TY - JOUR

T1 - Estimating Soil Organic Carbon using multitemporal PRISMA imaging spectroscopy data

AU - Ward, Kathrin J.

AU - Foerster, Saskia

AU - Chabrillat, Sabine

N1 - Publisher Copyright: © 2024 The Authors

PY - 2024/10

Y1 - 2024/10

N2 - Soils are the largest terrestrial carbon pool and a valuable good that provides important ecosystem services. Since soils are threatened by degradation and in order to fight climate change the knowledge of the status quo especially of its soil organic carbon (SOC) content is required. A promising tool to map and monitor our soils are spaceborne imaging spectrometers which are able to produce up-to-date, inexpensive and spatially explicit maps. Especially the recent launch of new imaging spectroscopy sensors with a high signal-to-noise ratio opens up new possibilities. One of those is the combination of multitemporal spaceborne imaging spectroscopy data into SOC composite maps with a higher spatial coverage. This study explores different multitemporal combination workflows in order to support finding a best practice. To our knowledge for the first time, a spatially more complete SOC composite map was generated using four PRISMA images recorded over the same study site in northern Germany. Two different workflows of computation were compared: workflow one, creates a synthetical bare soil composite using averaged spectra as a basis for model development. Workflow two uses compositing after individual SOC modeling for each image. Within these workflows, different approaches were tested to estimate the SOC content, amongst them are a range of SOC spectral features and a two-step local PLSR which replaces the wet-chemistry SOC analyses for model calibration and validation by laboratory spectra and a large scale soil spectral library. Results show that the best method to produce a multitemporal composite SOC map based on imaging spectroscopy data was workflow two: the SOC maps composite, using the SOC spectral feature approach (R2 = 0.83, RPD = 2.42). While workflow two and the traditional PLSR approach was more robust for all input dates (R2 = 0.79, RPD = 2.21). Best results of the single images reached R2 values of 0.76-0.91 and RPD values ranging between 2.06-3.42. Three of the tested SOC spectral features provided accuracies comparable to the modeling approaches. These results are promising regarding the improvement of the spatial coverage and the refinement of the mapping and monitoring of SOC and other soil parameters. Further investigations in this direction are needed as they are precursors of what will be feasible by upcoming operational imaging spectroscopy missions with increased availability.

AB - Soils are the largest terrestrial carbon pool and a valuable good that provides important ecosystem services. Since soils are threatened by degradation and in order to fight climate change the knowledge of the status quo especially of its soil organic carbon (SOC) content is required. A promising tool to map and monitor our soils are spaceborne imaging spectrometers which are able to produce up-to-date, inexpensive and spatially explicit maps. Especially the recent launch of new imaging spectroscopy sensors with a high signal-to-noise ratio opens up new possibilities. One of those is the combination of multitemporal spaceborne imaging spectroscopy data into SOC composite maps with a higher spatial coverage. This study explores different multitemporal combination workflows in order to support finding a best practice. To our knowledge for the first time, a spatially more complete SOC composite map was generated using four PRISMA images recorded over the same study site in northern Germany. Two different workflows of computation were compared: workflow one, creates a synthetical bare soil composite using averaged spectra as a basis for model development. Workflow two uses compositing after individual SOC modeling for each image. Within these workflows, different approaches were tested to estimate the SOC content, amongst them are a range of SOC spectral features and a two-step local PLSR which replaces the wet-chemistry SOC analyses for model calibration and validation by laboratory spectra and a large scale soil spectral library. Results show that the best method to produce a multitemporal composite SOC map based on imaging spectroscopy data was workflow two: the SOC maps composite, using the SOC spectral feature approach (R2 = 0.83, RPD = 2.42). While workflow two and the traditional PLSR approach was more robust for all input dates (R2 = 0.79, RPD = 2.21). Best results of the single images reached R2 values of 0.76-0.91 and RPD values ranging between 2.06-3.42. Three of the tested SOC spectral features provided accuracies comparable to the modeling approaches. These results are promising regarding the improvement of the spatial coverage and the refinement of the mapping and monitoring of SOC and other soil parameters. Further investigations in this direction are needed as they are precursors of what will be feasible by upcoming operational imaging spectroscopy missions with increased availability.

KW - Hyperspectral

KW - Imaging spectroscopy

KW - Multitemporal

KW - PRISMA

KW - SOC maps composite

KW - Soil organic carbon

UR - http://www.scopus.com/inward/record.url?scp=85199779976&partnerID=8YFLogxK

U2 - 10.1016/j.geoderma.2024.117025

DO - 10.1016/j.geoderma.2024.117025

M3 - Article

AN - SCOPUS:85199779976

VL - 450

JO - GEODERMA

JF - GEODERMA

SN - 0016-7061

M1 - 117025

ER -