THE BIG PICTURE - SATELLITE REMOTE SENSING APPLICATIONS IN RANGELAND ASSESSMENT AND CROP INSURANCE

Livestock Production/Industries, Risk and Uncertainty,

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

The Economic Value of Remote Sensing of Earth Resources from Space: An ERTS Overview and the Value of Continuity of Service. Volume 1: Summary

An overview of the ERTS program is given to determine the magnitude of the benefits that can be reasonably expected to flow from an Earth Resources Survey (ERS) Program, and to assess the benefits foregone in the event of a one or two-year gap in ERS services. An independent evaluation of the benefits attributable to ERS-derived information in key application areas is presented. These include two case studies in agriculture-distribution, production and import/export, and one study in water management. The cost-effectiveness of satellites in an ERS system is studied by means of a land cover case study. The annual benefits achieveable from an ERS system are measured by the in-depth case studies to be in the range of $430 to$746 million. Benefits foregone in the event of a one-year gap in ERS service are estimated to be $147 to$220 million and $274 to$420 million for a two-year gap in ERS service

Weather, climate, and hydrologic forecasting for the US Southwest: A survey

As part of a regional integrated assessment of climate vulnerability, a survey was conducted from June 1998 to May 2000 of weather, climate, and hydrologic forecasts with coverage of the US Southwest and an emphasis on the Colorado River Basin. The survey addresses the types of forecasts that were issued, the organizations that provided them, and techniques used in their generation. It reflects discussions with key personnel from organizations involved in producing or issuing forecasts, providing data for making forecasts, or serving as a link for communicating forecasts. During the survey period, users faced a complex and constantly changing mix of forecast products available from a variety of sources. The abundance of forecasts was not matched in the provision of corresponding interpretive materials, documentation about how the forecasts were generated, or reviews of past performance. Potential existed for confusing experimental and research products with others that had undergone a thorough review process, including official products issued by the National Weather Service. Contrasts between the state of meteorologic and hydrologic forecasting were notable, especially in the former's greater operational flexibility and more rapid incorporation of new observations and research products. Greater attention should be given to forecast content and communication, including visualization, expression of probabilistic forecasts and presentation of ancillary information. Regional climate models and use of climate forecasts in water supply forecasting offer rapid improvements in predictive capabilities for the Southwest. Forecasts and production details should be archived, and publicly available forecasts should be accompanied by performance evaluations that are relevant to users

A review on the current Status of Numerical Weather Prediction in Portugal 2021: surface–atmosphere interactions

Earth system modelling is currently playing an increasing role in weather forecasting and understanding climate change, however, the operation, deployment and development of numerical Earth system models are extremely demanding in terms of computational resources and human effort. Merging synergies has become a natural process by which national meteorological services assess and contribute to the development of such systems. With the advent of joining synergies at the national level, the second edition of the workshop on Numerical Weather Prediction in Portugal was promoted by the Portuguese Institute for the Sea and Atmosphere, I.P. (IPMA), in cooperation with several Portuguese Universities. The event was hosted by the University of Évora, during the period of 11–12 of November 2021. It was dedicated to surface–atmosphere interactions and allowed the exchange of experiences between experts, students and newcomers. The workshop provided a refreshed overview of ongoing research and development topics in Portugal on surface–atmosphere interaction modelling and its applications and an opportunity to revisit some of the concepts associated with this area of atmospheric sciences. This article reports on the main aspects discussed and offers guidance on the many technical and scientific modelling platforms currently under study.info:eu-repo/semantics/publishedVersio

Collinsville solar thermal project: yield forecasting (final report)

