www.isadmc.ir

mzernani@yazd.ac.ir

NDWI

.

http://www.isadmc.irmailto:mzernani@yazd.ac.ir

Dong et al.,2015

Njoku et al., 1996

Van

et al ., 2012

2011et al.Das2008et al

Ghulam2007et alMallickNjoku.1996&

EntekhabiPaloscia et al.2006Pierdicca et

al.2010Sano et al. 1998et al. 2008VivoniZhang

Wegehenkel2006

Lu et al.,2012

Gokman et al., 2012

Bastiaanssen et al., 1998

Allen et al., 2002

SEBS

Gokman et al., 2012

BEAREX08

Neale et

al.,2012

TSEB

TSEB

TSEB

TSEB

TSEB

Ju et al.,2010

Bushland Evapotranspiration and Agricultural Remote

Sensing Experiment

ASCAT MetOP

AMSR-E Aqua

Brocca et al.,2011

AMSR

SEBAL

Co

CoCo

MODIS

ILWIS

Level 1B

HDF

ENVI 5Image

UTM

SEB

SEBI-SSEBAL

METRICSEBS

LE

)S(T

2w/mnR2w/mG

2w/mH2w/m

SEBAL

nR

Allen et al., 2002

Surface Energy Balance (SEB) Surface Energy Balance Index Surface Energy Balance Algorithm for Land Mapping Evapo Transpiration at high Resolution with

Internalized Calibration Surface Energy Balance System

w/m2

w/m2

w/m20

Allen et al., 2002

toa;-Radiance

Bastiaanssen, 2000

ii

Esuni

Bi

w/m2Rs0.7×106 Kmds

H1-J.S 34-10×6.262C

m/s 810×2.998KJ/k 23-10×1.381

i:T

Liang, 2001

scG21367,w/mCos

dr

swR2w/m

Allen et al., 2002

NDVI

0

NDVI

0

VP

Zhao et al., 2009

sT

nB_nTn

KnLnn

m810×=1.11910661C410×=1.4382C

31T32T

K

Allen et al., 2002

a

4/K2W/m 8-10×5.67aT

K

G

nG/R

Allen et al., 2002

sT

GnR

3Kg/mpC1004

J/Kg/KdT:2T-1T-1Z

2ZKahr

m/s

rah

dT

NDVI

NDVI

(dT)

dT

.

.(Bastiaanssen et al., 1998)

24ET

24ETfrET

frET

rET

rET

.(Bastiaanssen, 2000)

:Rn-day

J/Kg

.

NDVI

.

MODIS MODIS

LST

MODIS

LST

LST

LST LST

MODIS

MODIS

RN

2W/m RNRN

MODIS

MODIS

G

G G

MODIS

MODIS

H

H H MODIS MODIS

RnH

G

ETinst

Bastiansen., 2000

mm/day

ET ETMODIS MODIS

0

10

20

30

40

0 0.2 0.4 0.6 0.8 1

ws%

( )

( )

( )

)

SEBS

=0.57)2(r

Gokman et al., 2012

Neale

Bearex08

TSEB

TSEB

SUTSEBAL

3. Allen, R.G., Tasumi, M., Trezza, R., & Bastiaanssen, W.G.M.( 2002). SEBAL (Surface Energy Balance Algorithms for Land). Advanced Training and Users Manual

4. Ahmad, S., Kalra, A., Stephen, &Haroon, (2010). Estimating soil moisture using remote sensing data: a machine learning approach. Advances in Water Resources 33, 69 80.

5. Bastiaanssen, W. (2000). SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of Hydrology, 229(1): 87-100

6. Brocca, L.S., Hasenauer, T., Lacava, F., Melone, T., Moramarco, W., Wagner, W., Dorigo, P., Matgen, J., Martínez-Fernández, P., Llorens, J., Latron, C. & Martin, M.Bittelli. (2011). Soil Moisture Estimation through Ascat and Amsr-E Sensors: An Intercomparison and Validation Study Across Europe. Remote Sensing Of Environment J, 115, 3390 3408

7. Betts, A.K., Ball, J.H., & Beljaars, A.C.M., (1996). The land surface-atmosphere interaction: a rview based on observational and global modeling perspectives. Journal of Geophysical Research 101, 72097225.

