Passive microwave remote sensing of cloud liquid water over land regions

Jones, Andrew S.; Haar, Thomas H. Vonder

doi:10.1029/jd095id10p16673

Cited by 53 publications

(25 citation statements)

References 28 publications

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“…The original Jones and Vonder Haar [1990] method is extended using an improved cloud detection algorithm and new multisensor data processing technology [Jones et al, 1995], in addition to other refinements to be mentioned in the following text. The new method discussed in this paper is an improvement over the previous method in that the microwave surface emittance is able to be retrieved over a much larger geographic region and period.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of microwave surface emittance over land using coincident microwave and infrared satellite measurements

Jones¹,

Haar²

1997

J. Geophys. Res.

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Abstract. A method for routinely generating microwave surface emittance over land from coincident microwave and infrared satellite data has been developed. The method includes a surface skin temperature retrieval, and a dynamic cloud discrimination method, in addition to an explicit atmospheric correction based on in situ atmospheric sounding information. An example of results from the method are presented for a 70-day period over the central United States. Atmospheric-corrected microwave surface emittance results are shown to enhance the use of the microwave data sets for determining land surface characteristics, especially in regards to analysis of the data's frequency dependencies. Several problems that affect the use of the microwave brightness temperature data were examined, including natural characteristics of the spatial and temporal variability of the microwave background signature. The microwave surface emittance was found to be sensitive to numerous rain events captured in the data set. Application of this method to generate longer-term climatologies of microwave surface emittance should improve the understanding of the microwave surface emittance behavior. In particular, realistic spatial distributions of microwave surface emittance should enhance current atmospheric microwave remote sensing efforts over land. The microwave surface emittance data set also shows potential in nonforested regions for flood-monitoring purposes using change detection methods. Modeling work such as this and related retrieval methods based on these models would benefit from having high-quality atmospheric corrected microwave surface emittance data sets for validation and for comparison purposes. 13,609

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Section: Introductionmentioning

confidence: 99%

Retrieval of microwave surface emittance over land using coincident microwave and infrared satellite measurements

Jones¹,

Haar²

1997

J. Geophys. Res.

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“…According to Equation (3), the MVI_B parameter can be expressed as a ratio of the polarization difference of surface emissivity at two frequencies when the atmosphere effect is not considered. When considering the effect of the atmosphere, and assuming that the atmosphere is a non-scattering, plane-parallel atmosphere with a non-blackbody surface boundary condition, the brightness temperature received by the microwave sensor onboard a satellite can be expressed as follows [15][16][17]:…”

Section: Basic Theorymentioning

confidence: 99%

Atmospheric Effect Analysis and Correction of the Microwave Vegetation Index

Shi

Letu

et al. 2017

Remote Sensing

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Microwave vegetation index (MVI) is a vegetation index defined in microwave bands. It has been developed based on observations from AMSR-E and widely used to monitor global vegetation. Recently, our study found that MVI was influenced by the atmosphere, although it was calculated from microwave bands. Ignoring the atmospheric influence might bring obvious uncertainty to the study of global vegetation. In this study, an atmospheric effect sensitivity analysis for MVI was carried out, and an atmospheric correction algorithm was developed to reduce the influence of the atmosphere. The sensitivity analysis showed that water vapor, clouds and precipitation were main parameters that had an influence on MVI. The result of the atmospheric correction on MVI was validated at both temporal and spatial scales. The validation showed that the atmospheric correction algorithm developed in this study could obviously improve the underestimation of MVI on most land surfaces. Seasonal patterns in the uncorrected MVI were obviously related to atmospheric water content besides vegetation changes. In addition, global maps of MVI showed significant differences before and after atmospheric correction in the northern hemisphere in the northern summer. The atmospheric correction will make the MVI more reliable and improve its performance in calculating vegetation biomass.

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“…The apparent scene brightness temperature is related to the emissivity and temperature of the surface and is modified by the intervening atmosphere. By appropriate selection of operating frequencies in several microwave bands, the temperature and moisture content of the atmosphere 22 , as well as key surface properties such as land surface temperature 37 , soil and plant moisture 20,42 , sea-ice mapping 54 , snow cover classification 16 , and wind speed (over the ocean) 57 , can be retrieved. Radiometer data is being operationally used in weather forecasting and sea-ice monitoring.…”

Section: Spaceborne Microwave Sensorsmentioning

confidence: 99%

Reconstruction and Resolution Enhancement Techniques for Microwave Sensors

Long¹,

Chen²

2003

Frontiers of Remote Sensing Information Processing

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Microwave remote sensing instruments such as radiometers and scatterometers have proven themselves effective in a variety of Earth Science studies. The resolution of these sensors, while adequate for many applications, is a limiting factor to their application in other studies. As a result, there is a strong interest in developing ground processing methods which can enhance the spatial resolution of the data. A number of resolution enhancement algorithms have been developed based on inverse filtering and irregular sampling reconstruction. This Chapter discusses the use of resolution enhancement and reconstruction algorithms in microwave remote sensing. While the focus is on microwave instruments, the techniques and algorithms considered are applicable to a variety of sensors, including those not originally designed for imaging.

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Passive microwave remote sensing of cloud liquid water over land regions

Cited by 53 publications

References 28 publications

Retrieval of microwave surface emittance over land using coincident microwave and infrared satellite measurements

Retrieval of microwave surface emittance over land using coincident microwave and infrared satellite measurements

Atmospheric Effect Analysis and Correction of the Microwave Vegetation Index

Reconstruction and Resolution Enhancement Techniques for Microwave Sensors

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