基于GF-1遥感数据预测区域森林土壤有机质含量
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S159-3;S714

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广西自然科学基金项目(2018GXNSFBA138035;2018GXNSFAA050135)和广东省林业科技计划项目(2019-07)资助。


Prediction of Soil Organic Matter Content Based on Artificial Neural Network Model and GF-1 Remote Sensing Data
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    摘要:

    为探索国产卫星GF-1预测土壤有机质(SOM)的能力,本研究以广东省云浮市的罗定市为研究区,以GF-1多光谱遥感影像衍生的9个遥感变量和DEM提取的9个地形水文变量为预测因子,建立2种人工神经网络模型(A模型:地形水文;B模型:地形水文+遥感),对5个土壤深度(L1:0~20 cm,L2:20~40 cm,L3:40~60 cm,L4:60~80 cm,L5:80~100 cm)的SOM进行预测。结果表明:5个深度的B模型全都比A模型的精度高,尤其是L1、L2土层,精度提升明显,其R²分别提高了13%和10%;而深层土壤(L3、L4、L5)的精度提升较小,仅提高了4%、5%和4%。另外,两个评价指标RMSE和ROA ±10%也表现出相似的趋势。总体而言,GF-1遥感数据显著改善了上层(0~40 cm)森林土壤人工神经网络模型的预测精度,对下层(40~100 cm)森林土壤模型改善尺度较低,是预测森林土壤SOM含量可观的新遥感数据源。

    Abstract:

    To explore the capability of GF-1 satellite to predict soil organic matter (SOM), Luoding City of Yunfu City, Guangdong Province was taken as the study area, and 9 multi-spectral remote sensing variables retrieved from GF-1 and 9 terrain variables derived from DEM were used as predictors to establish two kinds of artificial neural network models (Model A: terrain; Model B: terrain & remote sensing) for predicting soil organic matter (SOM) at five soil depths (L1: 0-20 cm, L2: 20-40 cm, L3: 40-60 cm, L4: 60-80 cm, and L5: 80-100 cm). The results showed that the accuracies of SOM full-variable B model at five depths was higher than those of A model with topographic variables only. Especially for the L1 and L2 layers of soil, the accuracy was obviously improved. The R² of the L1 and L2 layers of SOM were increased by 13% and 10% respectively. However, the accuracies of deep soils (L3, L4, L5) were only improved by 4%, 5% and 4%, respectively, and RMSE and ROA±10% also showed a similar trend. The results show that GF-1 remote sensing image can be used as a new data source to predict SOM.

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李莹莹,赵正勇,杨旗,丁晓纲,孙冬晓,韦孙玮.基于GF-1遥感数据预测区域森林土壤有机质含量[J].土壤,2022,54(1):191-197. LI Yingying, ZHAO Zhengyong, YANG Qi, DING Xiaogang, SUN Dongxiao, WEI Sunwei. Prediction of Soil Organic Matter Content Based on Artificial Neural Network Model and GF-1 Remote Sensing Data[J]. Soils,2022,54(1):191-197

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  • 收稿日期:2021-04-16
  • 最后修改日期:2021-06-08
  • 录用日期:2021-06-16
  • 在线发布日期: 2022-02-11
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