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

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

Exploring the limits of variational passive microwave retrievals

2017 Summer.Includes bibliographical references.Passive microwave observations from satellite platforms constitute one of the most important data records of the global observing system. Operational since the late 1970s, passive microwave data underpin climate records of precipitation, sea ice extent, water vapor, and more, and contribute significantly to numerical weather prediction via data assimilation. Detailed understanding of the observation errors in these data is key to maximizing their utility for research and operational applications alike. However, the treatment of observation errors in this data record has been lacking and somewhat divergent when considering the retrieval and data assimilation communities. In this study, some limits of passive microwave imager data are considered in light of more holistic treatment of observation errors. A variational retrieval, named the CSU 1DVAR, was developed for microwave imagers and applied to the GMI and AMSR2 sensors for ocean scenes. Via an innovative method to determine forward model error, this retrieval accounts for error covariances across all channels used in the iteration. This improves validation in more complex scenes such as high wind speed and persistently cloudy regimes. In addition, it validates on par with a benchmark dataset without any tuning to in-situ observations. The algorithm yields full posterior error diagnostics and its physical forward model is applicable to other sensors, pending intercalibration. This retrieval is used to explore the viability of retrieving parameters at the limits of the available information content from a typical microwave imager. Retrieval of warm rain, marginal sea ice, and falling snow are explored with the variational retrieval. Warm rain retrieval shows some promise, with greater sensitivity than operational GPM algorithms due to leveraging CloudSat data and accounting for drop size distribution variability. Marginal sea ice is also detected with greater sensitivity than a standard operational retrieval. These studies ultimately show that while a variational algorithm maximizes the effective signal to noise ratio of these observations, hard limitations exist due to the finite information content afforded by a typical microwave imager

Satellite microwave assessment of Northern Hemisphere lake ice phenology from 2002 to 2015

A new automated method enabling consistent satellite assessment of seasonal lake ice phenology at 5 km resolution was developed for all lake pixels (water coverage  ≥  90 %) in the Northern Hemisphere using 36.5 GHz H-polarized brightness temperature (Tb) observations from the Advanced Microwave Scanning Radiometer for EOS and Advanced Microwave Scanning Radiometer 2 (AMSR-E/2) sensors. The lake phenology metrics include seasonal timing and duration of annual ice cover. A moving t test (MTT) algorithm allows for automated lake ice retrievals with daily temporal fidelity and 5 km resolution gridding. The resulting ice phenology record shows strong agreement with available ground-based observations from the Global Lake and River Ice Phenology Database (95.4 % temporal agreement) and favorable correlations (R) with alternative ice phenology records from the Interactive Multisensor Snow and Ice Mapping System (R = 0.84 for water clear of ice (WCI) dates; R = 0.41 for complete freeze over (CFO) dates) and Canadian Ice Service (R = 0.86 for WCI dates; R = 0.69 for CFO dates). Analysis of the resulting 12-year (2002–2015) AMSR-E/2 ice record indicates increasingly shorter ice cover duration for 43 out of 71 (60.6 %) Northern Hemisphere lakes examined, with significant (p  \u3c  0.05) regional trends toward earlier ice melting for only five lakes. Higher-latitude lakes reveal more widespread and larger trends toward shorter ice cover duration than lower-latitude lakes, consistent with enhanced polar warming. This study documents a new satellite-based approach for rapid assessment and regional monitoring of seasonal ice cover changes over large lakes, with resulting accuracy suitable for global change studies

GCOM-W AMSR2 Soil Moisture Product Validation Using Core Validation Sites

The Advanced Microwave Scanning Radiometer 2 (AMSR2) is part of the Global Change Observation Mission-Water (GCOM-W). AMSR2 has filled the gap in passive microwave observations left by the loss of the Advanced Microwave Scanning RadiometerEarth Observing System (AMSR-E) after almost 10 years of observations. Both missions provide brightness temperature observations that are used to retrieve soil moisture estimates at the near surface. A merged AMSR-E and AMSR2 data product will help build a consistent long-term dataset; however, before this can be done, it is necessary to conduct a thorough validation and assessment of the AMSR2 soil moisture products. This study focuses on the validation of the AMSR2 soil moisture products by comparison with in situ reference data from a set of core validation sites around the world. A total of three soil moisture products that rely on different algorithms were evaluated; the Japan Aerospace Exploration Agency (JAXA) soil moisture algorithm, the Land Parameter Retrieval Model (LPRM), and the Single Channel Algorithm (SCA). JAXA, SCA and LPRM soil moisture estimates capture the overall climatological features. The spatial features of the three products have similar overall spatial structure. The JAXA soil moisture product shows a lower dynamic range in the retrieved soil moisture with a satisfactory performance matrix when compared to in situ observations (ubRMSE0.059 m3m3, Bias-0.083 m3m3, R0.465). The SCA performs well over low and moderately vegetated areas (ubRMSE0.053 m3m3, Bias-0.039 m3m3, R0.549). The LPRM product has a large dynamic range compared to in situ observations with a wet bias (ubRMSE0.094 m3m3, Bias0.091 m3m3, R0.577). Some of the error is due to the difference in observation depth between the in situ sensors (5 cm) and satellite estimates (1 cm). Results indicate that overall the JAXA and SCA have the best performance based upon the metrics considered

