1. Landsacpe complexity & Soil Moisturevariation in south Georgia,USA,for remote sensinapplications Mario A. Girdalo , 2008 , Uni.GeorgiaUSA Present by : MARI ; FOROOTAN.

2. We adressed Temporal & spatial Variation Of SM In a heterogenous landscape

3. 0-History ! 1-OBJECTIVE &HYPOTHESIS 2-METHODOLOGY 3-RESULTS 4-CONCLUSION IN THESE PARTS

4. SM= soil moistue LRW=little river watershed=study area LULC=LandCover-LandUse ANOVA=analyze of variance LS=landscape H-P=hydra-probe Var. =variation P.C.C =pearson correlation coefficient Remember

5. Western et al. ,1999 SM is not a random phen. Sp. Var. according to Size of Samp. Area & Env. Var. Anderson et al. ,2003 Combination of Env. Factors (Soil.Veg.c,Topo,Climate) Create spatially distribution of SM Over dif.scales of space & time History

6. History USDA-NASA 2003 (SMEX03) In situ devices at LRW produce Relieble inf. Of SM Moran ,2004 Cashion ,2005 Under a complex LS. Remote sensors with coarse spatial resolution produce inaccurate estimates

7. Investigate Spacial var. In ground-based SM data collection From small plots (30*30) Similar to pixel size of Small EFOV remote sensors TM , ETM+& ASTER &ALI In a heterogenous landscape OBJECTIVE

8. We want toAssess Suitabolity of Using small plots For field validation Of Satellites remote sensing Instruments With small EFOV( < 30m )

9. TM,ETM+ ASTER & ALI All are expected To better capture Local field conditions Under landscape environmental Complexity

10. Dispite Heterogeneity of study area It is possible to find Temporally & Spatiolly Homogenous SM behavior Within small areas of 30*30. Our HYPOTHESIS

11. Study SM var. Within 5 dominate LULC In LRW Landscape. Another Objective

12. 1- increase field knowledge of SM behavior in Hetero.LS 2-lead to better interpretation of the Satellite estimates Our Results Will

13. Methodology

14. -Study Area -Plot Data collection -Statistical Analysis

15. Study Area

18. An in situ Network 27 ground stations As a source of ground-based Point Data To 1- validate remote sensing analysis of SM and Soil tempreture& Climate 2- long term Hydrological studies In South eastern of US 2002 - 2003

19. These 27 stations equipped with Steven-vitel Hydra-Probe(steven water monitoring sys.)

23. H-P-s Operated by USDA-ARS And record SM information &Ground tempreture At 3 depths 5 , 20 , 30 m Every 30 min Typ. Installed along Agriculture field boundaries, Fence rows & pasture areas & Typ. Surounded by Native grass veg.cover.

24. Plot Data collection

25. 8 of 27 sites Selected Surounding H-P & Associated with the In situ SM Monitoring station

27. 30 * 03 m area Surrounded H.p And Subsequently sampled for SM SM was measured using Portable theta capacitance probe (measure dielectric conductivity similar to H.p)

28. To minimize Errors We use Same equipment & Same personnel

29. 3 different Soil Dataduring 2005 - Jan2006

30. Data collected Randomly Within 8(30*30m) plots 1 8 16 26 32 40 50 63 66 For each plot 10-20 reading On 4 dif. Dates March11 March28 April12 May24 2005

31. testing temporal stability of SM reading over a fairly short time period (here 48 h) On 2 consecutive days *Nov 30 & Dec 1 (2005) *Jan 13 & Jan 14 (2006) Systematic sampling 2

32. Each reading was taken in 3 intervals in 4 directions From H.P station Total 20 sample per Plot

33. At some locations Fewer than 4 directions Were evaluated Due to the presence of obstacles Such as Road and Channels 4 perpendicular direction Sampling location Hdra-probe Station 30 M 30 M

34. collected from 5 field adjacent to plot areas Under the LULC; Grass ,Orchard,Bare land& Agriculture Cotton & Peanuts Associated with H.P sits: 50 , 32 , 66 & 40 3

35. Each LULC 8 – 10 SM readings At 3 intervals along a 25 – 30 m transect In 4 dif.times Nov – Dec 2005 & Jan 2006

36. Precipitation from each site  Raingages at H.P stations For 12-day period at intervals 5 min For each Sampling Dates

37. Statistical analysis

38. 1- ANOVA 2- time stability anaysis 3- Tukey & tamhane hoc analysis 4-Pearson Correlation Coefficient 5- t- test (Were used to analyze Data)

39. Results

40. Results of precipitation analysis

41. No. Of R.F For 12 day period previos to Sampling date Ranged between 3 - 6

42. Max average was 10 cm In 28 March Min average was less than 2.2 In 24 May

43. March 11 March 28

44. April12 May 24

45. Nov & Dec January 2006

46. During R.F events all sites received simultanous precipitation with small var. among them. SO Water supply was homogeneous for all sites prior to Sample Date SO SM dif. Can be considered As a result of Intrinsic & mostly independet of water supply

47. When we compared it with precipitation record: 8  greatest cumulative prec. only for 3 Sam.Date. While 63 , 16 , 50  greatest prec. inputs for 5 other days Aand For lowest 40 , 32  Prec. Record shows they have above average These observations support the Hypothesis: SM caused by intrinsic Enviromental var. Beyond prec.acting At the local scale Mean volumetric SM & infield Variation Recorded in 8 locations Presented  8  highest valus of Vol.SM 40  lowest valus of Vol.SM

48. ANOVA of SM

49. This analys use 2 sets of variation to perform Composition between plots Variation among groups Variation within groups

