Food, water, and fault lines: Remote sensing opportunities for earthquake-response management of agricultural water.

Earthquakes often cause destructive and unpredictable changes that can affect local hydrology (e.g. groundwater elevation or reduction) and thus disrupt land uses and human activities. Prolific agricultural regions overlie seismically active areas, emphasizing the importance to improve our understanding and monitoring of hydrologic and agricultural systems following a seismic event. A thorough data collection is necessary for adequate post-earthquake crop management response; however, the large spatial extent of earthquake's impact makes challenging the collection of robust data sets for identifying locations and magnitude of these impacts. Observing hydrologic responses to earthquakes is not a novel concept, yet there is a lack of methods and tools for assessing earthquake's impacts upon the regional hydrology and agricultural systems. The objective of this paper is to describe how remote sensing imagery, methods and tools allow detecting crop responses and damage incurred after earthquakes because a change in the regional hydrology. Many remote sensing datasets are long archived with extensive coverage and with well-documented methods to assess plant-water relations. We thus connect remote sensing of plant water relations to its utility in agriculture using a post-earthquake agrohydrologic remote sensing (PEARS) framework; specifically in agro-hydrologic relationships associated with recent earthquake events that will lead to improved water management.

[1]  David Riaño,et al.  Contributions of imaging spectroscopy to improve estimates of evapotranspiration , 2011 .

[2]  R. Crawford,et al.  Flooding and Plant Growth. , 1985 .

[3]  A. Lakso,et al.  Effect of rootstock on apple (Malus domestica) tree water relations , 1986 .

[4]  Ross W. Boulanger,et al.  Seismic Response of Levees in the Sacramento-San Joaquin Delta , 2009 .

[5]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[6]  Charles K. Huyck,et al.  A Comprehensive Analysis of Building Damage in the 2010 Haiti Earthquake Using High-Resolution Imagery and Crowdsourcing , 2015 .

[7]  S. Ustin,et al.  Estimating Vegetation Water content with Hyperspectral data for different Canopy scenarios: Relationships between AVIRIS and MODIS Indexes , 2006 .

[8]  M. Shinozuka,et al.  Resilient Disaster Response : Using Remote Sensing Technologies for Post-Earthquake Damage Detection by , 2003 .

[9]  E. Njoku,et al.  Passive microwave remote sensing of soil moisture , 1996 .

[10]  Anthony Morse,et al.  Use of the METRIC evapotranspiration model to compute water use by irrigated agriculture in Idaho , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Zachary Whitman,et al.  Agricultural land rehabilitation following 2010 Darfield (Canterbury) earthquake , 2010 .

[12]  Martha C. Anderson,et al.  Thermal Remote Sensing of Drought and Evapotranspiration , 2008 .

[13]  S. G. Nelson,et al.  Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape , 2008, Sensors.

[14]  Pamela L. Nagler,et al.  Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data , 2005 .

[15]  Huadong Guo,et al.  Assessment of damage to buildings and farms during the 2011 M 9.0 earthquake and tsunami in Japan from remote sensing data , 2011 .

[16]  A. M. J. Meijerink,et al.  Remote sensing applications to hydrology: groundwater , 1996 .

[17]  T. Kozlowski Flooding and Plant Growth , 1985 .

[18]  Nazzareno Pierdicca,et al.  Uplift and subsidence due to the 26 December 2004 Indonesian earthquake detected by SAR data , 2008 .

[19]  S. Hook,et al.  NASA’s Hyperspectral Infrared Imager (HyspIRI) , 2013 .

[20]  Marco Chini Earthquake Damage Mapping Techniques Using SAR and Optical Remote Sensing Satellite Data , 2009 .

[21]  C. Justice,et al.  Development of vegetation and soil indices for MODIS-EOS , 1994 .

[22]  Richard G. Allen,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model , 2007 .

[23]  Susan L Ustin,et al.  Remote sensing of plant functional types. , 2010, The New phytologist.

[24]  R. Goldstein,et al.  Mapping small elevation changes over large areas: Differential radar interferometry , 1989 .

[25]  S. Leroy,et al.  Impact of earthquakes on agriculture during the Roman–Byzantine period from pollen records of the Dead Sea laminated sediment , 2010, Quaternary Research.

[26]  Wim G.M. Bastiaanssen,et al.  Remote sensing for irrigated agriculture: examples from research and possible applications , 2000 .

[27]  Sergey V. Samsonov,et al.  Remote sensing and the disaster management cycle , 2009 .

[28]  P. Gamba,et al.  GIs and Image Understanding for Near-Real- Time Earthquake Damage Assessment , 1998 .

[29]  David Riaño,et al.  Detection of diurnal variation in orchard canopy water content using MODIS/ASTER airborne simulator (MASTER) data , 2013 .

[30]  I. Akhtar,et al.  Effect of Waterlogging and Drought Stress in Plants , 2013 .

