Earth Observations and Integrative Models in Support of Food and Water Security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries

Satellite and in situ observations for advancing global Earth surface modelling: a review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort

Observation and integrated Earth-system science: a roadmap for 2016–2025

This report is the response to a request by the Committee on Space Research of the International Council for Science to prepare a roadmap on observation and integrated Earth-system science for the coming ten years. Its focus is on the combined use of observations and modelling to address the functioning, predictability and projected evolution of interacting components of the Earth system on timescales out to a century or so. It discusses how observations support integrated Earth-system science and its applications, and identifies planned enhancements to the contributing observing systems and other requirements for observations and their processing. All types of observation are considered, but emphasis is placed on those made from space. The origins and development of the integrated view of the Earth system are outlined, noting the interactions between the main components that lead to requirements for integrated science and modelling, and for the observations that guide and support them. What constitutes an Earth-system model is discussed. Summaries are given of key cycles within the Earth system. The nature of Earth observation and the arrangements for international coordination essential for effective operation of global observing systems are introduced. Instances are given of present types of observation, what is already on the roadmap for 2016–2025 and some of the issues to be faced. Observations that are organised on a systematic basis and observations that are made for process understanding and model development, or other research or demonstration purposes, are covered. Specific accounts are given for many of the variables of the Earth system. The current status and prospects for Earth-system modelling are summarized. The evolution towards applying Earth-system models for environmental monitoring and prediction as well as for climate simulation and projection is outlined. General aspects of the improvement of models, whether through refining the representations of processes that are already incorporated or through adding new processes or components, are discussed. Some important elements of Earth-system models are considered more fully. Data assimilation is discussed not only because it uses observations and models to generate datasets for monitoring the Earth system and for initiating and evaluating predictions, in particular through reanalysis, but also because of the feedback it provides on the quality of both the observations and the models employed. Inverse methods for surface-flux or model-parameter estimation are also covered. Reviews are given of the way observations and the processed datasets based on them are used for evaluating models, and of the combined use of observations and models for monitoring and interpreting the behaviour of the Earth system and for predicting and projecting its future. A set of concluding discussions covers general developmental needs, requirements for continuity of space-based observing systems, further long-term requirements for observations and other data, technological advances and data challenges, and the importance of enhanced international co-operation

An evaluation of novel remotely sensed data to improve and verify ocean- atmosphere forecasting.

The aim of this study is to evaluate the use of novel remote observations and spatial data analysis to improve the skill of an ocean forecasting system for the central Mediterranean Sea. A high-resolution (0.042 by 0.042ๆ ocean forecasting system was setup consisting of an atmosphere model (NCEP Eta model) that was coupled to an ocean model (Princeton Ocean Model). This coupling consisted of the provision of surface atmospheric fluxes predicted at 3-hourly intervals to drive forward the ocean model. This research study dealt with a variety of aspects to improve this forecasting system using an inter-disciplinary approach. The main aspect of this thesis is an evaluation of novel, remotely- sensed data acquired by an orbiting passive microwave sensor as a tool to assess and improve ocean forecasting. Thus, SST derived by the Tropical Microwave Imager onboard the TRMM satellite was evaluated for its potential to define one of the lower boundary conditions of the Eta model. The impact was positive, and resulted in an average improvement of the skill of the model to predict lower surface marine winds by approximately 10%. TMI-data proved extremely useful to derive instantaneous turbulent heat fluxes and other surface geophysical fields that were needed to diagnose and fine-tune the skill of the Eta model to forecast these fields. The TMI SST product also proved to be a valuable data source for data assimilation by the ocean model. An optimised data assimilation scheme was derived resulting in a bias of just -0.05 С after a 15-day model integration run. This thesis shows how spatial data analysis can provide more detailed information about the high-resolution forecasts and their quality in addition to standard verification tools. Routines that explore the spatial data of the forecasts, observations and their relationship were developed and applied. Geostatistical analysis was used to model the spatial structure of the residual fields of the predictions and observations, and to translate the degree of spatial correlation in numerical and graphical terms

