Maxim Shoshany

Prof. Maxim Shoshany


Research Focus & Metrics

Academic Career Length

38Years (1989-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 November 2015 in ISPRS Journal of Photogrammetry and Remote Sensing
Verified
Journal Article
Closed access
TRTal Rapaport
Tal Rapaport
UHUri Hochberg
Uri Hochberg
MSMaxim Shoshany
Maxim Shoshany
AKArnon Karnieli
Arnon Karnieli
SRShimon Rachmilevitch
Shimon Rachmilevitch

1 January 2009 in Forest Ecology and Management
Verified
Journal Article
Closed access
YCYohay Carmel
Yohay Carmel
SPShlomit Paz
Shlomit Paz
FJFaris Jahashan
Faris Jahashan
MSMaxim Shoshany
Maxim Shoshany

1 March 2004 in Landscape and Urban Planning
Verified
Journal Article
Closed access
PKPua Kutiel
Pua Kutiel
OCOded Cohen
Oded Cohen
MSMaxim Shoshany
Maxim Shoshany
MSMerav Shub
Merav Shub

1 April 2002 in Land Use Policy
Verified
Journal Article
Closed access
MSMaxim Shoshany
Maxim Shoshany
NGNaftaly Goldshleger
Naftaly Goldshleger

28 May 2013 in International Journal of Remote Sensing
Verified
Journal Article
Closed access
MSMaxim Shoshany
Maxim Shoshany
NGNaftaly Goldshleger
Naftaly Goldshleger
ACAlexandra Chudnovsky
Alexandra Chudnovsky
Agricultural land degradation is a global problem that severely hampers the production of food needed to sustain the growing world population. Mapping of soil degradation by remote sensing is instrumental for understanding the spatial extent and rate of this problem. Methods aimed at detecting soil loss, soil drying, and soil-quality deterioration have been demonstrated in numerous studies utilizing passive and active remote sensors. This review presents a short description of each form of soil degradation, including data regarding known extents and rates, and then reviews the methods with respect to direct and indirect modelling approaches. Two types of obstacles to achieving wide regional detection of soil degradation are revealed. The first concerns the complex and non-unique relationships between remote-sensing indicators and different soil properties, such as roughness, soil moisture (SM), soil salinity, and organic matter content. The second concerns the difficulties involved in acquiring data on subsurface soil properties. In view of these difficulties, we recommend expanding the use of phenomenological models capable of estimating soil-degradation potential according to combinations of environmental conditions, thus enabling remote-sensing efforts to be focused on local areas where the environmental threat is highest. The second avenue for improving the contribution of remote sensing on a wide regional scale involves the application of integrative methods, such as those based on hyperspectral, multisensory, and multitemporal approaches, as well as those that incorporate environmental information (such as topography, soil types, and precipitation).