Development of Multi-Sensor Global Cloud and Radiance Composites for Earth Radiation Budget Monitoring from DSCOVR

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). Radiance observations and cloud property retrievals from low earth orbit and geostationary satellite imagers have to be co-located with EPIC pixels to provide scene identification in order to select anisotropic directional models needed to calculate shortwave and longwave fluxes. A new algorithm is proposed for optimal merging of selected radiances and cloud properties derived from multiple satellite imagers to obtain seamless global hourly composites at 5-kilometer resolution. An aggregated rating is employed to incorporate several factors and to select the best observation at the time nearest to the EPIC measurement. Spatial accuracy is improved using inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling. The composite data are subsequently remapped into EPIC-view domain by convolving composite pixels with the EPIC point spread function (PSF) defined with a half-pixel accuracy. PSF-weighted average radiances and cloud properties are computed separately for each cloud phase. The algorithm has demonstrated contiguous global coverage for any requested time of day with a temporal lag of under 2 hours in over 95 percent of the globe

Inter-Calibration of the OSIRIS-REx NavCams with Earth-Viewing Imagers

The Earth-viewed images acquired by the space probe OSIRIS-REx during its Earth gravity assist flyby maneuver on 22 September 2017 provided an opportunity to radiometrically calibrate the onboard NavCam imagers. Spatially-, temporally-, and angularly-matched radiances from the Earth viewing GOES-15 and DSCOVR-EPIC imagers were used as references for deriving the calibration gain of the NavCam sensors. An optimized all-sky tropical ocean ray-matching (ATO-RM) calibration approach that accounts for the spectral band differences, navigation errors, and angular geometry differences between NavCam and the reference imagers is formulated in this paper. Prior to ray-matching, the GOES-15 and EPIC pixel level radiances were mapped into the NavCam field of view. The NavCam 1 ATO-RM gain is found to be 9.874 x 10(-2) Wm(-2)sr(-1)mu m(-1)DN(-1) with an uncertainty of 3.7%. The ATO-RM approach predicted an offset of 164, which is close to the true space DN of 170. The pre-launch NavCam 1 and 2 gains were compared with the ATO-RM gain and were found to be within 2.1% and 2.8%, respectively, suggesting that sensor performance is stable in space. The ATO-RM calibration was found to be consistent within 3.9% over a factor of +/- 2 NavCam 2 exposure times. This approach can easily be adapted to inter-calibrate other space probe cameras given the current constellation of geostationary imagers.National Aeronautics & Space Administration (NASA) [NNM10AA11C]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

GIS based models for optimisation of marine cage aquaculture in Tenerife, Canary Islands

This study focused on the optimisation of offshore marine fish-cage farming in Tenerife, Canary Islands. The main objective was to select the most suitable sites for offshore cage culture. This is a key factor in any aquaculture operation, affecting both success and sustainability. Moreover, it can solve conflicts between different coastal activities, making a rational use of the coastal space. Site selection was achieved by using Geographical Information Systems (GIS) based models and related technology, such as satellite images and Global Positioning System (GPS), to support the decision-making process. Three different cage systems were selected and proposed for different areas around Tenerife. Finally, a particulate waste distribution model (uneaten feed and faeces) was developed, also using GIS, for future prediction of the dispersive nature of selected sites. This can reduce the number of sites previously identified as most suitable, by predicting possible environmental impacts on the benthos if aquaculture was to be developed on a specific site. The framework for spatial multi-criteria decision analysis used in this study began with a recognition and definition of the decision problem. Subsequently, 31 production functions (factors and constraints) were identified, defined and subdivided into 8 sub-models. These sub-models were then integrated into a GIS database in the form of thematic layers and later scored for standardization. At this stage, the database was verified by field sampling to establish the quality of data used. The decision maker's preferences were incorporated into the decision model by assigning weights of relative importance to the evaluation under consideration. These, together with the thematic layers, were integrated by using Multi-criteria Evaluation (MCE) and simple overlays to provide an overall assessment of possible alternatives. Finally, sensitivity analysis was performed to determine the model robustness. The integration, manipulations and presentation of the results by means of GIS-based models in this sequential and logical flow of steps proved to be very effective for helping the decision-making process of site selection in study. On the whole, this study revealed the usefulness of GIS as an aquaculture planning and management tool. Cage systems that can withstand harsh environments were found to be suitable for use over a broader area of Tenerife's coastline. Thus, the more robust self-tensioned cage (SeaStation®) could be used over a greater area than the weaker gravity cages (Corelsa®). From the 228 km2 of available area for siting cages in the coastal regions with depth of 50 m, the suitable area (sum of scores 6, 7 and 8) for siting SeaStation® cages was 61 km2, while the suitable area for SeaStation® and Corelsa® cages was 49 and 37 km2 respectively. Most of the variation between these three cage systems was found among the intermediate suitability scores. It was concluded that the biggest differences in suitable area among cage systems are between Corelsa® and SeaStation® systems, followed by differences between Corelsa® and OceanSpar® cages, and OceanSpar® and SeaStation® respectively. This variability was mostly located on the N and NNW of the island, where waves, both long and short-term, are higher

