Results from the Deep-Convective Clouds (DCC) Based Response Versus Scan-Angle (RVS) Characterization for the MODIS Reflective Solar Bands

The Terra and Aqua MODIS scan mirror reflectance is a function of the angle of incidence (AOI) and was characterized prior to launch by the instrument vendor. The relative change of the prelaunch response versus scan-angle (RVS) is tracked and linearly scaled on-orbit using observations at two AOIs of 11.2deg and 50.2deg corresponding to the moon view and solar diffuser, respectively. As the missions continue to operate well beyond their design life of 6 years, the assumption of linear scaling between the two AOIs is known to be inadequate in accurately characterizing the RVS, particularly at short wavelengths. Consequently, an enhanced approach of supplementing the on-board measurements with response trends from desert pseudo-invariant calibration sites (PICS) was formulated in MODIS Collection 6 (C6). An underlying assumption for the continued effectiveness of this approach is the long-term (multi-year) and short-term (month-to-month) stability of the PICS. Previous work has shown that the deep convective clouds (DCC) can also be used to monitor the on-orbit RVS performance with less trend uncertainties than desert sites. In this paper, the raw sensor response to the DCC is used to characterize the on-orbit RVS on a band and mirror side basis. These DCC-based RVS results are compared with the C6 PICS-based RVS, showing an agreement within 2% observed in most cases. The pros and cons of using a DCC-based RVS approach are also discussed in this paper. Although this reaffirms the efficacy of the C6 PICS-based RVS, the DCC-based RVS approach presents itself as an effective alternative for future considerations. Potential applications of this approach to other instruments such as SNPP and JPSS VIIRS are also discussed

Assessment of Terra MODIS Thermal Emissive Band Calibration Using Cold Targets and Measurements in Lunar Roll Events

Terra MODIS has provided continuous global observations for science research and applications for more than 18 years. The MODIS Thermal emissive bands (TEB) radiometric calibration uses a quadratic function for instrument response. The calibration coefficients are updated using the response of an on-board blackbody (BB) in quarterly warm-up and cool-down (WUCD) events. As instrument degradation and electronic crosstalk of long-wave infrared (LWIR) bands 27 to 30 developed substantial issues, accurate calibration is crucial for a high-quality L1B product. The on-board BB WUCD temperature ranges from 270 K to 315 K and the derived nonlinear response has a relatively large uncertainty for the offset, especially for these LWIR bands, which affects the measurements of low brightness temperature (BT) scenes. In this study, the TEB radiometric calibration impact on the L1B product is assessed using selected cold targets and the measurements during regular lunar rolls. The cold targets include Antarctic Dome Concordia (Dome-C) and deep convective clouds (DCC) for the calibration assessment, focusing on bands 27 to 30. Dome-C area is covered with uniformly-distributed permanent snow, and the atmospheric effect is small and relatively constant. Usually the DCC is treated as an invariant earth target to evaluate the reflective solar band calibration. The DCC can also be treated as a stable target to assess the performance of TEB calibration. During a scheduled lunar observation event with a spacecraft roll maneuver to view the moon through the space view port, the instrument cavity provides a stable reference for calibration assessment. The long-term trending of BT measurements and the relative difference between scan mirror sides and detectors are used for the assessment of the calibration consistency and stability. The comparison of L1B products over the selected targets before and after the calibration coefficients update can be used to assess the impact of a calibration look-up table (LUT) update. This assessment is beneficial for future calibration algorithm and LUT update procedure improvements for enhancing the L1B product quality

Evaluation of an Extended PICS (EPICS) for Calibration and Stability Monitoring of Optical Satellite Sensors

Pseudo Invariant Calibration Sites (PICS) have been increasingly used as an independent data source for on-orbit radiometric calibration and stability monitoring of optical satellite sensors. Generally, this would be a small region of land that is extremely stable in time and space, predominantly found in North Africa. Use of these small regions, referred to as traditional PICS, can be limited by: i) the spatial extent of an individual Region of Interest (ROI) and/or site; ii) and the frequency of how often the site can be acquired, based on orbital patterns and cloud cover at the site, both impacting the time required to construct a richly populated temporal dataset. This paper uses a new class of continental scaled PICS clusters (also known as Extended PICS or EPICS), to demonstrate their capability in increasing temporal frequency of the calibration time series which ultimately allows calibration and stability assessment at a much finer scale compared to the traditional PICSbased method while also reducing any single location’s potential impact to the overall assessment. The use of EPICS as a calibration site was evaluated using data from Landsat- 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Sentinel-2A&B Multispectral Instrument (MSI) images at their full spatial resolutions. Initial analysis suggests that EPICS, at its full potential and with nominal cloud consideration, can significantly decrease the temporal revisit interval of moderate resolution sensors to as much as of 0.33 day (3 collects/day). A traditional PICS is expected to have a temporal uncertainty (defined as the ratio of temporal standard deviation and temporal mean) of 2-5% for TOA reflectance. Over the same time period EPICS produced a temporal uncertainty of 3%. But the advantage to be leveraged is the ability to detect sensor change quicker due to the denser dataset and reduce the impact of any potential ‘local’ changes. Moreover, this approach can be extended to any on-orbit sensor. An initial attempt to quantify the minimum detectable change (a threshold slope value which must be exceeded by the reflectance trend to be considered statistically significant) suggests that the use of EPICS can decrease the time period up to approximately half of that found using traditional PICS-based approach

Evaluation of Machine Learning Algorithms for Lake Ice Classification from Optical Remote Sensing Data

