1. North American Croplands Teki Sankey and Richard Massey Northern Arizona University Flagstaff, AZ

2. Existing Sources – North America Coarse-resolution datasets – LULC maps (Loveland et al.,2000; Hansen et al., 2000) – National Land Cover Dataset (Homer et al., 2004) – GAP datasets Finer-resolution datasets – USDA National Agricultural Statistics Service Cropland Data Layer (CDL) 30 m resolution 2010 (and 2008-2006 for a few regions)

3. Existing Sources – North America Canada – 30 m – 2000 Mexico

4. North America Year 2000 MODIS Downloaded 8-day composites (MOD09Q1) B1 and B2 Reprojected into geographic projection

5. North America Mosaicked tiles into NA coverage B1 and B2 for 39 days covering NA over the year 2000

6. North America Stacked B1 and B2 for each day Calculated NDVI Stacked 39 days==NDVI time-series for NA

7. North America Subset NA NDVI time-series using the cropland extent map

8. North America Classification using NDVI stack and CE mask: – Unsupervised classification (15 classes)

9. North America

10. How to refine?? How to integrate additional information? Spectral feature fitting (ENVI)

11. North America Combine the MODIS-derived map with Landsat data Landsat data download: – Jun composite (few scenes/location) – LEDAP (only 500 scenes/order out of 861 scenes within the CE extent) – WELD (after 2003)

12. STARFM Gao et al. (2006) introduced the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and has been used in: – Forest disturbance mapping (Hilker et al., 2009) – Forest cover loss mapping (Potapov et al., 2008) – Forest cover change studies (Hansen et al., 2008) – Conservation tillage studies (Watts et al., 2010) Data fusion technique: Landsat and MODIS data Yields high-temporal Landsat data in calibrated spectral reflectance LANDSAT IMAGE 1MODIS IMAGE 1 LANDSAT IMAGE 1 MODIS IMAGE 2PREDICTED IMAGE 2 LANDSAT IMAGE 3MODIS IMAGE 3 LANDSAT IMAGE 3 MODIS IMAGE 4 ?MODIS IMAGE 5PREDICTED IMAGE 5 MODIS IMAGE 6 LANDSAT IMAGE 7MODIS IMAGE 7 LANDSAT IMAGE 7 Filled gap Supplemented data

13. Motivation Capitalize on advantageous characteristics of both Landsat and MODIS Increasing availability of data by adding to the available 30 m Landsat dataset Synthetic images will have lesser cloud contamination

14. Objectives Generate synthetic Landsat images with high frequency MODIS (8-day) and low frequency Landsat (16-day) data Identify crop types using the combined data set and NDVI time-series

15. Study Area Area Characteristics Located in North Central valley, California Subset of Landsat WRS-2 path 44/row 33 Subset dimensions: 1405 x 1498 Study Area

16. Data Availability and plan LandsatMODISPredictedCombined 6665 66 7374 8281 82 8990 9897 98 105106 114113 114 121 129 137138 145 153 162161162 169170 178177 178 185186 194193 194 201202 210209 210 217218 226225 226 233234 242241 242 249 257258 265 274273274 3/7/2010 10/1/2010 6/11/2010 3/23/2010 4/8/2010 4/24/2010 6/27/2010 7/13/2010 7/29/2010 8/14/2010 8/30/2010

17. Method T1 band 2 MODIS T2 band 2 MODIS T3 band 2 MODIS T1 band 4 Landsat T2 band 4 Landsat Output at Landsat resolution is based on spatially weighted difference Landsat and the MODIS scene pairs at T 1 and T 2 in addition to MODIS scene for T 3 are used to predict Landsat image for T 3 (T 1 < T 3 < T 2 ) T3 STARFM Predicted

18. Method MODIS Landsat data – Original dates – Predicted dates Calculate NDVI – STARFM-predicted bands 3 and 4 – STARFM-predicated NDVI

19. Classification Maximum Likelihood classification was used Training areas were selected based on temporal patterns of NDVI for different crops

20. Results: Accuracy Type Producer’s accuracy (avg) User’s accuracy (avg) Overall Landsat 86.21% 85.86%87.00% MODIS 71.67%71.17% 65.00% Landsat-Predicted Landsat (NDVI pre STARFM) 79.92%83.05% 83.16% Landsat-Predicted Landsat (NDVI post STARFM) 79.48%79.13% 82.50% Cropland data layer (CDL) land-use land-cover maps of California for 2010 were used

21. Conclusions STARFM-derived images were successfully used to classify croplands at Landsat resolution at shorter time intervals (8-day) STARFM maintains high spatial detail in the predicted scenes STARFM is useful in temporal analysis for regions with fewer Landsat scenes available Potential to supplement existing Landsat data for NA

22. North America How to refine?? Spectral feature fitting (ENVI) Other Landsat image sources?

24. Existing Sources—Most Recent to Older Yu et al., 2013: Global cropland extent in 30m resolution, 2010 Pittman et al., 2010: Global cropland extent in 250 m resolution, 2008 Thenkabail et al., 2009: Global irrigated area map, 2000 Biradar et al., 2009: Global map of rainfed croplands, 2000

25. Landsat Vs CDL

26. MODIS Vs CDL

27. Landsat-Predicted-Landsat (Pre STARFM) Vs CDL

28. Landsat-Predicted-Landsat (Post STARFM) Vs CDL