8. Coll, C., & Caselles, V. (1997). A split-window algorithm for land surface temperature from advanced very high resolution radiometer data: Validation and algorithm comparison. Journal of Geophysical Research, 102(D14): 16697-16616, 16713

9. Courault, D., Lacarrère, P., Clastre, P., Lecharpentier, P., Jacob, F., & Marloie, O., Et al. (2003). Estimation of Surface Fluxes in a Small Agricultural Area Using the Three-Dimensional AtmospHeric Model Meso-Nh and Remote Sensing Data. Canadian Journal Of Remote Sensing, 29(6), 741-754

10. Das, N.N., Mohanty, B.P., Cosh, M.H., & Jackson, T.J., (2008). Modeling and assimilation of root zone soil moisture using remote sensing observations in Walnut GulchWatershed during SMEX04. Remote Sensing of Environment 112, 412 429.

11. Dong J, Steele-Dunne SC, Judge J., & van de Giesen N. (2015). A particle batch smoother for soil moisture estimation using soil temperature observations. Advances in Water Resources 83: 111-22

12. Ju, W., Gao, P., Zhou, Y., & Zhang, X. (2010). Combining an Ecological. Model With Remote Sensing And Gis Techniques To Monitor Soil Water Content Of Croplands With A Monsoon Climate. Agricultural Water Management, 97, 1221-1231.

13. Jung, M., Reichstein, M., Ciais, P., Seneviratne, S. I., & Sheffield, J., & Goulden, .M. L., ET Al (2010). Recent Decline In The Global Land Evapotranspiration Trend Due To Limitedmoisture Supply. Nature, 467, 951 954

14. Ghulam, A., Qin, Q., Teyip, T., & Li, Z., (2007). Modified perpendicular drought index (MPDI): a real-time drought monitoring method. ISPRS Journal of Photogrammetry and Remote Sensing 62, 150 164.

15. Gokmen, M., Vekerdy, Z., Verhoef, A., Verhoef, W., Batelaan, O., & Van Der Tol, C. (2012). Integration of Soil Moisture In Sebs For Improving Vapotranspiration Estimation Under Water Stress Conditions. Remote Sensing Of Environment, 121, 261-274

16. Liang, S. (2001). Narrowband to broadband conversions of land surface albedo I: Algorithms. Remote Sensing of Environment, 76(2): 213-238

17. Lu, H., Koike, T., Yang, K., Hu, Z., Xu, X., Rasmy, M., & Tamagawa, K. (2012). Improving land surface soil moisture and energy flux simulations over the Tibetan plateau by the assimilation of the microwave

remote sensing data and the GCM output into a land surface model. International Journal of Applied Earth Observation and Geoinformation, 17, 43-54.

18. Mallick, K., Bhattacharya, B.K., & Patel, N.K., (2009). Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI. Agricultural and Forest Meteorology 149, 1327 1342.

19. Neale, C. M., Geli, H. M., Kustas, W. P., Alfieri, J. G., Gowda, P. H., Evett, S. R., & Howell, T. A. (2012). Soil water content estimation using a remote sensing based hybrid evapotranspiration modeling approach. Advances in Water Resources, 50, 152-161

20. Njoku, E.G., & Entekhabi, D., (1996). Passive microwave remote sensing of soil moisture. Journal of Hydrology 184, 101 129.

21. Paloscia, S., Macelloni, G., & Santi, E., (2006). Soil moisture estimates from AMSR-E brightness temperatures by using a dual-frequency algorithm. IEEE Transactions on Geoscience and Remote Sensing 44, 3135 3144.

22. Pierdicca, N., Pulvirenti, L., Bignami, C., 2010. Soil moisture estimation over vegetated terrains using multitemporal remote sensing data. Remote Sensing of Environment 114, 440 448.

23. Sano, E.E., Huete, A.R., Troufleau, D., Moran, M.S., & Vidal, A., (1998). Sensitivity analysis of ERS-1 synthetic aperture radar data to the surface moisture content of rocky soils in a semiarid rangeland. Water Resources Research 34, 1491 1498.

