U.S. Geological Survey U.S. Department of Interior GFSAD30m Global Cropland Extent Products of Nominal 250 m (GCE V2.0) Updates Pardhasaradhi Teluguntla and Prasad Thenkabail 19 th March, 2015

There are many methods of Cropland mapping. But, within this project, we are focused on, developing and implementing cropland mapping algorithms (CMAs) that are, as far as possible, automated to a great extent or fully. In this regard, we have chosen two methods for this project: 1. Spectral Matching Techniques; and 2. Automated cropland classification algorithm that is a refinement of decision trees; Note: a. Same methods will be applied to produce the 4 products; b. The two methods will be applied to each of the 3 Masks GFSAD30 GCE V2.0 Cropland Products of Nominal 250 m METHODS Used to Produce Cropland Products We will discuss today U.S. Geological Survey U.S. Department of Interior Recall…

GFSAD 250m Cropland Products of Australia Automated Cropland Classification Algorithm (ACCA) U.S. Geological Survey U.S. Department of Interior  Based on Ideal spectral signatures, developed Automated Cropland Classification Algorithm (ACCA) for each of 3 cropland masks of Australia to study cropland changes year to year ( )

U.S. Department of the Interior U.S. Geological Survey GFSAD 250m Cropland Products of Australia 2a. Develop Automated Cropland Classification Algorithm (ACCA) for: (a) rainfed cropland mask

U.S. Geological Survey U.S. Department of Interior Separation of cropland Classes within Rainfed Cropland Mask of Australia Ideal spectra MODIS NDVI signatures based on ground samples

MODIS 250m-16day NDVI time series (2014) Rainfed-cropland mask If ΣNDVI <= In time period 11to 17 then Croplands, rainfed, fallows Class#4 If ΣNDVI >= In time period 2to 5 then Croplands, rainfed, Season2 crops Class#3 If ΣNDVI <= In time period 6to 8 then Croplands, rainfed, Season1 all crops Class#1 If ΣNDVI > In time period 6to 8 then Croplands, rainfed, pastures Class#2 Cropland Algorithm for Australia for Year 2014 Automated Cropland Classification Algorithm (ACCA) for Rainfed Cropland Mask Area of Australia based on Ideal spectral signatures : 1 of 3 U.S. Geological Survey U.S. Department of Interior

U.S. Geological Survey U.S. Department of Interior Class Distribution within Rainfed Cropland Mask of Australia for Year 2014 SMT versus ACCA: Spatial Distribution of Classes Reference Product, 4 classes from rainfed cropland mask (based on SMT): Spatial distribution of croplands within rainfed cropland mask using SMT Algorithm Product, 4 classes from rainfed cropland mask (based on ACCA): Spatial distribution of croplands within rainfed cropland mask using ACCA

U.S. Department of the Interior U.S. Geological Survey GFSAD 250m Cropland Products of Australia 2b. Develop Automated Cropland Classification Algorithm (ACCA) for: (b) rainfed pasture mask

U.S. Geological Survey U.S. Department of Interior Separation of cropland Classes within Rainfed Pasture Mask of Australia Ideal spectra MODIS NDVI signatures based on ground samples

MODIS 250m-16day NDVI time series (2014) Rainfed-pasture mask If ΣNDVI <= In time period 11to 14 then Croplands, rainfed, fallows Class#13 Else Croplands, rainfed, Season1 all crops Class#11 NDVI(1)>3800 or If ΣNDVI > In time period 3to 6 or NDVI(4)>6000 or NDVI(5)>6000 or NDVI(6)>6000 or NDVI(7)>6000 or NDVI(8)>6000 Or NDVI(9)>6000 or If ΣNDVI > 9000 and ΣNDVI < In time period 15 to 17 then Croplands, rainfed, pastures Class#12 Cropland Algorithm for Australia for Year 2014 Automated Cropland Classification Algorithm (ACCA) for Rainfed Pasture Mask Area of Australia: 2 of 3 U.S. Geological Survey U.S. Department of Interior

U.S. Geological Survey U.S. Department of Interior Class Distribution within Rainfed Pasture Mask of Australia for Year 2014 SMT versus ACCA: Spatial Distribution of Classes Reference Product, 3 classes in rainfed pasture mask (based on SMT): Spatial distribution of croplands within rainfed pasture mask using SMT Algorithm Product, 3 classes in rainfed pasture mask (based on ACCA): Spatial distribution of croplands within rainfed pasture mask using ACCA

U.S. Department of the Interior U.S. Geological Survey GFSAD 250m Cropland Products of Australia 2c. Develop Automated Cropland Classification Algorithm (ACCA) for: (c) irrigated cropland mask

U.S. Geological Survey U.S. Department of Interior Separation of cropland Classes within Irrigated Cropland Mask of Australia Ideal spectra MODIS NDVI signatures based on ground samples

MODIS 250m-16day NDVI time series (2014) Irrigated cropland mask If ΣNDVI <= In time period 8 to 11 then Croplands, irrigated, fallows Class#27 If ΣNDVI >= In time period 19to 22 then Croplands, irrigated, continuous orchards High NDVI Class#26 If ΣNDVI >= In time period 2 to 4 and ΣNDVI <= In time period 8 to 10 and ΣNDVI > In time period 15 to 18 then Croplands, irrigated, double crop Class#24 If ΣNDVI >= In time period 2 to 4 and ΣNDVI <= 8000 In time period 12 to 13 then Croplands, irrigated, season#2Class#23 If ΣNDVI >= In time period 2 to 4 or (ΣNDVI = ) In time period 13 to 18 then Croplands, irrigated, continuous, orchards Class#25 If ΣNDVI >= In time period 8 to 10 then Croplands, irrigated, pastures Class#22 Else Croplands, irrigated, all crops Class#21 Cropland Algorithm for Australia for Year 2014 Automated Cropland Classification Algorithm (ACCA) for Irrigated Cropland Mask Area of Australia: 3 of 3 U.S. Geological Survey U.S. Department of Interior

U.S. Geological Survey U.S. Department of Interior Class Distribution within irrigated cropland Mask of Australia for Year 2014 SMT versus ACCA: Spatial Distribution of Classes Reference Product, 7 classes in irrigated cropland mask (based on SMT): Spatial distribution of croplands within irrigated cropland mask using SMT Algorithm Product, 7 classes in irrigated cropland mask (based on ACCA): Spatial distribution of croplands within irrigated cropland mask using ACCA

U.S. Department of the Interior U.S. Geological Survey GFSAD 250m Cropland Products of Australia 3. Develop Automated Cropland Classification Algorithm (ACCA) for: (d) all 3 cropland masks

U.S. Geological Survey U.S. Department of Interior Class Distribution within all 3 cropland masks of Australia for Year 2014 SMT versus ACCA: Spatial Distribution of aggregated 6 Classes Reference Product, 6 classes from all 3 masks (based on SMT): Spatial distribution of croplands within all 3 cropland masks using SMT Algorithm Product, 6 classes from all 3 masks (based on ACCA): Spatial distribution of croplands within all 3 cropland masks using ACCA

U.S. Geological Survey U.S. Department of Interior Class Distribution within all 3 cropland Masks of Australia for Year 2014 SMT versus ACCA: Accuracy Assessment Reference Product, 6 classes from all 3 masks (x-axis): based on SMT Algorithm derived product, 6 classes from all 3 masks (y-axis): based on ACCA

U.S. Geological Survey U.S. Department of Interior Work in Progress:  Cropland change dynamics ( )  Sub pixel area calculations  Accuracy assessment with FAO statistics  Accuracy assessment with Other available sources

U.S. Geological Survey U.S. Department of Interior