Estimating high resolution evapotranspiration from disaggregated thermal images

Remote Sensing of Environment

Volumn 187, Issue , 2016, Pages 423-433

  Bisquert, M ^a   Sanchez J M ^a   Lopez Urrea R ^b   Caselles, V ^c  

^a UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

^b Instituto Tecnico Agronomico Provincial de Albacete   (Spain)

^c UNIVERSITY OF VALENCIA   (Spain)

Author keywords

Disaggregation; Evapotranspiration; LST; MODIS; Sentinel 2; Spot 5 Take 5

Indexed keywords

ATMOSPHERIC TEMPERATURE; ENERGY BALANCE; ERRORS; ESTIMATION; EVAPOTRANSPIRATION; IMAGE RESOLUTION; INTERFACIAL ENERGY; RADIOMETERS; REMOTE SENSING; SURFACE PROPERTIES;

DISAGGREGATION; DISAGGREGATION TECHNIQUES; LAND SURFACE TEMPERATURE; MEAN ABSOLUTE DEVIATIONS; MODIS; SENTINEL 2; SPOT 5 TAKE 5; SURFACE HETEROGENEITIES;

HEAT FLUX;

ENERGY BALANCE; ESTIMATION METHOD; EVAPOTRANSPIRATION; HETEROGENEITY; LANDSAT THEMATIC MAPPER; MODIS; REMOTE SENSING; SATELLITE IMAGERY; SENSIBLE HEAT FLUX; SENSOR; SENTINEL; SPATIAL RESOLUTION; SPOT; SURFACE ENERGY; SURFACE TEMPERATURE; TEMPERATURE EFFECT;

EID: 84994593703     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2016.10.049     Document Type: Article

References (57)

