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Calibrating Remotely sensed Ocean Chlorophyll Data: An Application of the Blending Technique in Three Dimensions (3D)

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Open Journal of Marine Science 2017年第1期7卷 191-204页

作者： Mathias A. Onabid Department of Mathematics and Computer Sciences Faculty of Sciences University of Dschang Dschang Cameroon

In this article, the extension to three dimensions (3D) of the blending technique that has been widely used in two dimensions (2D) to calibrate ocean chlorophyll is presented. The results thus obtained revealed a very high degree of efficiency when predicting observed values of ocean chlorophyll. The mean squared difference between the predicted and observed values of ocean chlorophyll when 3D technique was used fell far below the tolerance level which was set to the difference between satellite and observed in-situ values. The resulting blended field did not only provide better predictions of the in situ observations in areas where bottle samples cannot be obtained but also provided a smooth variation of the distribution of ocean chlorophyll throughout the year. An added advantage is its computational efficiency since data that would have been treated at least four times would be treated only once. With the advent of these results, it is believed that the modelling of the ocean life cycle will become more realistic.

关键词： In-Situ 3D-Blending Satellite Over-Relaxation Method Calibration Remotely sensed Data

Using Boosted Regression Trees and Remotely sensed Data to Drive Decision-Making

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Open Journal of Statistics 2017年第5期7卷 859-875页

作者： Brigitte Colin Samuel Clifford Paul Wu Samuel Rathmanner Kerrie Mengersen School of Mathematical Sciences Queensland University of Technology Brisbane Australia

Challenges in Big Data analysis arise due to the way the data are recorded, maintained, processed and stored. We demonstrate that a hierarchical, multivariate, statistical machine learning algorithm, namely Boosted Regression Tree (BRT) can address Big Data challenges to drive decision making. The challenge of this study is lack of interoperability since the data, a collection of GIS shapefiles, remotely sensed imagery, and aggregated and interpolated spatio-temporal information, are stored in monolithic hardware components. For the modelling process, it was necessary to create one common input file. By merging the data sources together, a structured but noisy input file, showing inconsistencies and redundancies, was created. Here, it is shown that BRT can process different data granularities, heterogeneous data and missingness. In particular, BRT has the advantage of dealing with missing data by default by allowing a split on whether or not a value is missing as well as what the value is. Most importantly, the BRT offers a wide range of possibilities regarding the interpretation of results and variable selection is automatically performed by considering how frequently a variable is used to define a split in the tree. A comparison with two similar regression models (Random Forests and Least Absolute Shrinkage and Selection Operator, LASSO) shows that BRT outperforms these in this instance. BRT can also be a starting point for sophisticated hierarchical modelling in real world scenarios. For example, a single or ensemble approach of BRT could be tested with existing models in order to improve results for a wide range of data-driven decisions and applications.

关键词： Boosted Regression Trees Remotely sensed Data Big Data Modelling Approach Missing Data

The spatio-temporal distribution of thermodynamic forces at the land-atmosphere interface in China

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Science China Earth Sciences 2010年第11期53卷 42-53页

作者： LEVY Jason K. L. Douglas Wilder School of Government and Public Affairs Virginia Commonwealth University

Correlation analysis and the complementary relationship model are used to indirectly measure and characterize the dynamic and thermodynamic properties at the land-atmosphere interface in *** show that thermodynamic forces driving the exchange fluxes of energy,water and carbon between the land surface and the overlying atmosphere are significantly enhanced along the mid-upper reaches of the Yangtze River basin around Sichuan Basin and the northern region of *** thermodynamic forces exhibit a decreasing trend in southern Xinjiang and some regions of *** cloud cover was shown to be a significant factor leading to changes in thermodynamic properties at the land-atmosphere *** Pacific Subtropical High (WPSH) and storm data provide strong evidence that a changing synoptic system is likely a principle factor causing the abnormal spatiotemporal distribution of thermodynamic forces over the land surface in *** data also suggests that cyclical and quasi-cyclical fluctuations of low cloud cover in most regions of China are responsible for annual rainfall *** the same time,NOAA-AVHRR satellite remote sensing data illustrates that changes to land surface characteristics may cause atypical spatiotemporal distributions of thermodynamic forces over the land *** show that thermodynamic forces are weakening in regions with increasing ***,where urbanization has decreased the quantity of vegetation,thermodynamic forces are ***,an increase in large-scale thermodynamic forces is associated with a reduction in low cloud cover due to changes in the synoptic system.

