Comparison of spatial-temporal analysis modelling with purely spatial analysis modelling using temperature data obtained by remote sensing

Received: March 25th, 2021 ; Accepted: May 20th, 2021 ; Published: October 5th, 2021 ; Correspondence: [email protected] in climatic elements directly affect the productivity of agricultural activities. Temperature is one of the climatic elements that varies in space and time.Therefore, understanding spatial variations in temperature is essential for many activities. Given the above, the objective of this work was to compare the performance of the proposed spatiotemporal analysis model with that of purely spatial analysis using temperature data obtained by remote sensing. The experimental data were arranged in a grid with 403 spatial locations, with 22 samples collected in a 24-hour period. The statistical software R Core Team (2020) was used to perform the analysis. The packages used in the analyses were ‘geoR’, ‘CompRandFld’, ‘scatterplot3d’, and ‘fields’. For making the maps, the software ArcGIS was used. The behavioural analysis of spatiotemporal dependence indicated, through the covariogram graph of the data, that there is a strong spatial dependence. For the cases of purely spatial analysis of phenomena, a separate spatial model for each time is justified because this type of model presents a smaller prediction error and requires simpler processing than the space-time model. It was possible to compare the space-time analysis with the purely spatial analysis using temperature data obtained by remote sensing images. The data modelled with the purely spatial analysis had, on average, lower error than those with the space-time model

Solar irradiance modeling and forecasting using novel statistical techniques

Ph.DDOCTOR OF PHILOSOPH

SOLAR IRRADIANCE FORECASTING FOR TROPICAL REGIONS USING NOVEL STATISTICAL ANALYSIS AND MACHINE LEARNING

Ph.DDOCTOR OF PHILOSOPH

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences

Quantitative Mapping of Soil Property Based on Laboratory and Airborne Hyperspectral Data Using Machine Learning

Soil visible and near-infrared spectroscopy provides a non-destructive, rapid and low-cost approach to quantify various soil physical and chemical properties based on their reflectance in the spectral range of 400–2500 nm. With an increasing number of large-scale soil spectral libraries established across the world and new space-borne hyperspectral sensors, there is a need to explore methods to extract informative features from reflectance spectra and produce accurate soil spectroscopic models using machine learning. Features generated from regional or large-scale soil spectral data play a key role in the quantitative spectroscopic model for soil properties. The Land Use/Land Cover Area Frame Survey (LUCAS) soil library was used to explore PLS-derived components and fractal features generated from soil spectra in this study. The gradient-boosting method performed well when coupled with extracted features on the estimation of several soil properties. Transfer learning based on convolutional neural networks (CNNs) was proposed to make the model developed from laboratory data transferable for airborne hyperspectral data. The soil clay map was successfully derived using HyMap imagery and the fine-tuned CNN model developed from LUCAS mineral soils, as deep learning has the potential to learn transferable features that generalise from the source domain to target domain. The external environmental factors like the presence of vegetation restrain the application of imaging spectroscopy. The reflectance data can be transformed into a vegetation suppressed domain with a force invariance approach, the performance of which was evaluated in an agricultural area using CASI airborne hyperspectral data. However, the relationship between vegetation and acquired spectra is complicated, and more efforts should put on removing the effects of external factors to make the model transferable from one sensor to another.:Abstract I Kurzfassung III Table of Contents V List of Figures IX List of Tables XIII List of Abbreviations XV 1 Introduction 1 1.1 Motivation 1 1.2 Soil spectra from different platforms 2 1.3 Soil property quantification using spectral data 4 1.4 Feature representation of soil spectra 5 1.5 Objectives 6 1.6 Thesis structure 7 2 Combining Partial Least Squares and the Gradient-Boosting Method for Soil Property Retrieval Using Visible Near-Infrared Shortwave Infrared Spectra 9 2.1 Abstract 10 2.2 Introduction 10 2.3 Materials and methods 13 2.3.1 The LUCAS soil spectral library 13 2.3.2 Partial least squares algorithm 15 2.3.3 Gradient-Boosted Decision Trees 15 2.3.4 Calculation of relative variable importance 16 2.3.5 Assessment 17 2.4 Results 17 2.4.1 Overview of the spectral measurement 17 2.4.2 Results of PLS regression for the estimation of soil properties 19 2.4.3 Results of PLS-GBDT for the estimation of soil properties 21 2.4.4 Relative important variables derived from PLS regression and the gradient-boosting method 24 2.5 Discussion 28 2.5.1 Dimension reduction for high-dimensional soil spectra 28 2.5.2 GBDT for quantitative soil spectroscopic modelling 29 2.6 Conclusions 30 3 Quantitative Retrieval of Organic Soil Properties from Visible Near-Infrared Shortwave Infrared Spectroscopy Using Fractal-Based Feature Extraction 31 3.1 Abstract 32 3.2 Introduction 32 3.3 Materials and Methods 35 3.3.1 The LUCAS topsoil dataset 35 3.3.2 Fractal feature extraction method 37 3.3.3 Gradient-boosting regression model 37 3.3.4 Evaluation 41 3.4 Results 42 3.4.1 Fractal features for soil spectroscopy 42 3.4.2 Effects of different step and window size on extracted fractal features 45 3.4.3 Modelling soil properties with fractal features 47 3.4.3 Comparison with PLS regression 49 3.5 Discussion 51 3.5.1 The importance of fractal dimension for soil spectra 51 3.5.2 Modelling soil properties with fractal features 52 3.6 Conclusions 53 4 Transfer Learning for Soil Spectroscopy Based on Convolutional Neural Networks and Its Application in Soil Clay Content Mapping Using Hyperspectral Imagery 55 4.1 Abstract 55 4.2 Introduction 56 4.3 Materials and Methods 59 4.3.1 Datasets 59 4.3.2 Methods 62 4.3.3 Assessment 67 4.4 Results and Discussion 67 4.4.1 Interpretation of mineral and organic soils from LUCAS dataset 67 4.4.2 1D-CNN and spectral index for LUCAS soil clay content estimation 69 4.4.3 Application of transfer learning for soil clay content mapping using the pre-trained 1D-CNN model 72 4.4.4 Comparison between spectral index and transfer learning 74 4.4.5 Large-scale soil spectral library for digital soil mapping at the local scale using hyperspectral imagery 75 4.5 Conclusions 75 5 A Case Study of Forced Invariance Approach for Soil Salinity Estimation in Vegetation-Covered Terrain Using Airborne Hyperspectral Imagery 77 5.1 Abstract 78 5.2 Introduction 78 5.3 Materials and Methods 81 5.3.1 Study area of Zhangye Oasis 81 5.3.2 Data description 82 5.3.3 Methods 83 5.3.3 Model performance assessment 85 5.4 Results and Discussion 86 5.4.1 The correlation between NDVI and soil salinity 86 5.4.2 Vegetation suppression performance using the Forced Invariance Approach 86 5.4.3 Estimation of soil properties using airborne hyperspectral data 88 5.5 Conclusions 90 6 Conclusions and Outlook 93 Bibliography 97 Acknowledgements 11