[31]  D. Huntley On the detection of shallow aquifers using thermal infrared imagery , 1978 .

[32]  Pamela L. Nagler,et al.  Evapotranspiration on western U.S. rivers estimated using the Enhanced Vegetation Index from MODIS and data from eddy covariance and Bowen ratio flux towers , 2005 .

[33]  Gerald W. Bawden,et al.  Tectonic contraction across Los Angeles after removal of groundwater pumping effects , 2001, Nature.

[34]  F. R. Troeh,et al.  Soil and Water Conservation for Productivity and Environmental Protection , 1981 .

[35]  Susan L. Cutter,et al.  Levee Failures and Social Vulnerability in the Sacramento-San Joaquin Delta Area, California , 2008 .

[36]  Cristina Aguilar,et al.  NDVI as an indicator for changes in water availability to woody vegetation , 2012 .

[37]  V. Chowdary,et al.  Groundwater management and development by integrated remote sensing and geographic information systems: prospects and constraints , 2007 .

[38]  Sergey V. Samsonov,et al.  A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters , 2009 .

[39]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[40]  D. Roberts,et al.  Special issue on the Hyperspectral Infrared Imager (HyspIRI): Emerging science in terrestrial and aquatic ecology, radiation balance and hazards , 2015 .

[41]  Theodore T. Kozlowski,et al.  Responses of woody plants to flooding and salinity , 1997 .

[42]  Irina Emelyanova,et al.  Mapping groundwater‐dependent ecosystems using remote sensing measures of vegetation and moisture dynamics , 2014 .

[43]  G. Blewitt,et al.  Uplift and seismicity driven by groundwater depletion in central California , 2014, Nature.

[44]  David Riaño,et al.  Water content estimation from hyperspectral images and MODIS indexes in Southeastern Arizona , 2008 .

[45]  D. Montgomery,et al.  Streamflow and Water Well Responses to Earthquakes , 2003, Science.

[46]  Shailesh Nayak,et al.  Mapping the liquefaction induced soil moisture changes using remote sensing technique: an attempt to map the earthquake induced liquefaction around Bhuj, Gujarat, India , 2006 .

[47]  G. A. Blackburn,et al.  Hyperspectral remote sensing of plant pigments. , 2006, Journal of experimental botany.

[48]  Karen E. Joyce,et al.  Incorporating Remote Sensing into Emergency Management , 2010 .

[49]  Matthew W Becker,et al.  Potential for Satellite Remote Sensing of Ground Water , 2006, Ground water.

[50]  A. T. Jeyaseelan,et al.  DROUGHTS & FLOODS ASSESSMENT AND MONITORING USING REMOTE SENSING AND GIS , 2005 .

[51]  Chen-Wuing Liu,et al.  Changes in hydrogeological properties of the River Choushui alluvial fan aquifer due to the 1999 Chi-Chi earthquake, Taiwan , 2008 .

[52]  D. Riaño,et al.  Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating , 2004 .

[53]  Chi‐yuen Wang,et al.  Temporal change in groundwater level following the 1999 (Mw = 7.5) Chi‐Chi earthquake, Taiwan , 2004 .

[54]  P. Smart,et al.  Applications of remote sensing to groundwater hydrology , 1990 .

[55]  James L. Wright,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Applications , 2007 .

[56]  R. Suddeth Levee Decisions and Sustainability for the Sacramento-San Joaquin Delta , 2010 .

[57]  Tao Cheng,et al.  Spectroscopic determination of leaf water content using continuous wavelet analysis , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[58]  E. Noordman,et al.  SEBAL model with remotely sensed data to improve water-resources management under actual field conditions , 2005 .

[59]  Matthew Rodell,et al.  The potential for satellite-based monitoring of groundwater storage changes using GRACE: the High Plains aquifer, Central US , 2002 .

[60]  R. Green,et al.  An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities , 2015 .

[61]  J. Vu,et al.  Influence of paclobutrazol in the soil on growth, nutrient elements in the leaves, and flood/freeze tolerance of citrus rootstock seedlings , 1995, Journal of Plant Growth Regulation.

[62]  Ayse Irmak,et al.  Satellite‐based ET estimation in agriculture using SEBAL and METRIC , 2011 .

[63]  S. Ustin,et al.  LEAF OPTICAL PROPERTIES: A STATE OF THE ART , 2000 .

[64]  Michael Manga,et al.  Earthquakes and Water , 2010 .

[65]  Jay R. Lund,et al.  Levee Decisions and Sustainability for the , 2010 .

[66]  A. O'Geen,et al.  Soil suitability index identifies potential areas for groundwater banking on agriculturallands , 2015 .

[67]  A. Huete,et al.  Vegetation Index Methods for Estimating Evapotranspiration by Remote Sensing , 2010 .

[68]  W. Ellsworth Injection-Induced Earthquakes , 2013, Science.