Validating Diurnal Climatology Logic of the MT-Clim Model Across a Climatic Gradient in Oregon

This study tests diurnal climatology assumptions made in the MT—CLIM model by examining two microclimate variables driven by diurnal atmospheric dynamics: incident solar radiation (in kilojoules per square metre), and humidity, expressed as vapor pressure deficit, VPD (in kilopascals). The relative VPD humidity comparison was used to test our hypothesis that night minimum temperatures can function as a surrogate for dew—point temperatures. VPD was chosen as the humidity measure for these tests since plants are more directly sensitive of this measure than relative humidity. For the observed vs. examined vapor pressure deficit models, we obtained coefficients of determination (R2) ranging from 0.66 to 0.84. Incident solar radiation is calculated in the model using an algorithm that relates diurnal temperature amplitude to atmospheric transmissivity, coupled with a potential radiation model to compute diffuse and direct radiation. Correlations for incident solar radiation models indicate generally good agreement, with coefficients of determination ranging from R2 = 0.82 to 0.89. These results suggest that MT—CLIM may be a useful way to provide the climatology that many ecological/hydrological models require, particularly for larger scale spatial modeling applications where precise meteorology may not be as important as a good general characterization of the regional climatology

An extended global Earth system data record on daily landscape freeze–thaw status determined from satellite passive microwave remote sensing

The landscape freeze–thaw (FT) signal determined from satellite microwave brightness temperature (Tb) observations has been widely used to define frozen temperature controls on land surface water mobility and ecological processes. Calibrated 37 GHz Tb retrievals from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and SSM/I Sounder (SSMIS) were used to produce a consistent and continuous global daily data record of landscape FT status at 25 km grid cell resolution. The resulting FT Earth system data record (FT-ESDR) is derived from a refined classification algorithm and extends over a larger domain and longer period (1979–2014) than prior FT-ESDR releases. The global domain encompasses all land areas affected by seasonal frozen temperatures, including urban, snow- and ice-dominant and barren land, which were not represented by prior FT-ESDR versions. The FT retrieval is obtained using a modified seasonal threshold algorithm (MSTA) that classifies daily Tb variations in relation to grid-cell-wise FT thresholds calibrated using surface air temperature data from model reanalysis. The resulting FT record shows respective mean annual spatial classification accuracies of 90.3 and 84.3 % for evening (PM) and morning (AM) overpass retrievals relative to global weather station measurements. Detailed data quality metrics are derived characterizing the effects of sub-grid-scale open water and terrain heterogeneity, as well as algorithm uncertainties on FT classification accuracy. The FT-ESDR results are also verified against other independent cryospheric data, including in situ lake and river ice phenology, and satellite observations of Greenland surface melt. The expanded FT-ESDR enables new investigations encompassing snow- and ice-dominant land areas, while the longer record and favorable accuracy allow for refined global change assessments that can better distinguish transient weather extremes, landscape phenological shifts, and climate anomalies from longer-term trends extending over multiple decades. The dataset is freely available online (doi:10.5067/MEASURES/CRYOSPHERE/nsidc-0477.003)

Pre-Launch Tasks Proposed in our Contract of December 1991

We propose, during the pre-EOS phase to: (1) develop, with other MODIS Team Members, a means of discriminating different major biome types with NDVI and other AVHRR-based data. (2) develop a simple ecosystem process model for each of these biomes, BIOME-BGC (3) relate the seasonal trend of weekly composite NDVI to vegetation phenology and temperature limits to develop a satellite defined growing season for vegetation; and (4) define physiologically based energy to mass conversion factors for carbon and water for each biome. Our final core at-launch product will be simplified, completely satellite driven biome specific models for net primary production. We will build these biome specific satellite driven algorithms using a family of simple ecosystem process models as calibration models, collectively called BIOME-BGC, and establish coordination with an existing network of ecological study sites in order to test and validate these products. Field datasets will then be available for both BIOME-BGC development and testing, use for algorithm developments of other MODIS Team Members, and ultimately be our first test point for MODIS land vegetation products upon launch. We will use field sites from the National Science Foundation Long-Term Ecological Research network, and develop Glacier National Park as a major site for intensive validation

An Operational Remote Sensing Algorithm of Land Surface Evaporation

Partitioning of solar energy at the Earth surface has significant implications in climate dynamics, hydrology, and ecology. Consequently, spatial mapping of energy partitioning from satellite remote sensing data has been an active research area for over two decades. We developed an algorithm for estimating evaporation fraction (EF), expressed as a ratio of actual evapotranspiration (ET) to the available energy (sum of ET and sensible heat flux), from satellite data. The algorithm is a simple two-source model of ET. We characterize a landscape as a mixture of bare soil and vegetation and thus we estimate EF as a mixture of EF of bare soil and EF of vegetation. In the estimation of EF of vegetation, we use the complementary relationship of the actual and the potential ET for the formulation of EF. In that, we use the canopy conductance model for describing vegetation physiology. On the other hand, we use “VI-Ts” (vegetation index-surface temperature) diagram for estimation of EF of bare soil. As operational production of EF globally is our goal, the algorithm is primarily driven by remote sensing data but flexible enough to ingest ancillary data when available. We validated EF from this prototype algorithm using NOAA/AVHRR data with actual observations of EF at AmeriFlux stations (standard error ≅ 0.17 and R2 ≅ 0.71). Global distribution of EF every 8 days will be operationally produced by this algorithm using the data of MODIS on EOS-PM (Aqua) satellite

An Earth System Data Record for Land Surface Freeze/Thaw State. Algorithm Theoretical Basis Document (ATBD), Version 1

This document represents and Algorithm Theoretical Basis Document (ATBD) for developing an Earth System Data Record (ESDR) quantifying global vegetated land surface freeze/thaw state (F/T) dynamics. The freeze/thaw ESDR (FT_ESDR) will be developed using multi-frequency satellite passive and active microwave remote sensing time series spanning multiple missions and sensors, including passive microwave radiometery from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and radar scatterometry from SeaWinds-on-QuikSCAT. These records are global in extent and provide a contiguous time series extending from 1979 onward with some overlap between missions