The observation of surface water bodies in all weather conditions and better knowledge about inundation patterns are important for water resource management and flood early warning. Microwave radiometers at 37 GHz were applied to observe and study the inundation pattern in large subtropical floodplains in China, i.e. the Poyang Lake and Dongting Lake floodplains, due to the trade-off between the capability to penetrate hydrometeors and vegetation, revisiting time, and spatial coverage and resolution. Taking the shallow sensing depth at 37 GHz into account, open water, inundated area and water saturated soil surface all determine the surface emittance measured by the radiometer. Thus, Water Saturated Surface (WSS) is defined as the combination of these three land surface elements. In subtropical regions, seasonal changes in vegetation cover and various surface roughness conditions are the major challenges for the observation of surface water bodies with microwave radiometers. Atmospheric attenuation, observation gaps and errors in the microwave observations reduce the quality of daily radiometric observations. To deal with the attenuation due to vegetation and surface roughness, a two-step model was developed: the first step is to retrieve the polarization difference emissivity from Polarization Difference Brightness Temperature (PDBT) at 37 GHz with the simplified radiative transfer model and the vegetation optical thickness at 37 GHz parameterized from Normalized Difference Vegetation Index (NDVI) ; the second step is to retrieve the fractional area of WSS from the emissivity difference with a linear model, which can be parameterized according to the Qp surface roughness model. To remove the noise and extract the surface signal (including surface emittance and vegetation attenuation) from the daily PDBT time series, the Time Series Analysis Procedure (TSAP) was developed to identify the spectral features of noisy components in the frequency domain and remove them with a proper filter. The overall method combined the TSAP and the two-step model to derive daily observation of WSS area. The retrieved WSS area in the Poyang Lake floodplain was in a good agreement with the lake area observed from MODerate-resolution Imaging Spectroradiometer (MODIS) and Advanced Synthetic Aperture Radar (ASAR). The observations and analysis of the inundation patterns in the Poyang Lake and Dongting Lake floodplains with this method illustrated the close relationship between inundated area, precipitation and stream flow. Furthermore, a lumped hydrological model, named the discrete rainfall-runoff model, was developed to fully use the retrieved WSS area and to study the role of inundated area in stream flow production. This model simulates stream flow as the integration of contributions of antecedent precipitation in a certain period. Three implementations of the model were developed with the help of ground water table depth and the retrieved WSS area. The case study in the Xiangjiang River basin (upstream catchment of the Dongting Lake floodplain), China, illustrated that: 1) the longest duration of antecedent precipitation is a key parameter to determine model performance; 2) long duration would increase the model uncertainty and lead to overfitting; 3) the application of the WSS area can reduce the duration required to achieve a reasonable accuracy. The model parameters indicated the interaction between stream flow and various water storages, and the calibration results of three implementations implied the recharge period of ground water.

Daily time series of microwave radiometer data obtained in one-orbit direction are full of observation gaps due to satellite configuration and errors from spatial sampling. Such time series carry information about the surface signal including surface emittance and vegetation attenuation, and the atmospheric signal including atmosphere emittance and atmospheric attenuation. To extract the surface signal from this noisy time series, the Time Series Analysis Procedure (TSAP) was developed, based on the properties of the Discrete Fourier Transform (DFT). TSAP includes two stages: (1) identify the spectral features of observation gaps and errors and remove them with a modified boxcar filter; and (2) identify the spectral features of the surface signal and reconstruct it with the Harmonic Analysis of Time Series (HANTS) algorithm. Polarization Difference Brightness Temperature (PDBT) at 37 GHz data were used to illustrate the problems, to explain the implementation of TSAP and to validate this method, due to the PDBT sensitivity to the water content both at the land surface and in the atmosphere. We carried out a case study on a limited heterogeneous crop land and lake area, where the power spectrum of the PDBT time series showed that the harmonic components associated with observation gaps and errors have periods ≤8 days. After applying the modified boxcar filter with a length of 10 days, the RMSD between raw and filtered time series was above 11 K, mainly related to the power reduction in the frequency range associated with observation gaps and errors. Noise reduction is beneficial when applying PDBT observations to monitor wet areas and open water, since the PDBT range between dryland and open water is about 20 K. The spectral features of the atmospheric signal can be revealed by time series analysis of rain-gauge data, since the PDBT at 37 GHz is mainly attenuated by hydrometeors that yield precipitation. Thus, the spectral features of the surface signal were identified in the PDBT time series with the help of the rain-gauge data. HANTS reconstructed the upper envelope of the signal, i.e., correcting for atmospheric influence, while retaining the spectral features of the surface signal. To evaluate the impact of TSAP on retrieval accuracy, the fraction of Water Saturated Surface (WSS) in the region of Poyang Lake was retrieved with 37 GHz observations. The retrievals were evaluated against estimations of the lake area obtained with MODerate-resolution Imaging Spectroradiometer (MODIS) and Advanced Synthetic Aperture Radar (ASAR) data. The Relative RMSE on WSS was 39.5% with unfiltered data and 23% after applying TSAP, i.e., using the estimated surface signal only.

Satellite observations of the terrestrial biosphere cover a period of time sufficiently extended to allow the calculation of a reliable climatology. The latter is particularly relevant for studies of vegetation response to climate variability. Observations from space of the land surface are hampered by clouds at shorter wavelength and affected by water in the atmosphere in the microwave range. Both polar orbiting and geostationary satellites have a revisit frequency high enough to allow for some redundancy relative to the processes being observed, so that time series where a fraction of observations are removed and the resulting gaps filled are still very useful to monitor land surface processes. Two examples illustrate this concept in two different spectral regions: Thermal Infrared (TIR) and observations of land surface temperature to study the thermal behavior of the land surface in response to weather and climate and 37 GHz observations of the polarization difference in brightness temperature to retrieve the fractional abundance of watersaturated soil. Three applications of time series of land surface temperature are presented: (a) monitoring of spectral thermal admittance of the land surface; (b) estimation and mapping of air temperature and (c) monitoring of thermal load to assess the risk of forest fires. Two methods were applied to identify and remove anomalous observations (outliers) and to fill the resulting gaps: Harmonic ANalysis of Time Series (HANTS) and theMultichannel Singular Spectrum Analysis (M-SSA). The HANTS algorithm has been widely used to reconstruct time series of Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Surface Temperature (LST) as well as the polarization difference brightness temperature (PDBT) during the past 20 years to remove random noise or eliminate cloud/snow contamination. The M-SSA, an advanced methodology for time series analysis, was utilized to reconstruct gap-free LST time series using both the spatial and the temporal information content in the data set.