24. Van der Tol, C. (2012). Validation of remote sensing of bare soil ground heat flux. Remote Sensing of Environment, 121, 275-286.

25. Vivoni, E.R., Gebremichael, M., Watts, C.J., Bindlish, R., & Jackson, T.J., (2008). Comparison of ground-based and remotely-sensed surface soil moisture estimates over complex terrain during SMEX04. Remote Sensing of Environment 112,314 325.

26. Wang, K. C., Wang, P., Li, Z. Q., Cribb, M., & Sparrow, M. (2007). A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature. Journal of Geophysical Research-Atmospheres, 112, 14

27. Zhang, Y.Q., & Wegehenkel, M., (2006). Integration ofMODISdata into a simple model for the spatial distributed simulation of soil water content and evapotranspiration. Remote Sensing of Environment 104, 393 408.

28. Zhao, S., Qin, Q., Yang, Y., Xiong, Y., & Qiu, G. (2009). Comparison of two split-window methods for retrieving land surface temperature from MODIS data. Journal of Earth System Science, 118(4): 345-353

29. Zhao S, Yang Y, Qiu G, Qin Q, Yao Y, et al. (2010). Remote detection of bare soil moisture using a surface-temperature-based soil evaporation transfer coefficient. International Journal of Applied Earth Observation and Geoinformation

No. 6, Autumn & Winter, 2016, pp 90-107

Deser t Managementwww.isadmc.ir

Iranian Scientific Association of Desert Management and Control

Assessment of Surface Energy Balance Algorithm for Land (SEBAL) model and biophysical parameters derived from remotely- sensed data in estimating of soil

moisture in arid lands (Case study: Jarghoye, Isfahan)

R. Sadeghzade Poode1, M. Zare2 M. H. Mokhtari3, M. Akhavan Ghalibaf3

1. MSc of Natural Resources Engineering- Arid Lands Management, Faculty of Natural Resources andEremology, Yazd University, Iran 2. Assistant Professor, Faculty of Natural Resources and Eremology, Yazd University, Iran3. Assistant Professor, Faculty of Natural Resources and Eremology, Yazd University, Iran*Corresponding Author, E-mail: mzernani@yazd.ac.ir

Received date: 23/01/2016 Accepted date: 08/03/2016

Abstract

One of the key components of energy and hydrological processes is soil moisture, which is measured indirectly, because of exiting some problems in measuring directly. Some existing methods such as thermal inertia, vegetation indices, temperature and water indices (e.g. NDWI) has certain limitations such as difficulties in capturing images of day and night times, and differences in method of calculating of thermal inertia for different hours in a day. Therefore, finding a new method for calculating of soil moisture based soil temperature or water and soil spectral changes is very necessary. Although soil moisture is not calculated directly in the Surface Energy Balance Algorithm for Land (SEBAL), since all parameters that have effect on soil moisture changes, consider for calculating evapotranspiration in SEBAL, this model can be used to calculate soil moisture. Jarghoye-Sofla, as the study area, is located adjacent to the Gavkhoni playa, Isfahan province. The study area, has faced with declining in soil moisture, as a result of climatic fluctuations, and drying of the wetlands in recent years. The purpose of this research is assessing of the remote sensed based surface energy balance model, and evaluation of biophysical parameters derived from satellite imagery to estimate soil moisture. Data used in this research, consisted of MODIS satellite images and measurements of 33 soil samples taken at depth of 0-30 cm. By measuring point soil moisture, and calculating volumetric soil moisture, the rate of evapotranspiration was estimated using the SEBAL model. Then, correlation between the parameters used in the SEBAL and ground measurements of soil moisture was evaluated. Results show high correlation between parameters of the SEBAL and soil moisture. The highest correlation was determined between the SEBAL algorithm daily evapotranspiration and soil moisture for days of 16 and 17 November, 2014 with values of 0.51 and 0.68, respectively.

Keywords: Soil moisture; MODIS; SEBAL; Playa; Arid lands

http://www.isadmc.irmailto:mzernani@yazd.ac.ir