Abstract
Visible and near-infrared (Vis–NIR) spectroscopy can provide a rapid and inexpensive estimation for soil organic carbon (SOC). However, with respect to field in situ spectroscopy, external environmental factors likely degrade the model accuracy. Among these factors, moisture has the greatest effect on soil spectra. The external parameter orthogonalization (EPO) algorithm in combination with the Chinese soil spectroscopic database (Dataset A, 1566 samples) was investigated to eliminate the interference of the external parameters for SOC estimation. Two different methods of EPO development, namely, laboratory-rewetting archive soil samples and field-collecting actual moist samples, were compared to balance model performance and analytical cost. Memory-based learning (MBL), a local modeling technique, was introduced to compare with partial least square (PLS), a global modeling method. A total of 250 soil samples from Central China were collected. Of these samples, 120 dry ground samples (Dataset B) were rewetted to different moisture levels to develop EPO P1 matrix. Seventy samples (Dataset C) containing field-moist intact and laboratory dry ground soils were used to establish EPO P2 matrix. The remaining 60 samples (Dataset D) also containing field-moist intact and laboratory dry ground soils were employed to validate the spectral models developed based on Dataset A. Results showed that EPO could correct the effect of external factors on soil spectra. For PLS, the validation statistics were as follows: no correction, validation R2 = 0.02; P1 correction, validation R2 = 0.56; and P2 correction, validation R2 = 0.57. For MBL, the validation results were as follows: no correction, validation R2 = 0.06; P1 correction, validation R2 = 0.65; and P2 correction, validation R2 = 0.69. The P2 consistently yielded better results than P1 did but simultaneously increased the sampling time and economic cost. The use of the P1 matrix and the MBL algorithm was recommended because it could reduce the cost of establishing in situ models for SOC.
Highlights
Soil organic carbon (SOC) plays an important role in reducing greenhouse gas emissions into the atmosphere [1–5]
With advancements in proximal soil sensing, visible and near-infrared (Vis–NIR) spectroscopy has become an effective technique for enriching conventional soil surveys to reduce cost and quantify multiple soil attributes [8–12]
The soil data used in this study was divided into four independent datasets: Dataset A: This subset consisted of 1566 topsoil (0–20 cm) samples gathered from 14 provinces in China
Summary
Soil organic carbon (SOC) plays an important role in reducing greenhouse gas emissions into the atmosphere [1–5]. It is an important indicator of soil quality and can improve soil biological productivity and agricultural sustainability [6,7]. SOC content should be accurately and timely assessed for managing, enhancing, and improving. 2022, 14, 1303 the utilization of this resource. With advancements in proximal soil sensing, visible and near-infrared (Vis–NIR) spectroscopy has become an effective technique for enriching conventional soil surveys to reduce cost and quantify multiple soil attributes [8–12]. The. Vis–NIR spectra contain comprehensive soil information, including color, particle size, organic matter, and clay mineral. In comparison with traditional methods for determining
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have