U.S. Geological Survey U.S. Department of Interior Automated Cropland Mapping Algorithms (ACMA) for Global Food Security Assessment Across Years Pardhasaradhi Teluguntla, Prasad Thenkabail, and Jun Xiong Jun 24-26, 2014 Fourth Workshop on Global Food Security Analysis 30 m (GFSAD30) Sioux Falls, SD, USA

U.S. Geological Survey U.S. Department of Interior Need for Rapid Food Security Assessment

U.S. Geological Survey U.S. Department of Interior Source: Rembold et al. (upcoming) How do Croplands Change from year to year? Rapid Assessment of Past, Current, and Future Once we map cropland areas accurately; our goal ought to monitor them across years; rapidly through automated algorithms

U.S. Geological Survey U.S. Department of Interior Average yield gaps for major cereal crops, maize, wheat and rice (Source: Mueller et al., 2012; in Atzberger et al. Upcoming) Future food security will have to focus on target areas where there is significant yield gap; so we can produce more in these poorly performing areas through better crop productivity and better water productivity using smart technologies Where Can We Produce More? to Ensure Food Security

U.S. Geological Survey U.S. Department of Interior Goals and Objectives

U.S. Geological Survey U.S. Department of Interior Automated Cropland Mapping Algorithms (ACMA) Overarching Goal and Specific Objectives Overarching Goal: To develop an automated cropland mapping algorithm (ACMA) that can rapidly and accurately produce, year by year, cropland dynamics relative to a normal year in order to quantitatively assess food security scenario. Specific Objectives are: 1.Develop an automated cropland mapping algorithm (ACMA) to automatically and accurately reproduce croplands over very large areas (e.g., country, region, world), year after year (past, current, future), at any spatial resolution; 2.Assess accuracies of such products; 3.Disseminate products and ACMA; 4.Establish a protocol for global food security analysis.

U.S. Geological Survey U.S. Department of Interior Automated Cropland Mapping Algorithm (ACMA) Baseline\Reference Cropland Product

U.S. Geological Survey U.S. Department of Interior Automated Cropland Mapping Algorithms (ACMA) Baseline\Reference (normal) year Australia 2001 was considered baseline\reference year because it was a normal agricultural year in the time period: 2000 through 2013 Note: 30.8 Mha is the area occupied by 250 m size MODIS pixels over Australia (~5 million 250 m size pixels) These 250 m size ~5 million pixels is where one should focus on assessing food security for Australia

U.S. Geological Survey U.S. Department of Interior Automated Cropland Mapping Algorithm (ACMA) Concept

U.S. Geological Survey U.S. Department of Interior Automated Cropland Mapping Algorithms (ACMA) Concept: Comparing all Years to a Baseline\Reference (normal) year\s Establish an Accurate Cropland Map of An Area (e.g., Australia for year 2001) for a normal year through any process Determine how agriculture has changed in other target years……rapidly and automatically…… using automated cropland mapping algorithm (ACMA) Year 2002 Year 2003 Year 2018 Year 2014 Note: ACMA requires same type of data used in its development in all years (e.g., MODIS, Landsat)………

U.S. Geological Survey U.S. Department of Interior If 2009ndvi> 1.1 *2001ndvi in at least 5 images (9-20) or at least 3 images (14-21) Better than normal If 2009ndvi> 0.9*2001ndvi and <= 1.1 *2001ndvi in at least 5 images (9-20) or at least 3 images (14-21) Normal scenario If 2009ndvi> 0.8*2001ndvi and <= 0.9 *2001ndvi in at least 5 images (9-20) or at least 3 images (14-21) Mild drought If 2009ndvi> 0.7* 2001ndvi and <= 0.8 *2001ndvi in at least 5 images (9-20) or at least 3 images (14-21) Medium drought If 2009ndvi> 0.6*2001ndvi and <= 0.7 *2001ndvi in at least 5 images (9-20) or at least 3 images (14-21) Severe drought If 2009ndvi> 0.5*2001ndvi and <= 0.6 *2001ndvi in at least 5 images (9-20) or at least 3 images (14-21) Reference year\s 2001 NDVI Target years (e.g., 2009 NDVI) Extreme drought/ Crop fail/Fallow Automated Cropland Mapping Algorithms (ACMA) Concept: Comparing all Years to a Baseline\Reference (normal) year\s Note 1: Each of these conditions include: (a) forward shift, (b) backward shift But retaining the conditions Note 2: conditions do not show change in cropping phenology

U.S. Geological Survey U.S. Department of Interior Normal Mild drought Medium drought Severe drought Extreme drought Fallow Above normal Automated Cropland Mapping Algorithms (ACMA) Baseline\Reference (normal) year\s (2001; see dotted line) and Conditions for: (a) convergence (normal, above normal), (b) divergence (drought, fallows) Note 1: Each of these conditions include: (a) forward shift, (b) backward shift, But retaining the conditions Note 2: conditions do not show change in cropping phenology

