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Remote Sensing for Assessing Crop Residue Cover and Soil Tillage Intensity
 

Remote Sensing for Assessing Crop Residue Cover and Soil Tillage Intensity

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Remote sensing –Beyond images ...

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)

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    Remote Sensing for Assessing Crop Residue Cover and Soil Tillage Intensity Remote Sensing for Assessing Crop Residue Cover and Soil Tillage Intensity Presentation Transcript

    • 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
    • Tillage intensity is defined by crop residue cover. Crop residue = Portion of a crop that is left in the field after harvest. Conservation till >30% cover Reduced till 15-30% cover o Crop residues on the soil surface: oDecrease soil erosion oIncrease soil organic matter oIncrease water infiltration oImprove water quality oAlter surface energy balance o Soil tillage and biofuel harvesting oReduce residue amount and cover Intensive till <15% cover
    • Current Methods of Measuring Crop Residue Cover Line 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. Traditional methods of measuring crop residue cover are inadequate for many fields and large areas.
    • Reflectance Spectra Corn residue Bare soil Green vegetation • 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
    • Scaling-up: Field Reflectance Spectra 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: 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
    • Crop Residue Cover vs. Cellulose Absorption Index Ground-based Satellite-based
    • 100 Slope of line is similar to ground-based (ASD) and aircraft (AVIRIS & AISA) data in MD, IN, and IA. 80 R e s id u e C o v e r, % Central Iowa 2004 C o ve r = 2 8 .1 + 8 .8 6 C A I 2 r = 0 .7 8 5 60 40 Corn Soybean 20 H yp e rio n D a ta M ay 3, 2004 0 -4 -2 0 Planting progress for May 9 (Iowa Crop & Weather, 2004) Corn: 93% planted;39% emerged Soybeans: 54% planted; 4% emerged Residue cover was measured: May 10-12 Hyperion Imagery was acquired: May 3 Daughtry et al. 2006. Soil Tillage Research 91:101-108 2 CAI 4 6 8 + Residue Cover 2004 Crops 2003
    • Residue Cover Category Tillage Class = Intensive Reduced Conservation <15% 15-30% >30% 2003 Crop % % % Corn 18 36 46 Soybean 35 40 25 Overall 25 38 37 <15% 15-30% >30% 2004 Crop % % % Corn 7 38 55 Soybean 3 21 76 Overall 5 31 64 3 May 2004 22 May 2005 Weather at planting influences tillage intensity. 2004: warm, dry = more intense tillage 2005: cool, wet = less intense tillage
    • 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.
    • 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
    • Corn and Soybean Fields with Different Tillage Intensities SPOT Bands Corn No-Till Soybean No-Till Corn Reduced Till Corn & Soybean Intensive Till Corn Intensive Till Reduced Till Corn No-Till Soybean Till Soybean No-Till
    • Overall Accuracy, % 2009 2010 2011 Landsat 76 74 71 SPOT 78 89 81
    • Summary Broad 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.
    • Decision Support Tools  Evaluate the impact of removing corn residues for biofuel on water quality and soil carbon in a watershed in central Iowa.
    • South Fork of the Iowa River Conservation Effects Assessment Project • One of 15 CEAP watersheds • Area = 788 km2 • 84% Cropland • 99% Corn + Soybean • Hydric soils • Potholes •Tile drainage 16
    • South Fork Watershed - Shifting towards corn-dominated production 17
    • Decision Support Tools for Modeling Scenarios: SWAT (watershed scale) APEX (field scale) EPIC (field scale) CQESTR (soil carbon) 1. 2. 3. 4. 5. 6. Water Budget Nutrient Transport Sediment Transport Crop Yield Soil Carbon … 18
    • 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.
    • Watershed-Scale Simulations - SWAT 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 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.
    • 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.
    • Field-Scale Simulations Erosion Productivity Impact Calculator- EPIC 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 Initial Intensity Status Residue Removed 0% 80% Stable Soil Carbon, Mg/ha Conventional 92.1 93.5 88.5 No-till 92.1 96.1 93.3 Annual Sediment Loss, Mg/ha Conventional 2.3 2.8 6.1 No-till 2.3 0.5 1.1 Caveats: • 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.
    • 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.
    • Thank you! Questions? ASABE Annual Meeting– Aug 7-10, 2011 24