Estimation of Evapotranspiration Over South Florida Using Remote Sensing Data. Shafiqul Islam Le Jiang Elfatih Eltahir

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1 Estimation of Evapotranspiration Over South Florida Using Remote Sensing Data Shafiqul Islam Le Jiang Elfatih Eltahir

2 Outline Introduction Proposed methodology Step-by by-step procedure Demonstration of Results Comparison with surface observations Concluding Remarks

3 Introduction

4 Surface Energy Balance R n H λε G Energy balance: R n G = H + λe (1)

5 Evaporation estimation methods 1. Direct method (Penman-Monteith Equation) λe where = ( R G) n + ρc e r sa p + γ (1 + e r r s a * ( T r, r r surface, s aerodynamic resistance ρ, C constants s a p e * a, e vapor, saturated vapor pressure * slope of e ) s r ) a e a (2)

6 2. Indirect method - Residual method λe = ( Rn G ) H (3) where (4) However, Taero H C T = ρ aero T p r rah virtual, and need several other surface & atmospheric parameters ah a

7 Single site vs. large area Remote sensing Single site estimation: physically based model and ground based data. Large area estimation: physically based model + effective parameters for a large area

8 Use of remote sensing data - Provide some parameters for single sites. - Provide information for aggregation schemes to obtain effective parameters over large areas. - Difficult Issues: * Has to be incorporated into single site based methodology frameworks. * land surface heterogeneity, non-linearity; limited number of parameters from Remote Sensing.

9 Objectives Develop a simple scheme to estimate evapotranspiration over large areas using primarily remote sensing data. Compare and contrast with surface based Observations.

10 Proposed Methodology Goal: Estimate ET map from remote sensing with little or no ground based information. Simplified framework suitable for distributed estimation and efficient for large amount of remote sensing data. Generally applicable, no site specific tuning necessary.

11 Formulation: λe Simplified to: where, = β A γ (Priestley-Taylor Eq. α =1.26 * Evaporative Fraction: γ ( ) ( * ) R G + B f ( u ) e e n + γ λ E = φ( R G ) n + γ + γ psychrometric constant. slope of saturated evaporation pressure. λe = α( R G) valid for wet surface conditions ) EF = φ n + γ a + γ a, and (5) (6)

12 Challenge:: obtain spatially distributed φ Implication for remote sensing application: - spatial context of RS image provides bounds for φ For example, dry pixel: high T 0, high surface reflectance, no evaporation, H = R G and φ = φ = min 0 n wet pixel: low T 0, low albedo (reflectance), no sensible heat, λ = and φ = φ max E R n G

13 What does remote sensing tell? Surface reflectance (albedo( albedo): VIS channel Surface temperature (IR channels) Vegetation index (VIS + IR channels) Example Advanced Very High Resolution Radiometer (AVHRR) - Polar orbiting, 837 km above earth surface, 2 overpasses/day; - Multi-channel: 1 VIS, 1 near IR, 3 other thermal channels; - Resolution: 1km.

14 Remotely sensed land surface parameters from AVHRR T 0: radiometric surface temperature T 0 = f (ch4, ch5) NDVI: normalized difference vegetation index NDVI = ch2 ch2 + ch1 ch1 ch1: VIS (Red); ch4, ch5: Thermal ch2: near IR;

15 T 0 vs. NDVI space T 0 NDVI

16 Spatial interpolation scheme for φ T 0 φ min ( NDVI, max i T i, φ min i ) ( NDVI x, T x, φx ) ( NDVI, min i T i φ max, φ max i ) NDVI

17 NDVI T May 5, 1997 NDVI T May 14, 1997

Land use/land cover (re-classified into 10-class based on the FLORIDA LANDUSE COVER AND FORMS CLASSIFICATION SYSTEM (FLUCCS)-1995 provided by SFWMD 0: outside of district or no

18 Land use/land cover (re-classified into 10-class based on the FLORIDA LANDUSE COVER AND FORMS CLASSIFICATION SYSTEM (FLUCCS)-1995 provided by SFWMD 0: outside of district or no value 1: residential, human inhabited 2: pastures, farm, crops, 3: scrub and brush 4: woods 5: water & ocean 6: swamps, wet land 7: beaches 8: facilities:airports/hwys,etc. 9: wet prairie

19 Interpretation of T4 vs. NDVI plots - T4 vs. NDVI plots are important for successfully applying the proposed algorithm. - What we anticipate to see is at least a warm edge, and/or a triangle (trapezoid) space in the scatter plot of T4 and NDVI. - Another most important issue is to be able to determine the Tmax, Tmin for the bounding triangle, or the Tmax, Tmin and Te for the trapezoid. T T max T e T min NDVI min NDVI max NDVI

