Review of Soil Moisture and Plant Water Stress Models Based on Satellite Thermal Imagery

Artur Łopatka, Tomasz Miturski, Rafał Pudełko, Jerzy Kozyra, Piotr Koza

Abstract


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.


Keywords


soil moisture; plant water stress; remote sensing; thermal imagery; heat balance

Full Text:

PDF

References


[ 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

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

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

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

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

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

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

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

IMGW-PIB, 2014. Sprawozdanie z działalności w roku 2013, Warszawa

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

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

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

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

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

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

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

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

Petropoulos G.P., 2013. Remote sensing of energy fluxes and soil moisture content. CRC Press.

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

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

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

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.

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

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

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




DOI: http://dx.doi.org/10.17951/pjss.2016.49.1.73
Date of publication: 2017-01-03 00:00:00
Date of submission: 2017-01-11 12:33:29


Statistics


Total abstract view - 1212
Downloads (from 2020-06-17) - PDF - 749

Indicators



Refbacks

  • There are currently no refbacks.


Copyright (c) 2017 Artur Łopatka, Tomasz Miturski, Rafał Pudełko, Jerzy Kozyra, Piotr Koza

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.