U.S. Geological Survey U.S. Department of Interior Application of Automated Cropland Mapping Algorithm (ACMA) for Year 2009, Australia Establishing Above Normal

U.S. Geological Survey U.S. Department of Interior Normal reference year Very good Y2009 (43%) Very good with forward shift Y2009 (3%) Total ~46% Automated Cropland Mapping Algorithms (ACMA) for Australia Very Good Conditions (> 10% NDVI) in 2009 relative to Baseline\Reference (normal) year 2001 during 3 or more months of crop growing period ACMA showed us that of ~5 million pixels of croplands of Australia: A.43% of all cropland pixels in 2009 did better than 2001 following nearly same phenology as 2001; B.3% of all cropland pixels in 2009 did better than 2001 with a forward shift in phenology (about 1-2 month later sowing and 1-2 month late harvest)

U.S. Geological Survey U.S. Department of Interior Automated Cropland Mapping Algorithms (ACMA) for Australia Very Good Conditions (> 10% NDVI) in 2009 relative to Baseline\Reference (normal) year 2001 during crop growing period

U.S. Geological Survey U.S. Department of Interior Application of Automated Cropland Mapping Algorithm (ACMA) for Year 2009, Australia Establishing Normal

U.S. Geological Survey U.S. Department of Interior Normal reference yearWith in normal range 26 % normal range with forward shift (6.8%) Total ~32% Automated Cropland Mapping Algorithms (ACMA) for Australia Normal (+10% deviation in NDVI ) Conditions in 2009 relative to Baseline\Reference (normal) year 2001 during crop growing period ACMA showed us that of ~5 million pixels of croplands in Australia: A.26% of all cropland pixels in 2009 had similar (+10%) conditions when compared with 2001 without forward shift (same phenology in both years); B.6.8% of all cropland pixels in 2009 had similar (+10%) conditions when compared with 2001 with a forward shift (1-2 month delay in sowing and harvest)

U.S. Geological Survey U.S. Department of Interior Automated Cropland Mapping Algorithms (ACMA) for Australia Normal (+10% deviation in NDVI ) Conditions in 2009 relative to Baseline\Reference (normal) year 2001 during crop growing period

U.S. Geological Survey U.S. Department of Interior Application of Automated Cropland Mapping Algorithm (ACMA) for Year 2009, Australia Establishing Mild Drought

U.S. Geological Survey U.S. Department of Interior Normal reference year> -20% and < -10% relative to normal year 2.7% Total ~6.3% with forward shift (3.6%) Automated Cropland Mapping Algorithms (ACMA) for Australia Mild Drought (>-20%, <-10% in NDVI ) Conditions in 2009 relative to Baseline\Reference (normal) year 2001 in 3 or more months during crop growing period ACMA showed us that of ~5 million pixels of croplands in Australia: A.2.7% of all cropland pixels in 2009 had Mild Drought (>-20%, <-10% in NDVI ) conditions when compared with 2001 without forward shift (same phenology in both years); B.3.6% of all cropland pixels in 2009 had Mild Drought (>-20%, <-10% in NDVI ) conditions when compared with 2001 with a forward shift (1-2 month delay in sowing and harvest) With forward shift = 3.6%

U.S. Geological Survey U.S. Department of Interior Automated Cropland Mapping Algorithms (ACMA) for Australia Mild Drought (>-20%, <-10% in NDVI ) Conditions in 2009 relative to Baseline\Reference (normal) year 2001 in 3 or more months during crop growing period

U.S. Geological Survey U.S. Department of Interior Application of Automated Cropland Mapping Algorithm (ACMA) for Year 2009, Australia Establishing Medium Drought

U.S. Geological Survey U.S. Department of Interior Normal reference year> -30% and < -20% relative to normal year 0.9% with forward shift (3.2%) Total ~4.1% Automated Cropland Mapping Algorithms (ACMA) for Australia Medium Drought (>-30%, <-20% in NDVI ) Conditions in 2009 relative to Baseline\Reference (normal) year 2001 in 3 or more months during crop growing period ACMA showed us that of ~5 million pixels of croplands in Australia: A.0.9% of all cropland pixels in 2009 had Medium Drought (>-30%, <-20% in NDVI ) conditions when compared with 2001 without forward shift (same phenology in both years); B.3.2% of all cropland pixels in 2009 had Medium Drought (>-30%, <-20% in NDVI ) conditions when compared with 2001 with a forward shift (1-2 month delay in sowing and harvest)

U.S. Geological Survey U.S. Department of Interior Automated Cropland Mapping Algorithms (ACMA) for Australia Medium Drought (>-30%, <-20% in NDVI ) Conditions in 2009 relative to Baseline\Reference (normal) year 2001 in 3 or more months during crop growing period

U.S. Geological Survey U.S. Department of Interior Application of Automated Cropland Mapping Algorithm (ACMA) for Year 2009, Australia Establishing Severe Drought

