Impact of day/night time land surface temperature in soil moisture disaggregation algorithms

References

• Anderson M.C., Norman J.M., Diak G.R., Kustas W.P., Mecikalski J.R. (1997) -A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sensing of Environment, 60 (2): 195–216. doi: http://dx.doi.org/10.1016/S0034-4257(96)00215-5.
   Web of Science ®Google Scholar
• Anderson M.C., Norman J.M., Mecikalski J.R., Otkin J.A., Kustas W.P. (2007)—A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation. Journal of Geophysical Research, 112 (D10): 1–7. doi: http://dx.doi.org/10.1029/2006JD007506.
   Web of Science ®Google Scholar
• Aminou D.M.A., Jaquet B., Pasternak F. (1997)—Characteristics of the Meteosat Second Generation Radiometer/Imager: SEVIRI. Proceedings of Society of Photo-optical Instrumentation Engineers, 3221: 19–31. doi: http://dx.doi.org/10.1117/12.298084.
   Google Scholar
• BEC Team (2015)—SMOS-BEC Ocean and Land Products Description. Technical Report, Barcelona, Spain Available online at: http://cp34-bec.cmima.csic.es/doc/BEC-SMOS-0001-PD.pdf.
   Google Scholar
• Bruscantini C.A., Grings F.M., Barber M., Franco M., Entekhabi D., Karszenbaum H. (2015)—A novel downscaling methodology for intermediate resolution radiometer data for SMAP. IEEE International Geoscience and Remote Sensing Symposium, 1972–1975. doi: http://dx.doi.org/10.1109/IGARSS.2015.7326183.
   Google Scholar
• Carlson T.N., Gillies R.R., Perry E.M. (1994)—A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sensing Reviews, 9 (1–2): 161–173. doi: http://dx.doi.org/10.1080/02757259409532220.
   Google Scholar
• Carlson T.N. (2007)—An Overview of the “Triangle Method” for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery. Sensors, 7 (8): 16121629. doi: http://dx.doi.org/10.3390/s7081612.
   Google Scholar
• Chan S.K., Bindlish R., O'Neill P.E., Njoku E., Jackson T.J., Colliander A., Chen F., Burgin M., Dunbar S., Piepmeier J., Yueh S., Entekhabi D., Cosh M.H., Caldwell T., Walker J., Wu X., Berg A., Rowlandson T., Pacheco A., McNairn H., Thibeault M., Martínez- Fernández J., González-Zamora A., Seyfried M., Bosch D., Starks P., Goodrich D. Prueger J., Palecki M., Small E.E., Zreda M., Calvet J.C., Crow W.T., Kerr Y.H. (2016)—Assessment of the SMAP Passive Soil Moisture Product. IEEE Transactions on Geoscience and Remote Sensing, 54 (8): 4994–5007. doi: http://dx.doi.org/10.1109/TGRS.2016.2561938.
   Web of Science ®Google Scholar
• Das N.N., Entekhabi D., Njoku E. (2011)—Algorithm for merging SMAP radiometer and radar data for high resolution soil moisture retrieval. IEEE Transactions on Geoscience and Remote Sensing, 49 (5): 1504–1512. doi: http://dx.doi.org/10.1109/TGRS.2010.2089526.
   Google Scholar
• Das N.N., Entekhabi D., Njoku E.G., Shi J.J.C., Johnson J.T., Colliander A. (2014)—Tests of the SMAP Combined Radar and Radiometer Algorithm Using Airborne Field Campaign Observations and Simulated Data. IEEE Transactions on Geoscience and Remote Sensing, 52 (4): 2018–2018. doi: http://dx.doi.org/10.1109/TGRS.2013.2257605.
   Web of Science ®Google Scholar
• Entekhabi D., Njoku E.G., O'Neill P.E., Kellogg K.H., Crow W.T., Edelstein W.N., Entin J.K., Goodman S.D., Jackson T.J., Johnson J., Kimball J., Piepmeier J.R., Koster R.D., Martin N., McDonald K.C., Moghaddam M., Moran S., Reichle R., Shi J.-C., Spencer M.W., Thurman S.W., Leung T., Van Zyl J. (2010a)—The Soil Moisture Active Passive (SMAP) mission. Proceedings of the IEEE, 98 (5): 704–716. doi: http://dx.doi.org/10.1109/JPROC.2010.2043918.