Generating high resolution precipitation conditional on rainfall observations and satellite data

This study is part of the high resolution reanalysis project proposed for Germany and Europe (Bollmeyer et. al., 2014) within the framework of the Hans Ertel Centre for Weather Research (HErZ). The reanalysis for Germany assimilates among other variables high resolution rainfall rates. For the most recent years, radar data is assimilated, however, for periods before 2007 this data is not available and another radar-like dataset is required. This study proposes the method HIRAIN to generate an ensemble of probable space-time precipitation fields given a set of observational data. HIRAIN works in two steps. First, a Bayesian statistical model conditional on observations from synoptic stations and on satellite information simulates the latent spatial Gaussian process that drives the occurrence of precipitation exceeding a selected threshold. In a second step, realisations of occurrence/non-occurrence of precipitation exceeding the same thresholds are obtained given the simulated latent process. The occurrence/non-occurrence of precipitation is generated through two different methodologies. HIRAIN is extended to several thresholds of precipitation amount and the final precipitation product is generated from the fields occurrence/non-occurrence of the individual thresholds. A Bayesian approach is used in HIRAIN to provide more realistic fields than those produced by interpolation methods. In the Bayesian approach the data at the observation locations are honored and the spatial covariance structure of the spatial process is reproduced in each realisation. Moreover, the ability to generate ensemble of possible precipitation patterns provides valuable information of precipitation uncertainties that plays also an important role in ensemble reanalysis. HIRAIN produces precipitation dataset with hourly and 4 km resolution. This product presents a more appropriate resolution for the purposes of the reanalysis than the rainfall datasets available by the time the Germany reanalysis project started

A Greenhouse-Gas Information System: Monitoring and Validating Emissions Reporting and Mitigation

This study and report focus on attributes of a greenhouse-gas information system (GHGIS) needed to support MRV&V needs. These needs set the function of such a system apart from scientific/research monitoring of GHGs and carbon-cycle systems, and include (not exclusively): the need for a GHGIS that is operational, as required for decision-support; the need for a system that meets specifications derived from imposed requirements; the need for rigorous calibration, verification, and validation (CV&V) standards, processes, and records for all measurement and modeling/data-inversion data; the need to develop and adopt an uncertainty-quantification (UQ) regimen for all measurement and modeling data; and the requirement that GHGIS products can be subjected to third-party questioning and scientific scrutiny. This report examines and assesses presently available capabilities that could contribute to a future GHGIS. These capabilities include sensors and measurement technologies; data analysis and data uncertainty quantification (UQ) practices and methods; and model-based data-inversion practices, methods, and their associated UQ. The report further examines the need for traceable calibration, verification, and validation processes and attached metadata; differences between present science-/research-oriented needs and those that would be required for an operational GHGIS; the development, operation, and maintenance of a GHGIS missions-operations center (GMOC); and the complex systems engineering and integration that would be required to develop, operate, and evolve a future GHGIS