Toward vicarious calibration of microwave remote-sensing satellites in arid environments

The Soil Moisture and Ocean Salinity (SMOS) satellite marks the commencement of dedicated global surface soil moisture missions, and the first mission to make passive microwave observations at L-band. On-orbit calibration is an essential part of the instrument calibration strategy, but on-board beam-filling targets are not practical for such large apertures. Therefore, areas to serve as vicarious calibration targets need to be identified. Such sites can only be identified through field experiments including both in situ and airborne measurements. For this purpose, two field experiments were performed in central Australia. Three areas are studied as follows: 1) Lake Eyre, a typically dry salt lake; 2) Wirrangula Hill, with sparse vegetation and a dense cover of surface rock; and 3) Simpson Desert, characterized by dry sand dunes. Of those sites, only Wirrangula Hill and the Simpson Desert are found to be potentially suitable targets, as they have a spatial variation in brightness temperatures of <4 K under normal conditions. However, some limitations are observed for the Simpson Desert, where a bias of 15 K in vertical and 20 K in horizontal polarization exists between model predictions and observations, suggesting a lack of understanding of the underlying physics in this environment. Subsequent comparison with model predictions indicates a SMOS bias of 5 K in vertical and 11 K in horizontal polarization, and an unbiased root mean square difference of 10 K in both polarizations for Wirrangula Hill. Most importantly, the SMOS observations show that the brightness temperature evolution is dominated by regular seasonal patterns and that precipitation events have only little impact.

Prediction of Land Surface Temperature under Cloudy Conditions using Microwave Remote Sensing and ANN

Land surface temperature (LST) is an important variable in climate, hydrologic, ecological, biophysical and biochemical studies (Mildrexler et al., 2011). The most effective way to obtain LST measurements is through satellites. Presently, LST from moderate resolution imaging spectroradiometer (MODIS) sensor is applied in various fields due to its high spatial and temporal availability over the globe, but quite difficult to provide observations in cloudy conditions. This study evolves of prediction of LST under clear and cloudy conditions using microwave vegetation indices (MVIs), elevation, latitude, longitude and Julian day as inputs employing an artificial neural network (ANN) model. MVIs can be obtained even under cloudy condition, since microwave radiation has an ability to penetrate through clouds. In this study LST and MVIs data of the year 2010 for the Cauvery basin on a daily basis were obtained from MODIS and advanced microwave scanning radiometer (AMSR-E) sensors of aqua satellite respectively. Separate ANN models were trained and tested for the grid cells for which both LST and MVI were available. The performance of the models was evaluated based on standard evaluation measures. The best performing model was used to predict LST where MVIs were available. Results revealed that predictions of LST using ANN are in good agreement with the observed values. The ANN approach presented in this study promises to be useful for predicting LST using satellite observations even in cloudy conditions. (C) 2015 The Authors. Published by Elsevier B.V.

Microwave remote sensing of stratospheric trace gases using digital Fast Fourier Transform spectrometers

The Institute of Applied Physics observes middle atmospheric trace gases, such as ozone and water vapour, by microwave radiometry. We report on the comparison of measurements using a novel digital Fast Fourier Transform and accousto optical spectrometers. First tests made on ground are presented as well as first experience about the use of such spectrometers under aircraft conditions.