The topic of lake ice cover mapping from satellite remote sensing data has gained interest in recent years since it allows the extent of lake ice and the dynamics of ice phenology over large areas to be monitored. Mapping lake ice extent can record the loss of the perennial ice cover for lakes located in the High Arctic. Moreover, ice phenology dates, retrieved from lake ice maps, are useful for assessing long-term trends and variability in climate, particularly due to their sensitivity to changes in near-surface air temperature. However, existing knowledge-driven (threshold-based) retrieval algorithms for lake ice-water classification that use top-of-the-atmosphere (TOA) reflectance products do not perform well under the condition of large solar zenith angles, resulting in low TOA reflectance. Machine learning (ML) techniques have received considerable attention in the remote sensing field for the past several decades, but they have not yet been applied in lake ice classification from optical remote sensing imagery. Therefore, this research has evaluated the capability of ML classifiers to enhance lake ice mapping using multispectral optical remote sensing data (MODIS L1B (TOA) product). Chapter 3, the main manuscript of this thesis, presents an investigation of four ML classifiers (i.e. multinomial logistic regression, MLR; support vector machine, SVM; random forest, RF; gradient boosting trees, GBT) in lake ice classification. Results are reported using 17 lakes located in the Northern Hemisphere, which represent different characteristics regarding area, altitude, freezing frequency, and ice cover duration. According to the overall accuracy assessment using a random k-fold cross-validation (k = 100), all ML classifiers were able to produce classification accuracies above 94%, and RF and GBT provided above 98% classification accuracies. Moreover, the RF and GBT algorithms provided a more visually accurate depiction of lake ice cover under challenging conditions (i.e., high solar zenith angles, black ice, and thin cloud cover). The two tree-based classifiers were found to provide the most robust spatial transferability over the 17 lakes and performed consistently well across three ice seasons, better than the other classifiers. Moreover, RF was insensitive to the choice of the hyperparameters compared to the other three classifiers. The results demonstrate that RF and GBT provide a great potential to map accurately lake ice cover globally over a long time-series. Additionally, a case study applying a convolution neural network (CNN) model for ice classification in Great Slave Lake, Canada is presented in Appendix A. Eighteen images acquired during the the ice season of 2009-2010 were used in this study. The proposed CNN produced a 98.03% accuracy with the testing dataset; however, the accuracy dropped to 90.13% using an independent (out-of-sample) validation dataset. Results show the powerful learning performance of the proposed CNN with the testing data accuracy obtained. At the same time, the accuracy reduction of the validation dataset indicates the overfitting behavior of the proposed model. A follow-up investigation would be needed to improve its performance. This thesis investigated the capability of ML algorithms (both pixel-based and spatial-based) in lake ice classification from the MODIS L1B product. Overall, ML techniques showed promising performances for lake ice cover mapping from the optical remote sensing data. The tree-based classifiers (pixel-based) exhibited the potential to produce accurate lake ice classification at a large-scale over long time-series. In addition, more work would be of benefit for improving the application of CNN in lake ice cover mapping from optical remote sensing imagery

Earth resources: A continuing bibliography with indexes (issue 61)

This bibliography lists 606 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1 and March 31, 1989. 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, oceanography and marine resources, hydrology and water management, data processing and distribution systems, and instrumentation and sensors, and economic analysis

CIRA annual report FY 2014/2015

Reporting period July 1, 2014-March 31, 2015

CIRA annual report FY 2015/2016

Reporting period April 1, 2015-March 31, 2016

CIRA annual report FY 2016/2017

Reporting period April 1, 2016-March 31, 2017

Feasibility Study for an Aquatic Ecosystem Earth Observing System Version 1.2.

International audienceMany Earth observing sensors have been designed, built and launched with primary objectives of either terrestrial or ocean remote sensing applications. Often the data from these sensors are also used for freshwater, estuarine and coastal water quality observations, bathymetry and benthic mapping. However, such land and ocean specific sensors are not designed for these complex aquatic environments and consequently are not likely to perform as well as a dedicated sensor would. As a CEOS action, CSIRO and DLR have taken the lead on a feasibility assessment to determine the benefits and technological difficulties of designing an Earth observing satellite mission focused on the biogeochemistry of inland, estuarine, deltaic and near coastal waters as well as mapping macrophytes, macro-algae, sea grasses and coral reefs. These environments need higher spatial resolution than current and planned ocean colour sensors offer and need higher spectral resolution than current and planned land Earth observing sensors offer (with the exception of several R&D type imaging spectrometry satellite missions). The results indicate that a dedicated sensor of (non-oceanic) aquatic ecosystems could be a multispectral sensor with ~26 bands in the 380-780 nm wavelength range for retrieving the aquatic ecosystem variables as well as another 15 spectral bands between 360-380 nm and 780-1400 nm for removing atmospheric and air-water interface effects. These requirements are very close to defining an imaging spectrometer with spectral bands between 360 and 1000 nm (suitable for Si based detectors), possibly augmented by a SWIR imaging spectrometer. In that case the spectral bands would ideally have 5 nm spacing and Full Width Half Maximum (FWHM), although it may be necessary to go to 8 nm wide spectral bands (between 380 to 780nm where the fine spectral features occur -mainly due to photosynthetic or accessory pigments) to obtain enough signal to noise. The spatial resolution of such a global mapping mission would be between ~17 and ~33 m enabling imaging of the vast majority of water bodies (lakes, reservoirs, lagoons, estuaries etc.) larger than 0.2 ha and ~25% of river reaches globally (at ~17 m resolution) whilst maintaining sufficient radiometric resolution