Behavior of multitemporal and multisensor passive microwave indices in Southern Hemisphere ecosystems

©2014. American Geophysical Union. All Rights Reserved. This study focused on the time series analysis of passive microwave and optical satellite data collected from six Southern Hemisphere ecosystems in Australia and Argentina. The selected ecosystems represent a wide range of land cover types, including deciduous open forest, temperate forest, tropical and semiarid savannas, and grasslands. We used two microwave indices, the frequency index (FI) and polarization index (PI), to assess the relative contributions of soil and vegetation properties (moisture and structure) to the observations. Optical-based satellite vegetation products from the Moderate Resolution Imaging Spectroradiometer were also included to aid in the analysis. We studied the X and Ka bands of the Advanced Microwave Scanning Radiometer-EOS and Wind Satellite, resulting in up to four observations per day (1:30, 6:00, 13:30, and 18:00-h). Both the seasonal and hourly variations of each of the indices were examined. Environmental drivers (precipitation and temperature) and eddy covariance measurements (gross ecosystem productivity and latent energy) were also analyzed. It was found that in moderately dense forests, FI was dependent on canopy properties (leaf area index and vegetation moisture). In tropical woody savannas, a significant regression (R2) was found between FI and PI with precipitation (R2->-0.5) and soil moisture (R2->-0.6). In the areas of semiarid savanna and grassland ecosystems, FI variations found to be significantly related to soil moisture (R2->-0.7) and evapotranspiration (R2->-0.5), while PI varied with vegetation phenology. Significant differences (p-<-0.01) were found among FI values calculated at the four local times. Key Points Passive microwave indices can be used to estimate vegetation moisture Microwave observations were supported by flux data Passive microwave indices could be used to estimate evapotranspiratio

The sensitivity of land emissivity estimates from AMSR-E at C and X bands to surface properties

Microwave observations at low frequencies exhibit more sensitivity to surface and subsurface properties with little interference from the atmosphere. The objective of this study is to develop a global land emissivity product using passive microwave observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and to investigate its sensitivity to land surface properties. The developed product complements existing land emissivity products from SSM/I and AMSU by adding land emissivity estimates at two lower frequencies, 6.9 and 10.65 GHz (C- and X-band, respectively). Observations at these low frequencies penetrate deeper into the soil layer. Ancillary data used in the analysis, such as surface skin temperature and cloud mask, are obtained from International Satellite Cloud Climatology Project (ISCCP). Atmospheric properties are obtained from the TIROS Operational Vertical Sounder (TOVS) observations to determine the small upwelling and downwelling atmospheric emissions as well as the atmospheric transmission. A sensitivity test confirms the small effect of the atmosphere but shows that skin temperature accuracy can significantly affect emissivity estimates. Retrieved emissivities at C- and X-bands and their polarization differences exhibit similar patterns of variation with changes in land cover type, soil moisture, and vegetation density as seen at SSM/I-like frequencies (Ka and Ku bands). The emissivity maps from AMSR-E at these higher frequencies agree reasonably well with the existing SSM/I-based product. The inherent discrepancy introduced by the difference between SSM/I and AMSR-E frequencies, incidence angles, and calibration has been assessed. Significantly greater standard deviation of estimated emissivities compared to SSM/I land emissivity product was found over desert regions. Large differences between emissivity estimates from ascending and descending overpasses were found at lower frequencies due to the inconsistency between thermal IR skin temperatures and passive microwave brightness temperatures which can originate from below the surface. The mismatch between day and night AMSR-E emissivities is greater than ascending and descending differences of SSM/I emissivity. This is because of unique orbit time of AMSR-E (01:30 a.m./p.m. LT) while other microwave sensors have orbit time of 06:00 to 09:00 (a.m./p.m.). This highlights the importance of considering the penetration depth of the microwave signal and diurnal variability of the temperature in emissivity retrieval. The effect of these factors is greater for AMSR-E observations than SSM/I observations, as AMSR-E observations exhibit a greater difference between day and night measures. This issue must be addressed in future studies to improve the accuracy of the emissivity estimates especially at AMSR-E lower frequencies

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

A Method for Objectively Integrating Soil Moisture Satellite Observations and Model Simulations Toward a Blended Drought Index

With satellite soil moisture (SM) retrievals becoming widely and continuously available, we aim to develop a method to objectively integrate the drought indices into one that is more accurate and consistently reliable. The datasets used in this paper include the Noah land surface modelbased SM estimations, AtmosphereLandExchangeInverse modelbased Evaporative Stress Index, and the satellite SM products from the Advanced Scatterometer, WindSat, Soil Moisture and Ocean Salinity, and Soil Moisture Operational Product System. Using the Triple Collocation Error Model (TCEM) to quantify the uncertainties of these data, we developed an optically blended drought index (BDI_b) that objectively integrates drought estimations with the lowest TCEMderived rootmeansquareerrors in this paper. With respect to the reported drought records and the drought monitoring benchmarks including the U.S. Drought Monitor, the Palmer Drought Severity Index and the standardized precipitation evapotranspiration index products, the BDI_b was compared with the sample average blending drought index (BDI_s) and the RMSEweighted average blending drought indices (BDI_w). Relative to the BDI_s and the BDI_w, the BDI_b performs more consistently with the drought monitoring benchmarks. With respect to the official drought records, the developed BDI_b shows the best performance on tracking drought development in terms of time evolution and spatial patterns of 2010Russia, 2011USA, 2013New Zealand droughts and other reported agricultural drought occurrences over the 20092014 period. These results suggest that model simulations and remotely sensed observations of SM can be objectively translated into useful information for drought monitoring and early warning, in turn can reduce drought risk and impacts

