首页|艾比湖流域不同土地覆盖类型土壤养分高光谱反演模型研究

艾比湖流域不同土地覆盖类型土壤养分高光谱反演模型研究

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
土壤养分影响着土壤的质量,也影响着植被、农作物等的生长。为快速准确地估测艾比湖流域土壤养分状况,选择艾比湖流域精河县作为研究区,以精河县内不同地表覆盖类型土壤为研究对象,基于实地采集的75个土壤样品的室内ASD FieldSpec3实测光谱数据和3种光谱变换形式,利用10 nm间隔重采样进行去噪处理,再结合多元逐步回归法(SMLR)、偏最小二乘法回归法(PLSR)、人工神经网络法(ANN)分别建立土壤养分预测模型,以探索最优模型。结果表明：土壤实测光谱的一阶微分、二阶微分变换形式能显著提高光谱与土壤养分之间的相关性,尤其是一阶微分变换与土壤有机质和全氮的相关性最高分别达0.87和0.91,光谱变换技术能显著增强土壤养分与高光谱之间的敏感度,达到更好的建模效果； SMLR、PLSR和ANN这3种模型都具有良好的预测能力,其中, ANN建立的模型预测效果最好,二阶微分变换的ANN模型对有机质、全氮的预测决定系数(R2)分别为0.886和0.984,均方根误差(RMSE)分别为2.614和0.147, PLSR次之；全氮的预测效果明显优于有机质的预测效果,说明高光谱和全氮之间的敏感性更高。总体来说,光谱二阶微分变换形式的人工神经网络模型可以最精确稳定地完成土壤养分含量的快速预测,能够实现艾比湖流域的土壤养分空间分布状况和动态变化特征的动态监测。
Hyper-spectral retrieval of soil nutrient content of various land-cover types in Ebinur Lake Basin
AbstractThe soil nutrient affects soil quality, vegetation type, crops growth and yield. To rapidly and accurately determine soil nutrient contents, an indoor spectral data (measured by ASD FieldSpec3) of 75 soil samples of Jinghe County of Ebinur Lake Basin were analyzed. Then the collected data were processed at resampling interval of 10 nm to suppress noise. Soil nutrient hyper-spectral forecast models were used to forecasts soil nutrient contents in three transformation conditions. The performance of the models was evaluated based on stepwise multiple linear regression (SMLR) analysis, partial least squares regression (PLSR) analysis and artificial neural network (ANN) analysis and the optimal model determined by comparison. The results showed that the transformation of first-order and second-order differential dramatically enhanced correlation between spectroscopy data and soil nutrient content. Specifically, the first-order differential of soil spectroscopy had a good correction with soil nutrient content. The correlation coefficients for organic matter and total nitrogen were 0.87 and 0.91, respectively. In conclusion, spectral transformation technique increased the sensitivity of high spectral data to soil nutrient change, and it produced far better forecasting results. Although all the three models had good predictive ability, ANN model had the best predictive effect, followed by PLSR model. The ANN model estimation test based on the second-order differential of spectroscopy data with independent datasets from different soil samples respectively producedR2 and RMSE values of 0.885 and 0.984 for organic matter and 2.614 and 0.147 for total nitrogen. The prediction effect of total nitrogen was obviously better than that of organic matter. This indicated that the sensitivity of soil hyper-spectral reflectance to the soil total nitrogen content was much better. Overall, the ANN model based on the second-order differential of spectroscopy data rapidly and precisely predicted soil nutrients contents. It was beneficial for monitoring spatial distributions and dynamic changes of soil nutrients in Ebinur Lake Basin.

Soil nutrientHyper-spectrum remote sensingStepwise multiple linear regressionPartial least squares regressionArtificial neural networkEbinur Lake Basin

蒋烨林、王让会、李焱、李成、彭擎、吴晓全

展开 >

南京信息工程大学应用气象学院南京 210044

大气环境与装备技术协同创新中心南京 210044

土壤养分 高光谱遥感 多元逐步回归 偏最小二乘法 人工神经网络 艾比湖流域

中国清洁发展机制基金国家科技支撑计划项目国家科技支撑计划项目

20130132012BAD16B03052012BAC23B01

2016

10.13930/j.cnki.cjea.160547

中国生态农业学报(中英文)
中国科学院遗传与发育生物学研究所 中国生态经济学会

中国生态农业学报(中英文)

CSTPCDCSCD北大核心
影响因子：1.937
ISSN：2096-6237
年,卷(期)：2016.24(11)
  • 15
  • 26