20 Given the previous plot, the φ value for any pixel in a given NDVI class within the trapezoid is φ = T T max max T T Our analysis to obtain Tmax and Tmin from the 14-year climatology appears to be ineffective large bias exists between the coarse resolution (16 km) re-sampled temperature and the high resolution (1 km) temperature. Here, we arrive at the following empirical approach after re-evaluating the practical constraints for instantaneous remote sensing image, i.e., Tmin=mean(remotely sensed in-land water surface temperature) Where, Tmin take the mean value of remotely sensed in-land water body temperature (exclude ocean temperature from consideration), and min φ max Tmax can be extrapolated from the warm edge (1)

21 Demonstration of triangular/trapezoid space of T vs. NDVI May 5,1997 May 14,1997

22 a. Phi map with cloud mask blue indicates cloud May 5, 1997 b. Phi map, cloud mask removed by interp. scheme. a. Phi map with cloud mask May 14, 1997 b. Phi map, cloud mask removed by interp. scheme.

23 Step-by-step procedure a. Input data acquisition Raw data - High Resolution Picture Transmission (HRPT), 8-bit format scanline dataset from Satellite Active Archive (SAA) Resampling resample scanline data into predefined SFWMD lat/lon projection grid. Coverage: longitude: ~ latitude: ~ col by 466-row rectangular area with resolution of degree. b. Convert Channel data into physical values and calculate NDVI, T NDVI=(albedo2-albedo1)/(albedo2+albedo1) T: calculated from AVHRR channel 4

24 c. Estimation of Evaporative Fraction (EF) Construct triangular space of T vs. NDVI, and calculate φ, as well as EF and EF, T φ = T max max EF = φ T T min + γ φ max Different scenarios: -clear days: above approach - partial cloudy days: above approach + spatial interpolation using both clear pixels and ground based data - complete cloudy days: spatial interpolation (using ground based data) + temporal smoothing (gap filling, median filtering, smoothing)

25 d. Derive Rn map using ground based data Spatial interpolation of Rn map using ground based daily Rn observations (Inverse Distance Square method, shortcoming: too smooth). To control the quality, mean and standard deviation of ground based daily Rn were calculated for each day and any observations outside the [mean-2*std, mean+2*std] were excluded from the ground truth for interpolation. e. Estimation of ET ET = Rn * EF

26 Demonstration for Clear, Partially Cloudy, and Cloudy days For each day maps of NDVI, T4, EF, Rn, and ET are shown

27 Clear Day 32: 02/01/97 NDVI T EF Rn ET

28 Mostly Clear Day 134: 05/14/97 NDVI T EF Rn ET

29 Mostly Cloudy Day 161: 06/10/97 NDVI T EF Rn ET

30 Overcast Day 270: 09/27/97 NDVI T EF Rn ET

31 Poor Image Quality Day 220: 08/08/97 NDVI T EF Rn ET

32 Comparison with Ground Based Observations Derived ET maps series for 365 days in 1997 are compared to 27 available ground based stations within the SFWMD domain. Among these stations, there are 9 used by German (2000), 14 Pan evaporation stations, and 4 ET stations. ET map is calculated from EF map (which is the main goal of the proposed algorithm) and Rn map (which is interpolated using available ground Rn stations within the domain). Among the comparison stations: 8 of the German s stations were used in the EF derivation for cloudy days (e.g. spatial interpolation using land use class information). All other stations should be viewed as independent source for validation. Comparisons were done for both the raw observation series and the smoothed observation series using a median filter (e.g., for a 3 point smoothing filter provides running mean.

33 Surface flux, Pan evaporation, and Evapotranspiration stations mapped into SFWMD domain

34 ET time series: a).estimated; b). observed; c).observed with smoothing (by size 3 median filter) Scatter plots: a). Estimated ET vs. obs. ET; b). Estimated ET vs. obs. ET with smoothing

35 ET time series: a).estimated; b). observed; c).observed with smoothing (by size 3 median filter) Scatter plots: a). Estimated ET vs. obs. ET; b). Estimated ET vs. obs. ET with smoothing

36 ET time series: a).estimated; b). observed; c).observed with smoothing (by size 3 median filter) Scatter plots: a). Estimated ET vs. obs. ET; b). Estimated ET vs. obs. ET with smoothing

37 ET time series: a).estimated; b). observed; c).observed with smoothing (by size 3 median filter) Scatter plots: a). Estimated ET vs. obs. ET; b). Estimated ET vs. obs. ET with smoothing

38 ET time series: a).estimated; b). observed; c).observed with smoothing (by size 3 median filter) Scatter plots: a). Estimated ET vs. obs. ET; b). Estimated ET vs. obs. ET with smoothing

39 ET time series: a).estimated; b). observed; c).observed with smoothing (by size 3 median filter) Scatter plots: a). Estimated ET vs. obs. ET; b). Estimated ET vs. obs. ET with smoothing