Land Surface Phenologies and Seasonalities Using Cool Earthlight in Temperate and Tropical Croplands

In today’s world of increasing food insecurity due to more frequent and extreme events (droughts, floods), a comprehensive understanding of global cropland dynamics is critically needed. Land surface parameters derived from the passive microwave Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and AMSR2 data enable monitoring of cropland dynamics and they can complement visible to near infrared (VNIR) and thermal infrared (TIR) data. Passive microwave data are less sensitive to atmospheric effects, cloud contamination, and solar illumination constraints resulting in finer temporal resolution suitable to track the temporal progression of cropland cover development compared to the VNIR data that has coarser temporal resolution due to compositing to lessen the atmospheric effects. Both VNIR and TIR data have moderate to fine spatial resolution compared to passive microwaves, due to the faint microwave flux from the planetary surface. I used AMSR, MODIS, TRMM, and simplified surface energy balance (SSEB) data to study cropland dynamics from 2003-2015 in North Dakota, USA, the Canadian Prairie Provinces, Northern Eurasia, and East Africa: a contrast between crop exporting regions and a food insecure region. Croplands in the temperate region are better studied compared to that of the tropics. The objective of this research was to characterize cropland dynamics in the tropics based on the knowledge gained about the microwave products in the temperate croplands. This study also aimed at assessing the utility of passive microwave data for cropland dynamics study, especially for tropical cropland regions that are often cloud-obscured during the growing season and have sparse in situ data networks. Using MODIS land cover data, I identified 162 AMSR grid cells (25km*25km=625km2) dominated by croplands within the study regions. To fit the passive microwave time series data to environmental forcings, I used the convex quadratic (CxQ) model fit that has been successfully applied with the VNIR and TIR data to herbaceous vegetation in temperate and boreal ecoregions. Land surface dynamics in the thermally-limited temperate croplands were characterized as a function of temperature; whereas, a function of moisture to model land surface dynamics in the tropical croplands. In the temperate croplands, growing degree-day (GDD), NDVI, and vegetation optical depth (VOD) were modeled as a convex quadratic function of accumulated GDD (AGDD) derived from AMSR air temperature data, yielding high coefficients of determination (0.88≤ r2≤0.98) Deviations of GDD from the long term average CxQ model by site corresponded to peak VI producing negative residuals (arising from higher latent heat flux) and low VI at beginning and end of growing season producing positive residuals (arising from higher sensible heat flux). In Northern Eurasia, sites at lower latitude (44° - 48° N) that grow winter grains showed either a longer unimodal growing season or a bimodal growing season; whereas, sites at higher latitude (48° - 56° N) where spring grains are cultivated showed shorter, unimodal growing seasons. Peak VOD showed strong linear correspondence with peak greenness (NDVI) with r2\u3e0.8, but with a one-week lag. The AMSR data were able to capture the effects of the 2010 and 2007 heat waves that devastated grain production in southwestern Russia and Northern Kazakhstan, and Ukraine, respectively, better than the MODIS data. In East African croplands, the AMSR, TRMM, and SSEB datasets modeled as a convex quadratic function of cumulative water-vapor-days displayed distinct cropland dynamics in space and time, including unimodal and bimodal growing seasons. Interannual moisture variability is at its highest at the beginning of the growing season affecting planting times of crops. Moisture time to peak from AMSR and TRMM land surface parameters displayed strong correspondence (r2 \u3e 0.80) and logical lags among variables. Characterizing cropland dynamics based on the synergistic use of complementary remote sensing data should help to advance and improve agricultural monitoring in tropical croplands that are often associated with food insecurity

Assessing Global Surface Water Inundation Dynamics Using Combined Satellite Information from SMAP, AMSR2 and Landsat

