不同粒径湿地土壤高光谱特征及碳氮磷含量反演模型研究

Hyperspectral characteristics and inversion model of carbon,nitrogen,and phosphorus contents in wetland soil with different particle sizes

河流湿地是非常重要的湿地类型,其中河流湿地土壤能够有效维持河流湿地生态系统的稳定性。土壤碳氮磷是支撑湿地土壤质量和植被生长的关键营养元素,利用高光谱遥感数据对其进行估算对实现湿地土壤养分信息的快速和准确检测具有重要意义。土壤粒径作为土壤最重要的属性之一,对土壤样本的光谱反射率有着重要影响,并且是影响土壤结构、阳离子交换能力、植物养分可用性等的重要因素。以陕西黄河湿地省级自然保护区为研究区,于2022年8—9月采集477份湿地表层土壤样本,经过室内过筛处理后得到1.0 mm、0.3 mm、0.2 mm、0.1 mm四种不同粒径的土壤样本。基于原始光谱数据及一阶微分转换光谱数据对土壤碳、氮、磷含量建立不同粒径的偏最小二乘回归、随机森林、高斯过程回归3种预测模型,比较建模R^(2)以及RMSR选择最优模型,并筛选敏感波段构建模型进行评价。研究结果显示:(1)光谱反射率数值随土壤粒径的减小而增大,0.1 mm粒径的预测模型相比于其他粒径始终有着更好的精度;(2)基于一阶微分光谱建立的土壤有机碳、全氮、全磷含量估算模型均具有更高的精度;(3)基于敏感波段建立的偏最小二乘回归模型,建模R^(2)范围0.62—0.98,验证R^(2)范围0.36—0.94,相比其他模型具有更优秀更稳定的反演效果。研究结果表明通过控制土壤粒径建立土壤碳、氮、磷含量的估算模型是可行的,选择合适的粒径大小能够提高反演模型估算精度。而偏最小二乘回归作为具有较高精度的反演模型可以帮助提高模型的稳定性和预测能力,从而更准确地估算土壤中的碳、氮、磷含量。研究结果为基于高光谱遥感的不同粒径处理的湿地表层土壤碳、氮、磷定量反演提供坚实的理论支撑与技术支持。

River wetlands are a critically important type of wetland,with soil playing a vital role in maintaining the stability of riverine wetland ecosystems.Soil carbon,nitrogen,and phosphorus are key nutrient elements supporting soil quality and vegetation growth in wetlands.Utilizing hyperspectral remote sensing data to estimate them is of significant importance for rapid and accurate detection of soil nutrient information in wetlands.Soil particle size,as one of the most important soil properties,has a significant impact on the spectral reflectance of soil samples and is a crucial factor affecting soil structure,cation exchange capacity,and plant nutrient availability.Taking the Shaanxi Yellow River Wetland Provincial Nature Reserve as the study area,477 surface soil samples were collected from August to September 2022,and after sieving in the laboratory,four different particle-sized soil samples of 1.0 mm,0.3 mm,0.2 mm,and 0.1 mm were obtained.Three prediction models,namely partial least squares regression(PLSR),random forest(RF),and Gaussian process regression(GPR),were established for soil carbon,nitrogen,and phosphorus content based on original spectral data and first-order differential transformed spectral data of different particle sizes.The models were compared in terms of modeling R^(2) and RMSR to select the optimal model,and sensitive bands were selected to construct the model for evaluation.The research results showed that:(1)The numerical values of spectral reflectance increased with decreasing soil particle size,and the prediction model for 0.1 mm particle size consistently exhibited better accuracy than other particle sizes;(2)Models for estimating soil organic carbon,total nitrogen,and total phosphorus content based on first-order differential spectra had higher accuracy;(3)Partial least squares regression(PLSR)models based on sensitive bands had a modeling R^(2) range of 0.62—0.98 and a validation R^(2) range of 0.36—0.94,demonstrating superior and more stable inversion results compared to other models.The study results indicate that establishing models to estimate soil carbon,nitrogen,and phosphorus content by controlling soil particle size is feasible,and selecting appropriate particle sizes can improve the accuracy of inversion models.Partial least squares regression(PLSR),as a high-precision inversion model,can help enhance the stability and predictive capability of the model,thereby more accurately estimating carbon,nitrogen,and phosphorus content in the soil.The research results provide solid theoretical and technical support for the quantitative inversion of surface soil carbon,nitrogen,and phosphorus in wetlands with different particle size treatments based on hyperspectral remote sensing.