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)

1. 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

2. 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

3. 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.

4. 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

5. 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.

6. 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

7. Crop Residue Cover vs. Cellulose Absorption Index
Ground-based
Satellite-based

8. 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

9. 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

10. 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.

11. 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

12. 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

13. Overall Accuracy, %
2009
2010
2011
Landsat
76
74
71
SPOT
78
89
81

14. 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.

15. 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 River
Conservation Effects
Assessment Project
• One of 15 CEAP
watersheds
• Area = 788 km2
• 84% Cropland
• 99% Corn + Soybean
• Hydric soils
• Potholes
•Tile drainage
16

17. South Fork Watershed - Shifting towards corn-dominated production
17

18. 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

19. 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.

20. 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.

21. 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.

22. 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.

23. 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.

24. Thank you!
Questions?
ASABE Annual Meeting– Aug 7-10, 2011
24