TY - JOUR
T1 - VisNIR integrated multi-sensing penetrometer for in situ high-resolution vertical soil sensing
AU - Wijewardane, Nuwan K.
AU - Hetrick, Sarah
AU - Ackerson, Jason
AU - Morgan, Cristine L.S.
AU - Ge, Yufeng
N1 - Funding Information:
This work was funded by USDA-NIFA (Award #: 2017-67021-26248) and The Climate Corporation. The Authors would like to thank Mr. Tyler Smith for his assistance in the field testing of the VisNIR integrated multi-sensing penetrometer, and Kellogg Soil Survey Lab of USDA-NRCS for providing the dry-ground soil VisNIR spectral library.
Funding Information:
This work was funded by USDA-NIFA (Award #: 2017-67021-26248) and The Climate Corporation. The Authors would like to thank Mr. Tyler Smith for his assistance in the field testing of the VisNIR integrated multi-sensing penetrometer, and Kellogg Soil Survey Lab of USDA-NRCS for providing the dry-ground soil VisNIR spectral library.
Publisher Copyright:
© 2020 The Authors
PY - 2020/5
Y1 - 2020/5
N2 - An in situ penetrometer system that can measure profile soil properties rapidly, cost-effectively, and at high vertical resolution would benefit the soil science and agriculture communities. A visible and near infrared (VisNIR) integrated multi-sensing penetrometer system was developed to automatically measure in situ soil VisNIR reflectance spectra, penetration resistance, and insertion depth along a soil profile. The system was tested in 11 agricultural fields in Nebraska, Illinois, Iowa, and South Dakota of the U.S. An independent soil VisNIR spectral library was used to build calibration models for soil property prediction. External Parameter Orthogonalization (EPO) was used to correct for the field intactness of in situ VisNIR spectra. The results showed that EPO was effective in correcting for the spectral disparity between in situ and dry-ground VisNIR spectra. The EPO correction showed an improvement of prediction accuracy of soil total carbon (R2 and RMSE improved from 0.29 and 3.06 % to 0.5 and 0.79 %, respectively) and total nitrogen (R2 and RMSE improved from 0.51 and 0.36 % to 0.62 and 0.06 %, respectively). The system also predicted soil bulk density with an RMSE of 0.12 g cm−3 and R2 of 0.80. It is concluded that the VisNIR multi-sensing penetrometer, along with the use of external soil spectral libraries and the spectral correction algorithm EPO, can lead to a rapid, robust and cost-effective system for in situ high resolution vertical soil sensing.
AB - An in situ penetrometer system that can measure profile soil properties rapidly, cost-effectively, and at high vertical resolution would benefit the soil science and agriculture communities. A visible and near infrared (VisNIR) integrated multi-sensing penetrometer system was developed to automatically measure in situ soil VisNIR reflectance spectra, penetration resistance, and insertion depth along a soil profile. The system was tested in 11 agricultural fields in Nebraska, Illinois, Iowa, and South Dakota of the U.S. An independent soil VisNIR spectral library was used to build calibration models for soil property prediction. External Parameter Orthogonalization (EPO) was used to correct for the field intactness of in situ VisNIR spectra. The results showed that EPO was effective in correcting for the spectral disparity between in situ and dry-ground VisNIR spectra. The EPO correction showed an improvement of prediction accuracy of soil total carbon (R2 and RMSE improved from 0.29 and 3.06 % to 0.5 and 0.79 %, respectively) and total nitrogen (R2 and RMSE improved from 0.51 and 0.36 % to 0.62 and 0.06 %, respectively). The system also predicted soil bulk density with an RMSE of 0.12 g cm−3 and R2 of 0.80. It is concluded that the VisNIR multi-sensing penetrometer, along with the use of external soil spectral libraries and the spectral correction algorithm EPO, can lead to a rapid, robust and cost-effective system for in situ high resolution vertical soil sensing.
KW - Bulk density
KW - External parameter orthogonalization
KW - Machine learning
KW - Partial least squares regression
KW - Soil profile
KW - Spectral library
KW - Support vector regression
KW - Total carbon
KW - Total nitrogen
UR - http://www.scopus.com/inward/record.url?scp=85079329253&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079329253&partnerID=8YFLogxK
U2 - 10.1016/j.still.2020.104604
DO - 10.1016/j.still.2020.104604
M3 - Article
AN - SCOPUS:85079329253
SN - 0167-1987
VL - 199
JO - Soil and Tillage Research
JF - Soil and Tillage Research
M1 - 104604
ER -