Lucas A. Jones

Satellite Microwave Remote Sensing of Daily Land Surface Air Temperature Minima and Maxima From AMSR-E

We present an approach to retrieve daily minimum and maximum 2-m height air temperatures from 18.7, and 23.8 GHz H and V polarized brightness temperature from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) during the snow free season. The approach accounts, with minimal ancillary data, for vertically integrated atmospheric water vapor, and variable surface emissivity due to open water and vegetation. Retrieved temperatures were evaluated using Northern Hemisphere weather stations and independent satellite-based air temperatures from the Atmosphere Infrared Sounder and Advanced Microwave Sounding Unit (AIRS/AMSU; hereafter AIRS) sensors on Aqua. The retrieved temperatures are within 1.0 - 3.5 K of surface weather station measurements for vegetated locations, but uncertainty can exceed 4 K for desert and sparsely vegetated regions, mainly due to site to site biases. The AIRS and AMSR-E temperature retrievals generally agree more closely with one another than with weather stations and are generally within 1.0-2.8 K over vegetated regions, but with less agreement ( > 4 K ) over desert and mountainous regions. Additional useful information produced by our approach includes open water fraction, vegetation optical depth and atmospheric water vapor. The results of this study provide inputs for land surface models and a new approach for monitoring of land surface air temperatures with well quantified accuracy and precision.

Evaluation of MERRA Land Surface Estimates in Preparation for the Soil Moisture Active Passive Mission

AbstractThe authors evaluated several land surface variables from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) product that are important for global ecological and hydrological studies, including daily maximum (Tmax) and minimum (Tmin) surface air temperatures, atmosphere vapor pressure deficit (VPD), incident solar radiation (SWrad), and surface soil moisture. The MERRA results were evaluated against in situ measurements, similar global products derived from satellite microwave [the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E)] remote sensing and earlier generation atmospheric analysis [Goddard Earth Observing System version 4 (GEOS-4)] products. Relative to GEOS-4, MERRA is generally warmer (~0.5°C for Tmin and Tmax) and drier (~50 Pa for VPD) for low- and middle-latitude regions ( 3°C) in mountainous areas, tro...

Satellite Microwave Remote Sensing of Boreal and Arctic Soil Temperatures From AMSR-E

Methods are developed and evaluated to retrieve surface soil temperature information for the advanced microwave scanning radiometer on earth observing system for seven boreal forest and Arctic tundra biophysical monitoring sites across Alaska and Northern Canada. A multiple-band iterative radiative transfer process-based method producing dynamic vegetation and snow cover correction quantities and an empirical multiple regression method using several frequencies are employed. The seasonal pattern of microwave emission and relative accuracy of the soil temperature retrievals are influenced strongly by landscape properties, including the presence of open water, vegetation type and seasonal phenology, snow cover, and freeze-thaw transitions. The retrieval of soil temperature is similar for the two methods with an overall root-mean-square error of 3.1-3.9 K during summer thawed conditions, with a larger error occurring in winter during periods of dynamic snow cover and freeze-thaw state. These results indicate that at high latitudes, the influence of the atmosphere may be less important than that of surface conditions in determining the relative accuracy of the estimated soil temperature. Impacts of surface conditions on surface emissivity, observed brightness temperature, and estimated soil temperature are discussed.

A Satellite Approach to Estimate Land–Atmosphere

Northern ecosystems are a major sink for atmospheric CO2 and contain much of the worlds soil organic carbon (SOC) that is potentially reactive to near-term climate change. We introduce a simple terrestrial carbon flux (TCF) model driven by satellite remote sensing inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to estimate surface (<10-cm depth) SOC stocks, daily respiration, and net ecosystem carbon exchange (NEE). Soil temperature and moisture information from AMSR-E provide environmental constraints to soil heterotrophic respiration (R h), while gross primary production (GPP) information from MODIS provides estimates of the total photosynthesis and autotrophic respiration. The model results were evaluated across a North American network of boreal forest, grassland, and tundra monitoring sites using alternative carbon measures derived from tower CO2 flux measurements and BIOME-BGC model simulations. Root-mean-square-error (rmse) differences between TCF model estimates and tower observations were 1.2, 0.7, and 1.2 g middot C middot m-2 middot day-1 for GPP, ecosystem respiration (Rtot) and NEE, while mean residual differences were 43% of the rmse. Similar accuracies were observed for both TCF and BIOME-BGC model simulations relative to tower results. TCF-model-derived SOC was in general agreement with soil inventory data and indicates that the dominant SOC source for Rh has a mean residence time of less than five years, while R h is approximately 43% and 55% of R tot for respective summer and annual fluxes. An error sensitivity analysis determined that meaningful flux estimates could be derived under prevailing climatic conditions at the study locations, given documented error levels in the remote sensing inputs.

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The development and continuity of consistent long-term data records from similar overlapping satellite observations is critical for global monitoring and environmental change assessments. We developed an empirical approach for inter-calibration of satellite microwave brightness temperature (Tb) records over land from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and Microwave Scanning Radiometer 2 (AMSR2) using overlapping Tb observations from the Microwave Radiation Imager (MWRI). Double Differencing (DD) calculations revealed significant AMSR2 and MWRI biases relative to AMSR-E. Pixel-wise linear relationships were established from overlapping Tb records and used for calibrating MWRI and AMSR2 records to the AMSR-E baseline. The integrated multi-sensor Tb record was largely consistent over the major global vegetation and climate zones; sensor biases were generally well calibrated, though residual Tb differences inherent to different sensor configurations were still present. Daily surface air temperature estimates from the calibrated AMSR2 Tb inputs also showed favorable accuracy against independent measurements from 142 global weather stations (R2 ≥ 0.75, RMSE ≤ 3.64 °C), but with slightly lower accuracy than the AMSR-E baseline (R2 ≥ 0.78, RMSE ≤ 3.46 °C). The proposed method is promising for generating consistent, uninterrupted global land parameter records spanning the AMSR-E and continuing AMSR2 missions.

