Hiroyuki Tsutsui

Field-Supported Verification and Improvement of a Passive Microwave Surface Emission Model for Rough, Bare, and Wet Soil Surfaces by Incorporating Shadowing Effects

To investigate the potential of passive microwave techniques for observing the atmosphere over land, it is important to understand the nature of emissions from the land surface. The heterogeneity of large-scale land surface emissions has been cited as a major impediment in conducting observations of the atmosphere over land. Many models, both theoretical and empirical, have been developed to explain the surface emission with varying degrees of success. In the past, most field-supported research in soil observations using microwave techniques has concentrated on lower frequencies (L-band). This paper reports on a study, supported by field data, that seeks to improve our understanding of surface emission at various frequencies using passive microwave radiometers. This provides a crucial link between remote sensing of the land surface and the atmosphere. We show that it is important to consider shadowing associated with rough wet surfaces. By incorporating shadowing effects, the advanced integral equation model (AIEM) shows remarkable agreement with observations at all frequencies and polarizations. Although the roughness parameters obtained during our experiment correspond to very rough conditions, by including shadowing effects the AIEM model is able to transition from the not so rough natural condition as observed from space to the very rough as obtained during field experiments

A Field Verification of an Algorithm for Retrieving Vegetation Water Content From Passive Microwave Observations

We present a field-verified algorithm for retrieving vegetation water content (VWC), which is the mass of water in vegetation tissue per ground area, using observed microwave brightness temperatures (TBs). We can use 6.925and 10.65-GHz microwave observations to minimize the species dependence of the relationship between vegetation optical depth (VOD) and VWC. Then, we can easily estimate the VWC after obtaining the VOD. Although the VOD retrieved at these frequencies is highly affected by uncertainties in the surface roughness, we found that the effects of the bias of the roughness parameters in a radiative transfer model can effectively be eliminated by introducing leaf area index (LAI) data. The TBs observed using ground-based microwave radiometer and field-observed LAI were used to successfully reproduce the field-observed VWC below 2.0 kg/m2 (R2 = 0.712 and RMSE = 0.393 kg/m2). This strategy of surface roughness correction also positively impacts the performance of soil moisture retrieval. This field-verified algorithm can contribute to globalscale VWC retrieval using satellite microwave radiometers such as the Advanced Microwave Scanning Radiometer on Earth Observation Systems (AMSR-E) and its successor (AMSR2).

Snow Cover Conditions in the Tibetan Plateau Observed during the Winter of 2003/2004

ABSTRACT Surface conditions in the non-mountainous areas of the central Tibetan Plateau were measured in a field survey in February 2004, and water balance parameters such as precipitation, sublimation, and water equivalent of snow cover were examined through the 2003/2004 winter by in situ automated measurements. Snow cover was shallow and coexisted with snow-free areas, producing large surface temperature heterogeneity under strong insolation. Clear diurnal variation was found in the meteorological observation. The precipitation and total sublimation from November 2003 to January 2004 were estimated as 15 mm and 17 mm, respectively, and the remaining equivalent snow water quantity in the beginning of February 2004 was 8 mm. Imbalance of the water budget was mainly due to the uncertainty of snow cover proportion within the mesoscale area. Importance of a redistribution process of the snow was proposed to explain the consistency of surface heating and remaining snow cover.

Improving the AMSR-E Soil Moisture Algorithm of the University of Tokyo through Field Experiments and Parameters Optimization

This paper reports the progresses of AMSR-E soil moisture algorithm development at the University of Tokyo. The first progress is made through improving the forward model, i.e. radiative transfer model (RTM), based on field experiment and numerical simulation. The second progress is the development of a new parameterization method, through which the parameters necessary for the algorithm are optimized by a land data assimilation system developed at the University of Tokyo (LDAS-UT). The capability of LDAS-UT was validated successfully with winter wheat experiment data. Finally, the new RTM and parameterization method was validated on AMSR-E match up data set. The results demonstrate that the simulated brightness temperature is in good agreements with the one observed by AMSR-E.

A coupled Land Atmosphere Radiative-Transfer Model (LA-RTM) for multi-frequency passive microwave remote sensing: development and application over Wakasa Bay and the Tibetan Plateau

Multi-frequency passive microwave sensors herald a new dawn for combined land and atmosphere observations. Past efforts to utilize microwave remote sensing of atmosphere and land surface have proceeded by treating these two areas in a parallel fashion. In this research, a unified approach is presented that can be used to improve both quantitative and qualitative understanding of land and atmosphere constituents. A coupled Land Atmosphere Radiative-Transfer Model (LA-RTM) that can be used as a forward model in retrieval algorithms, or as an observation operator in data-assimilation schemes is developed. This model is validated using data collected during the 2003 Advanced Microwave Scanning Radiometer on board the Earth Observing Satellite (AMSR/AMSR-E) validation experiment over Wakasa Bay in Japan and the Coordinated Enhanced Observing Period (CEOP) dataset for the Tibetan Plateau collected in April and August 2004. These datasets comprise satellite (AMSR-R) observations, ground-based microwave radiometers (GBMRs) and radiosonde atmosphere soundings. In both sites, good agreement between simulated and observed brightness temperatures is demonstrated. To facilitate fast retrievals, a retrieval scheme is proposed that uses LA-RTM as a forward model to generate a look-up table (LUT) for varying land-surface conditions. This LUT is used to retrieve soil-moisture and surface-roughness conditions for the target site. Using this scheme, retrieved soil moisture at in situ stations was shown to have fairly good agreement with observations.

