remote sensing for assessing crop residue cover and soil tillage intensity
DESCRIPTION
Remote sensing –Beyond images Mexico 14-15 December 2013 The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)TRANSCRIPT
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Remote Sensing for Assessing Crop Residue Cover and Soil Tillage Intensity
Craig Daughtry, Peter Beeson, Ray Hunt, and Ali Sadeghi
1USDA-ARS, Beltsville, Maryland USA
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Tillage intensity is defined by crop residue cover.
Crop residue = Portion of a crop that is left in the field after harvest.
oCrop residues on the soil surface:oDecrease soil erosionoIncrease soil organic matteroIncrease water infiltrationoImprove water qualityoAlter surface energy balance
oSoil tillage and biofuel harvestingoReduce residue amount and cover
Conservation till>30% cover
Reduced till15-30% cover
Intensive till<15% cover
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Traditional methods of measuring crop residue cover are inadequate for many fields and large areas.
Current Methods of Measuring Crop Residue CoverLine Point Transect • Stretch Line-Point Transect across rows and count the number of
markers that intersect residue.• Accuracy depends on length of line, number of points, and size of
residue pieces.Windshield Survey• Trained observers stop at intervals along a fixed route and assess fields
on both sides of road.• Errors due to subjective interpretation and limited observation of field
conditions near the road.
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Corn residue
Bare soil
Green vegetation
Reflectance Spectra
• Cellulose Absorption Index• CAI = 100 [0.5 (R2.0 + R2.2) - R2.1] Where:
R2.0 = reflectance at 2030 nm
R2.1 = reflectance at 2100 nm
R2.0 = reflectance at 2210 nm
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Reflectance spectra• ASD Spectroradiometer
• 18-degree fore optics• 350-2500 nm wavelength range
• Referenced to Spectralon panel
• Digital Camera•Aligned with FOV•Cover fractions determined using dot grid overlay.
Scaling-up: Field Reflectance Spectra
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Scaling-up: Airborne & Satellite Imaging Spectrometers
EO-1 Hyperion • 400-2500 nm • ~10 nm
bands • 30 m pixels;
AVIRIS (NASA)• 400-2450 nm • ~10 nm bands • 20 m pixels;
AISA Sensor (SpecTIR) • 400-2450 nm • ~5-10 nm bands • 0.5 to 4 m pixels
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Crop Residue Cover vs. Cellulose Absorption Index
Ground-based Satellite-based
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CAI-4 -2 0 2 4 6 8
Re
sid
ue
Co
ver,
%
0
20
40
60
80
100Cover = 28.1 + 8.86 CAI
r2
= 0.785
Hyperion DataMay 3, 2004
CornSoybean
+Crops2003
Residue Cover 2004
Planting progress for May 9 (Iowa Crop & Weather, 2004)
Corn: 93% planted;39% emergedSoybeans: 54% planted; 4% emerged
Residue cover was measured: May 10-12
Hyperion Imagery was acquired: May 3
CentralIowa 2004
Slope of line is similar to ground-based (ASD) and aircraft (AVIRIS & AISA) data in MD, IN, and IA.
Daughtry et al. 2006. Soil Tillage Research 91:101-108
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3 May 2004 <15%
15-30%
>30%
2003 Crop % % %
Corn 18 36 46
Soybean 35 40 25
Overall 25 38 37
Residue Cover Category
22 May 2005 <15% 15-30% >30%
2004 Crop % % %
Corn 7 38 55
Soybean 3 21 76
Overall 5 31 64
Intensive ReducedConservation
Weather at planting influences tillage intensity. 2004: warm, dry = more intense tillage 2005: cool, wet = less intense tillage
Tillage Class =
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Summary
Narrow band spectral indices that measure the intensity of absorption features are linearly related to crop residue cover.
Relationships developed with ground-based spectroradiometers (ASD) are extendable to airborne (AISA & AVIRIS) and space-borne (Hyperion & ASTER) sensors.
Maps and inventories of crop residue cover and soil tillage intensity across agricultural landscapes are possible.
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Advanced Remote Sensing Imagery Reality Check
• Satellite imaging spectrometers• NASA Hyperion is aging (launched in 2000).• German EnMAP scheduled launch: 2015.• NASA HyspIRI anticipated launch: >2018.
