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Mapping soil properties with fixed rank kriging of proximally sensed soil data fused with Sentinel-2 biophysical parameter

Nikolaos Karapetsas, Thomas K. Alexandridis, George Bilas, Muhammad Abdul Munnaf (UGent) , Angela Patricia Guerrero Castillo, Maria Calera, Anna Osann, Anne Gobin, Tomas Reznik, Dimitrios Moshou, et al.
(2022) REMOTE SENSING. 14(7).
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Abstract
Soil surveys with line-scanning platforms appear to have great advantages over the traditional methods used to collect soil information for the development of field-scale soil mapping and applications. These carry VNIR (visible and near infrared) spectrometers and have been used in recent years extensively for the assessment of soil fertility at the field scale, and the delineation of site-specific management zones (MZ). A challenging feature of VNIR applications in precision agriculture (PA) is the massiveness of the derived datasets that contain point predictions of soil properties, and the interpolation techniques involved in incorporating these data into site-specific management plans. In this study, fixed-rank kriging (FRK) geostatistical interpolation, which is a flexible, non-stationary spatial interpolation method especially suited to handling huge datasets, was applied to massive VNIR soil scanner data for the production of useful, smooth interpolated maps, appropriate for the delineation of site-specific MZ maps. Moreover, auxiliary Sentinel-2 data-based biophysical parameters NDVI (normalized difference vegetation index) and fAPAR (fraction of photosynthetically active radiation absorbed by the canopy) were included as covariates to improve the filtering performance of the interpolator and the ability to generate uniform patterns of spatial variation from which it is easier to receive a meaningful interpretation in PA applications. Results from the VNIR prediction dataset obtained from a pivot-irrigated field in Albacete, southeastern Spain, during 2019, have shown that FRK variants outperform ordinary kriging in terms of filtering capacity, by doubling the noise removal metrics while keeping the computation cost reasonably low. Such features, along with the capacity to handle a large volume of spatial information, nominate the method as ideal for PA applications with massive proximal and remote sensing datasets.
Keywords
SPATIAL VARIABILITY, MANAGEMENT ZONES, DATA FUSION, PHYSICAL-PROPERTIES, PREDICTION, ONLINE, DELINEATION, VARIABLES, QUALITY, MODELS, geostatistical interpolation, VNIR spectrometer, NDVI, fAPAR, precision, agriculture

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Citation

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MLA
Karapetsas, Nikolaos, et al. “Mapping Soil Properties with Fixed Rank Kriging of Proximally Sensed Soil Data Fused with Sentinel-2 Biophysical Parameter.” REMOTE SENSING, vol. 14, no. 7, 2022, doi:10.3390/rs14071639.
APA
Karapetsas, N., Alexandridis, T. K., Bilas, G., Munnaf, M. A., Guerrero Castillo, A. P., Calera, M., … Mouazen, A. (2022). Mapping soil properties with fixed rank kriging of proximally sensed soil data fused with Sentinel-2 biophysical parameter. REMOTE SENSING, 14(7). https://doi.org/10.3390/rs14071639
Chicago author-date
Karapetsas, Nikolaos, Thomas K. Alexandridis, George Bilas, Muhammad Abdul Munnaf, Angela Patricia Guerrero Castillo, Maria Calera, Anna Osann, et al. 2022. “Mapping Soil Properties with Fixed Rank Kriging of Proximally Sensed Soil Data Fused with Sentinel-2 Biophysical Parameter.” REMOTE SENSING 14 (7). https://doi.org/10.3390/rs14071639.
Chicago author-date (all authors)
Karapetsas, Nikolaos, Thomas K. Alexandridis, George Bilas, Muhammad Abdul Munnaf, Angela Patricia Guerrero Castillo, Maria Calera, Anna Osann, Anne Gobin, Tomas Reznik, Dimitrios Moshou, and Abdul Mouazen. 2022. “Mapping Soil Properties with Fixed Rank Kriging of Proximally Sensed Soil Data Fused with Sentinel-2 Biophysical Parameter.” REMOTE SENSING 14 (7). doi:10.3390/rs14071639.
Vancouver
1.
Karapetsas N, Alexandridis TK, Bilas G, Munnaf MA, Guerrero Castillo AP, Calera M, et al. Mapping soil properties with fixed rank kriging of proximally sensed soil data fused with Sentinel-2 biophysical parameter. REMOTE SENSING. 2022;14(7).
IEEE
[1]
N. Karapetsas et al., “Mapping soil properties with fixed rank kriging of proximally sensed soil data fused with Sentinel-2 biophysical parameter,” REMOTE SENSING, vol. 14, no. 7, 2022.
@article{8751352,
  abstract     = {{Soil surveys with line-scanning platforms appear to have great advantages over the traditional methods used to collect soil information for the development of field-scale soil mapping and applications. These carry VNIR (visible and near infrared) spectrometers and have been used in recent years extensively for the assessment of soil fertility at the field scale, and the delineation of site-specific management zones (MZ). A challenging feature of VNIR applications in precision agriculture (PA) is the massiveness of the derived datasets that contain point predictions of soil properties, and the interpolation techniques involved in incorporating these data into site-specific management plans. In this study, fixed-rank kriging (FRK) geostatistical interpolation, which is a flexible, non-stationary spatial interpolation method especially suited to handling huge datasets, was applied to massive VNIR soil scanner data for the production of useful, smooth interpolated maps, appropriate for the delineation of site-specific MZ maps. Moreover, auxiliary Sentinel-2 data-based biophysical parameters NDVI (normalized difference vegetation index) and fAPAR (fraction of photosynthetically active radiation absorbed by the canopy) were included as covariates to improve the filtering performance of the interpolator and the ability to generate uniform patterns of spatial variation from which it is easier to receive a meaningful interpretation in PA applications. Results from the VNIR prediction dataset obtained from a pivot-irrigated field in Albacete, southeastern Spain, during 2019, have shown that FRK variants outperform ordinary kriging in terms of filtering capacity, by doubling the noise removal metrics while keeping the computation cost reasonably low. Such features, along with the capacity to handle a large volume of spatial information, nominate the method as ideal for PA applications with massive proximal and remote sensing datasets.}},
  articleno    = {{1639}},
  author       = {{Karapetsas, Nikolaos and Alexandridis, Thomas K. and Bilas, George and Munnaf, Muhammad Abdul and Guerrero Castillo, Angela Patricia and Calera, Maria and Osann, Anna and Gobin, Anne and Reznik, Tomas and Moshou, Dimitrios and Mouazen, Abdul}},
  issn         = {{2072-4292}},
  journal      = {{REMOTE SENSING}},
  keywords     = {{SPATIAL VARIABILITY,MANAGEMENT ZONES,DATA FUSION,PHYSICAL-PROPERTIES,PREDICTION,ONLINE,DELINEATION,VARIABLES,QUALITY,MODELS,geostatistical interpolation,VNIR spectrometer,NDVI,fAPAR,precision,agriculture}},
  language     = {{eng}},
  number       = {{7}},
  pages        = {{22}},
  title        = {{Mapping soil properties with fixed rank kriging of proximally sensed soil data fused with Sentinel-2 biophysical parameter}},
  url          = {{http://doi.org/10.3390/rs14071639}},
  volume       = {{14}},
  year         = {{2022}},
}
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