Abstract

Quantifying roughness effects on ground surface emissivity is an important step in obtaining high-quality soil moisture products from large-scale passive microwave sensors. In this study, we used a semi-empirical method to evaluate roughness effects (parameterized here by the parameter) on a global scale from AMSR-E (Advanced Microwave Scanning Radiometer for EOS) observations. AMSR-E brightness temperatures at 6.9 GHz obtained from January 2009 to September 2011, together with estimations of soil moisture from the SMOS (Soil Moisture and Ocean Salinity) L3 products and of soil temperature from ECMWF’s (European Centre for Medium-range Weather Forecasting) were used as inputs in a retrieval process. In the first step, we retrieved a parameter (referred to as the parameter) accounting for the combined effects of roughness and vegetation. Then, global MODIS NDVI data were used to decouple the effects of vegetation from those of surface roughness. Finally, global maps of the Hr parameters were produced and discussed. Initial results showed that some spatial patterns in the values could be associated with the main vegetation types (higher values of were retrieved generally in forested regions, intermediate values were obtained over crops and grasslands, and lower values were obtained over shrubs and desert) and topography. For instance, over the USA, lower values of were retrieved in relatively flat regions while relatively higher values were retrieved in hilly regions.

Highlights

  • Soil moisture (SM), which plays an essential role in energy transfer between the soil and the atmosphere, is a major variable in hydrological processes [1]

  • 6.9 GHz TB observations, SMOS soil moisture products and European Centre for Medium-range Weather Forecasting (ECMWF)’s soil temperature, we developed a simple approach to globally estimate the spatial variations in the parameter

  • To illustrate the use of the method developed in this study, Figure 2a–d show a comparison between the retrieved values of ∗, using the equations described above, and the MODIS Normalized Difference Vegetation Index (NDVI) values over the four selected SCAN sites, from January 2010 to September 2011

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Summary

Introduction

Soil moisture (SM), which plays an essential role in energy transfer between the soil and the atmosphere, is a major variable in hydrological processes [1]. Soil moisture is a key variable for weather forecasting [2] and climate predictions [3]. The passive microwave remote sensing technique, which has a large spatial coverage and high temporal resolution and is sensitive to surface water content, has been shown to be an efficient approach for large scale soil moisture monitoring [4,5,6]. The characteristics of the vegetation (such as vegetation structure and vegetation water content) and the soil (such as soil moisture content, surface roughness and texture) have a significant impact on the surface microwave emissivity [7,8,9,10,11]. The parameterization of these two effects is the key to obtaining high quality estimates of soil moisture

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