• Aircraft imaging spectrometers• NASA AVIRIS • SpecTIR AISA
• Satellite multispectral systems with SWIR bands • NASA/Japan – ASTER (SWIR detectors failed in 2008)• Digital Globe – WorldView-3 anticipated launch: 2014
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Corn Intensive Till Soybean No-TillSoybean TillCorn No-TillReduced Till
Corn & Soybean Intensive Till
Soybean No-Till
Corn No-Till
Corn Reduced Till
Corn and Soybean Fields with Different Tillage Intensities SPOT Bands
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Overall Accuracy, %2009
2010 2011
Landsat
76 74 71
SPOT 78 89 81
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SummaryBroad band residue indices are not robust.A few residue cover categories may be
identified in multispectral images.Training statistics are not extendable in time or
space. Soil type, crop residue age, scene moisture, and
atmospheric conditions affect classifications.
Challenge:How to best use a few hyperspectral images and
many multispectral images to produce regional surveys of soil tillage intensity.
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Decision Support Tools
Evaluate the impact of removing corn residues for biofuel on water quality and soil carbon in a watershed in central Iowa.
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South Fork of the Iowa RiverConservation
Effects Assessment Project
• One of 15 CEAP watersheds
• Area = 788 km2
• 84% Cropland• 99% Corn + Soybean
• Hydric soils• Potholes•Tile drainage
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South Fork Watershed - Shifting towards corn-dominated production
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Decision Support Tools for Modeling Scenarios:
1. Water Budget2. Nutrient
Transport3. Sediment
Transport4. Crop Yield5. Soil Carbon6. …
CQESTR(soil carbon)
EPIC(field scale)
APEX(field scale)
SWAT(watershed
scale)
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Watershed-Scale Simulations
• Soil and Water Assessment Tool - SWAT• Quasi-physically based water quality simulation model• SWAT predicts the effects of management practices on
water, sediment, and agricultural chemicals in watersheds.
• South Fork Watershed• Water budget was calibrated with observation data.
• 2000-2010• Sediment discharges were well correlated with measured
sediment discharges.• Scenarios simulated with using measured weather data.
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Scenarios• Continuous corn for all
cropland in watershed.
• Tillage intensity scenarios:• Conventional (<30% cover)
• Conservation (30 – 60%)
• No Till (>60% cover)
• Residue removal scenarios:
• No residue removed• 80% residue removed
Watershed-Scale Simulations - SWAT
Results• Residue removal increased sediment discharge for all tillage intensities.• Sediment discharge was greater in wet years than in normal years.• Proactive management strategies include:
•Reduce tillage intensity.•Establish filter strips and grass waterways.
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Field–Scale Simulations Background• A farmer has a field
• Silty Clay Loam (5% sand, 60% silt, 35% clay)• Average slope = 2% • Corn-soybean rotation • Conventional tillage for >10 years.
Scenarios• Evaluate effects of harvesting corn residue for biofuel on the
soil carbon and soil erosion over the next 10 years.
• His tillage and biofuel harvesting options:• Continue with conventional tillage with no residue removed• Continue with conventional tillage with 80% residue
removed• Switch to no-till with no residue removed• Switch to no-till with 80% residue removed
• Erosion Productivity Impact Calculator – EPIC• Ecosystem model for simulating management practices on
crop growth, yield, water balance, and nutrient cycling at field unit.
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After 10 years:• No-till increased soil
carbon and reduced sediment loss.
• Residue removal is sustainable for no-till on these nearly flat soils.
Tillage Intensity
InitialStatus
Residue Removed
0% 80%
Stable Soil Carbon, Mg/haConventional
92.1 93.5 88.5
No-till 92.1 96.1 93.3
Annual Sediment Loss, Mg/haConventional
2.3 2.8 6.1
No-till 2.3 0.5 1.1Caveats:• As slope increases, the amount of crop residue that can be
harvested in a sustainable manner is much lower.• The effects of harvesting crop residue can be different for other
geographic regions, e.g., Coastal Plain region of southeastern U.S.
Field-Scale SimulationsErosion Productivity Impact Calculator-
EPIC
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Conclusions• Remote sensing offers methods to account for variability in soil tillage intensity across agricultural landscapes.
•Watershed-scale simulations help identify proactive management strategies.
•Field-scale simulations evaluate sustainability of biofuel harvesting for specific soils and tillage intensities.
•A suite of models is required to address complex agronomic, environmental, and economic issues related to harvesting crop residues for biofuels.
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24ASABE Annual Meeting– Aug 7-10, 2011
Thank you!
Questions?