月表热参数模型与月壤厚度的被动微波遥感理论分析

利用被动微波遥感亮度温度数据反演月壤厚度是“嫦娥”探月工程的科学目标之一，也是人类探测月壤厚度的一种新的尝试。深入研究月表太阳辐射、月球内部热流以及月表温度的分布和变化规律，是解译遥感数据，反演月壤厚度的前提条件，也为进一步开展月球探测、开发利用月球资源乃至建立月球基地相关研究工作提供必要的参考。 本文根据月表有效太阳辐照度与太阳常数、日月距离和太阳辐射入射角的关系，建立了月表有效太阳辐照度的实时模型如下： (1) 其中， (2) (3) 通过对月表有效太阳辐照度实时模型的各个参数分析发现，影响月表有效太阳辐照度变化的主要因素是日地距离和太阳辐射入射角的变化。对模型的误差分析表明，从1950年到2050年的100年内，月表有效太阳辐照度计算结果的误差百分比小于0.28％，能更准确地反映月表有效太阳辐照度的变化情况。从2007年月表有效太阳辐照度的计算结果发现，该年内的月表有效太阳辐照度变化在1321.5～1416.6 W•m-2之间，平均为1368.0 W•m-2，一个月内的变化最小幅度为6.0 W•m-2，最大幅度为23.6 W•m-2。 在月表有效太阳辐照度的实时模型基础上，根据能量守恒和Stefan-Boltzmann定律，本文还得出了月表温度分布模型如下： (4) 其中，初始条件由下式决定， (5) 通过与月表温度实际观测结果的比较发现，当月表反射率、热发射率和热惯量分别取0.127、0.94和125 J•m-2•s-1/2•K-1时，模型的计算结果与实际观测值比较符合，能较好地预测理想条件下的月表温度。 月表热参数研究的一个重要应用就是解译对月被动微波遥感的亮度温度数据。在对月被动微波遥感探测中，辐射计获得的亮度温度反映了月球表层的热辐射特性。月球表层的热辐射与其自身的热状况紧密相关，结合文中建立的月表热参数模型，根据辐射传播理论进一步分析了对月微波遥感探测中，月球表层在不同情况下对亮度温度的贡献，确定了亮度温度随月表温度和月壤厚度的变化关系，对被动微波遥感探测月壤厚度的可能性和可能达到的精度进行了估算。 对月球表层的热辐射传播的分析发现，对月被动微波遥感探测获得的亮度温度受月球表层热辐射的控制，与月壤厚度具有指数相关性，并受到月表温度的影响。当月壤和月岩的复介电常数分别为2 + 0.005 j和9 + 1 j、相对磁导率均为1时，对应3.0GHz、7.8GHz、19.35GHz和37.0GHz四个频率的亮度温度与月壤厚度及月表温度的关系可分别近似表示为， 3.0GHz亮度温度： (6) 7.8GHz亮度温度： (7) 19.35GHz亮度温度： (8) 37.0GHz亮度温度： (9) 当月壤厚度和月表温度分别在0.5m~30m和100K~400K之间变化时，上述四个频率的亮度温度变化范围分别在212.5K～252.8K、207.4 K～266.7K、193.8 K～288.6K和174.0 K～310.9K之间。对于较低频率的被动微波遥感，亮度温度随月壤厚度的增大逐渐增大并趋于稳定；对较高频率的被动微波遥感，亮度温度随月壤厚度的增大会产生起伏波动，不利于用单波段反演月壤厚度。亮度温度梯度在频率较高时梯度较大，在很小的月壤厚度范围内很快就趋于0，不利于厚度较大时的月壤厚度反演，但对于厚度较小时的月壤厚度反演精度较高；同时，除3.0GHz外，7.8GHz、19.35GHz和37.0GHz三个频率的亮度温度梯度随月表温度的升高降幅较大，尤其是19.35GHz，适合在夜间对月壤厚度较小的地区进行更精确的探测。对于3.0GHz，其亮度温度梯度受月表温度变化的影响很小，能反映出较深层月壤厚度的信息，可以对月球进行全球全天时探测。若辐射计的分辨率为0.02K，3.0GHz频率对10m厚月壤的判别精度达到0.07m；对于20m厚月壤的精度为1.4m。当月壤厚度小于0.5m时，随着月壤厚度从0到0.5m增加，月球表层的亮度温度贡献呈先减小后增大的趋势，从而使某一亮度温度值可能对应存在两种不同的月壤厚度。因此，对于月壤厚度小于0.5m的区域，利用单波段被动微波遥感亮度温度反演月壤厚度是比较困难的。 在对月被动微波遥感探测中，可以利用月球夜晚时段的亮度温度数据判别月壤厚度是否小于0.5m。当月表温度为100K时，3.0GHz、7.8GHz、19.35GHz和37.0GHz四个频率的亮度温度判别参考值分别为212.9K、207.4K、193.5K和174.1K；月表温度为240K时，上述四个频率的亮度温度判别参考值分别为220.8K、226.8K、234.1K和237.2K。当亮度温度小于参考值时表示月壤厚度小于0.5m，反之，表示月壤厚度大于0.5m。更进一步地，可以根据月表温度的影响系数对月岩是否裸露于月表进行判断。当3.0GHz、7.8GHz、19.35GHz和37.0GHz四个频率的月表温度影响系数接近0.77、0.82、0.84和0.85时，可以认为月岩直接暴露于月表。