   Web of Science ®Google Scholar
• Entekhabi D., Reichle R.H., Koster R.D., Crow W.T. (2010b)—Performance Metrics for Soil Moisture Retrievals and Application Requirements. Journal of Hydrometeorology, 11 (3): 832–840. doi: http://dx.doi.org/10.1175/2010JHM1223.1.
   Web of Science ®Google Scholar
• Entekhabi D., Yueh S., O'Neill P., Kellogg K., Allen A., Bindlish R., Brown M., Chan S., Colliander A., Crow W.T., Das N., De Lannoy G., Dunbar R.S., Edelstein W.N., Entin J.K., Escobar V., Goodman S.D., Jackson T.J., Jai B., Johnson J., Kim E., Kim S., Kimball J., Koster R.D., Leon A., McDonald K.C., Moghaddam M., Mohamed P., Moran S., Njoku E.G., Piepmeier J.R., Reichle R., Rogez F., Shi J.C., Spencer M.W., Thurman S.W., Tsang L., Van Zyl J., Weiss B., West R. (2014)—SMAP Handbook. JPL Publication, 400–1567, Jet Propulsion Laboratory, Pasadena, California, USA, http://smap.jpl.nasa.gov/system/internal_resources/details/original/178_SMAP_Handbook_FINAL_1_JULY_2014_Web.pdf.
   Google Scholar
• Fang B., Lakshmi V. (2014)—Soil moisture at watershed scale: remote sensing techniques. Journal of Hydrology, 516: 258–272. doi: http://dx.doi.org/10.1016/j.jhydrol.2013.12.008.
   Web of Science ®Google Scholar
• Font J., Camps A., Borges A., Martin-Neira M., Boutin J., Reul N., Kerr Y.H., Hahne A., Mecklenburg S. (2010)—SMOS: The Challenging Sea Surface Salinity Measurement From Space. Proceedings of the IEEE, 98 (5): 649–665. doi: http://dx.doi.org/10.1109/JPROC.2009.2033096.
   Google Scholar
• GCOS (2010)—Implementation Plan for the Global Observing System for Climate in support of the UNFCCC, GCOS-138 (GOOS-184, GTOS-76, WMO-TD/No. 1523). Available online at: https://www.wmo.int/pages/prog/gcos/Publications/gcos-138.pdf.
   Google Scholar
• Gillies R.R., Carlson T.N. (1995)—Thermal Remote Sensing of Surface Soil Water Content with Partial Vegetation Cover for Incorporation into Climate Models. Journal of Applied Meteorology, 34 (4): 745–756. doi: http://dx.doi.org/10.1175/1520-0450(1995)034<0745:TRSOSS>2.0.CO;2.
   Google Scholar
• González-Zamora A., Sánchez N., Martínez-Fernández J., Gumuzzio A., Piles M., Olmedo E. (2015)—Long-term SMOS soil moisture products: A comprehensive evaluation across scales and methods in the Duero Basin (Spain). Physics and Chemistry of the Earth, Parts A/B/C, 83–84: 123–136. doi: http://dx.doi.org/10.1016/j.pce.2015.05.009.
   Google Scholar
• Guo P., Shi J., Zhao T. (2013)—A downscaling algorithm for combining radar and radiometer observations for SMAP soil moisture retrieval. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 731–734. doi: http://dx.doi.org/10.1109/IGARSS.2013.6721261.
   Google Scholar
• IPCC (2014)—Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Geneva, Switzerland.
   Google Scholar
• Kerr Y.H., Waldteufel P., Wigneron J.-P., Delwart S., Cabot F., Boutin J., Escorihuela M.-J., Font J., Reul N., Gruhier C., Juglea S.E., Drinkwater M.R., Hahne A., Martin-Neira M., Mecklenburg S. (2010)—The SMOS Mission: New Tool for Monitoring Key Elements of the Global Water Cycle. Proceedings of the IEEE, 98 (5): 666–687. doi: http://dx.doi.org/10.1109/JPROC.2010.2043032.
