土壤有机碳预测模型数据

可以用于土壤有机碳预测,输入为土壤样本的光谱400nm吸光度，400.5nm吸光度，401nm吸光度，401.5nm吸光度，输出为土壤有机碳预测。该模型帮助解决了土壤有机碳和土壤光谱吸光度关系建模的问题。使用光谱仪器采集土壤光谱数据，将采集的土壤光谱数据使用传统算法，多元线性回归算法等方式以预测土壤有机碳。该模型通过输入土壤光谱400nm吸光度，400.5nm吸光度，401nm吸光度，401.5nm吸光度数据，来输出预测的土壤有机碳。

This dataset can be utilized for soil organic carbon (SOC) prediction. The inputs are the spectral absorbance values of soil samples at 400 nm, 400.5 nm, 401 nm, and 401.5 nm, while the output is the predicted soil organic carbon content. This model addresses the challenge of establishing the correlation between soil organic carbon and soil spectral absorbance. Soil spectral data are collected using spectrometers, and traditional algorithms such as multiple linear regression can be applied to the collected data for SOC prediction. Specifically, this model takes the absorbance values at the four specified wavelengths of soil spectra as inputs to produce the predicted soil organic carbon content.

搜集汇总

数据集介绍

特点

该数据集名为土壤有机碳预测模型数据，主要用于通过土壤样本的光谱吸光度数据预测有机碳含量。数据集包含19037条记录，每年更新，应用多元线性回归等算法，已在区块链平台存证。