Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil's well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.

High-Resolution Global Maps of 21st-Century Forest Cover Change

https://cimss.ssec.wisc.edu/dbs/China2011/Day2/Lectures/MODIS_MOD13_NDVI_referenc.pdf

Overview of the radiometric and biophysical performance of the MODIS vegetation indices

Google Earth Engine: Planetary-scale geospatial analysis for everyone

Black carbon aerosol plays a unique and important role in Earth's climate system. Black carbon is a type of carbonaceous material with a unique combination of physical properties. This assessment provides an evaluation of black-carbon climate forcing that is comprehensive in its inclusion of all known and relevant processes and that is quantitative in providing best estimates and uncertainties of the main forcing terms: direct solar absorption; influence on liquid, mixed phase, and ice clouds; and deposition on snow and ice. These effects are calculated with climate models, but when possible, they are evaluated with both microphysical measurements and field observations. Predominant sources are combustion related, namely, fossil fuels for transportation, solid fuels for industrial and residential uses, and open burning of biomass. Total global emissions of black carbon using bottom-up inventory methods are 7500 Gg yr−1 in the year 2000 with an uncertainty range of 2000 to 29000. However, global atmospheric absorption attributable to black carbon is too low in many models and should be increased by a factor of almost 3. After this scaling, the best estimate for the industrial-era (1750 to 2005) direct radiative forcing of atmospheric black carbon is +0.71 W m−2 with 90% uncertainty bounds of (+0.08, +1.27) W m−2. Total direct forcing by all black carbon sources, without subtracting the preindustrial background, is estimated as +0.88 (+0.17, +1.48) W m−2. Direct radiative forcing alone does not capture important rapid adjustment mechanisms. A framework is described and used for quantifying climate forcings, including rapid adjustments. The best estimate of industrial-era climate forcing of black carbon through all forcing mechanisms, including clouds and cryosphere forcing, is +1.1 W m−2 with 90% uncertainty bounds of +0.17 to +2.1 W m−2. Thus, there is a very high probability that black carbon emissions, independent of co-emitted species, have a positive forcing and warm the climate. We estimate that black carbon, with a total climate forcing of +1.1 W m−2, is the second most important human emission in terms of its climate forcing in the present-day atmosphere; only carbon dioxide is estimated to have a greater forcing. Sources that emit black carbon also emit other short-lived species that may either cool or warm climate. Climate forcings from co-emitted species are estimated and used in the framework described herein. When the principal effects of short-lived co-emissions, including cooling agents such as sulfur dioxide, are included in net forcing, energy-related sources (fossil fuel and biofuel) have an industrial-era climate forcing of +0.22 (−0.50 to +1.08) W m−2 during the first year after emission. For a few of these sources, such as diesel engines and possibly residential biofuels, warming is strong enough that eliminating all short-lived emissions from these sources would reduce net climate forcing (i.e., produce cooling). When open burning emissions, which emit high levels of organic matter, are included in the total, the best estimate of net industrial-era climate forcing by all short-lived species from black-carbon-rich sources becomes slightly negative (−0.06 W m−2 with 90% uncertainty bounds of −1.45 to +1.29 W m−2). The uncertainties in net climate forcing from black-carbon-rich sources are substantial, largely due to lack of knowledge about cloud interactions with both black carbon and co-emitted organic carbon. In prioritizing potential black-carbon mitigation actions, non-science factors, such as technical feasibility, costs, policy design, and implementation feasibility play important roles. The major sources of black carbon are presently in different stages with regard to the feasibility for near-term mitigation. This assessment, by evaluating the large number and complexity of the associated physical and radiative processes in black-carbon climate forcing, sets a baseline from which to improve future climate forcing estimates.

/pdf/bounding-the-role-of-black-carbon-in-the-climate-system-a-1fc3novd9r.pdf

Bounding the role of black carbon in the climate system: A scientific assessment

PhenoCrop: An integrated satellite-based framework to estimate physiological growth stages of corn and soybeans

Although many approaches will be used to validate land products from the Earth Observing System (EOS), we describe the system being developed for the collection and comparison of field-measured parameters with EOS products. The most unique component is EOS Land Validation Core Sites, a global network of 24 high-intensity field measurement sites. These sites will provide the user community with the most timely and comprehensive ground, aircraft and satellite data available under EOS. Furthermore, data protocols and World Wide Web sites are being developed to promote public participation and long-term validation data preservation. Results of these validation activities will be conveyed to the community through both published literature and metadata embedded within product data sets.

EOS global land validation network

Executive Summary____________________________________ v 1 . 0 Introduction _____________________________________________ 6 1.1 Scientific Objectives ___________________________________________________6 1.2 Missions ____________________________________________________________6 1.3 Science Data Products _________________________________________________6 2 . 0 Validation Overview ______________________________________ 7 2.1 Overall Approach ____________________________________________________7 2.2 EOS AM-1 Validation Investigations ______________________________________7 2.3 Measures of Success ___________________________________________________9 3.0 Validation Sites ____________________________________________ 9 3.1 AM-1 Land Validation Core Sites________________________________________10 3.2 Land Product sites ___________________________________________________14 3.2.1 Land Cover sites ______________________________________________________________ 14 3.2.2 Land Cover change sites ________________________________________________________ 14 3.2.3 Land Surface Temperature sites ___________________________________________________ 15 3.2.4 Snow and Sea-Ice sites__________________________________________________________ 16 3.3 Operational Surface Networks __________________________________________17 4.0 Pre Launch Activities______________________________________ 1 8 4.1 Field Data __________________________________________________________18 4.1.1 Independently-led Campaigns_____________________________________________________ 19 4.1.2 MODLAND-led Campaigns _____________________________________________________ 19 4.2 Interaction with other Sensor Data and Teams______________________________22 4.3 Instrument Development_______________________________________________23 5.0 Post-launch Activities _____________________________________ 2 4 5.1 MODLAND Quick Airborne Looks______________________________________25 5.2 Field Campaigns_____________________________________________________26 5.3 Other Satellite Data __________________________________________________28 MODIS Land Validation Plan iii 5.4 Geometric Registration Sites____________________________________________29 6.0 Implementation of Validation Results _________________________ 2 9 6.1 Approach and Role of EOSDIS _________________________________________29 6.2 In situ Validation Data Processing, Distribution, and Archiving ________________30 6.3 Image Validation Data Processing, Distribution and Archiving ________________26 7.0 Summary________________________________________________ 2 7 8.0 References ______________________________________________ 3 3 9.0 Acronyms _______________________________________________ 3 4 MODIS Land Validation Plan

/pdf/modis-land-validation-plan-2c2d9xon9g.pdf

Christopher O. Justice

Papers

PhenoCrop: An integrated satellite-based framework to estimate physiological growth stages of corn and soybeans

EOS global land validation network

MODIS Land Validation Plan

The EOS land validation core sites: background information and current status

Validation of Global Land-Cover Products by the Committee on Earth Observing Satellites