Spatial Downscaling of Passive Microwave Data With Visible-to-Infrared Information for High-Resolution Soil Moisture Mapping

Earth observation satellites working on the range of the microwave frequencies allow deriving quantitative estimates of surface soil moisture (SM). The first two space missions ever launched to measure the Earth’s surface SM had L-band microwave sensors onboard: the ESA’s Soil Moisture and Ocean Salinity and NASA’s Soil Moisture Active Passive. Also, microwave satellites operating at C- and X-bands can provide SM information under sparse vegetated regions. While the accuracy in microwave-derived SM products available from space keeps improving (4% vol.), their spatial resolution (40 km) limits their use in most hydrological and agricultural applications. Observations at visible and infrared frequencies, in turn, have a high spatial resolution (< 1 km) but provide only indirect information on soil water content. This chapter provides an overview of existing techniques that leverage the strengths and synergies of microwave data and visible-to-infrared information for high-resolution SM sensing. Experimental results using satellite and airborne observations are presented.

[1]  I. Sandholt,et al.  A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status , 2002 .

[2]  S. Ustin,et al.  Development of angle indexes for soil moisture estimation, dry matter detection and land-cover discrimination , 2007 .

[3]  T. Carlson,et al.  A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover , 1994 .

[4]  Adriano Camps,et al.  A sensitivity study of land surface temperature to soil moisture using in-situ and spaceborne observations , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[5]  Yann Kerr,et al.  SMOS: The Challenging Sea Surface Salinity Measurement From Space , 2010, Proceedings of the IEEE.

[6]  Adriano Camps,et al.  Airborne soil moisture determination using a data fusion approach at regional level , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[7]  Hongjie Xie,et al.  Estimating root zone soil moisture at distant sites using MODIS NDVI and EVI in a semi-arid region of southwestern USA , 2010, Ecol. Informatics.

[8]  Adriano Camps,et al.  Review of the CALIMAS Team Contributions to European Space Agency's Soil Moisture and Ocean Salinity Mission Calibration and Validation , 2012, Remote. Sens..

[9]  Eric F. Lambin,et al.  The surface temperature-vegetation index space for land cover and land-cover change analysis , 1996 .

[10]  Wenjiang Huang,et al.  A method of estimating soil moisture based on the linear decomposition of mixture pixels , 2013, Math. Comput. Model..

[11]  Adriano Camps,et al.  Hyperspectral Optical, Thermal, and Microwave L-Band Observations For Soil Moisture Retrieval at Very High Spatial Resolution , 2014 .

[12]  J. Adegoke,et al.  Relations between Soil Moisture and Satellite Vegetation Indices in the U.S. Corn Belt , 2002 .

[13]  W. Verstraeten,et al.  Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests , 2006 .

[14]  Philippe Richaume,et al.  Disaggregation of SMOS Soil Moisture in Southeastern Australia , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Alberto Alonso Arroyo,et al.  On the Synergy of Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation , 2015, Remote. Sens..

[16]  Raul Onrubia Ibáñez,et al.  The light airborne reflectometer for GNSS-R observations (LARGO) instrument: Initial results from airborne and Rover field campaigns , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[17]  Terri S. Hogue,et al.  Improving Spatial Soil Moisture Representation Through Integration of AMSR-E and MODIS Products , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[18]  A. Al Bitar,et al.  An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data , 2010 .

[19]  M. Ashton,et al.  Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications , 2004 .

[20]  M. Piles,et al.  Comparing surface-soil moisture from the SMOS mission and the ORCHIDEE land-surface model over the Iberian Peninsula , 2016 .

[21]  S. Sánchez-Ruiz,et al.  Combining SMOS with visible and near/shortwave/thermal infrared satellite data for high resolution soil moisture estimates , 2014 .

[22]  Yann Kerr,et al.  Downscaling SMOS-Derived Soil Moisture Using MODIS Visible/Infrared Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Adriano Camps,et al.  Design and First Results of an UAV-Borne L-Band Radiometer for Multiple Monitoring Purposes , 2010, Remote. Sens..

[24]  George P. Petropoulos,et al.  Towards improved spatio-temporal resolution soil moisture retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations , 2016 .