1. Agam, N., Kustas, W.P., Anderson, M.C., et al. A vegetation index based technique for spatial sharpening of thermal imagery. Remote Sens. Environ. 107 (2007), 545–558, 10.1016/j.rse.2006.10.006.
2. Allen, R., Tasumi, M., Trezza, R., Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—model. J. Irrig. Drain. Eng. 133 (2007), 380–394, 10.1061/(ASCE)0733-9437(2007)133:4(380).
3. Amorós-López, J., Gómez-Chova, L., Alonso, L., et al. Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring. Int. J. Appl. Earth Obs. Geoinf. 23 (2013), 132–141, 10.1016/j.jag.2012.12.004.
4. Anderson, M.C., Norman, J.M., Diak, G.R., et al. A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens. Environ. 60 (1997), 195–216, 10.1016/S0034-4257(96)00215-5.
5. Anderson, M.C., Norman, J.M., Mecikalski, J.R., et al. A multiscale remote sensing model for disaggregating regional fluxes to micrometeorological scales. J. Hydrometeor. 5 (2004), 343–363, 10.1175/1525-7541(2004)005<0343:AMRSMF>2.0.CO;2.
6. Anderson, M.C., Norman, J.M., Mecikalski, J.R., et al. A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. J. Geophys. Res., 2007, 10.1029/2006JD007507.
7. Barsi, J.A., Barker, J.L., Schott, J.R., An atmospheric correction parameter calculator for a single thermal band earth-sensing instrument. Geoscience and Remote Sensing Symposium, 2003. IGARSS'03. Proceedings. 2003 IEEE International. IEEE, 2003, 3014–3016.
8. Barsi JA, Schott JR, Palluconi FD, Hook SJ (2005) Validation of a Web-based Atmospheric Correction Tool for Single Thermal Band Instruments. In: Butler JJ (ed). (p 58820E–1–58820E–7).
9. Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A., Holtslag, A.A.M., A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J. Hydrol. 212–213 (1998), 198–212, 10.1016/S0022-1694(98)00253-4.
10. Berger, M., Rast, M., Wursteisen, P., et al. The DAISEX campaigns in support of a future land-surface-processes mission. ESA Bull. 105 (2001), 101–111.
11. Bindhu, V.M., Narasimhan, B., Sudheer, K.P., Development and verification of a non-linear disaggregation method (NL-DisTrad) to downscale MODIS land surface temperature to the spatial scale of Landsat thermal data to estimate evapotranspiration. Remote Sens. Environ. 135 (2013), 118–129, 10.1016/j.rse.2013.03.023.
12. Bisquert, M., Sánchez, J.M., Caselles, V., Evaluation of disaggregation methods for downscaling Modis land surface temperature to Landsat spatial resolution in Barrax test site. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 9 (2016), 1430–1438.
13. Cammalleri, C., Anderson, M.C., Gao, F., et al. A data fusion approach for mapping daily evapotranspiration at field scale: DATA fusion approach for mapping daily ET. Water Resour. Res. 49 (2013), 4672–4686, 10.1002/wrcr.20349.
14. Cammalleri, C., Anderson, M.C., Gao, F., et al. Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion. Agric. For. Meteorol. 186 (2014), 1–11, 10.1016/j.agrformet.2013.11.001.
15. Choudhury, B.J., Idso, S.B., Reginato, R.J., Analysis of an empirical model for soil heat flux under a growing wheat crop for estimating evaporation by an infrared-temperature based energy balance equation. Agric. For. Meteorol. 39 (1987), 283–297, 10.1016/0168-1923(87)90021-9.
16. Coll, C., Caselles, V., Galve, J.M., et al. Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data. Remote Sens. Environ. 97 (2005), 288–300, 10.1016/j.rse.2005.05.007.
17. Coll, C., Wan, Z., Galve, J.M., Temperature-based and radiance-based validations of the V5 MODIS land surface temperature product. J. Geophys. Res., 2009, 10.1029/2009JD012038.
18. Corbari, C., Bissolati, M., Mancini, M., Multi-scales and multi-satellites estimates of evapotranspiration with a residual energy balance model in the Muzza agricultural district in Northern Italy. J. Hydrol. 524 (2015), 243–254, 10.1016/j.jhydrol.2015.02.041.
19. Doña, C., Chang, N.-B., Caselles, V., et al. Integrated satellite data fusion and mining for monitoring lake water quality status of the Albufera de Valencia in Spain. J. Environ. Manag. 151 (2015), 416–426, 10.1016/j.jenvman.2014.12.003.
20. Dorigo, W.A., Zurita-Milla, R., de Wit, A.J.W., et al. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. Int. J. Appl. Earth Obs. Geoinf. 9 (2007), 165–193, 10.1016/j.jag.2006.05.003.
21. French, A.N., Hunsaker, D.J., Thorp, K.R., Remote sensing of evapotranspiration over cotton using the TSEB and METRIC energy balance models. Remote Sens. Environ. 158 (2015), 281–294, 10.1016/j.rse.2014.11.003.
22. Gao, F., Masek, J., Schwaller, M., Hall, F., On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens. 44 (2006), 2207–2218, 10.1109/TGRS.2006.872081.
23. Gao, F., Kustas, W., Anderson, M., A data mining approach for sharpening thermal satellite imagery over land. Remote Sens. 4 (2012), 3287–3319, 10.3390/rs4113287.
24. Hagolle, O., Huc, M., Pascual, D., Dedieu, G., A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENS and Sentinel-2 images. Remote Sens. 7 (2015), 2668–2691, 10.3390/rs70302668.
25. Hagolle, O., Sylvander, S., Huc, M., et al. Spot-4 (Take 5): simulation of Sentinel-2 time series on 45 large sites. Remote Sens. 7 (2015), 12242–12264, 10.3390/rs70912242.
26. Inglada, J., Arias, M., Tardy, B., et al. Assessment of an operational system for crop type map production using high temporal and spatial resolution satellite optical imagery. Remote Sens. 7 (2015), 12356–12379, 10.3390/rs70912356.
27. Katul, G.G., Oren, R., Manzoni, S., et al. Evapotranspiration: A process driving mass transport and energy exchange in the soil-plant-atmosphere-climate system: evapotranspiration and climate. Rev. Geophys., 2012, 10.1029/2011RG000366.
28. Kustas, W.P., Norman, J.M., Anderson, M.C., French, A.N., Estimating subpixel surface temperatures and energy fluxes from the vegetation index–radiometric temperature relationship. Remote Sens. Environ. 85 (2003), 429–440, 10.1016/S0034-4257(03)00036-1.
