Surface topography and mixed-pixel effects on the simulated L-band brightness temperatures.

The impact of topography and mixed pixels on L-band radiometric observations over land needs to be quantified to improve the accuracy of soil moisture retrievals. For this purpose, a series of simulations has been performed with an improved version of the Soil Moisture and Ocean Salinity (SMOS) End-to-End Performance Simulator (SEPS). The brightness temperature (TB) generator of SEPS has been modified to include a 100-m-resolution land cover map and a 30-m-resolution digital elevation map of Catalonia (northeast of Spain). This highresolution TB generator allows the assessment of the errors in soil moisture retrieval algorithms due to limited spatial resolution and provides a basis for the development of pixel disaggregation techniques. Variation of the local incidence angle, shadowing, and atmospheric effects (up- and downwelling radiation) due to surface topography has been analyzed. THE AVAILABILITY of high-resolution brightness temperature (TB) maps at L-band is crucial to analyze important issues dealing with bare and vegetation-covered land emission and to develop inversion algorithms in preparation for real Soil Moisture and Ocean Salinity (SMOS) mission data. Mixed-pixel, coastlines, shadowing, and topography effects on the measured brightness temperatures need further study, but the lack of global geophysical data at sufficient temporal and spatial resolution and the large amount of data involved in the generation of high-resolution TB maps on a global basis complicate the issue. In fact, in spite of the existence of global digital elevation models with sufficient spatial resolution, accurate land cover data do not exist for most parts of the world. To address these issues, a series of simulations has been performed with an improved version of the SMOS End-to-End Performance Simulator (SEPS) [1], [2], in which, to date, all points on Earth have been assumed to be at sea level. The study has been done over the region of Catalonia, on the northeastern coast of Spain, because of its many different land cover types, topography, and the presence of a coastline. A 30-m-resolution digital elevation map [3] and a 100-m-resolution land coverage map of Catalonia [4] have been used as inputs, and SEPS has been conveniently modified to generate high-resolution TB maps of this area. A variety of soil and land cover types (crops, bushes, marshes, etc.) have been parameterized using the values obtained from field experiments and literature [5]–[10], [12].The impact of topography and mixed pixels on L-band radiometric observations over land needs to be quantified to improve the accuracy of soil moisture retrievals. For this purpose, a series of simulations has been performed with an improved version of the soil moisture and ocean salinity (SMOS) end-to-end performance simulator (SEPS). The brightness temperature generator of SEPS has been modified to include a 100-m-resolution land cover map and a 30-m-resolution digital elevation map of Catalonia (northeast of Spain). This high-resolution generator allows the assessment of the errors in soil moisture retrieval algorithms due to limited spatial resolution and provides a basis for the development of pixel disaggregation techniques. Variation of the local incidence angle, shadowing, and atmospheric effects (up- and downwelling radiation) due to surface topography has been analyzed. Results are compared to brightness temperatures that are computed under the assumption of an ellipsoidal Earth

Journal of Telecommunications and Information Technology, 2007, nr 1

kwartalni

Satellite Positioning

Satellite positioning techniques, particularly global navigation satellite systems (GNSS), are capable of measuring small changes of the Earths shape and atmosphere, as well as surface characteristics with an unprecedented accuracy. This book is devoted to presenting recent results and development in satellite positioning technique and applications, including GNSS positioning methods, models, atmospheric sounding, and reflectometry as well their applications in the atmosphere, land, oceans and cryosphere. This book provides a good reference for satellite positioning techniques, engineers, scientists as well as user community

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

Forests are suffering water stress due to climate change; in some parts of the globe, forests are being exposed to the highest temperatures historically recorded. Machine learning techniques combined with robotic platforms and artificial vision systems have been used to provide remote monitoring of the health of the forest, including moisture content, chlorophyll, and nitrogen estimation, forest canopy, and forest degradation, among others. However, artificial intelligence techniques evolve fast associated with the computational resources; data acquisition, and processing change accordingly. This article is aimed at gathering the latest developments in remote monitoring of the health of the forests, with special emphasis on the most important vegetation parameters (structural and morphological), using machine learning techniques. The analysis presented here gathered 108 articles from the last 5 years, and we conclude by showing the newest developments in AI tools that might be used in the near future

Contributions to ionospheric determination with global positioning system: solar flare detection and prediction of global maps of total electron content

