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The paper analyzes the advantages and disadvantages of the most commonly used groups of models of soil moisture and plant water stress based on satellite thermal imagery. We present a simple proof of linking NDTI and CWSI indicators with plants water stress and quantitative justification for the shape of the points cloud on the chart Ts-NDVI.
[ 1 ] Allen R.G., Pereira L.S., Raes D., Smith M., 1998. Crop evapotranspiration-Guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56. FAO, Rome, 300, 9.
[ 2 ] Allen R.G., Tasumi M., Morse A., Trezza R., Wright J.L., Bastiaanssen W., Kramber W., LoriteI., Robison C.W., 2007. Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Applications. Journal of Irrigation and Drainage Engineering, 133, 4: 395–406. http://dx.doi.org/10.1061/
(ASCE)0733-9437(2007)133:4(395)
[ 3 ] 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. http://dx.doi.org/10.1016/S0034-4257(96)00215-5
[ 4 ] Bastiaanssen W.G.M., Menenti M., Feddes R.A., Holtslag A.A.M., a1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, 212–213: 198–212.
http://dx.doi.org/10.1016/S0022-1694(98)00253-4
[ 5 ] Bastiaanssen W.G.M., Pelgrum H., Wang J., Ma Y., Moreno J.F., Roerink G.J., van der Wal T., b1998. A remote sensing surface energy balance algorithm for land (SEBAL). Part 2: Validation. Journal of Hydrology, 212–213: 213–229. http://dx.doi.org/10.1016/S0022-1694(98)00254-6
[6] Begum S., OtungI.E., 2009. Rain cell size distribution inferred from rain gauge and radar data in the UK. Radio Science, 44, 2: 44. http://dx.doi.org/10.1029/2008RS003984
[7] Carlson T.N., Ripley D.A., 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62, 3: 241–252. http://dx.doi.org/10.1016/S0034-4257(97)00104-1
[8] Chen J.-H., Kan C.-E., Tan C.-H., Shih S.-F., 2002. Use of spectral information for wetland evapotranspiration assessment. Agricultural Water Management, 55, 3: 239–248. http://dx.doi.org/10.1016/S0378-3774(01)00143-3
[9] Choudhury B., Ahmed N., Idso S., Reginato R., Draughtry C., 1994. Relations between evaporation coefficients and vegetation indices studied by model simulations. Remote Sensing of Environment, 50, 1: 1–17. http://dx.doi.org/10.1016/0034-4257(94)90090-6
[10] French A.N., Schmugge T.J., Kustas W.P., 2002. Estimating evapotranspiration over El Reno, Oklahoma with ASTER imagery. Agronomie, 22, 1: 105–106. http://dx.doi.org/10.1051/agro:2001010
[11] Hasager C.B., Jensen N.O., 1999. Surface-flux aggregation in heterogeneous terrain. Quarterly Journal of the Royal Meteorological Society, 125, 558: 2075–2102. http://dx.doi.org/10.1002/qj.49712555808
[12] Idso S., Jackson R., Pinter P., Reginato R., Hatfield J., 1981. Normalizing the stress-degree-day parameter for environmental variability. Agricultural Meteorology, 24: 45–55. http://dx.doi.org/10.1016/0002-1571(81)90032-7
[13] IMGW-PIB, 2014. Sprawozdanie z działalności w roku 2013, Warszawa
[14] Jackson R.D., Idso S.B., Reginato R.J., Pinter P.J., 1981. Canopy temperature as a crop water stress indicator. Water Resources Research, 17, 4: 1133–1138. http://dx.doi.org/10.1029/WR017i004p01133
[15] Jiang L., Islam S., 2001. Estimation of surface evaporation map over Southern Great Plains using remote sensing data. Water Resources Research, 37, 2: 329–340. http://dx.doi.org/10.1029/2000WR900255
[16] Kustas W.P., Norman J.M., Anderson M.C., French A.N., 2003. Estimating subpixel surface temperatures and energy fluxes from the vegetation index–radiometric temperature relationship. Remote Sensing of Environment, 85, 4: 429–440. http://dx.doi.org/10.1016/S0034-4257(03)00036-1
[17] McVicar T.R., Jupp D.L., 2002. Using covariates to spatially interpolate moisture availability in the Murray–Darling Basin. Remote Sensing of Environment, 79, 2-3: 199–212. http://dx.doi.org/10.1016/S0034-4257(01)00273-5
[18] Meyers T.P., Hollinger S.E., 2004. An assessment of storage terms in the surface energy balance of maize and soybean. Agricultural and Forest Meteorology, 125, 1–2: 105–115. http://dx.doi.org/10.1016/j.agrformet.2004.03.001
[19] Monteith J.L., Szeicz G., 1962. Radiative temperature in the heat balance of natural surfaces. Quarterly Journal of the Royal Meteorological Society, 88, 378: 496–507. http://dx.doi.org/10.1002/qj.49708837811
[20] 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. http://dx.doi.org/10.1016/0034-4257(94)90020-5
[21] Norman J.M., Anderson M.C., Kustas W.P., French A.N., Mecikalski J., Torn R., Diak G.R., Schmugge T.J., Tanner B.C.W., 2003. Remote sensing of surface energy fluxes at 10 1 -m pixel resolutions. Water Resources Research, 39, 8: n/a. http://dx.doi.org/10.1029/2002WR001775
[22] Petropoulos G.P., 2013. Remote sensing of energy fluxes and soil moisture content. CRC Press.
[23] Price J., 1990. Using spatial context in satellite data to infer regional scale evapotranspiration. IEEE Transactions on Geoscience and Remote Sensing, 28, 5: 940–948. http://dx.doi.org/10.1109/36.58983
[24] Price J.C., 1977. Thermal inertia mapping: A new view of the Earth. Journal of Geophysical Research, 82, 18: 2582–2590. http://dx.doi.org/10.1029/JC082i018p02582
[25] Roerink G., Su Z., Menenti M., 2000. S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 25, 2: 147–157. http://dx.doi.org/10.1016/S1464-1909(99)00128-8
[26] Rouse J.W., JR., Haas R.H., Schell J.A., Deering D.W., 1994. Monitoring Vegetation Systems in the Great Plains with Erts. Proceedings, 3rd Earth Resource Technology Satellite (ERTS) Symposium, 1: 48–62.
[27] Sánchez J.M., Kustas W.P., Caselles V., Anderson M.C., 2008. Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations. Remote Sensing of Environment, 112, 3: 1130–1143. http://dx.doi.org/10.1016/j.rse.2007.07.018
[28] Su Z., 2002. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrology and Earth System Science, 6, 1: 85–100. http://dx.doi.org/10.5194/hess-6-85-2002
[29] Wang K., Li Z., Cribb M., 2006. Estimation of evaporative fraction from a combination of day and night land surface temperatures and NDVI: A new method to determine the Priestley–Taylor parameter. Remote Sensing of Environment, 102, 3–4: 293–305. http://dx.doi.org/10.1016/j.rse.2006.02.007