Application of Remote Sensing derived land surface information to enhance implementation of management practices in SWAT Presented by Jeba Princy R

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1 Application of Remote Sensing derived land surface information to enhance implementation of management practices in SWAT Presented by Jeba Princy R Balaji Narashimhan V.M.Bindhu, S.M. Kirthiga Annie Issac

2 2 OUTLINE BACKGROUND OF THE STUDY OBJECTIVE OVERALL METHODOGY RESULTS AND DISCUSSION CONCLUSION FUTURE WORK

3 3 BACKGROUND OF THE STUDY Water management and water saving are evolving as crucial factors in the context of sustainable development. Distributed hydrological models are widely used for water balance studies. The accuracy of estimation of these water balance components are more dependent on the availability of the input data. Mainly in agricultural dominated region the land use representation and the crop management practices play a dominant role.

4 4 WATER USE IN INDIAN CONTEXT India is an agricultural dominated country and 9% water consumption is accounted by agriculture. Heterogeneity in landuse and spatio-temporal variability in agricultural practices needs to be addressed in Hydrologic models. Accounting these variability in Hydrological models will increase the models performance in simulating the water balance components effectively. Conventional methods of acquiring land management related information through field scale surveys, cropping related reports etc., are appropriate for model simulations at a field scale.

5 5 LAND USE/LAND COVER MAP The LULC map of 27-8 The LULC map represents agricultural cultivated areas as season specific classes, namely: kharif only, rabi only, zaid only and double/triple cropped areas. Source: NRSC

6 6 CROPPING SEASONS IN INDIA Cropping Season in INDIA May June July Aug Sep Oct Nov Dec Jan Feb Mar April May Kharif Rabi Zaid Source:

7 NDVI 7 CROP PHENOLOGY USING REMOTE SENSING Times series of MODIS NDVI 6-day 25m spatial resolution MODIS VI LAYERSTACKING Sowing date Harvesting date. Length of the growing season DAYS

8 NDVI Kharif Rabi Days Upstream - entirely paddy

9 NDVI Kharif Rabi Downstream - paddy mixed with perennial

10 OBJECTIVE Developing an automated algorithm to extract the crop phenology parameters from remote sensing data to prepare a crop calendar for the whole nation. Improving the parameterization of the agro-hydrological model SWAT by incorporating the crop related information and management practices.

11 METHODOLOGY MODIS VEGETATION INDICES DATA DISTRICT WISE SEASONAL CROP STATISTICS DENOISING NDVI AUTOMATED ALGORITHM USING DERIVATIVE METHOD CROP MAPPING & CROP CALENDAR FOR NATIONAL SCALE EXTRACTION OF CROP SOWING, HARVESTING DATES & CROP GROWTH LENGTH

12 2 DENOISING OF TIME SERIES DATA Removal of Cloudy pixels Masking of agricultural pixels from NRSC LULC data Gap filling Smoothening of the time series data Savitzky-Golay Filter g i = n= n R n= n L c n f i+n Removal of contaminated pixels

13 3 Time series raw NDVI and smoothened NDVI

14 DERIVATIVE METHOD 4 The st derivative of Lagrangian interpolation, L k x = y j l j= j k i=,j!=j l j = [ x j x i k x x m m=,m!=(i,j) ] x j x m Where y represents the NDVI value and x represents the composite day of the year of the i. The 3-point lagrangian interpolation is used for the study, the st derivative at point j along the NDVI time series calculated

15 NDVI 5 RESULTS AND DISCUSSION 29- crop year is selected for the preliminary study..7 NDVI and Derivative profile.5.3. NDVI Derivative 4/7/29 5/27/29 7/6/29 9/4/29 /24/29 2/3/29 2//2 3/23/2 5/2/ Days

16 NDVI NDVI 6 KHARIF SEASON PADDY PIXEL.5.3 NDVI. Derivative -. 3/28/29 5/7/29 7/6/29 8/25/29 /4/29 2/3/29 /22/2 3/3/2 5/2/2 6/2/ DAYS SUGARCANE.7.5 NDVI Lagrangian_Derivative.3. Sow_date Harv date /6/29 4//29 CGP: 2 DAYS Sow_date Harv date 3/4/29 9/2/29 CGP : 232 DAYS 3/28/ /7/29 7/6/29 8/25/29 /4/29 2/3/29 /22/2 3/3/2 5/2/2 - DAYS

17 NDVI MIXED PIXEL RICE MIXED WITH PLANTATION.3. NDVI LAGRANGIAN_DERIVATIVE 3/28/29 5/7/29 7/6/29 8/25/29 /4/29 2/3/29 /22/2 3/3/2 5/2/ DAYS Sow_date Harv date 5/4/29 2//2 CGP: 28 DAYS

18 NDVI NDVI 8.8 Double crop - Paddy with another crop 3/28/29-5/7/29 7/6/29 8/25/29 /4/29 2/3/29 /22/2 3/3/2 5/2/2 6/2/2 NDVI Derivative - DAYS Double crop - Paddy -Paddy.8 NDVI Derivative 3/28/29-5/7/29 7/6/29 8/25/29 /4/29 2/3/29 /22/2 3/3/2 5/2/2 6/2/2 - - DAYS

19 9 SPATIAL VARIABILITY OF SOWING & HARVESTING DATES

20 2 CONCLUSIONS The algorithm was effective for pure pixels of single and double crops. Exception handling is required for various degrees of mixed pixel.

21 2 FUTURE WORK The algorithm will be applied for the time series of MODIS composite data from the period of 2 till date Also, it will be implemented and validated for various seasons and crops across India. The crop information extracted using this methodology will be used in SWAT for modelling large river basins.

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