29. Latorre, C., Camacho, F., de la Cruz, F., et al. Seasonal monitoring of FAPAR over the Barrax cropland site in Spain, in support of the validation of PROBA-V products at 333 m. Recent Advances in Quantitative Remote Sensing. Torrent (Spain), 2014, 431–435.
30. López-Urrea, R., Martín de Santa Olalla, F., Fabeiro, C., Moratalla, A., Testing evapotranspiration equations using lysimeter observations in a semiarid climate. Agric. Water Manag. 85 (2006), 15–26, 10.1016/j.agwat.2006.03.014.
31. López-Urrea, R., Montoro, A., González-Piqueras, J., et al. Water use of spring wheat to raise water productivity. Agric. Water Manag. 96 (2009), 1305–1310, 10.1016/j.agwat.2009.04.015.
32. López-Urrea, R., Montoro, A., Mañas, F., et al. Evapotranspiration and crop coefficients from lysimeter measurements of mature “Tempranillo” wine grapes. Agric. Water Manag. 112 (2012), 13–20, 10.1016/j.agwat.2012.05.009.
33. Moran, M.S., New imaging sensor technologies suitable for agricultural management. Asp. Appl. Biol. 60 (2000), 1–10.
34. Moran, M.S., Inoue, Y., Barnes, E.M., Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens. Environ. 61 (1997), 319–346, 10.1016/S0034-4257(97)00045-X.
35. Moreno, J.F., Alonso, L., Fernàndez, G., et al. The SPECTRA Barrax campaign (SPARC): overview and first results from CHRIS data. Proceedings of the Second Workshop CHRIS/Proba, 2004, European Space Agency, (Special Publication) ESA SP, Frascati (Italy), 30–39.
36. Mu, Q., Heinsch, F.A., Zhao, M., Running, S.W., Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ. 111 (2007), 519–536, 10.1016/j.rse.2007.04.015.
37. Mu, Q., Zhao, M., Running, S.W., Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115 (2011), 1781–1800, 10.1016/j.rse.2011.02.019.
38. Niclos, R., Rivas, R., Garcia-Santos, V., et al. SMOS level-2 soil moisture product evaluation in rain-fed croplands of the Pampean Region of Argentina. IEEE Trans. Geosci. Remote Sens. 54 (2016), 499–512, 10.1109/TGRS.2015.2460332.
39. Norman, J.M., Kustas, W., Humes, K., A two-source approach for estimating soil and vegetation energy fluxes from observations of directional radiometric surface temperature. Agric. For. Meteorol. 77 (1995), 263–293.
40. Roy, D.P., Ju, J., Lewis, P., et al. Multi-temporal MODIS–Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sens. Environ. 112 (2008), 3112–3130, 10.1016/j.rse.2008.03.009.
41. Rubio, E., Caselles, V., Coll, C., et al. Thermal–infrared emissivities of natural surfaces: improvements on the experimental set-up and new measurements. Int. J. Remote Sens. 24 (2003), 5379–5390, 10.1080/0143116031000102412.
42. Sánchez, J.M., Caselles, V., Niclòs, R., et al. Evaluation of the B-method for determining actual evapotranspiration in a boreal forest from MODIS data. Int. J. Remote Sens. 28 (2007), 1231–1250.
43. Sánchez, J.M., Kustas, W.P., Caselles, V., Anderson, M.C., Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations. Remote Sens. Environ. 112 (2008), 1130–1143, 10.1016/j.rse.2007.07.018.
44. Sánchez, J.M., Scavone, G., Caselles, V., et al. Monitoring daily evapotranspiration at a regional scale from Landsat-TM and ETM + data: Application to the Basilicata region. J. Hydrol. 351 (2008), 58–70, 10.1016/j.jhydrol.2007.11.041.
45. Sánchez, J.M., López-Urrea, R., Rubio, E., Caselles, V., Determining water use of sorghum from two-source energy balance and radiometric temperatures. Hydrol. Earth Syst. Sci. 15 (2011), 3061–3070, 10.5194/hess-15-3061-2011.
46. Sánchez, J.M., López-Urrea, R., Rubio, E., et al. Assessing crop coefficients of sunflower and canola using two-source energy balance and thermal radiometry. Agric. Water Manag. 137 (2014), 23–29, 10.1016/j.agwat.2014.02.002.
47. Schmidt, M., Lucas, R., Bunting, P., et al. Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia. Remote Sens. Environ. 158 (2015), 156–168, 10.1016/j.rse.2014.11.015.
48. Schneider, P., Ghent, D., Corlett, G., et al. Land Surface Temperature Validation Protocol. 2012.
49. Seguin, B., Itier, B., Using midday surface temperature to estimate daily evaporation from satellite thermal IR data. Int. J. Remote Sens. 4 (1983), 371–383, 10.1080/01431168308948554.
50. Semmens, K.A., Anderson, M.C., Kustas, W.P., et al. Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sens. Environ., 2015, 10.1016/j.rse.2015.10.025.
51. Senf, C., Leitão, P.J., Pflugmacher, D., et al. Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery. Remote Sens. Environ. 156 (2015), 527–536, 10.1016/j.rse.2014.10.018.
52. Sobrino, J.A., Gómez, M., Jiménez-Muñoz, J.C., et al. A simple algorithm to estimate evapotranspiration from DAIS data: Application to the DAISEX campaigns. J. Hydrol. 315 (2005), 117–125, 10.1016/j.jhydrol.2005.03.027.
53. Sobrino, J.A., Jiménez-Muñoz, J.C., Sòria, G., et al. Thermal remote sensing in the framework of the SEN2FLEX project: field measurements, airborne data and applications. Int. J. Remote Sens. 29 (2008), 4961–4991, 10.1080/01431160802036516.
54. Valor, E., Caselles, V., Mapping land surface emissivity from NDVI: application to European, African, and South American areas. Remote Sens. Environ. 57 (1996), 167–184, 10.1016/0034-4257(96)00039-9.
55. Vancutsem, C., Ceccato, P., Dinku, T., Connor, S.J., Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sens. Environ. 114 (2010), 449–465, 10.1016/j.rse.2009.10.002.
56. Wang, K., Dickinson, R.E., A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability: GLOBAL TERRESTRIAL EVAPOTRANSPIRATION. Rev. Geophys., 2012, 10.1029/2011RG000373.
57. Zarco-Tejada, P.J., Ustin, S.L., Whiting, M.L., Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agron. J., 97, 2005, 641, 10.2134/agronj2003.0257.