Assessing satellite-derived land product quality for earth system science applications: results from the ceos lpv sub-group

The value of satellite derived land products for science applications and research is dependent upon the known accuracy of the data. CEOS (Committee on Earth Observation Satellites), the space arm of the Group on Earth Observations (GEO), plays a key role in coordinating the land product validation process. The Land Product Validation (LPV) sub-group of the CEOS Working Group on Calibration and Validation (WGCV) aims to address the challenges associated with the validation of global land products. This paper provides an overview of LPV sub-group focus area activities, which cover seven terrestrial Essential Climate Variables (ECVs). The contribution will enhance coordination of the scientific needs of the Earth system communities with global LPV activities

Assessing the utility of geospatial technologies to investigate environmental change within lake systems

Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future

The Sensitivity of Land Emissivity Estimates From AMSR-E as C and X Bands to Surface Properties

Microwave observations at low frequencies exhibit more sensitivity to surface and subsurface properties with little interference from the atmosphere. The objective of this study is to develop a global land emissivity product using passive microwave observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and to investigate its sensitivity to land surface properties. The developed product complements existing land emissivity products from SSM/I and AMSU by adding land emissivity estimates at two lower frequencies, 6.9 and 10.65 GHz (C- and X-band, respectively). Observations at these low frequencies penetrate deeper into the soil layer. Ancillary data used in the analysis, such as surface skin temperature and cloud mask, are obtained from International Satellite Cloud Climatology Project (ISCCP). Atmospheric properties are obtained from the TIROS Operational Vertical Sounder (TOVS) observations to determine the small upwelling and downwelling atmospheric emissions as well as the atmospheric transmission. A sensitivity test confirms the small effect of the atmosphere but shows that skin temperature accuracy can significantly affect emissivity estimates. Retrieved emissivities at C- and X-bands and their polarization differences exhibit similar patterns of variation with changes in land cover type, soil moisture, and vegetation density as seen at SSM/I-like frequencies (Ka and Ku bands). The emissivity maps from AMSR-E at these higher frequencies agree reasonably well with the existing SSM/I-based product. The inherent discrepancy introduced by the difference between SSM/I and AMSR-E frequencies, incidence angles, and calibration has been assessed. Significantly greater standard deviation of estimated emissivities compared to SSM/I land emissivity product was found over desert regions. Large differences between emissivity estimates from ascending and descending overpasses were found at lower frequencies due to the inconsistency between thermal IR skin temperatures and passive microwave brightness temperatures which can originate from below the surface. The mismatch between day and night AMSR-E emissivities is greater than ascending and descending differences of SSM/I emissivity. This is because of unique orbit time of AMSR-E (01:30 a.m./p.m. LT) while other microwave sensors have orbit time of 06:00 to 09:00 (a.m./p.m.). This highlights the importance of considering the penetration depth of the microwave signal and diurnal variability of the temperature in emissivity retrieval. The effect of these factors is greater for AMSR-E observations than SSM/I observations, as AMSR-E observations exhibit a greater difference between day and night measures. This issue must be addressed in future studies to improve the accuracy of the emissivity estimates especially at AMSR-E lower frequencies

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

An artificial neural network approach for soil moisture retrieval using passive microwave data

Soil moisture is a key variable that defines land surface-atmosphere (boundary layer) interactions, by contributing directly to the surface energy and water balance. Soil moisture values derived from remote sensing platforms only accounts for the near surface soil layers, generally the top 5cm. Passive microwave data at L-band (1.4 GHz, 21cm wavelength) measurements are shown to be a very effective observation for surface soil moisture retrieval. The first space-borne L-band mission dedicated to observing soil moisture, the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, was launched on 2nd November 2009.Artificial Neural Network (ANN) methods have been used to empirically ascertain the complex statistical relationship between soil moisture and brightness temperature in the presence of vegetation cover. The current problem faced by this method is its inability to predict soil moisture values that are 'out-of-range' of the training data.In this research, an optimization model is developed for the Backpropagation Neural Network model. This optimization model utilizes the combination of the mean and standard deviation of the soil moisture values, together with the prediction process at different pre-determined, equal size regions to cope with the spatial and temporal variation of soil moisture values. This optimized model coupled with an ANN of optimum architecture, in terms of inputs and the number of neurons in the hidden layers, is developed to predict scale-to-scale and downscaling of soil moisture values. The dependency on the accuracy of the mean and standard deviation values of soil moisture data is also studied in this research by simulating the soil moisture values using a multiple regression model. This model obtains very encouraging results for these research problems.The data used to develop and evaluate the model in this research has been obtained from the National Airborne Field Experiments in 2005