Surface soil moisture is one of the crucial variables in hydrological processes, which influences the exchange of water and energy fluxes at the land surface/ atmosphere interface. Accurate estimate of the spatial and temporal variations of soil moisture is critical for numerous environmental studies. Recent technological advances in satellite remote sensing have shown that soil moisture can be measured by a variety of remote sensing techniques, each with its own strengths and weaknesses. This paper presents a comprehensive review of the progress in remote sensing of soil moisture, with focus on technique approaches for soil moisture estimation from optical, thermal, passive microwave, and active microwave measurements. The physical principles and the status of current retrieval methods are summarized. Limitations existing in current soil moisture estimation algorithms and key issues that have to be addressed in the near future are also discussed.

A new index, the Normalized Multi‐band Drought Index (NMDI), is proposed for monitoring soil and vegetation moisture from space. NMDI is defined as , where R represents the apparent reflectance observed by a satellite sensor. Similar to the Normalized Difference Water Index, NMDI uses the 860 nm channel as the reference; instead of using a single liquid water absorption channel, however, it uses the difference between two liquid water absorption channels centered at 1640 nm and 2130 nm as the soil and vegetation moisture sensitive band. Analysis revealed that by combining information from multiple near infrared, and short wave infrared channels, NMDI has enhanced the sensitivity to drought severity, and is well suited to estimate both soil and vegetation moisture. Typical soil reflectance spectra and satellite‐acquired reflectances, are used to validate the usefulness of NMDI. Its application to areas with moderate vegetation coverage, however, needs further investigation.

[1] Trends in Chinese horizontal visibility, the frequency of visibility >19 km, and haziness for the period between 1981 and 2005 were evaluated based on data for daily horizontal visibility. Annual means were calculated for each station and for China as a whole. Linear regression analysis was used to characterize long-term annual trends in these variables. Over the past 25 years, there has been a significant decrease in horizontal visibility (À2.1 km per decade from 1990 to 2005) and the frequency of visibility >19 km (À3.5% per decade) but a significant increase in the 75th percentile annual extinction coefficients (25% per 25 year). According to rapid increase of total energy consumption, the consistent spatial and temporal variations of visibility and haze support the speculation that increased aerosol loadings were responsible for the observed decreases in horizontal visibility over much of East China.

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Sand and dust storms (SDSs), which present environmental risks and affect the regional climate, have been worsened in the East Asian regions over the last decade. Monitoring SDS from space using satellite remote sensing (RS) has become one of the most important issues in this field. At present, satellite RS of SDS is limited to using true-color images or aerosol optical thickness (AOT), or a new algorithm called "Deep Blue." Using current existing approaches makes it difficult to identify SDS from clouds. The authors have detected SDS by combining Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) solar reflectance band (SRB) measurements. Based on the dust spectral characteristic, this letter proposes a normalized difference dust index (NDDI) using MODIS reflectance measurements and applies it to the Asian SDS cases. The simple NDDI index is found to be able to identify SDS and clouds easily. The results suggest that NDDI could be used to detect SDS over bright surfaces where the MODIS AOT product is not available.Index Terms-Aerosol, Asian, Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference dust index (NDDI), sand and dust storm (SDS), satellite remote sensing (RS), Terra and Aqua.