Two research studies have been addressed in this thesis. Both of them are of actual scientific interest and are based on processing GNSS data. The first part of this thesis is devoted to GNSS detection and monitoring of solar flares. The second one is devoted to GNSS prediction of ionospheric Total Electron Content. Regarding the first study, a new solar flare detector called SISTED has been designed and implemented. Its goal is to provide a simple and efficient way of detecting the most number of powerful X-class solar flares in real time operation. In addition, it can send early warning messages to prevent the harmful consequences of the increase of ejected particles from the Sun that may reach the Earth after a solar flare, especially in case of a Coronal Mass Ejection. The main benefit of SISTED regarding other detection techniques is that it does not require data from external providers out of the GNSS community. In addition, it can run in real-time operation and could provide value added data to GNSS users. The results show that SISTED was able to detect up to the 95% of the X-class flares reported by GOES for more than a half solar cycle. Regarding the second study, a new approach to predict Global Ionospheric vertical TEC Maps has been designed and implemented in the context of the IGS Ionosphere Working Group. The motivation to develop a UPC Predicted product was the interest of ESA's SMOS mission. A recent application using UPC Predicted products is the generation of real-time global VTEC maps as background model. In addition, the predicted VTEC maps are used to generate the combined IGS Predicted products. The results obtained in this thesis show that the model performs well when the results are compared with those obtained by the other IGS analysis centers. In addition, applying the prediction model leads to better results than the use of time-invariant ionosphere for two days ahead. In relation with this research, 4 publications in international journals indexed in JCR/ISI have been generated (and another one is under review process), and 7 presentations have been authored in international meetings, among the new UPC predicted product contributing to IGS, and the contribution to two competitive projects funded by the European Space Agency (AGIM and MONITOR)

Plant Characteristic Estimation Using Sonar, Multispectral Reflectance, and Electromagnetic Response

The goal of this study was to design, test and validate three methods of remotely estimating plant physical and physiological characteristics. A free-space parallel plate electrostatic sensing system operating at medium radio frequency range was used to estimate water content and plant dry biomass. An ultrasound distance sensing system and a multispectral imaging system was used to directly estimate plant height and top view surface area and indirectly estimate plant biomass. NDVI was calculated from the multispectral imaging system data. Combining NDVI with the plant height and top view surface area estimates, a correlation was observed between plant biomass, chlorophyll content and chlorophyll concentration. Plant water content and dry biomass of greenhouse grown spinach were estimated using a free-space electrostatic sensing system (rsup#2/sup# = 0.95). Ultrasonic sensor-based height estimates and top view surface area multispectral image data provided plant biomass estimates in corn and spinach (rsup#Biosystems and Agricultural Engineerin

BDS GNSS for Earth Observation

For millennia, human communities have wondered about the possibility of observing phenomena in their surroundings, and in particular those affecting the Earth on which they live. More generally, it can be conceptually defined as Earth observation (EO) and is the collection of information about the biological, chemical and physical systems of planet Earth. It can be undertaken through sensors in direct contact with the ground or airborne platforms (such as weather balloons and stations) or remote-sensing technologies. However, the definition of EO has only become significant in the last 50 years, since it has been possible to send artificial satellites out of Earth’s orbit. Referring strictly to civil applications, satellites of this type were initially designed to provide satellite images; later, their purpose expanded to include the study of information on land characteristics, growing vegetation, crops, and environmental pollution. The data collected are used for several purposes, including the identification of natural resources and the production of accurate cartography. Satellite observations can cover the land, the atmosphere, and the oceans. Remote-sensing satellites may be equipped with passive instrumentation such as infrared or cameras for imaging the visible or active instrumentation such as radar. Generally, such satellites are non-geostationary satellites, i.e., they move at a certain speed along orbits inclined with respect to the Earth’s equatorial plane, often in polar orbit, at low or medium altitude, Low Earth Orbit (LEO) and Medium Earth Orbit (MEO), thus covering the entire Earth’s surface in a certain scan time (properly called ’temporal resolution’), i.e., in a certain number of orbits around the Earth. The first remote-sensing satellites were the American NASA/USGS Landsat Program; subsequently, the European: ENVISAT (ENVironmental SATellite), ERS (European Remote-Sensing satellite), RapidEye, the French SPOT (Satellite Pour l’Observation de laTerre), and the Canadian RADARSAT satellites were launched. The IKONOS, QuickBird, and GeoEye-1 satellites were dedicated to cartography. The WorldView-1 and WorldView-2 satellites and the COSMO-SkyMed system are more recent. The latest generation are the low payloads called Small Satellites, e.g., the Chinese BuFeng-1 and Fengyun-3 series. Also, Global Navigation Satellite Systems (GNSSs) have captured the attention of researchers worldwide for a multitude of Earth monitoring and exploration applications. On the other hand, over the past 40 years, GNSSs have become an essential part of many human activities. As is widely noted, there are currently four fully operational GNSSs; two of these were developed for military purposes (American NAVstar GPS and Russian GLONASS), whilst two others were developed for civil purposes such as the Chinese BeiDou satellite navigation system (BDS) and the European Galileo. In addition, many other regional GNSSs, such as the South Korean Regional Positioning System (KPS), the Japanese quasi-zenital satellite system (QZSS), and the Indian Regional Navigation Satellite System (IRNSS/NavIC), will become available in the next few years, which will have enormous potential for scientific applications and geomatics professionals. In addition to their traditional role of providing global positioning, navigation, and timing (PNT) information, GNSS navigation signals are now being used in new and innovative ways. Across the globe, new fields of scientific study are opening up to examine how signals can provide information about the characteristics of the atmosphere and even the surfaces from which they are reflected before being collected by a receiver. EO researchers monitor global environmental systems using in situ and remote monitoring tools. Their findings provide tools to support decision makers in various areas of interest, from security to the natural environment. GNSS signals are considered an important new source of information because they are a free, real-time, and globally available resource for the EO community