Ground Truth of Passive Microwave Radiative Transfer on Vegetated Land Surfaces

In this paper, we implemented the in-situ observation of surface soil moisture (SSM), vegetation water content (VWC), and microwave brightness temperatures. By analyzing this in-situ observation dataset and the numerical simulation, we investigated the source of the uncertainty of the current algorithms for Advanced Microwave Scanning Radiometer for Earth observation system (AMSR-E) and AMSR2 to retrieve SSM and vegetation dynamics. Our findings are: (1) the microwave radiative transfer at C-band and X-band is not strongly affected by the shape of vegetation and the existing algorithm can be applied to a wide variety of plant types; (2) the diversity of surface soil roughness significantly affects the indices which are used by the current algorithms and addressing the uncertainty of surface soil roughness is necessary to improve the retrieval algorithms; (3) At C-band, SSM of the homogeneous vegetated land surfaces can be detected only when their VWC is less than approximately 0.25 (kg/m2); (4) the state-of-the-art Radiative Transfer Model (RTM) can predict our observed dataset although we have some biases in simulating brightness temperatures at a higher frequency. The new in-situ observation dataset produced by this study can be the guideline for both developers and users of passive microwave land observations to consider the uncertainties of their products.

Improving land surface energy and water fluxes simulation over the Tibetan Plateau with using a land data assimilation system

The land-atmosphere interaction in the Tibetan Plateau plays an important role in the Asian summer monsoon and the global energy and water cycle. This study presents a method to improve the land surface water and energy fluxes simulation by using a land data assimilation system (LDAS), which merging microwave remote sensing data and GCM output into a land surface model. NCEP reanalysis data is used as the background field and also as the meteorological forcing for the land surface model. Two experiments were designed as by driving LDAS-UT with two sets of atmospheric forcing data, (1) with in situ observed forcing data and (2) with NCEP reanalysis data at Gaize and Naqu sites. Results show that LDAS is able to estimate land surface soil moisture and energy fluxes accurately. The RMSE of soil moisture simulation is around 0.03–0.05 and RMSE of net radiation simulation is around 30W/M2. This study reveals the potential for using satellite remote sensing data to improve land surface fluxes estimation.

A Radiative transfer Model for Soil Media with Considering the volume Effects of Soil Particles: field observation and Numerical Simulation

This paper presents the development of an improved soil radiative transfer model (RTM) which considering the volume scattering effect of soil particles, an unexplored part of traditional RTMs, through field experiments and numerical simulations. The field observations were conducted by using the ground based passive microwave radiometer (GBMR) to measure the brightness temperature of dry sand layer over background materials, metal plates or absorbers. The existence of volume scattering effects in the dry sand was demonstrated through field experiments. Then, the observed data were simulated by the dense media radiative transfer (DMRT) model. The simulation results show that the DMRT model which includes the volume scattering effects performers better than the generally used surface emission model which does not include volume scattering effects.

Possibility of Estimating Seasonal Snow Depth Based Solely on Passive Microwave Remote Sensing on the Greenland Ice Sheet in Spring

Sea level rise related to the melting and thinning of the Greenland Ice Sheet (GrIS), a subject of growing concern in recent years, will eventually affect the global climate. Although the melting of snow on the GrIS is actively monitored by passive microwave remote sensing, very few studies have estimated the seasonal GrIS snow depth using this technique. In this study, to estimate seasonal snowpack on GrIS, we investigated the microwave property and optimum physical parameters. We used our microwave radiative transfer model to create a lookup table and a simple satellite retrieval algorithm to estimate seasonal snow depth on GrIS in spring, based on the microwave satellite brightness temperature from AMSR-E and AMSR2. Our research suggests there is potential for estimating snow depth based solely on GrIS passive microwave remote sensing data. We validated these estimates against in situ snow depths at several sites and compared them with the snow spatial distributions over the entire GrIS of several major products (ERA-interim, MAR ver. 5.3.1 and GLDAS-CLM) that evaluate snow depth.

Monitoring vegetation water content by using optical vegetation index and microwave vegetation index: Field experiments and applications

Information about vegetation status has widespread utility in agriculture, forestry, hydrology, and land-atmosphere interaction study. This paper presents an attempt to monitor the vegetation water content (VWC) by merging visible/infrared remote sensing and microwave remote sensing. We first derived a relationship between VWC and microwave vegetation index (MVI) through a field experiment. The relationships between VWC and Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were also studied. We found that MVI has the largest correlation coefficient with VWC, while NDWI has the second largest one. The VWC-MVI relationship derived from field experiment was used to estimate VWC from AMSR-E data at Mongolia sites. The results were compared with AMSR-E standard VWC products. We found the new method overestimated VWC but highly correlated to the AMSR-E products. This study reveals the potential of MVI to monitoring VWC variation.