Exchange for Boreal and Arctic Biomes Using MODIS and AMSR-E

The rate of vegetation recovery from boreal wildfire influences terrestrial carbon cycle processes and climate feedbacks by affecting the surface energy budget and land-atmosphere carbon exchange. Previous forest recovery assessments using satellite optical-infrared normalized difference vegetation index (NDVI) and tower CO(2) eddy covariance techniques indicate rapid vegetation recovery within 5-10 years, but these techniques are not directly sensitive to changes in vegetation biomass. Alternatively, the vegetation optical depth (VOD) parameter from satellite passive microwave remote sensing can detect changes in canopy biomass structure and may provide a useful metric of post-fire vegetation response to inform regional recovery assessments. We analyzed a multi-year (2003-2010) satellite VOD record from the NASA AMSR-E (Advanced Microwave Scanning Radiometer for EOS) sensor to estimate forest recovery trajectories for 14 large boreal fires from 2004 in Alaska and Canada. The VOD record indicated initial post-fire canopy biomass recovery within 3-7 years, lagging NDVI recovery by 1-5 years. The VOD lag was attributed to slower non-photosynthetic (woody) and photosynthetic (foliar) canopy biomass recovery, relative to the faster canopy greenness response indicated from the NDVI. The duration of VOD recovery to pre-burn conditions was also directly proportional (P < 0.01) to satellite (moderate resolution imaging spectroradiometer) estimated tree cover loss used as a metric of fire severity. Our results indicate that vegetation biomass recovery from boreal fire disturbance is generally slower than reported from previous assessments based solely on satellite optical-infrared remote sensing, while the VOD parameter enables more comprehensive assessments of boreal forest recovery.

Inter-Calibration of Satellite Passive Microwave Land Observations from AMSR-E and AMSR2 Using Overlapping FY3B-MWRI Sensor Measurements

Accurate mapping of long-term global soil moisture is of great importance to earth science studies and a variety of applications. An approach for deriving volumetric soil moisture using satellite passive microwave radiometry from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) was developed in this study. Unlike the major AMSR-E retrieval algorithms that assume fixed scattering albedo values over the globe, the proposed algorithm adopts a weighted averaging strategy for soil moisture estimation based on a dynamic selection of albedo values that are empirically determined. The resulting soil moisture retrievals demonstrate more realistic global patterns and seasonal dynamics relative to the baseline University of Montana soil moisture product. Quantitative analysis of the new approach against in situ soil moisture measurements over four study regions also indicates improvements over the baseline algorithm, with coefficients of determination (R2) between the retrievals and in situ measurements increasing by approximately 16.9% and 41.5% and bias-corrected root-mean-square errors decreasing by about 25.0% and 38.2% for ascending and descending orbital data records, respectively. The resulting algorithm is readily applied to similar microwave sensors, including the Advanced Microwave Scanning Radiometer 2, and its retrieval strategy is also applicable to other passive microwave sensors, including lower frequency (L-band) observations from the National Aeronautics and Space Administration Soil Moisture Active Passive mission.

Satellite microwave detection of boreal forest recovery from the extreme 2004 wildfires in Alaska and Canada

An approach for deriving atmosphere total precipitable water vapor (PWV) and surface air temperature over land using satellite passive microwave radiometry from the Advanced Microwave Scanning Radiometer 2 (AMSR2) was developed in this study. The PWV algorithm is based on theoretical analysis and comparisons against similar retrievals from the Atmospheric Infrared Sounder (AIRS). The AMSR2 PWV retrievals compare favorably with AIRS operational PWV products (R2 ≥ 0.80 and rmse: 4.4-5.6 mm) and independent PWV observations from the SuomiNet North American Global Positioning System station network, with an overall mean rmse of 4.7 mm and more than 78% of absolute retrieval errors below 5 mm. The PWV retrievals were then applied within an AMSR2 multifrequency brightness temperature algorithm for deriving atmosphere-corrected surface air temperatures. The estimated temperatures agree favorably (R2 > 0.80 and rmse <; 3.5 K) with independent weather station daily air temperature measurements spanning global climate and land cover variability. The resulting PWV estimates increase surface air temperature retrieval accuracy in our algorithm scheme. The AMSR2 algorithm is readily applied to similar microwave sensors including the AMSR for EOS and provides suitable performance and accuracy to support hydrologic, ecosystem, and climate change studies.

Passive Microwave Remote Sensing of Soil Moisture Based on Dynamic Vegetation Scattering Properties for AMSR-E

We developed an algorithm to estimate surface soil moisture, vegetation optical depth and fractional open water cover using satellite microwave radiometry. Soil moisture results compare favorably with a simple antecedent site precipitation index, and respond rapidly to precipitation events indicated by TRMM. High optical depth reduces soil moisture sensitivity in forests and croplands during peak biomass, although tundra locations maintain soil moisture sensitivity despite high optical depth. Optical depth varies with characteristic seasonality across vegetation cover types and tracks measures of vegetation canopy cover from MODIS. The algorithm developed in this study is able to monitor the daily variability of several important land surface state variables.

Satellite Microwave Retrieval of Total Precipitable Water Vapor and Surface Air Temperature Over Land From AMSR2

AbstractThe Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requiremen...