The Cooperative VAS Program with the Marshall Space Flight Center

Work was divided between the analysis/forecast model development and evaluation of the impact of satellite data in mesoscale numerical weather prediction (NWP), development of the Multispectral Atmospheric Mapping Sensor (MAMS), and other related research. The Cooperative Institute for Meteorological Satellite Studies (CIMSS) Synoptic Scale Model (SSM) has progressed from a relatively basic analysis/forecast system to a package which includes such features as nonlinear vertical mode initialization, comprehensive Planetary Boundary Layer (PBL) physics, and the core of a fully four-dimensional data assimilation package. The MAMS effort has produced a calibrated visible and infrared sensor that produces imager at high spatial resolution. The MAMS was developed in order to study small scale atmospheric moisture variability, to monitor and classify clouds, and to investigate the role of surface characteristics in the production of clouds, precipitation, and severe storms

Global Modeling and Assimilation Office Annual Report and Research Highlights 2011-2012

Over the last year, the Global Modeling and Assimilation Office (GMAO) has continued to advance our GEOS-5-based systems, updating products for both weather and climate applications. We contributed hindcasts and forecasts to the National Multi-Model Ensemble (NMME) of seasonal forecasts and the suite of decadal predictions to the Coupled Model Intercomparison Project (CMIP5)

North American Land Data Assimilation System: A Framework for Merging Model and Satellite Data for Improved Drought Monitoring

Drought is a pervasive natural climate hazard that has widespread impacts on human activity and the environment. In the United States, droughts are billion-dollar disasters, comparable to hurricanes and tropical storms and with greater economic impacts than extratropical storms, wildfires, blizzards, and ice storms combined (NCDC, 2009). Reduction of the impacts and increased preparedness for drought requires the use and improvement of monitoring and prediction tools. These tools are reliant on the availability of spatially extensive and accurate data for representing the occurrence and characteristics (such as duration and severity) of drought and their related forcing mechanisms. It is increasingly recognized that the utility of drought data is highly dependent on the application (e.g., agricultural monitoring versus water resource management) and time (e.g., short- versus long-term dryness) and space (e.g., local versus national) scales involved. A comprehensive set of drought indices that considers all components of the hydrological–ecological–human system is necessary. Because of the dearth of near-real-time in situ hydrologic data collected over large regions, modeled data are often useful surrogates, especially when combined with observations from remote sensing and in situ sources. This chapter provides an overview of drought-related activities associated with the North American Land Data Assimilation System (NLDAS), which purports to provide an incremental step toward improved drought monitoring and forecasting. The NLDAS was originally conceived to improve short-term weather forecasting by providing better land surface initial conditions for operational weather forecast models. This reflects increased recognition of the role of land surface water and energy states, such as surface temperature, soil moisture, and snowpack, to atmospheric processes via feedbacks through the coupling of the water and energy cycles. Phase I of the NLDAS (NLDAS-1; Mitchell et al., 2004) made tremendous progress toward developing an operational system that gave high-resolution land hydrologic products in near real time. The system consists of multiple land surface models (LSMs) that are driven by an observation-based meteorological data set both in real time and retrospectively. This work resulted in a series of scientific papers that evaluated the retrospective data (meteorology and model output) in terms of their ability to reflect observations of the water and energy cycles and the uncertainties in the simulations as measured by the spread among individual models (Pan et al., 2003; Robock et al., 2003; Sheffield et al., 2003; Lohmann et al., 2004; Mitchell et al., 2004; Schaake et al., 2004). These evaluations led to the implementation of significant improvements to the LSMs in the form of new model physics and adjustments to parameter values and to the methods and input meteorological data (Xia et al., 2012). The system has since expanded in scope to include model intercomparison studies, real-time monitoring, and hydrologic prediction and has inspired other activities such as high-resolution land surface modeling and global land data assimilation systems (e.g., the Global Land Data Assimilation System [GLDAS], Rodell et al., 2004; the Land Information System [LIS], Kumar et al., 2006)

North American Land Data Assimilation System: A Framework for Merging Model and Satellite Data for Improved Drought Monitoring

