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.

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

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.

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

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

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

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

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 =

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 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

Overall Accuracy, %2009

2010 2011

Landsat

76 74 71

SPOT 78 89 81

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.

Decision Support Tools

Evaluate the impact of removing corn residues for biofuel on water quality and soil carbon in a watershed in central Iowa.

16

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

17

South Fork Watershed - Shifting towards corn-dominated production

18

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)

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.

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.

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.

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

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.

24ASABE Annual Meeting– Aug 7-10, 2011

Thank you!

Questions?