冬小麦遥感冠层温度监测土壤含水量的试验研究

在冬小麦主要生育期(2002年4月初到5月底),对不灌溉的冬小麦测定了冠层温度、地温、气温以及土壤含水量,计算了冠气温差且分析了冠层温度和冠气温差与不同土层厚度的土壤含水量相关关系。结果表明:14:00的冠层温度能较好地反映20cm土层的土壤含水量变化,但与其它各土层相关性有较大的波动性;14:00的冠气温差能较好地反映40cm以上土层的土壤含水量变化,二者的相关性很高,在20cm、40cm土层,两者相关系数R2分别为0.98866、0.99389,这为用区域遥感数据反演主要生育期冬小麦的冠气温差进而监测区域40cm土壤含水量提供了实验性的依据;拔节期和灌浆期,用14:00冠气温差来拟合各土壤层的土壤含水量有较高的精度,从而为用区域遥感数据监测区域土壤含水量提供了经验性的模型。     

Isotopic order, biogeochemical processes, and earth history: Goldschmidt lecture, Davos, Switzerland, August 2002

The impetus to interpret carbon isotopic signals comes from an understanding of isotopic fractionations imposed by living organisms. That understanding rests in turn on studies of enzymatic isotope effects, on fruitful concepts of isotopic order, and on studies of the distribution of ¹³C both between and within biosynthetic products. In sum, these studies have shown that the isotopic compositions of biological products are governed by reaction kinetics and by pathways of carbon flow.Isotopic compositions of individual compounds can indicate specific processes or environments. Examples include biomarkers which record the isotopic compositions of primary products in aquatic communities, which indicate that certain bacteria have used methane as a carbon source, and which show that some portions of marine photic zones have been anaerobic. In such studies, the combination of structural and isotopic lines of evidence reveals relationships between compounds and leads to process-related thinking. These are large steps. Reconstruction of the sources and histories of molecular fossils redeems much of the early promise of organic geochemistry by resolving and clarifying paleoenviron-mental signals. In turn, contemplation of this new information is driving geochemists to study microbial ecology and evolution, oceanography, and sedimentology.     

Apparent and real bias in numerical transfer functions in palaeolimnology

We present an evaluation of the procedure by which model prediction bias is examined in palaeolimnological transfer function inference models. We argue that most of the prediction biases commonly reported in the literature are, in fact, fallacious, and are the artificial consequence of the inappropriate manner in which residuals are traditionally examined. We show that the extent of the specious model bias is entirely predictable from first principles and is essentially determined by the strength of the predictive model. We suggest that the analysis of residuals should always be examined as a function of the model's predictions and we discuss the implications of the old and new approaches.