关键词： thermodynamic forces Western Pacific Subtropical High low cloud cover remotely sensed data

Assessing Drought Conditions in Cloudy Regions Using Reconstructed Land Surface Temperature

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Journal of Meteorological Research 2020年第2期34卷 264-279页

作者： Shiqi YANG Dejun ZHANG Liang SUN Yongqian WANG Yanghua GAO Chongqing Institute of Meteorological Sciences Chongqing 401147 College of Resources and Environment Chengdu University of Information TechnologyChengdu 610225 Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Remote SensingMinistry of Agriculture and Rural Affairs of the People’s Republic of ChinaBeijing 100081

Temperature vegetation dryness index(TVDI)in a triangular or trapezoidal feature space can be calculated from the land surface temperature(LST)and normalized difference vegetation index(NDVI),and has been widely applied to regional drought ***,thermal infrared sensors cannot penetrate clouds to detect surface information of sub-cloud *** cloudy areas,LST data include a large number of cloudy pixels,seriously degrading the spatial and temporal continuity of drought *** this paper,the Remotely sensed Daily Land Surface Temperature Reconstruction model(RSDAST)is combined with the LST reconstructed(RLST)by the RSDAST and applied to drought monitoring in a cloudy *** drought monitoring capability of the reconstructed temperature vegetation drought index(RTVDI)under cloudy conditions is evaluated by comparing the correlation between land surface observations for soil moisture and the TVDI before and after surface temperature *** show that the effective duration and area of the RTVDI in the study area were larger than those of the original TVDI(OTVDI)in *** addition,RLST/NDVI scatter plots cover a wide range of values,with the fitted dry–wet boundaries more representative of real soil moisture *** continuously cloudy conditions,the OTVDI inverted from the original LST(OLST)loses its drought monitoring capability,whereas RTVDI can completely and accurately reconstruct surface moisture conditions across the entire study *** correlation between TVDI and soil moisture is stronger for RTVDI(R=-0.45)than that for OTVDI(R=-0.33).In terms of the spatial and temporal distributions,the R value for correlation between RTVDI and soil moisture was higher than that for ***,in continuously cloudy areas,RTVDI not only expands drought monitoring capability in time and space,but also improves the accuracy of surface soil moisture monitoring and enhances the applicability and reliability of thermal infrared

关键词： land surface temperature reconstruction Remotely sensed Daily Land Surface Temperature Reconstruction model(RSDAST) temperature vegetation dryness index(TVDI) soil moisture drought

The spatial scaling effect of continuous canopy Leaves Area Index retrieved by remote sensing

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Science China Earth Sciences 2009年第3期52卷 393-401页

作者： XU XiRu FAN WenJie TAO Xin Institute of Remote Sensing and GIS Peking UniversityBeijing 100871China

Leave Area Index (LAI) is one of the most basic parameters to describe the geometric structure of plant canopies. It is also important input data for climatic model and interaction model between Earth surface and atmosphere, and some other things. The spatial scaling of retrieved LAI has been widely studied in recent years. Based on the new canopy reflectance model, the mechanism of the scaling effect of con- tinuous canopy Leaf Area Index is studied, and the scaling transform formula among different scales is found. Both the numerical simulation and the field validation show that the scale transform formula is reliable.

关键词： spatial scaling effect remotely sensed LAI the true LAI the reflectance model of continuous canopy

Application of MODIS-Based Monthly Evapotranspiration Rates in Runoff Modeling: A Case Study in Nebraska,USA

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Open Journal of Modern Hydrology 2013年第4期3卷 172-178页

作者： Jozsef Szilagyi Department of Hydraulic and Water Resources Engineering Budapest University of Technology and Economics Budapest Hun- gary 2School of Natural Resources University of Nebraska-Lincoln Lincoln USA.

Daily and monthly flow-rates of the Little Nemaha River in Nebraska were simulated by the lumped-parameter Jakeman-Hornberger as well as a distributed-parameter water-balance accounting procedure for the 2003-2008 and 2000-2009 periods, respectively, with and without the help of the MODIS-based monthly estimates of evapotranspiration (ET) rates. While the daily lumped-parameter model simulation accuracy remained practically unchanged with the inclusion of the monthly MODIS-based ET rates interpolated into daily values (R2 of 0.66 vs 0.68, simulated to measured runoff ratio remaining the same 96%), the monthly water-balance accounting model outcomes did improve to some extent (from an R2 of 0.67 to 0.7 with simulated to measured runoff ratio of 72% vs 115%). In both cases the models had to be slightly modified for accommodation of the ET rates as predefined input values, not present in the original model setups. These results indicate the potential practical usefulness of satellite-derived ET estimates (CREMAP values in the present case) in monthly water-balance modeling. CREMAP is a calibration-free ET estimation method based on MODIS-derived daytime surface temperature values in combination of basic climatic variables, such as air temperature, humidity and solar radiation within a Complementary Relationship framework of evaporation.

关键词： Remotely sensed Evapotranspiration Complementary Relationship Runoff Modeling