   Web of Science ®Google Scholar
• Kerr Y.H., Al-Yaari A., Rodriguez-Fernandez N., Parrens M., Molero B., Leroux D., Bircher S., Mahmoodi A., Mialon A., Richaume P., Delwart S., Al Bitar A., Pellarin T., Bindlish R., Jackson T.J., Rüdiger C., Waldteufel P., Mecklenburg S., Wigneron J.-P. (2016)—Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation. Remote Sensing of Environment, 180: 40–63. doi: http://dx.doi.org/10.1016/j.rse.2016.02.042.
   Web of Science ®Google Scholar
• Leone D. (2015)—NASA Focused on Sentinel as Replacement for SMAP Radar. Space News. Available online at: http://spacenews.com/nasa-focused-on-sentinel-as-replacement-for-smap-radar.
   Google Scholar
• Martínez-Fernández J., Ceballos A. (2003)—Temporal Stability of Soil Moisture in a Large- Field Experiment in Spain. Soil Science Society of America Journal, 67 (6): 1647–1656. doi: http://dx.doi.org/10.2136/sssaj2003.1647.
   Google Scholar
• Masson V., Champeau J.-L., Chauvin F., Meriguet C., Lacaze R. (2003)—A global data base of land surface parameters at 1 km resolution in meteorological and climate models. Journal of Climate, 16 (9): 1261–1282. doi: http://dx.doi.org/10.1175/1520-0442-16.9.1261.
   Web of Science ®Google Scholar
• McMullan K.D., Brown M.A., Martin-Neira M., Rits W., Ekholm S., Marti J., Lemanczyk J. (2008)—SMOS: The Payload. IEEE Transactions in Geoscience and Remote Sensing, 46 (3): 594–605. doi: http://dx.doi.org/10.1109/TGRS.2007.914809.
   Google Scholar
• Merlin O., Rudiger C., Al Bitar A., Richaume P., Walker J.P., Kerr Y.H. (2012)—Disaggregation of SMOS Soil Moisture in Southeastern Australia. IEEE Transactions on Geoscience and Remote Sensing, 50 (5): 1556–1571. doi: http://dx.doi.org/10.1109/TGRS.2011.2175000.
   Google Scholar
• Moran M.S., Clarke T.R., Inoue Y., Vidal A. (1994)—Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sensing of Environment, 49 (3): 246–263. doi: http://dx.doi.org/10.1016/0034-4257(94)90020-5.
   Web of Science ®Google Scholar
• Ochsner T.E., Cosh M.H., Cuenca R.H., Dorigo W.A., Draper C.S., Hagimoto Y., Kerr Y.H., Njoku E.G., Small E.E., Zreda M. (2013)—State of the art in large-scale soil moisture monitoring. Soil Science Society of America Journal, 77 (6): 1888–1919. doi: http://dx.doi.org/10.2136/sssaj2013.03.0093.
   Google Scholar
• Pablos M., Piles M., Sánchez N., Gonzalez-Gambau V., Vall-llossera M., Camps A., Martínez-Fernandez J. (2014)—A sensitivity study of land surface temperature to soil moisture using in-situ and spaceborne observations. IEEE International Geoscience on Remote Sensing Symphosium, pp. 3267–3269. doi: http://dx.doi.org/10.1109/IGARSS.2014.6947176.
   Google Scholar
• Pablos M., Martínez-Fernández J., Piles M., Sánchez N., Vall-llossera M., Camps A. (2016)—Multi-temporal evaluation of soil moisture and land surface temperature dynamics using in situ and satellite observations. Remote Sensing, 8 (7): 587. doi: http://dx.doi.org/10.3390/rs8070587.
   Google Scholar
• Petropoulos G.P., Carlson T.N., Wooster M.J., Islam S. (2009)—A review of Ts/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil moisture. Progress in Physical Geography, 33 (2): 224–250. doi: http://dx.doi.org/10.1177/0309133309338997.
   Web of Science ®Google Scholar
• Piles M., Entekhabi D., Camps A. (2009)—A Change Detection Algorithm for Retrieving High-Resolution Soil Moisture From SMAP Radar and Radiometer Observations. IEEE Transactions on Geoscience and Remote Sensing, 47 (12): 4125–4131. doi: http://dx.doi.org/10.1109/TGRS.2009.2022088.
