometer, a x-ray fluorescence (PXRF) spectrometer, a probe connected to the VisNIR spectroradiometer and the PXRF spectrometer, one or more processors communicably coupled to the VisNIR spectroradiometer and the PXRF spectrometer, and one or more input/output interfaces communicably coupled to the one or more processors;
scanning the soil sample using the VisNIR spectroradiometer;
scanning the soil sample using the PXRF spectrometer;
receiving a diffuse reflectance spectra from the VisNIR spectroradiometer and elemental data from the PXRF spectrometer;
determining one or more properties of the soil sample using the one or more processors and a predictive model that relates the diffuse reflectance spectra and the elemental data to the one or more properties; and
providing the one or more properties of the soil sample to the one or more input/output interfaces.

2. The method as recited in claim 1 , further comprising the step of receiving a set of multispectral reflectance values related to the soil sample from a remote sensing device, and wherein the predictive model further relates the set of multispectral reflectance values to the one or more properties.

3. The method as recited in claim 2 , wherein the remote sensing device comprises a satellite and further comprising the step of extracting the multispectral reflectance values from a satellite imagery using a soil and vegetation based indices.

4. The method as recited in claim 2 , wherein the step of receiving the set of multispectral reflectance values related to the soil sample from the remote sensing device comprises the step of retrieving the set of multispectral reflectance values from a memory or a data storage communicably coupled to the one or more processors.

5. The method as recited in claim 1 , further comprising the step of reducing dimensionality and qualitative discrimination of the diffuse reflectance data.

6. The method as recited in claim 1 , wherein the predictive model uses a partial least squares regression (PLS) multivariate algorithm or a support vector regression (SVR) multivariate algorithm.

7. The method as recited in claim 1 , further comprising the step of placing the probe in contact with or proximate to the soil sample.

8. The method as recited in claim 1 , further comprising the step of calibrating the predictive model.

9. The method as recited in claim 1 , wherein the one or more properties comprise one or more chemical properties of the soil sample, one or more physical properties of the soil sample or a combination thereof.

10. The method as recited in claim 1 , wherein the one or more properties of the soil sample comprise a soil salinity and the elemental data comprises a Chlorine % and a Sulfur %.

11. The method as recited in claim 1 , wherein the one or more properties of the soil sample comprise textural constituents, soil pH, soil carbon content or clay mineralogy.

12. The method as recited in claim 1 , wherein the scanning, receiving, determining and providing steps are performed in situ.

13. The method as recited in claim 1 , further comprising the step of determining a geographic location of the soil sample using a space-based satellite navigation system.

14. The method as recited in claim 1 , further comprising the step of determining an elevation of the soil sample.

15. The method as recited in claim 1 , wherein the one or more input/output interfaces comprise a display, a data storage, a printer or a communications interface.

16. The method as recited in claim 1 , wherein the visible near infrared diffuse reflectance (VisNIR) spectroradiometer, the x-ray fluorescence (PXRF) spectrometer, the probe, the one or more processors, and the one or more input/output interfaces are integrated into a portable device.

17. The method as recited in claim 1 , wherein the elemental data comprises one or more elements selected a list of elements detectable by the PXRF spectrometer.

18. The method as recited in claim 17 , further comprising selecting the one or more elements based on the one or more properties of the soil sample to be determined.

19. The method as recited in claim 1 , wherein the diffuse reflectance spectra is a primary predictor and the elemental data is an auxiliary predictor within the predictive model.

20. The method as recited in claim 1 , further comprising step of retrieving a set of multispectral reflectance values from a memory or a data storage communicably coupled to the one or more processors, and using the set of multispectral reflectance values in combination with the diffuse reflectance spectra and the elemental data to determine the one or more properties of the soil sample.

21. The method as recited in claim 1 , wherein the scanning, receiving, determining and providing steps are performed on site proximate to where the soil sample was taken.

22. An apparatus comprising:
a probe;
a visible near infrared diffuse reflectance (VisNIR) spectroradiometer connected to the probe;
a x-ray fluorescence (PXRF) spectrometer connected to the probe;
one or more processors communicably coupled to the VisNIR spectroradiometer and PXRF spectrometer;
one or more input/output interfaces communicably coupled to the one or more processors; and
the one or more processors scan the soil sample using the VisNIR spectroradiometer, scan the soil sample using the PXRF spectrometer, receive a diffuse reflectance spectra from the VisNIR spectroradiometer and elemental data from the PXRF spectrometer, determine one or more properties of the soil sample using the one or more processors and a predictive model that relates the diffuse reflectance spectra and the elemental data to the one or more properties, and provide the one or more properties of the soil sample to the one or more input/output interfaces.

23. The apparatus as recited in claim 22 , wherein the one or more processors further receive a set of multispectral reflectance values related to the soil sample from a remote sensing device, and the predictive model further relates the set of multispectral reflectance values to the one or more properties.

24. The apparatus as recited in claim 23 , wherein the remote sensing device comprises a satellite and the one or more processors further extract the multispectral reflectance values from a satellite imagery using a soil and vegetation based indices.

25. The apparatus as recited in claim 23 , wherein the one or more processors receive the set of multispectral reflectance values related to the soil sample from the remote sensing device by retrieving the set of multispectral reflectance values from a memory or a data storage communicably coupled to the one or more processors.

26. The apparatus as recited in claim 22 , wherein the one or more processors further reduce dimensionality and qualitative discrimination of the diffuse reflectance data.

27. The apparatus as recited in claim 22 , wherein the predictive model uses a partial least squares regression (PLS) multivariate algorithm or a support vector regression (SVR) multivariate algorithm.

28. The apparatus as recited in claim 22 , wherein the one or more processors further calibrate the predictive model.

Assignees

• Univ Texas Tech System
• Ramakrishna Mission Vivekananda Univ

Inventors

• Weindorf David
• Chakraborty Somsubhra

Classifications

• G01N23/2206 Primary

  Combination of two or more measurements, at least one measurement being that of secondary emission, e.g. combination of secondary electron [SE] measurement and back-scattered electron [BSE] measurement · CPC title

• G01N2223/33

  scanning, i.e. relative motion for measurement of successive object-parts · CPC title

• G01N21/3563

  for analysing solids; Preparation of samples therefor · CPC title

• G01N23/223 Primary

  by irradiating the sample with X-rays or gamma-rays and by measuring X-ray fluorescence · CPC title

• G01N33/24

  Earth materials (G01N33/42 takes precedence) · CPC title