Drought is a pervasive natural climate hazard that has widespread impacts on human activity and the environment. In the United States, droughts are billion-dollar disasters, comparable to hurricanes and tropical storms and with greater economic impacts than extratropical storms, wildfires, blizzards, and ice storms combined (NCDC, 2009). Reduction of the impacts and increased preparedness for drought requires the use and improvement of monitoring and prediction tools. These tools are reliant on the availability of spatially extensive and accurate data for representing the occurrence and characteristics (such as duration and severity) of drought and their related forcing mechanisms. It is increasingly recognized that the utility of drought data is highly dependent on the application (e.g., agricultural monitoring versus water resource management) and time (e.g., short- versus long-term dryness) and space (e.g., local versus national) scales involved. A comprehensive set of drought indices that considers all components of the hydrological–ecological–human system is necessary. Because of the dearth of near-real-time in situ hydrologic data collected over large regions, modeled data are often useful surrogates, especially when combined with observations from remote sensing and in situ sources. This chapter provides an overview of drought-related activities associated with the North American Land Data Assimilation System (NLDAS), which purports to provide an incremental step toward improved drought monitoring and forecasting. The NLDAS was originally conceived to improve short-term weather forecasting by providing better land surface initial conditions for operational weather forecast models. This reflects increased recognition of the role of land surface water and energy states, such as surface temperature, soil moisture, and snowpack, to atmospheric processes via feedbacks through the coupling of the water and energy cycles. Phase I of the NLDAS (NLDAS-1; Mitchell et al., 2004) made tremendous progress toward developing an operational system that gave high-resolution land hydrologic products in near real time. The system consists of multiple land surface models (LSMs) that are driven by an observation-based meteorological data set both in real time and retrospectively. This work resulted in a series of scientific papers that evaluated the retrospective data (meteorology and model output) in terms of their ability to reflect observations of the water and energy cycles and the uncertainties in the simulations as measured by the spread among individual models (Pan et al., 2003; Robock et al., 2003; Sheffield et al., 2003; Lohmann et al., 2004; Mitchell et al., 2004; Schaake et al., 2004). These evaluations led to the implementation of significant improvements to the LSMs in the form of new model physics and adjustments to parameter values and to the methods and input meteorological data (Xia et al., 2012). The system has since expanded in scope to include model intercomparison studies, real-time monitoring, and hydrologic prediction and has inspired other activities such as high-resolution land surface modeling and global land data assimilation systems (e.g., the Global Land Data Assimilation System [GLDAS], Rodell et al., 2004; the Land Information System [LIS], Kumar et al., 2006)

Empleo de técnicas de teledetección con diferentes niveles de resolución para la mejora de la gestión del riego