   Web of Science ®Google Scholar
• Piles M., Camps A., Vall-Llossera M., Corbella I., Panciera R., Rudiger C., Kerr Y.H., Walker J. (2011)—Downscaling SMOS-derived soil moisture using MODIS visible/infrared data. IEEE Transactions on Geoscience and Remote Sensing, 49 (9): 3156–3166. doi: http://dx.doi.org/10.1109/TGRS.2011.2120615.
   Web of Science ®Google Scholar
• Piles M., Vall-llossera M., Laguna L., Camps A. (2012)—A downscaling approach to combine SMOS multi-angular and full-polarimetric observations with MODIS VIS/IR data into high resolution soil moisture maps. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1247–1250. doi: http://dx.doi.org/10.1109/IGARSS.2012.6351316.
   Google Scholar
• Piles M., Sánchez N., Vall-llossera M., Camps A., Martínez-Fernandez J., Martínez J., González-Gambau V. (2014)—A Downscaling Approach for SMOS Land Observations: Evaluation of High-Resolution Soil Moisture Maps Over the Iberian Peninsula. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 (9): 845–3857. doi: http://dx.doi.org/10.1109/JSTARS.2014.2325398.
   Google Scholar
• Piles M., Sánchez N. (2016)—Spatial downscaling of passive microwave data with visible-to-infrared information for high-resolution soil moisture mapping. Chapter of: Satellite Soil Moisture Retrieval: Techniques and Applications, Srivastava P.K, Petropoulos G., Kerr Y.H. (Eds.), Elsevier.
   Google Scholar
• Piles M., Petropoulos G.P., Sánchez N., González-Zamora A., Ireland G. (2016)—Towards improved spatio-temporal resolution soil moisture retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations. Remote Sensing of Environment, 180: 403–417. doi: http://dx.doi.org/10.1016/j.rse.2016.02.048.
   Web of Science ®Google Scholar
• Price J.C. (1980)—The potential of remotely sensed thermal infrared data to infer surface soil moisture and evaporation. Water Resources Research, 16 (4): 787–795. doi: http://dx.doi.org/10.1029/WR016i004p00787.
   Web of Science ®Google Scholar
• Sánchez N., Martínez-Fernández J., Scaini A., Pérez-Gutiérrez C. (2012)—Validation of the SMOS L2 soil moisture data in the REMEDHUS network (Spain). IEEE Transactions on Geoscience and Remote Sensing, 50 (5): 1602–1611. doi: http://dx.doi.org/10.1109/TGRS.2012.2186971.
   Google Scholar
• Sánchez-Ruiz S., Piles M., Sánchez N., Martínez-Fernández J., Vall-llossera M., Camps A. (2014)—Combining SMOS with visible and near/shortwave/thermal infrared satellite data for high resolution soil moisture estimates. Journal of Hydrology, 49: 3156–3166. doi: http://dx.doi.org/10.1016/jjhydrol.2013.12.047.
   Google Scholar
• Sandholt I., Rasmussen K., Andersen J. (2002)—A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 79 (2–3): 213–224. doi: http://dx.doi.org/10.1016/S00344257(01)00274-7.
   Web of Science ®Google Scholar
• Seneviratne S.I., Corti T., Davin E.L., Hirschi M., Jaeger E.B., Lehner I., Orlowsky B., Teuling A.J. (2010)—Investigating soil moisture-climate interactions in a changing climate: A review. Earth-Science Reviews, 99: 125–161.
   Web of Science ®Google Scholar
• Song C., Jia L. (2013)—An improved method for downscaling soil moisture retrieved by SMOS with MODIS LST/NDVI. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2696–2699. doi: http://dx.doi.org/10.1109/IGARSS.2013.6723379.
   Google Scholar
• Stisen S., Sandholt I., Norgaard A., Fensholt R., Jensen R.K. (2008)—Combining the triangle method with thermal inertia to estimate regional evapotranspiration—Applied to MSG-SEVIRI data in the Senegal River basin. Remote Sensing of Environment, 112 (3): 1242–1255. doi: http://dx.doi.org/10.1016/j.rse.2007.08.013.
   Web of Science ®Google Scholar
• Wan Z., Snyder W. (1999)—MODIS land-surface temperature algorithm theoretical basis document version 3.3. Institute for Computational Earth System Science, University of California, USA.
   Google Scholar