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Strategy of efficient estimation of soil organic content at the local scale based on the national spectral database
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  • Hongyi Li,
  • Yuheng Li,
  • Meihua Yang,
  • Songchao Chen,
  • Shi Zhou
Hongyi Li
Jiangxi University of Finance and Economics

Corresponding Author:[email protected]

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Yuheng Li
Jiangxi University of Finance and Economics
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Meihua Yang
Yuzhang Normal University
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Songchao Chen
INRA
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Shi Zhou
Zhejiang University
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Abstract

The aim of this paper was to compare the prediction performance of three strategies: general global Partial least squares regression (PLSR) using CSSL with and without spiking samples, memory-based learning (MBL) using CSSL with and without spiking samples and general PLSR using only spiking samples to predict soil organic matter in the target area. When using spiked subsets, we also investigated the prediction performance of the extra-weighted subsets. A series of spiking subsets randomly selected from the total spiking samples were selected by conditioned Latin hypercube sampling (cLHS) from the target sites. We calculated the mean squared Euclidean distance (msd) of different spiking subsets with the distribution density function of their vis–NIR spectra only and statistically inferred the optimal sampling set size to be 30. Our study showed that when the number of spiking were lower than 30, the predicted accuracy derived from global PLSR using CSSL spiked with and without extra-weighted samples was greater than the predicted accuracy derived from the general PLSR using the corresponding number of spiking samples only (RMSE 5.57–5.98 v.s. RMSE 6.76). Global PLSR using CSSL spiked with the statistically optimal local samples can achieve higher predicted performance (with a mean RMSE of 5.75). MBL spiked with five extra-weighted optimal spiking samples achieved the best accuracy with an RMSE of 3.98, an R2 of 0.70, a bias of 0.04 and an LCCC of 0.81. The msd is a simple and effective method to determine an adequate spiking size using only vis–NIR data.
09 Sep 2021Submitted to Land Degradation & Development
13 Sep 2021Submission Checks Completed
13 Sep 2021Assigned to Editor
28 Sep 2021Reviewer(s) Assigned
24 Oct 2021Review(s) Completed, Editorial Evaluation Pending
30 Oct 2021Editorial Decision: Revise Major
20 Dec 20211st Revision Received
21 Dec 2021Submission Checks Completed
21 Dec 2021Assigned to Editor
14 Jan 2022Review(s) Completed, Editorial Evaluation Pending
14 Jan 2022Editorial Decision: Revise Minor
18 Jan 20222nd Revision Received
23 Jan 2022Submission Checks Completed
23 Jan 2022Assigned to Editor
23 Jan 2022Review(s) Completed, Editorial Evaluation Pending
23 Jan 2022Editorial Decision: Revise Minor
24 Jan 20223rd Revision Received
25 Jan 2022Submission Checks Completed
25 Jan 2022Assigned to Editor
29 Jan 2022Review(s) Completed, Editorial Evaluation Pending
29 Jan 2022Editorial Decision: Accept
Jun 2022Published in Land Degradation & Development volume 33 issue 10 on pages 1649-1661. 10.1002/ldr.4223