Radio-Frequency Interference Identification Over Oceans for C- and X-Band AMSR2 Channels

A new method for radio-frequency interference (RFI) contamination identification over open oceans for the two C-subbands and X-band of Advanced Microwave Scanning Radiometer 2 (AMSR2) channel measurements is suggested. The method is based both on the AMSR2 brightness temperature (T-B) modeling and on the analysis of AMSR2 measurements over oceans. The joint analysis of T-B spectral differences allowed to identify the relations between them and the limits of their variability, which are ensured by the changes in the environmental conditions. It was found that the constraints, based on the ratio of spectral differences, are more regionally and seasonally independent than the spectral differences themselves. Although not all possible RFI combinations are considered, the developed simple criteria can be used to detect most RFI-contaminated pixels over the World Ocean for AMSR2 measurements in two C-subbands and the X-band.

Satellite passive microwave observations of the upper Colorado River snowpack

Seasonal snow cover in the mountains of the Upper Colorado River Basin is a major source of water for a large portion of the southwestern United States. The extent and amount of this snowpack not only reflects changes in weather patterns and climate but also influences the general circulation through modification of the energy exchange between land and atmosphere. ... Satellite observations and remote sensing techniques can enhance the standard snowpack observations to provide the temporal and spatial measurements required for understanding the role of snow in the surface energy balance and improving the management of water resources.

Effects of sub-pixel heterogeneity on the retrieval of soil moisture from passive microwave radiometry

A simple formulation relating the L-band microwave brightness temperature detected by a passive microwave radiometer to the near surface soil moisture was developed using MICRO-SWEAT, a coupled microwave emission model and soil-vegetation-atmosphere-transfer (SVAT) scheme. This simple model provides an ideal tool with which to explore the impact of sub-pixel heterogeneity on the retrieval of soil moisture from microwave brightness temperatures. In the case of a bare soil pixel, the relationship between apparent emissivity and surface soil moisture is approximately linear, with the clay content of the soil influencing just the intercept of this relationship. It is shown that there are no errors in the retrieved soil moisture from a bare soil pixel that is heterogeneous in soil moisture and texture. However, in the case of a vegetated pixel, the slope of the relationship between apparent emissivity and surface soil moisture decreases with increasing vegetation. Therefore for a pixel that is heterogeneous in vegetation and soil moisture, errors can be introduced into the retrieved soil moisture. Generally, under moderate conditions, the retrieved soil moisture is within 3% of the actual soil moisture. Examples illustrating this discussion use data collected during the Southern Great Plains '97 Experiment (SGP97).

Global estimates of snow water equivalent from passive microwave instruments: history, challenges and future developments

Snow properties have been retrieved from satellite data for many decades. While snow extent is generally felt to be obtained reliably from visible-band data, there is less confidence in the measurements of snow mass or water equivalent derived from passive microwave instruments. This paper briefly reviews historical passive microwave instruments and products, and compares the large-scale patterns from these sources to those of general circulation models and leading reanalysis products. Differences are seen to be large between the datasets, particularly over Siberia. A better understanding of the errors in both the model-based and measurement-based datasets is required to exploit both fully. Techniques to apply to the satellite measurements for improved large-scale snow data are suggested.