Essay Example on Soil moisture SM is an essential contributor to Climate









Surface soil moisture SM is an essential contributor to climate and hydrological sciences which plays an important role in disaster predictions such as flooding and drought environmental monitoring such as dust storms and erosion and hydrological applications The water content of soil affects a variety of physical and biological processes in the biosphere and links the Earth's surface and atmosphere through its influence on surface energy and moisture fluxes It is counted as a source of water for the atmosphere through processes leading to evapotranspiration from land which include mostly vegetable transpiration and bare soil evaporation In addition antecedent SM conditions affect the hydrologic behavior of land surfaces by controlling in part the infiltration capacity of soils and the portioning of precipitation into runoff and storage terms From the ecohydrology perspective of water limited environments focused on understanding the linkages between vegetation water climate relationships have been found to have a complicated dependence on availability of SM dynamics Mulebeke et al 2013 Garcia Estringana et al 2013 All these processes are highly characterized by nonlinearity functions of SM and complex feedback mechanisms 

Because of this quantified SM conditions are critical inputs for models in agriculture hydrology meteorology climatology and biology The dynamics of SM at the land surface are governed by the set of components with diverse time and spatial scales Variances in both weather and climate are therefore influenced by the SM state Reynolds 1970 distinguished between static e g soil texture topography and dynamic e g precipitation vegetation controlling factors So the evaluation of SM patterns differ depending on related variables condition Many of these factors are interrelated and vary spatially and or temporally making it challenging to recognize explicit cause and effect relationships between SM and its driving variables Landscape factors including topography vegetation and land use are important controlling factors of SM spatial and temporal variations SM spatial variation was found to be significantly correlated to terrain attributes e g slope elevation and topographic wetness index Therefore terrain attributes have been used to predict SM variation via regression geospatial and hydrological modeling in several studies Western et al 1999 2004 Lin et al 2006 Takagi and Lin 2012 Influence of vegetation e g type cover and distribution on SM variation has also been reported in several studies and spatial information on vegetation usually interpreted from remote sensing image has been used to simulate SM variation 

Mohanty et al 2000 Hupet and Vanclooster 2002 Generally long term time series of SM quantities can reveal trends in the water cycle related to climate or hydrological condition in the area Due to the fact that at a large area basis the number of networks and gauging SM in particular on a continuous basis is still restricted because of labor intensive very slow and may be very expensive and furthermore it is challenging to get reliable approximations at the large scale from point measurements because of the high variability and the low degree of observed autocorrelation so for various applications the large number of satellite based SM products show promise in assisting hydrologists to describe and measure the surface SM condition for large areas Since the 1970 s a number of remote sensing techniques have been created to investigate and mapping SM by measuring different areas of the electromagnetic spectrum from the optical to microwave regions Musick and Pelletier 1988 Engman 1991 Wang and Qu 2009 As remote sensors do not measure SM content directly mathematical models that describe the connection between the measured signals and SM content must be derived Microwave remote sensing methods such as the Advanced Microwave Scanning Radiometer Earth observing system AMSR E on board the Aqua satellite since 2002 Soil moisture and ocean salinity satellite SMOS since 2009 Multi frequency Scanning Microwave Radiometer MSMR since 1999 and Soil Moisture Active Passive SMAP since January 2015 are presently operational providing satellite data for the globe on a daily basis Although these methods offer many procedures to achieve SM at large scale they are almost low resolvable typically 25km and not appropriate in small catchment or field scale 

Optical thermal infrared remote sensing data known as Surface Temperature Vegetation Index Method provide finer resolution information 1km Recently Zhang and Zhou 2016 reviewed new technique that SM estimation can be extracted from optical thermal remote sensing which is mainly depends on the association between the SM and the surface reflectance and temperature or vegetation index Such retrieval methods in this field like thermal inertia methods emphasized on soil thermal characteristics or triangular relationship method that shows a relationship among SM Normalized Difference Vegetation Index NDVI and land surface temperature LST of a given region being used in practical applications but the lack of considering the other factors such as topography or just being capable for bare or low density vegetation cover is their weakness The remotely sensed based vegetation indices to estimate soil moisture e g NDVI Normalize Difference Water Index NDWI and Normalized Multi band Drought Index NMDI are good option but the distribution of SM cannot be predicted by a single parameter and the determining parameter changes between different land surface factor intensities Extensive efforts have been carried out over the past decades to estimate SM using satellite images through developing the relationship between remotely sensed LST and vegetation indexes has been reported by many researchers e g Hosseini and Saradjian 2011 Zhao and Li 2013 In addition to topography vegetation and LST can now be mapped with high spatial resolution from 30m to 1km with the use of remotely sensed imagery Therefor to predict SM conditions using the dependent landscape factors which is extracted from remotely sensed imagery instead of in situ measurement makes it possible to achieve the fast and real time monitoring of SM conditions

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