Recognizing and reducing effects of moisture-salt coexistence on soil organic matter spectral prediction：From laboratory to satellite - Wang, Danyang; Tan, Yayi; Li, Cheng; Xin, Jingda; Wang, Yunqi; Hou, Huagang; Gao, Lulu; Zhong, Changbo; Pan, Jianjun; Li, Zhaofu (Soil & tillage research, 2025)
Soil organic matter (SOM) mapping in salinized areas is crucial for scientific guidance on soil salinization. However, accurately mapping SOM is challenging due to the intricate interplay between soil moisture content (SMC) and soil salt content (SSC), which significantly influences soil spectra. Unlike prior research that has separately examined the impacts of moisture or salinity, this study delves into the combined effects of these factors on SOM spectra. The objective of this study is to develop and validate several spectral optimization algorithms at both the laboratory and satellite levels. In October 2020, a study was conducted using 291 ground-truth data to examine the impact of various moisture-salt stages (seven moisture stages, five salt stages) on hyperspectral data. Spectrum mechanism responsive for soil moisture and salinity were analyzed through spectral curves, correlation, and analysis of variance (Anova, AOV), and spectrum mechanism responsive for soil moisture and salinity model were built. Following that, the spectra were optimized using piecewise direct normalization (PDS)-AOV, non-negative matrix factorization (NMF)-AOV, and orthogonal signal correction (OSC)-AOV. The SOM prediction models were then built by integrating these optimized spectra with Stacking ensemble machine learning algorithms (RF, GBM, ANN). Eventually, the lab-optimized spectra were merged with satellite multispectral images to create new image (named REC) for SOM mapping. The results indicated varying impacts of SMC and SSC on spectra, particularly between 1400 nm to 2000 nm, revealing the influence of moisture-salt interaction; the best optimization algorithm (OSC-AOV) with Stacking mitigated the effect of moisture-salt coexistence on spectra (the R2 and RPD of the best models elevated by 0.005–0.267, 0.020–0.374 respectively, RMSE reduced by 0.137–1.817 g/kg); implementing this algorithm on REC significantly improved the accuracy of SOM mapping (R2 elevated by 0.185–0.259, RMSE reduced by 2.615–3.203 g/kg). This study extensively investigated the effects of moisture and salinity on spectra, spanning from laboratory to satellite, offering a novel approach to understanding and addressing the complexities in SOM mapping in salinized environments. •Development and validation of a novel spectral optimization algorithm.•Effects of moisture and salinity on soil spectra, from laboratory to satellite scales.•The stacking integration algorithm improves model robustness.•Substantial improvement in SOM mapping accuracy through the developed algorithm.