[25]  Ahmad Al Bitar,et al.  Retrieval and Multi-scale Validation of Soil Moisture from Multi-temporal SAR Data in a Semi-Arid Tropical Region , 2015, Remote. Sens..

[26]  Adriano Camps,et al.  A Downscaling Approach for SMOS Land Observations: Evaluation of High-Resolution Soil Moisture Maps Over the Iberian Peninsula , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  T. Carlson An Overview of the “Triangle Method” for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery , 2007, Sensors (Basel, Switzerland).

[28]  T. Jackson,et al.  Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands , 2005 .

[29]  Ad Stoffelen,et al.  Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target , 2014 .

[30]  S. Nicholson,et al.  The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana. I. NDVI response to rainfall , 1994 .

[31]  S. Ustin,et al.  Predicting water content using Gaussian model on soil spectra , 2004 .

[32]  S. Miller,et al.  Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach , 2003 .

[33]  Jiancheng Shi,et al.  The Soil Moisture Active Passive (SMAP) Mission , 2010, Proceedings of the IEEE.

[34]  B. Fang,et al.  Soil moisture at watershed scale: Remote sensing techniques , 2014 .

[35]  Yoann Malbéteau,et al.  Performance Metrics for Soil Moisture Downscaling Methods: Application to DISPATCH Data in Central Morocco , 2015, Remote. Sens..

[36]  Michael P. Finn,et al.  Remote Sensing of Soil Moisture Using Airborne Hyperspectral Data , 2011 .

[37]  Minha Choi,et al.  A microwave-optical/infrared disaggregation for improving spatial representation of soil moisture using AMSR-E and MODIS products , 2012 .

[38]  Adriano Camps,et al.  A downscaling approach to combine SMOS multi-angular and full-polarimetric observations with MODIS VIS/IR data into high resolution soil moisture maps , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[39]  Adriano Camps,et al.  Low soil moisture and high temperatures as indicators for forest fire occurrence and extent across the Iberian Peninsula , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[40]  M. S. Moran,et al.  Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index , 1994 .

[41]  Adriano Camps,et al.  SMOS and climate data applicability for analyzing forest decline and forest fires , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[42]  Sébastien Lambot,et al.  DIGISOIL: an integrated system of date collection technologies for mapping soil properties , 2008 .

[43]  Ahmad Al Bitar,et al.  Self-calibrated evaporation-based disaggregation of SMOS soil moisture: An evaluation study at 3 km and 100 m resolution in Catalunya, Spain , 2013 .

[44]  Yann Kerr,et al.  The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle , 2010, Proceedings of the IEEE.

[45]  Randall Rose,et al.  CYGNSS: NASA Earth Venture Tropical Cyclone Mission , 2014, SPIE Remote Sensing.

[46]  J. Sobrino,et al.  A method to estimate soil moisture from Airborne Hyperspectral Scanner (AHS) and ASTER data: Application to SEN2FLEX and SEN3EXP campaigns , 2012 .

[47]  G. Petropoulos,et al.  A review of Ts/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture , 2009 .

[48]  D. Lobell,et al.  Moisture effects on soil reflectance , 2002 .

[49]  Dara Entekhabi,et al.  An Algorithm for Merging SMAP Radiometer and Radar Data for High-Resolution Soil-Moisture Retrieval , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[50]  Elizabeth M. Middleton,et al.  Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion/EO-1 Data , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[51]  Ahmad Al Bitar,et al.  Copula-Based Downscaling of Coarse-Scale Soil Moisture Observations With Implicit Bias Correction , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[52]  Hongjie Xie,et al.  Different responses of MODIS-derived NDVI to root-zone soil moisture in semi-arid and humid regions , 2007 .

[53]  Yann Kerr,et al.  A disaggregation scheme for soil moisture based on topography and soil depth , 2003 .

[54]  R. Fensholt,et al.  Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment , 2003 .

[55]  Yann Kerr,et al.  A combined modeling and multispectral/multiresolution remote sensing approach for disaggregation of surface soil moisture: application to SMOS configuration , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[56]  Adriano Camps,et al.  On the synergy of SMOS and Terra/Aqua MODIS: High resolution soil moisture maps in near real-time , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[57]  N. Lu,et al.  Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia , 2013 .