Currently there is a growing interest in improving water management in Mediterranean agriculture due to the foreseeable results of climate change and to the competition with other sectors such as the environmental. For this reason different methodologies have been evaluated in this thesis to increase water use efficiency in Andalusian agriculture by means of the improvement in the estimation of crop irrigation water requirements, using different remote sensing techniques and spatial analysis. In this work the two main parameters involved in crop evapotranspiration determination were addressed: reference evapotranspiration (Chapters 1 and 2) and crop coefficient (Chapters 3 and 4). More specifically, in Chapter 1, different interpolation methods were applied to meteorological data and results were assessed in order to determine which of them provided the most accurate reference evapotranspiration (ETo) estimates. The ETo estimates obtained from the interpolation methods were compared with the ETo values provided by the Land Surface Analysis Satellite Application Facility (LSA SAF), based on the daily solar radiation derived from Meteosat Second Generation (MSG) and air temperature at 2 m forecasts provided by European Center for Medium-range Weather Forecasts (ECMWF). Additionally, new techniques were proposed for ETo estimation improvement in areas without a nearby weather station, which were based on the analysis of the spatial location of the weather stations and the temporal evolution of ETo. Also related to ETo estimation and its practical application for irrigation management, Chapter 2 presents an innovative methodology for performing irrigation schedules easily usable by farmers and technicians, using weather forecasts provided by the National Meteorological Agency (AEMET) and by ECMWF for ETo estimation. In addition, the effect that the different methods for ETo estimation has on the crop water requirements and on the crop yield simulated using the AquaCrop model was also assessed. Once accurate ETo values were determined by means of the methodologies developed in Chapters 1 and 2, it is necessary to determine crop coefficient values for the correct estimation of the crop water demands. This issue was addressed in Chapter 3, where different atmospheric corrections were applied to Landsat 7 satellite images, with the aim of eliminating the effect that the atmosphere causes during the image acquisition process. In this way, it was possible to obtain much more accurate surface temperature measurements, in order to assess the effect of the different atmospheric corrections on the determination of the olive crop coefficient. However, the effect that atmosphere has on the satellite images acquisition process analyzed in Chapter 3 is not the only issue to be taken into account when using remote sensing techniques. Thus, spatial resolution is also a key factor for the application of these techniques in irrigation management. Therefore, in Chapter 4 the influence of spatial resolution on the different energy balance components estimated by the METRIC energy balance model was evaluated, paying special attention to crop evapotranspiration.Actualmente existe un interés creciente por la mejora de la gestión del agua en la agricultura mediterránea debido a las previsibles consecuencias del cambio climático y a la competencia con otros sectores como el medioambiental. Por este motivo en esta tesis se han evaluado diferentes metodologías para incrementar la eficiencia en el uso del agua en la agricultura andaluza por medio de la mejora en la estimación de las necesidades de riego de los cultivos, empleando diferentes técnicas de teledetección y análisis espacial. De este modo, en este trabajo se abordó el estudio de los dos principales parámetros involucrados en la determinación de la evapotranspiración de cultivo: la evapotranspiración de referencia (Capítulos 1 y 2) y el coeficiente de cultivo (Capítulos 3 y 4). Más específicamente, en el Capítulo 1 se evaluaron diferentes métodos de interpolación de información obtenida desde estaciones meteorológicas para determinar cuál de ellos proporcionaba unas estimaciones de evapotranspiración de referencia (ETo) más precisas. Las estimaciones de ETo obtenidas con dichos métodos de interpolación se compararon con los valores de ETo proporcionados por Land Surface Analysis Satellite Application Facility (LSA SAF), a partir de la radiación solar diaria derivada de Meteosat Second Generation (MSG) y de las prediciones de la temperatura del aire a 2 m proporcionadas por European Centre for Medium-range Weather Forecasts (ECMWF). Adicionalmente, se propusieron técnicas para la mejora en la estimación de la ETo en zonas sin estación meteorológica cercana, basadas en el análisis de localización espacial de las estaciones meteorológicas y en la evolución temporal de ETo en las mismas. Relacionado también con la estimación de la ETo y su aplicación práctica para la gestión del riego, en el Capítulo 2 se presenta una innovadora metodología para la realización de calendarios de riego fácilmente utilizable por agricultores y técnicos, utilizando predicciones meteorológicas para la estimación de ETo proporcionadas por la Agencia Estatal de Meteorología (AEMET) y por el ECMWF. Además, se analizó el efecto de la consideración de diferentes métodos para la estimación de la ETo sobre las necesidades de riego y sobre el rendimiento del cultivo simulado utilizando el modelo AquaCrop. Una vez determinados valores fiables de ETo mediante las metodologías desarrolladas en los Capítulos 1 y 2, para la correcta estimación de las necesidades de riego de los cultivos, es preciso obtener valores de coeficiente de cultivo ajustados al estado de los mismos. Esta cuestión se trató en el Capítulo 3, donde se aplicaron diferentes correcciones atmosféricas sobre imágenes del satélite Landsat 7, con el objetivo de eliminar el efecto que la atmósfera causa durante el proceso de adquisición de las mismas. De este modo, se consiguió obtener unas medidas de temperatura superficial mucho más precisas, para finalmente conocer el efecto de las diferentes correcciones atmosféricas sobre la determinación del coeficiente de cultivo del olivar. Sin embargo, el efecto de la atmósfera en el proceso de adquisición de imágenes de satélite analizado en el Capítulo 3 no es el único aspecto a tener en cuenta al emplear técnicas de teledetección. Así, la resolución espacial también es un factor clave para la correcta aplicación de estas técnicas en la gestión del riego. Es por ello que en el Capítulo 4 se evaluó la influencia de la resolución espacial sobre los diferentes componentes de balance de energía estimados mediante el modelo de balance de energía METRIC, prestando especial atención a la evapotranspiración del cultivo