Characterization of geolocation accuracy of Suomi NPP Advanced Technology Microwave Sounder measurements

The Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership satellite has 22 channels at frequencies ranging from 23 to 183 GHz for probing the atmospheric temperature and moisture under all weather conditions. As part of the ATMS calibration and validation activities, the geolocation accuracy of ATMS data must be well characterized and documented. In this study, the coastline crossing method (CCM) and the land-sea fraction method (LFM) are utilized to characterize and quantify the ATMS geolocation accuracy. The CCM is based on the inflection points of the ATMS window channel measurements across the coastlines, whereas the LFM collocates the ATMS window channel data with high-resolution land-sea mask data sets. Since the ATMS measurements provide five pairs of latitude and longitude data for K, Ka, V, W, and G bands, respectively, the window channels 1, 2, 3, 16, and 17 from each of these five bands are chosen for assessing the overall geolocation accuracy. ATMS geolocation errors estimated from both methods are generally consistent from 40 cases in June 2014. The ATMS along-Track (cross-Track) errors at nadir are within ±4.2 km (±1.2 km) for K/Ka, ±2.6 km (±2.7 km) for V bands, and ±1.2 km (±0.6 km) at W and G bands, respectively. At the W band, the geolocation errors derived from both algorithms are probably less reliable due to a reduced contrast of brightness temperatures in coastal areas. These estimated ATMS along-Track and cross-Track geolocation errors are well within the uncertainty requirements for all bands. © 2016. American Geophysical Union. All Rights Reserved

Challenges of Harmonizing 40 Years of AVHRR Data: The TIMELINE Experience

Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper

Trends in surface temperature from new long–term homogenized thermal data by applying remote sensing techniques and its validation using in-situ data of five southern European lakes

