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241210s2024 xx |||||o 00| ||eng c |
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|a 10.1111/gcb.17608
|2 doi
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|a pubmed24n1628.xml
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|a (DE-627)NLM381377954
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|a (NLM)39651630
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Broeg, Tom
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Spatiotemporal Monitoring of Cropland Soil Organic Carbon Changes From Space
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| 264 |
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1 |
|c 2024
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 09.12.2024
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|a Date Revised 11.12.2024
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|a published: Print
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|a Citation Status MEDLINE
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| 520 |
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|a © 2024 The Author(s). Global Change Biology published by John Wiley & Sons Ltd.
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|a Soil monitoring requires accurate and spatially explicit information on soil organic carbon (SOC) trends and changes over time. Spatiotemporal SOC models based on Earth Observation (EO) satellite data can support large-scale SOC monitoring but often lack sufficient temporal validation based on long-term soil data. In this study, we used repeated SOC samples from 1986 to 2022 and a time series of multispectral bare soil observations (Landsat and Sentinel-2) to model high-resolution cropland SOC trends for almost four decades. An in-depth validation of the temporal model uncertainty and accuracy of the derived SOC trends was conducted based on a network of 100 long-term monitoring sites that were continuously resampled every 5 years. While the general SOC prediction accuracy was high (R2 = 0.61; RMSE = 5.6 g kg-1), the direct validation of the derived SOC trends revealed a significantly greater uncertainty (R2 = 0.16; p < 0.0001), even though predicted and measured values showed similar distributions. Classifying the results into declining and increasing SOC trends, we found that 95% of all sites were either correctly identified or predicted as stable (p < 0.001), highlighting the potential of our findings. Increased accuracies for SOC trends were found in soils with higher SOC contents (R2 = 0.4) and sites with reduced tillage (R2 = 0.26). Based on the signal-to-noise ratio and temporal model uncertainty, we were able to show that the necessary time frame to detect SOC trends strongly depends on the absolute SOC changes present in the soils. Our findings highlight the potential to generate significant cropland SOC trend maps based on EO data and underline the necessity for direct validation with repeated soil samples and long-term SOC measurements. This study marks an important step toward the usability and integration of EO-based SOC maps for large-scale soil carbon monitoring
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|a Journal Article
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| 650 |
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|a bare soil
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| 650 |
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|a carbon sequestration
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| 650 |
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4 |
|a change detection
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| 650 |
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4 |
|a climate change
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| 650 |
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4 |
|a earth observation
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| 650 |
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4 |
|a remote sensing
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| 650 |
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4 |
|a soil reflectance composite
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| 650 |
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4 |
|a space–time model
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| 650 |
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7 |
|a Soil
|2 NLM
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| 650 |
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7 |
|a Carbon
|2 NLM
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| 650 |
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7 |
|a 7440-44-0
|2 NLM
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| 700 |
1 |
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|a Don, Axel
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wiesmeier, Martin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Scholten, Thomas
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Erasmi, Stefan
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Global change biology
|d 1999
|g 30(2024), 12 vom: 09. Dez., Seite e17608
|w (DE-627)NLM098239996
|x 1365-2486
|7 nnns
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| 773 |
1 |
8 |
|g volume:30
|g year:2024
|g number:12
|g day:09
|g month:12
|g pages:e17608
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| 856 |
4 |
0 |
|u http://dx.doi.org/10.1111/gcb.17608
|3 Volltext
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