Executive Summary 1        Introduction This report’s primary aim is to provide yield projections for the proposed Linear Fresnel Reflector (LFR) technology plant at Collinsville, Queensland, Australia.  However, the techniques developed in this report to overcome inadequate datasets at Collinsville to produce the yield projections are of interest to a wider audience because inadequate datasets for renewable energy projects are commonplace.  Our subsequent report called ‘Energy economics and dispatch forecasting’ (Bell, Wild & Foster 2014a) uses the yield projections from this report to produce long-term wholesale market price and dispatch forecasts for the plant.  2        Literature review The literature review discusses the four drivers for yield for LFR technology: DNI (Direct Normal Irradiance) Temperature Humidity Pressure Collinsville lacks complete historical datasets of the four drivers to develop yield projections but its three nearby neighbours possess complete datasets, so could act as proxies for Collinsville.  However, analysing the four drivers for Collinsville and its three nearby sites shows that there is considerable difference in their climates.  This difference makes them unsuitable to act as proxies for yield calculations.  Therefore, the review investigates modelling the four drivers for Collinsville. We introduce the term “effective” DNI to help clarify and ameliorate concerns over the dust and dew effects on terrestrial DNI measurement and LFR technology. We also introduce a modified Typical Metrological Year (TMY) technique to overcome technology specific TMYs.  We discuss the effect of climate change and the El Niño Southern Oscillation (ENSO) on yield and their implications for a TMY. 2.1     Research questions Research questions arising from the literature review include: The overarching research question: Can modelling the weather with limited datasets produce greater yield predictive power than using the historically more complete datasets from nearby sites? This overarching question has a number of smaller supporting research questions: Does BoM adequately adjust its DNI satellite dataset for cloud cover at Collinsville? Given the dust and dew effects, is using raw satellite data sufficient to model yield? Does elevation between Collinsville and nearby sites affect yield? How does the ENSO cycle affect yield? Given the 2007-12 electricity demand data constraint, will the 2007-13 based TMY provide a “Typical” year over the ENSO cycle? How does climate change affect yield? Is the method to use raw satellite DNI data to calculate yield and retrospectively adjusting the calculated yield with an effective to satellite DNI energy per area ratio suitable? How has climate change affected the ENSO cycle? A further research question arises in the methodology but is included here for completeness. What is the expected frequency of oversupply from the Linear Fresnel Novatec Solar Boiler? 3        Methodology In the methodology section, we discuss the data preparation and the model selection process for the four drivers of yield.  We also discuss the development of the technology specific TMY and sensitivity analysis to address the research questions on climate change and elevation. 4        Results and analysis In the results section we present the selection process for the four driver models.  We also present the effective to satellite DNI ratio, the annual variation in gross yield, the selection of TMMs for the TMY based on monthly yield, the sensitivity analysis results on climate change and elevation, and the frequency of gross yield exceeding 30 MW. 5        Discussion We analyse the results within a wider context, in particular, we make a comparison with the yield calculations for Rockhampton to address the overarching research question.  We find that the modelling of weather at Collinsville using incomplete weather data has higher predictive performance that using the complete weather data at Rockhampton but recommend using the BoM’s one-minute solar data to improve the comparative test.  Other findings include the requirement to increase the current TMM’s selection period 2007-13 to incorporate more of the ENSO cycle.  There is less than 0.3% change in gross yield from the plant in the most likely case of climate change but there is a requirement to determine the effect of climate change on electricity demand and the ensuing change in wholesale electricity prices. 6        Conclusion In this report, we have addressed the key research questions, produced the yield projections for our subsequent report ‘Energy economics and dispatch forecasting’ (Bell, Wild & Foster 2014a) and made recommendations for further research

NDVI Short-Term Forecasting Using Recurrent Neural Networks

In this paper predictions of the Normalized Difference Vegetation Index (NDVI) data recorded by satellites over Ventspils Municipality in Courland, Latvia are discussed. NDVI is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. Artificial Neural Networks (ANN) are computational models and universal approximators, which are widely used for nonlinear, non-stationary and dynamical process modeling and forecasting. In this paper Elman Recurrent Neural Networks (ERNN) are used to make one-step-ahead prediction of univariate NDVI time series