52. A multiple comparesionamong the plots It one by one comparsionbetween sites

53. 0 = means weak relationship & More than 0 means have a relationship 1 , 2 , 3 ,… Bigger score Stronger relationship

54. SO Weakest relationship Were find between 8-26 /8-40 /8-66 / 40-50

55. SO Strongest Relationship are Between 26-32 / 32 – 66 / 50 -63

57. SO Site 8 Is most unique SM Behaviuor Site 66 High level of Similarity With other sites

58. Similarity of 26 & 32 *Precipitation record: similar and Less than 2.5 cm Dif.for all Samp.D. *Soil type: Both are belong to Tifton soil Series *Veg. cover: 32 short grass 26edge of agr.field in wich hay is cut for cattle consumption

59. Similarity of 16 & 50 *Precipitation: Dif. Cumulative rain 10 & even 18 cm *Soil type: 16 Tifton series 50Sunsweet series *Veg. cover: Similar homogenous grass &exposed to transit agri. Equipment Influenc soil physical characteristics

60. Most dissimilar sites were 8-26 / 8-40 /8-66 /40-50/50-32 Low assosiation was found between 16 &32 In total 6 Sam.D.

61. 8 – 26 / 8-40 / 8-66 Dif. Combination of Soil type SO Var. in water infiltration process SO Dif. SM content 40-50 / 50-32 Dif. Cumulative rainfall

62. Observation suggest that

63. In LRW LULC Is a greater factor affecting SM conditions Than Cumulative Rainfall

64. Influnce of LULC In SM Indicated by our results in LRW. LS. That make: Dif. Evatranspiration & Soil water usage

65. Spatial resolution of R.sensing Sensors That are More appropriate to Capture SM conditions under this LS. Will be defined by *LS. Fragmentation *LULC composition *Sizes of LULC fragments

66. High levels of fragmetation at LRW With fragments of Small size So Moderate to Fine spatial resolutionsensors (~ 3 0 m) Are expected better suit the countinoussutdy Of SM in LRW

67. SM time suitability

68. Parasmetermean relative diffrence: 1-Measure how particular sample compare with Av. SM of plot 2-Indicator of Infield Variation of surface SM

70. 26 has highest range on mean Dif. (36%-39%)

71. 16 has Lowest Var. (-4%-+4%)

72. Highest Av. Of mean relative Dif. Was 2.6% in 66 Followed by 8 , (2.1%) 16 (1.2 %) 26 (1.0%) Otherwise mean relative Dif. Was 0.1 % below the plot Dif. Approach Zero High level of Homogeneity Within each Sample plot

73. Results indicate Samp. Point within the plots are very close to each Plot mean SO Can accurately Estimate Surface SM behavior Of Entire 30*30 m plot

74. Our results shows High level of in field Homogeniety That’s in contrast with The results of similar research performed In large plots

75. in our research By selecting Small LS. fragments. The in field heterogenitycasued by Topography Diffrence Was Minimize

76. Stability in plots 40 & 50 Can be explained by their Relatively Homogenous LC 50  part of a larger pasture and entire plot covered by same grass type 40  part of a mechanized AGR.Field & change Veg-cover throughout growing season

77. So HemogeneityVeg.Cover & Homogenietyprecipitation,Slope,Soil physical characters  High stability of SM Recorded at these 2 sites

78. Pearson correlation coefficient

79. Was compare to evaluate if all in field locations experience similar SM VAR.s within a short period (one day in this case)

80. Correlations were significant in both sets But Only for 26 , 32 , 63

81. P.C.C Is an indicator of Spatial stability Of SM Point data

82. Low pcc with low significant level Process is unstable in space and for time lag In which Data collected

83. Two samples t-test

84. Computed to evaluate The Variation in Av.SM values From one day to next

86. Results suggest that Surface SM Will notnecesserily Reflect profile conditions

87. SO Remote sensing retrieving algorithms Based only in direct or indirect Quantification of Surface conditions As Temp. , SM May produce Errors When used in this LS.

88. These ERRORS Can be Minimized by incorporatig in to Rem.Sen. Analysis data of Precipitation events prior to Samp.D & Infiltration analysis for soil type dominated of study area

89. Statistical analysis foragriculture LUtransects

90. Discriptive statistics for SM On 4 dif. Date collection On 25-30 m long transects Related to Dif. LU Adajent to 5 plots

92. Results shows Orchard & grass Have the wettest conditions While Cotton& peanut fields Were the driest

93. Tilled AG.fiels lowest spatial variability with Av. Values around %1 Grass AG.field highest standard deviation with values btween %1.8 - %2.6 Bare land  Intermediate behavior

94. SM Var. Between LU

95. ANOVA Between SM valuse for five LULC Showed Significant statistical Dif. For all 4 days

96. Variation between goups showed by Anova Peanut & Cotton  Most similar with No stat.Dif Among them for any 4 days Greatest Dif. With orchard, grass & bareland Showing Stat.Dif on all Samp.Dates

97. Conclusion

98. AOVA high Diffrences in SM among the plots & high Hemogeneity within them Precipitation analysis similar rainfall conditions So SM.Var. explained by in situ local conditions

99. Temporal stability analysis SM has high stability within the small plots and single point can use to monitor SM Var. of all plot T-statistical analysisSMstatesin upper soil layer changes within 24H

100. And finally we found stat.Dif. In SM between Dif. LULC as there adjacent plots

101. finally

102. The results confirm that

103. A remote sensing approach that consider Homogeneous LULC ,LS fragments can be used to identify LS units of similar SM behavior under Heterogeneous landscapes

104. In addition The insitu USDA-ARS network will serve better in remote sensing studies in wich sensors with fine spatial resolution are evaluated

105. Thanx 4Your A 10tion Mari .forootan.1388