Recent studies, based on a combination of long-term in-situ and satellite derived temperature data indicate that lakes are rapidly warming at the global scale. Since Lake Surface Water Temperature (LSWT) is highly responsive to long-term modifications in the thermal structure of lakes, it is a good indicator of changes in lake characteristics. There have not been done many studies at a regional scale to understand the lakes’ response to climate change, mainly due to lack of high spatio-temporal data. Therefore, further studies are needed to understand variation in trends, impacts and consequences at a regional scale. It is essential to have highly frequent spatially explicit data to understand the spatiotemporal thermal variations of LSWT. Continuous in-situ water temperature data measured at high temporal resolution from permanently installed stations are becoming increasingly available through GLEON (Global Lake Ecological Observatory Network: http://gleon.org/) or NetLake (Networking Lake Observatories in Europe). But these data are often heterogeneous with different sources and time line, point based, and not available for many lakes around the globe. To establish permanent weather stations for all the large lakes in the world is also not economically viable. As an alternative to direct measurements, remote sensing is considered as a promising approach to reconstruct complete time series of LSWT where direct measurements are missing. Temperature of land/water surfaces is one of the direct and accurate measurements using satellite data acquired in the thermal infra-red spectral region. Furthermore, the availability of daily satellite data since the 1980s at a moderate resolution of 1 km from multiple polar orbiting satellites is an opportunity not to be missed. But owing to the complexities related to earlier satellite missions, and the need of high level of processing, the potential of the historical satellite data in deriving a homogenised LSWT is still not explored well. There is a gap in the availability of long-term time series of LSWT from the satellite data which could be used in understanding the patterns and drivers of thermal variations in large lakes. This thesis aims to fill this gap by developing reproducible and extendable methods to derive homogenised daily LSWT for thirty years from 1986 to 2015. Hence, the main objectives of this thesis are i) to reconstruct thirty years (1986-2015) of daily satellite thermal data as a homogenised time series of LSWT for five large Italian lakes by combining thermal data from multiple satellites, ii) to assess the quality of the satellite derived LSWT using long-term in-situ data collected from the same lakes, iii) to report the seasonal and annual trends in LSWT using robust statistical tests. The first part of the thesis deals with the accurate processing of historical Advanced Along-Track Scanning Radiometer (AVHRR) sensor data to derive time series of LSWT. A new method to resolve the complex geometrical issues with the earlier AVHRR data obtained from National Oceanic and Atmospheric Administration (NOAA) satellites has been developed. The new method can accurately process historical AVHRR data and develop time series of geometrically aligned thermal channels in the spectral range of 10.5-12.5 µm. The validation procedure to check the accuracy of image to image co-registration using 2000 random images (from a total of 22,507 images) reported sub-pixel accuracy with an overall Root Mean Square Error (RMSE) of 755.65 m. The usability of newly derived time series of thermal channels to derive LSWT for lakes were tested and validated. Furthermore, crossplatform and inter-platform validations were performed using corresponding same day observations which reported an overall RMSE of less than 1.5 °C. In the second part of the thesis, a new method was developed to derive homogenised daily LSWT standardized at 12:00 UTC from thermal channels of thirteen different satellites. The new method is implemented for Lake Garda in Northern Italy developing time series of homogenised daily LSWT for last thirty years from 1986 to 2015. The sensors used in this study are the AVHRR from multiple NOAA satellites, Along Track Scanning Radiometer (ATSR) series from European Remote Sensing (ERS) satellites and Moderate Resolution Imaging Spectroradiometer (MODIS) from Aqua and Terra satellites. The LSWT time series are then validated using long-term in-situ data obtained from a deep and a shallow sampling location in the lake. Validation of LSWT from individual satellites against corresponding in-situ data reported an overall RMSE of 0.92 °C. The validation between final homogenised LSWT and the in-situ data reported a coefficient of determination (R2) of 0.98 and a RMSE of 0.79 °C. In the third part of the thesis, homogenised daily LSWT for the last thirty years (1986-2015) were developed for five large lakes in Italy using the newly developed methods. The LSWT time series was validated against the in-situ data collected from the respective lakes. Furthermore, long-term trend analysis to study the seasonal and annual variations in LSWT over thirty years was performed over the newly developed LSWT data. The validation procedure reported an average RMSE and Mean Absolute Error (MAE) of 1.2 °C and 0.98 °C, respectively, over all the lakes. The trend analysis reported an overall regional summer warming rate of 0.03 °C yr-1 and an annual warming rate of 0.017 °C yr-1. During summer, all studied sub-Alpine lakes showed high coherence in LSWT to each other. The summer mean LSWT of Lake Garda, located in the sub-Alpine region also exhibit high temporal coherence with that of central Italian Lake Trasimeno. Annually, mean LSWT of all subAlpine lakes were found to be highly coherent to each other, while mean LSWT of Lake Trasimeno resulted less coherent to the other lakes. Overall, the thesis aims at contributing to the accurate processing of the various historical satellite data and the development of a new method which allows to merge them into a unified, longest possible time series of LSWT. The newly developed methods used open source geospatial software tools, which ensure the reproducibility and also extensibility to any other geographic location given the availability of satellite data. Although this study is using LSWT as the primary physical variable, the developed methods can be used to derive any other time series of land and water based regional products from satellite dat

Earth Resources: A continuing bibliography with indexes, issue 36

This bibliography lists 576 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System between October 1 and December 31, 1982. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application