U.S. Geological Survey U.S. Department of Interior Normal reference year> -40% and < -30% relative to normal year 0.5% with forward shift (2.3%) Total ~2.8% Automated Cropland Mapping Algorithms (ACMA) for Australia Severe Drought (>-40%, <-30% in NDVI ) Conditions in 2009 relative to Baseline\Reference (normal) year 2001 in 3 or more months during crop growing period ACMA showed us that of ~5 million pixels of croplands in Australia: A.0.9% of all cropland pixels in 2009 had Severe Drought (>-40%, <-30% in NDVI ) conditions when compared with 2001 without forward shift (same phenology in both years); B.3.2% of all cropland pixels in 2009 had Severe Drought (>-40%, <-30% in NDVI ) conditions when compared with 2001 with a forward shift (1-2 month delay in sowing and harvest)

U.S. Geological Survey U.S. Department of Interior Automated Cropland Mapping Algorithms (ACMA) for Australia Severe Drought (>-40%, <-30% in NDVI ) Conditions in 2009 relative to Baseline\Reference (normal) year 2001 in 3 or more months during crop growing period

U.S. Geological Survey U.S. Department of Interior Application of Automated Cropland Mapping Algorithm (ACMA) for Year 2009, Australia Establishing Extreme Drought\Cropland Fallow

U.S. Geological Survey U.S. Department of Interior Normal reference year< = -40% relative to normal year 0.3% with forward shift (4.3%) Total ~4.8% Automated Cropland Mapping Algorithms (ACMA) for Australia Extreme Drought\Cropland Fallows (<-40% in NDVI) Conditions in 2009 relative to Baseline\Reference (normal) year 2001 in 3 or more months during crop growing period ACMA showed us that of ~5 million pixels of croplands in Australia: A.0.3% of all cropland pixels in 2009 had Extreme Drought\Cropland Fallows (<-40% in NDVI) conditions when compared with 2001 without forward shift (same phenology in both years); B.4.3% of all cropland pixels in 2009 had Extreme Drought\Cropland Fallows (<-40% in NDVI) conditions when compared with 2001 with a forward shift (1-2 month delay in sowing and harvest)

U.S. Geological Survey U.S. Department of Interior Automated Cropland Mapping Algorithms (ACMA) for Australia Extreme Drought\Cropland Fallows (<-40% in NDVI) Conditions in 2009 relative to Baseline\Reference (normal) year 2001 in 3 or more months during crop growing period

U.S. Geological Survey U.S. Department of Interior Automated Cropland Mapping Algorithms (ACMA) for Australia Cropping pattern change in season1 in 2009 relative to Baseline\Reference (normal) year 2001 at least 2 of day composites > 20% normal year

U.S. Geological Survey U.S. Department of Interior Automated Cropland Mapping Algorithms (ACMA) for Australia Cropping pattern change in season2 at least 4 of day composites > 20% normal year

U.S. Geological Survey U.S. Department of Interior Application of Automated Cropland Mapping Algorithm (ACMA) for Year 2009, Australia Summary

U.S. Geological Survey U.S. Department of Interior Croplands of Australia for Year 2009 using MODIS 250 m based on Automated Cropland Mapping Algorithm (ACMA) ACMA derived croplands of Australia for year 2009 relative to year 2001, showed us that of ~5 million pixels of croplands in Australia: A.46.3% above normal; B.32.7% normal; C.6.6% drought (mild); D.4.0% drought (medium); E.2.8% drought (severe); F.4.8% drought (extreme\fallow); G.0.2% change in cropping pattern from season 2 to season 1; H.0.3% change in cropping pattern from season 1 to season 2; I.2.3% unresolved ~98% pixels resolved

U.S. Geological Survey U.S. Department of Interior Croplands of Australia for Year 2009 using MODIS 250 m based on Automated Cropland Mapping Algorithm (ACMA) ACMA derived croplands of Australia for year 2009 relative to year 2001, showed us that of ~5 million pixels of croplands in Australia: A.46.3% above normal; B.32.7% normal; C.6.6% drought (mild); D.4.0% drought (medium); E.2.8% drought (severe); F.4.8% drought (extreme\fallow); G.0.2% change in cropping pattern from season 2 to season 1; H.0.3% change in cropping pattern from season 1 to season 2; I.2.3% unresolved ~98% pixels resolved

U.S. Geological Survey U.S. Department of Interior Different Scenarios of Y 2009 Summary Source Year 2001 Target Year 2009 Scenario (Condition)Norm al Forward shift Backward shift Change NC to crop in S2 Total 1Above normal (> normal) 43.19%3.13%46.32% 2Normal (+/- 10%) 25.97%6.79%32.76% 3Mild drought (>-20% and < -10% ) 2.95%3.66%6.61% 4Medium drought (>-30% and <-20% ) 0.87%3.19%4.06% 5Severe drought (>-40% and <-30% ) 0.52%2.26%2.79% 6Extreme drought Fallow(<-40% ) 0.53%4.25%4.78% 7Cropping pattern change in season1 0.17% 8Cropping pattern change in season2 0.29% Total %74.03%23.28%0.17%0.29%97.8% Un-resolved 2.2%

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