40 ET time series: a).estimated; b). observed; c).observed with smoothing (by size 3 median filter) Scatter plots: a). Estimated ET vs. obs. ET; b). Estimated ET vs. obs. ET with smoothing

41 ET time series: a).estimated; b). observed; c).observed with smoothing (by size 3 median filter) Scatter plots: a). Estimated ET vs. obs. ET; b). Estimated ET vs. obs. ET with smoothing

42 ET ENR407 Pan WPB.EEDD ET ENR104 ET ENR307 ET ENRP Pan TAMITR40 Pan S7E Pan S65E Pan S5A Pna S140 Pan OKEE Pan KISS Pan HGS1 Pan FT.PIER Pan FT.PI_2 Pan CLEW Pan BELLE Pan BCBNAPLE_E German S9 German S8 German S7 German S6 German S5 German S4 German S3 German S2 German S1 Corr. coef rmse (inches) Bias (inches) Corr. coef rmse (inches) Bias (inches) station Note Compare to Smoothed Obs. series Compare to Obs. Series

43 Comparison of Model Errors (in % of Mean) for Daily ET Site S1 S2 S3 S4 S5 S6 S7 S8 S9 German (2000) Site Model 28% 14% 9% 12% 11% 10% 8% 15% 11% Proposed ET Model 0.5% Data Incomplete 16.6% 5.5% 0.4% 11.6% 6.2% 11% 17%

44 Summary It appears that the proposed algorithm is capable of producing a spatially consistent and temporally continuous ET estimates over the SFWMD domain using primarily remote sensing data. Given the once a day nature of satellite data and problems associated with satellite images for cloudy days and off nadir conditions, the proposed algorithm performs reasonably well in terms or low bias and root mean square error as well as high correlation coefficients. ET estimates are remarkably good for German (2000) stations, but care must be taken to properly interpret these results because some of the data from these stations were used in the spatial interpolation step. ET estimates for other sets of stations (i.e. 14 Pan Evap stations and 4 ET stations) are reasonably well. While comparing a spatially distributed estimates of ET with point observations, we must acknowledge (a) the scale discrepancy between the scale of estimation (~ 1 km) and scale of observation (point scale), (b) limited number of surface based observations and their spatial distributions, and (c) issues of mixed pixel and registration error between the remotely sensed pixel and the surface based observations.

45 Summary (contd.) The proposed approach is much simpler than the vast majority of land surface models which serve similar purposes. In addition, it requires no tuning of parameters. The proposed spatial-temporal algorithm appears to be powerful in resolving regional scale evapotranspiration pattern over large heterogeneous areas at high resolution; Uncertainty in ET is mainly related to poor satellite image quality (such as those affected by low and high level cloud, off-nadir observation angle) and estimates of net radiation. A major difficulty of the proposed approach is to overcome the cloudy conditions and develop a reliable estimates of net radiation map.

46 Future Work System development: Integrate the existing algorithm into an application software system. It will require intensive effort to combine different modules together and put into an usable and automated application system (potentially platform independent). Further research: combine remote sensing and ground based observations more readily and more meaningfully: - For example, construct observation networks that better capture the heterogeneous nature across the whole domain. This approach, however, could be costly from an implementation and maintenance perspective. It appears that remote sensing is an attractive alternative to develop high resolution map products.

47 Future Work (contd.) Challenges: Combine multi-platform remote sensing data, as well as available ground data. It appears to be beneficial to combine geo-stationary satellite and polar-orbiting satellite in future research and algorithm development by optimizing the strength of both, i.e. geo-stationary satellite (e.g. GOES resolution about 4km) has high time frequency in observation (e.g. 30-min) for a same region, which is quite useful to track the diurnal cloud and rainfall development and also useful to estimate daily solar insolation or even net radiation, while polar-orbiting satellite can give more accurate instantaneous estimates. Potential opportunity: Larger scale R & D effort is needed to address some of these challenges. Develop collaborative multi-year research and development project(s) with NOAA, SFWMD, IMSG, UC and MIT.

48 Thank you.

50 Class1: residential, human inhabited

51 Class2: pastures, farm, crops

52 Class Description No. of Pixels 1 residential, human inhabited pastures, farms, crops scrub and brush woods water and ocean swamps and wetlands beaches facilities (airports, highways, etc.) sea grass 38 TOTAL PIXELS

GEOG 4110/5100 Advanced Remote Sensing Lecture 2

GEOG 4110/5100 Advanced Remote Sensing Lecture 2 Data Quality Radiometric Distortion Radiometric Error Correction Relevant reading: Richards, sections 2.1 2.8; 2.10.1 2.10.3 Data Quality/Resolution Spatial