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针对无源微波遥感时间分辨率高可以克服云层影响获取地表温度的问题,该文应用AMSR-E微波亮度温度数据,分别选取了基于发射率估计的单通道反演法和多通道线性拟合法反演东北地区地表温度。在原有方法的基础上提出算法改进:对单通道反演法按照植被生长周期在生长季与非生长季分别建立发射率估计方程,探究各微波通道在每种地表覆被类型的反演能力并组合反演精度最高的通道,将微波极化差异指数作为表征发射率参数加入多通道拟合方程。结果显示,获取的地表温度剔除水体和冰雪无效像元后可用性达到100%,改进后的单通道反演法均方根误差由3.58~4.6降低至2.0~3.1,在75%的区域的误差小于2 K;多通道拟合法的最终均方根误差为2.6~3.5,同样有较高精度且只使用微波亮温数据就能获取地表温度。 相似文献
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本文基于AMSR2被动微波数据和0 cm地表温度数据,对比分析冻融判别函数算法和双指标算法在东北冻土区的判别精度及适用性。结果表明：（1）两种算法的Kappa系数都在0.7以上,总体判别精度在87%以上,具有较好的性能。（2）两种算法在升轨时期的总体判别精度高于降轨时期。在使用升轨数据时,双指标算法判别精度略高于冻融判别函数算法;使用降轨数据时,冻融判别函数算法判别精度更高。（3）冻融判别函数算法对冻土的判别精度高,双指标算法在识别融土方面具有优势。（4）两种算法对多年冻土区的土壤冻融判别精度高于季节冻土区。本研究的评价结果可为东北冻土区制备高精度、长时序地表冻融数据集提供基础数据资料,为选择合适的土壤冻融判别算法提供参考。 相似文献
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多年冻土区土壤碳、氮的可变性及对深层土壤特性了解的缺乏限制了人们对气候变化响应的理解。为明确东北大兴安岭多年冻土区森林土壤有机碳、有效氮(铵态氮、硝态氮)含量分布特征,于2020年秋季(9月末)采集呼玛河流域三种类型多年冻土区(不连续多年冻土区、零星多年冻土区和岛状多年冻土区)16个1 m深的土壤剖面,基于结构方程模型探讨海拔、气候、冻土区类型和植被类型等环境变量对森林土壤有机碳和有效氮含量的影响。结果表明:土壤有机碳和硝态氮含量在不连续多年冻土区高于零星多年冻土区和岛状多年冻土区,土壤铵态氮含量在零星多年冻土区高于岛状多年冻土区和不连续多年冻土区;在垂直剖面上,随着土壤深度的增加,土壤有机碳和有效氮含量呈降低趋势,且土壤有机碳与有效氮之间呈显著的负相关关系(P<0.05)。结构方程模型表明,植被类型和年平均温度是土壤有机碳含量变化的主要控制因素,年均降水量对土壤有机碳含量变化的影响最弱;冻土区类型和植被类型是土壤铵态氮和硝态氮含量变化的主要控制因素。研究结果能够为未来准确模拟和估算呼玛河流域多年冻土区森林土壤碳氮储量提供一定的数据支撑。 相似文献
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Soil freeze-thaw cycles have important effects on surface water and energy balance,and then affect vegetation growth,soil water content,carbon cycle and terrestrial ecosystem. Passive microwave plays an important role in monitoring global and regional surface freeze-thaw processes due to its high temporal resolution,abundant data and sensitivity to soil moisture. With the launch of passive microwave sensors at home and abroad,it provides conditions for the study of permafrost interannual variation,seasonal variation,diurnal variation and long time series of near-surface soil freeze-thaw cycle. In recent years,the study of surface freeze-thaw cycle using passive microwave data has gradually increased. Based on previous studies,this paper summarizes the types of passive microwave remote sensing data and the characteristics of the bands contained in them. Expounded the principle of passive microwave monitoring data used for freezing and thawing,focus on passive microwave data in five categories in the study of freezing and thawing monitoring algorithms,including double index algorithm,the decision tree algorithm,freeze-thaw discriminant algorithm,seasonal threshold algorithm and based on the freezing L-band relative factors discriminant algorithm threshold,and analysis of 5 kinds of algorithms are compared;The freeze-thaw products based on different algorithms and passive microwave data were combed. Finally,the problems and future research directions of passive microwave remote sensing in surface freeze-thaw applications are summarized. In the acquisition of passive microwave data,it is found that the passive microwave data is missing due to the physical characteristics of the sensor,the shape and orbit of the earth,and the low resolution of passive microwave data leads to the low precision of freeze-thaw discrimination. For the problem of missing passive microwave data,it is proposed to use the average value of passive microwave data before and after two days to fill the missing brightness temperature data,or establish statistical function to complement the missing data. For the problem of low passive microwave resolution,the current development trend is to scale down based on passive microwave data and combine with multiple data products,such as ground temperature and active microwave data,or perform probability discrimination on surface freezing-thawing state in pixels,so as to better describe surface freeze-thaw state. In terms of the algorithm for discriminating surface freezing-thawing,based on the problem that dual-index algorithm,decision tree algorithm,freezing-thawing discriminant algorithm and seasonal threshold algorithm cannot accurately distinguish snow and frozen soil,this paper proposes to adopt the method of data assimilation or start from the snow radiation and frozen soil dielectric model. Optimization of the algorithm for the snow covered surface can further improve the accuracy of freeze-thaw classification. Based on existing freeze-thaw products,Although SMAP freeze-thaw products continue to be updated,SAMP satellite was launched late,and SAMP freeze-thaw products have a short time series. In the future,the time span of this algorithm for freezing-thawing products can be extended by combining L-band data provided by SMOS satellite. The problems mentioned above and the direction of further research are of great significance for improving the accuracy of freezing and thawing discrimination and improving the understanding of the variation law of freezing and thawing cycles,and also have certain research space. © 2022 Science Press (China). 相似文献
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