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• The FOD technique refines soil spectral features, improving SOC mapping. • 3D spectral indices correlate more strongly with SOC compared to 2D indices. • Integrating low-order FOD and 3D indices maximizes SOC prediction accuracy. • ZY1-02D hyperspectral satellite demonstrated advanced soil property prediction. Monitoring soil organic carbon (SOC) content is crucial for climate change mitigation and sustaining ecological balance. Despite the unparalleled advantages of hyperspectral data in capturing nuanced variations in soil properties through its high spectral resolution, effectively extracting useful features from numerous bands via spectral processing techniques remains a formidable challenge. This study proposes an integrated approach combining fractional-order derivative (FOD) technique and optimal band combination algorithm using ZY1-02D satellite hyperspectral data to estimate SOC in Northeast China's Black soil region. Three modeling strategies were compared: (1) FOD-transformed reflectance (FOD spectra), (2) FOD spectra with traditional 2D spectral indices (FOD + 2D SI), and (3) FOD spectra with new 3D spectral indices (FOD + 3D SI). These strategies were implemented using the random forest model with the aim of the optimal SOC prediction. Results showed that the application of FOD technique for spectral transformation effectively addressed the challenges posed by overlapping peaks and baseline drift inherent in the original spectral reflectance. Additionally, FOD transformation enhanced subtle soil spectral features and yielded more pronounced spectral variations with increasing fractional order, as compared to the original spectral data and conventional integer-order derivatives (i.e., first and second-order derivatives). However, as the FOD order continued to increase beyond 1.4, the spectral curve exhibited amplified noise and distortion, thereby adversely impacting subsequent model development. The 3D spectral indices correlate more robustly with SOC than 2D indices. The model that combines 0.6-order FOD and 3D spectral indices achieved the best accuracy (R2 = 0.66, RMSE = 2.99 g/kg and MAE = 2.42 g/kg), significantly outperforming the models built by 0.6-order FOD spectra (R2 = 0.48, RMSE = 3.65 g kg−1, and MAE = 2.93 g kg−1) and 0.8-order FOD + 2D SI modeling strategy (R2 = 0.55, RMSE = 3.54 g kg−1, and MAE = 2.85 g kg−1). These findings indicated that FOD and 3D spectral indices exhibit superior synergistic performance in SOC prediction, demonstrating their feasibility and providing valuable insights for large-scale soil property prediction and mapping using satellite hyperspectral data. [ABSTRACT FROM AUTHOR]

• Investigate the most statistically significant covariates to predict topsoil (0–0.3 m) organic carbon (SOC). • Assess proximal and remote sensors versatility either in combination or alone. • Examine geostatistical, machine learning and hybrid models. • Identify optimal calibration sample size. • Determine nitrogen (N) fertiliser practices considering mineralisation from SOC. Nitrogen (N) is essential for sugarcane growth, with one source mineralisation of soil organic carbon (SOC, %). To assist farmers determine suitable N fertiliser rates, the Six-Easy-Steps nutrient guidelines were devised by Sugar Research Australia and based on topsoil (0–0.3 m) SOC. Given the heterogeneous nature of SOC, a digital soil mapping (DSM) approach may be useful to map SOC at the field scale. This study aims to examine various models to predict SOC, including ordinary kriging (OK), regression kriging (RK), machine learning (ML) such as random forest (RF) and support vector machine (SVM), and hybrid models that combine ML with RK (i.e., RFRK and SVMRK). Various digital data sources, including proximal (i.e., gamma-ray [γ-ray] and electromagnetic [EM] induction data) and remote (i.e., land surface temperature [LST, oC]) sensing data were explored in combination and alone. Minimum calibration sample size (i.e., 110–10) was also investigated. The comparisons were validated using a hold-out dataset, with prediction agreement (Lin's concordance correlation coefficient [LCCC]) and accuracy (ratio of performance to deviation [RPD]) assessed. The most statistically significant digital data used in combination included γ-ray (i.e., total count [TC]), EM (i.e., 1mHcon and 2mHcon), remote (i.e., LST) and northing. The SVMRK model performed best when using all calibration data, producing moderate agreement (LCCC = 0.73) and fair (RPD = 1.65) accuracy. This was also the case when sample size was >= 50, but when it was < 50, RFRF returned equivalent agreement (0.70) and accuracy (1.55). Similar results were achieved when only γ-ray (i.e., TC) was used, however, RFRK produced moderate agreement and fair accuracy using as few as 20 and as many as 70 samples, with the optimal being 40 (i.e., LCCC = 0.79 and RPD = 1.7). By comparison, OK and RK generally produced poor agreement (<= 0.65) and accuracy (<= 1.4) regardless of sample size. The final DSM using RFRK provided an indication of soil condition that limits soil capability to store N as a function of SOC and acts a means to replenish soil capital with N application rates according to the Six-Easy-Steps. Specifically, Tenosols and Rudosols require 130 kg/ha to 120 kg/ha, whereas Kandosols require 110 and 100 kg/ha. The DSM equates to a potential ∼ 22 % decrease in the cost of N fertiliser application, relative to a one-size fits all approach. [ABSTRACT FROM AUTHOR]