A method to assess global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fw(sub LBand)) retrievals were derived using SMAP H-polarization brightness temperature (Tb) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency (Tb) observations from AMSR2. The resulting (fw(sub LBand)) global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The (fw(sub LBand)) annual averages corresponded favourably (R=0.84, p<0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly (fw(sub LBand)) averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable (fw(sub LBand)) performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) (fw(sub LBand)) results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m (fw(sub LBand)) retrievals showed favourable spatial accuracy for water (70.71%) and land (98.99%) classifications and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new (fw(sub LBand)) algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics and potential flood risk

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

Observational needs of sea surface temperature

Sea surface temperature (SST) is a fundamental physical variable for understanding, quantifying and predicting complex interactions between the ocean and the atmosphere. Such processes determine how heat from the sun is redistributed across the global oceans, directly impacting large- and small-scale weather and climate patterns. The provision of daily maps of global SST for operational systems, climate modeling and the broader scientific community is now a mature and sustained service coordinated by the Group for High Resolution Sea Surface Temperature (GHRSST) and the CEOS SST Virtual Constellation (CEOS SST-VC). Data streams are shared, indexed, processed, quality controlled, analyzed, and documented within a Regional/Global Task Sharing (R/GTS) framework, which is implemented internationally in a distributed manner. Products rely on a combination of low-Earth orbit infrared and microwave satellite imagery, geostationary orbit infrared satellite imagery, and in situ data from moored and drifting buoys, Argo floats, and a suite of independent, fully characterized and traceable in situ measurements for product validation (Fiducial Reference Measurements, FRM). Research and development continues to tackle problems such as instrument calibration, algorithm development, diurnal variability, derivation of high-quality skin and depth temperatures, and areas of specific interest such as the high latitudes and coastal areas. In this white paper, we review progress versus the challenges we set out 10 years ago in a previous paper, highlight remaining and new research and development challenges for the next 10 years (such as the need for sustained continuity of passive microwave SST using a 6.9 GHz channel), and conclude with needs to achieve an integrated global high-resolution SST observing system, with focus on satellite observations exploited in conjunction with in situ SSTs. The paper directly relates to the theme of Data Information Systems and also contributes to Ocean Observing Governance and Ocean Technology and Networks within the OceanObs2019 objectives. Applications of SST contribute to all the seven societal benefits, covering Discovery; Ecosystem Health & Biodiversity; Climate Variability & Change; Water, Food, & Energy Security; Pollution & Human Health; Hazards and Maritime Safety; and the Blue Economy

Data Fusion and Synergy of Active and Passive Remote Sensing; An application for Freeze Thaw Detections

There has been a recent evolvement in the field of remote sensing after increase of number satellites and sensors data which could be fused to produce new data and products. These efforts are mainly focused on using of simultaneous observations from different platforms with different spatial and temporal resolutions. The research dissertation aims to enhance the synergy use of active and passive microwave observations and examine the results in detection land freeze and thaw (FT) predictions. Freeze thaw cycles particularly in high-latitude regions have a crucial role in many applications such as agriculture, biogeochemical transitions, hydrology and ecosystem studies. The dielectric change between frozen ice and melted water can dramatically affect the brightness temperature (TB) signal when water transits from the liquid to the solid phase which makes satellite-based microwave remote sensing unique for characterizing the surface freeze thaw status. Passive microwave (PMW) sensors with coarse resolution (about 25 km) but more frequent observations (at least twice a day and more frequent in polar regions) have been successfully utilized to define surface state in terms of freeze and thaw in the past. Alternatively, active microwave (AMW) sensors provide much higher spatial resolution (about 100 m or less) though with less temporal resolution (each 12 days). Therefore, an integration of microwave data coming from different sensors may provide a more complete estimation of land freeze thaw state. In this regard, the overarching goal of this research is to explore estimating high spatiotemporal freeze and thaw states using the combination of passive and active microwave observations. To obtain a high temporal resolution TB, this study primarily builds an improved diurnal variation of land surface temperature from integration of infrared sensors. In the next step, a half an hourly diurnal cycle of TB based on fusion of different passive sensors is estimated. It should be mentioned that each instrument has its own footprint, resolution, viewing angle, as well as frequency and consequently their data need to be harmonized in order to be combined. Later, data from an AMW sensor with fine spatial resolution are merged and compared to the corresponding passive data in order to find a relation between TB and backscatter data. Subsequently, PMW TB map can be downscaled to a higher spatial resolution or AMW backscatter timeseries can be generalized to high temporal resolution. Eventually, the final high spatiotemporal resolution TB product is used to examine the freeze thaw state for case studies areas in Northern latitudes