A Framework for Global Characterization of Soil Properties Using Repeat Hyperspectral Satellite Data

Debsunder Dutta, Praveen Kumar

Research output: Contribution to journalArticle

Abstract

Imaging spectroscopy offers the potential to quantify the soil properties over large areas based on its reflectance spectra. Soils are heterogeneous mixtures of minerals, air, water, and organic matter leading to complex manifestations of reflectance in the different parts of the visible-shortwave infrared spectra. Due to this complexity, data-driven modeling approaches are found to be most suitable for characterizing the relationships between soil spectra and the corresponding soil properties. Proposed spaceborne hyperspectral missions, such as Hyperspectral Infrared Imager, offer the possibility of repeating global spectral measurements in a 16- to 20-day revisit period. Soil attributes on the landscape vary at different rates. In particular, the soil textural attributes (percentage of sand, silt, and clay) may be assumed to remain invariant compared to chemical constituents during multiple consecutive 16- to 20-day satellite revisit period. We present a theoretical retrieval framework for assimilating repeat spaceborne soil spectral measurements into a previously developed lasso algorithm-based ensemble modeling framework for the global-scale characterization of soil textural attributes. The repeat spectral assimilation with each overpass of the satellite leads to the development of an enriched "dynamic soil spectral library" which spatially propagates the improvement in the characterization of soil textural properties globally, given the uncertain variations in other auxiliary factors, such as moisture and organic matter, affecting soil reflectance.

Original languageEnglish (US)
Article number8579521
Pages (from-to)3308-3323
Number of pages16
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume57
Issue number6
DOIs
StatePublished - Jun 1 2019

Fingerprint

satellite data
soil property
Satellites
Soils
reflectance
soil
soil dynamics
Biological materials
modeling
soil organic matter
silt
spectroscopy
Infrared radiation
moisture
organic matter
clay
Silt
sand
air
Image sensors

Keywords

  • Dynamic spectral library
  • Hyperspectral Infrared Imager (HyspIRI)
  • imaging spectroscopy
  • soil

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

Cite this

A Framework for Global Characterization of Soil Properties Using Repeat Hyperspectral Satellite Data. / Dutta, Debsunder; Kumar, Praveen.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 6, 8579521, 01.06.2019, p. 3308-3323.

Research output: Contribution to journalArticle

@article{7a36d167baf744d7a6b596a161129e02,
title = "A Framework for Global Characterization of Soil Properties Using Repeat Hyperspectral Satellite Data",
abstract = "Imaging spectroscopy offers the potential to quantify the soil properties over large areas based on its reflectance spectra. Soils are heterogeneous mixtures of minerals, air, water, and organic matter leading to complex manifestations of reflectance in the different parts of the visible-shortwave infrared spectra. Due to this complexity, data-driven modeling approaches are found to be most suitable for characterizing the relationships between soil spectra and the corresponding soil properties. Proposed spaceborne hyperspectral missions, such as Hyperspectral Infrared Imager, offer the possibility of repeating global spectral measurements in a 16- to 20-day revisit period. Soil attributes on the landscape vary at different rates. In particular, the soil textural attributes (percentage of sand, silt, and clay) may be assumed to remain invariant compared to chemical constituents during multiple consecutive 16- to 20-day satellite revisit period. We present a theoretical retrieval framework for assimilating repeat spaceborne soil spectral measurements into a previously developed lasso algorithm-based ensemble modeling framework for the global-scale characterization of soil textural attributes. The repeat spectral assimilation with each overpass of the satellite leads to the development of an enriched {"}dynamic soil spectral library{"} which spatially propagates the improvement in the characterization of soil textural properties globally, given the uncertain variations in other auxiliary factors, such as moisture and organic matter, affecting soil reflectance.",
keywords = "Dynamic spectral library, Hyperspectral Infrared Imager (HyspIRI), imaging spectroscopy, soil",
author = "Debsunder Dutta and Praveen Kumar",
year = "2019",
month = "6",
day = "1",
doi = "10.1109/TGRS.2018.2883311",
language = "English (US)",
volume = "57",
pages = "3308--3323",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "6",

}

TY - JOUR

T1 - A Framework for Global Characterization of Soil Properties Using Repeat Hyperspectral Satellite Data

AU - Dutta, Debsunder

AU - Kumar, Praveen

PY - 2019/6/1

Y1 - 2019/6/1

N2 - Imaging spectroscopy offers the potential to quantify the soil properties over large areas based on its reflectance spectra. Soils are heterogeneous mixtures of minerals, air, water, and organic matter leading to complex manifestations of reflectance in the different parts of the visible-shortwave infrared spectra. Due to this complexity, data-driven modeling approaches are found to be most suitable for characterizing the relationships between soil spectra and the corresponding soil properties. Proposed spaceborne hyperspectral missions, such as Hyperspectral Infrared Imager, offer the possibility of repeating global spectral measurements in a 16- to 20-day revisit period. Soil attributes on the landscape vary at different rates. In particular, the soil textural attributes (percentage of sand, silt, and clay) may be assumed to remain invariant compared to chemical constituents during multiple consecutive 16- to 20-day satellite revisit period. We present a theoretical retrieval framework for assimilating repeat spaceborne soil spectral measurements into a previously developed lasso algorithm-based ensemble modeling framework for the global-scale characterization of soil textural attributes. The repeat spectral assimilation with each overpass of the satellite leads to the development of an enriched "dynamic soil spectral library" which spatially propagates the improvement in the characterization of soil textural properties globally, given the uncertain variations in other auxiliary factors, such as moisture and organic matter, affecting soil reflectance.

AB - Imaging spectroscopy offers the potential to quantify the soil properties over large areas based on its reflectance spectra. Soils are heterogeneous mixtures of minerals, air, water, and organic matter leading to complex manifestations of reflectance in the different parts of the visible-shortwave infrared spectra. Due to this complexity, data-driven modeling approaches are found to be most suitable for characterizing the relationships between soil spectra and the corresponding soil properties. Proposed spaceborne hyperspectral missions, such as Hyperspectral Infrared Imager, offer the possibility of repeating global spectral measurements in a 16- to 20-day revisit period. Soil attributes on the landscape vary at different rates. In particular, the soil textural attributes (percentage of sand, silt, and clay) may be assumed to remain invariant compared to chemical constituents during multiple consecutive 16- to 20-day satellite revisit period. We present a theoretical retrieval framework for assimilating repeat spaceborne soil spectral measurements into a previously developed lasso algorithm-based ensemble modeling framework for the global-scale characterization of soil textural attributes. The repeat spectral assimilation with each overpass of the satellite leads to the development of an enriched "dynamic soil spectral library" which spatially propagates the improvement in the characterization of soil textural properties globally, given the uncertain variations in other auxiliary factors, such as moisture and organic matter, affecting soil reflectance.

KW - Dynamic spectral library

KW - Hyperspectral Infrared Imager (HyspIRI)

KW - imaging spectroscopy

KW - soil

UR - http://www.scopus.com/inward/record.url?scp=85058891431&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058891431&partnerID=8YFLogxK

U2 - 10.1109/TGRS.2018.2883311

DO - 10.1109/TGRS.2018.2883311

M3 - Article

VL - 57

SP - 3308

EP - 3323

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

IS - 6

M1 - 8579521

ER -