The document discusses the Daily Erosion Project (DEP), which uses remote sensing and modeling to estimate soil erosion on agricultural lands. The DEP utilizes the Water Erosion Prediction Project model with inputs like precipitation, elevation, soils, land use, and crop residue data. Residue levels are estimated from remote sensing indices compared to ground surveys. Over 280,000 flowpaths across 2,500 watersheds are modeled daily to map erosion. The results help farmers and managers understand soil loss and the DEP works with various partners and agencies.

1. Remote Sensing of Crop Residue
Conditions for the Daily Erosion Project
Brian Gelder
Iowa State University
1
High Resolution Estimates of Hydrologic
Processes in the Midwestern USA

2. The Daily Erosion Project
• Our mission
– To help farmers, land managers, and the public
better understand the dynamics and magnitude of
runoff and soil erosion through daily estimation of
these processes on agricultural areas and
dissemination of the estimates via the web
• https://dailyerosion.org
• Our beginning
– ISU Agronomy Dept. Endowment funding led to DEP
• Township based estimates of precipitation, runoff,
delivery, and soil moisture

3. • Modern spatial data and remote sensing makes it
possible to generate high-resolution inputs for
Water Erosion Prediction Project (WEPP) model
– Use discrete flowpaths from LiDAR-derived DEM
• Overlay on other appropriate data sources
– Precipitation, soil properties, crop and tillage practices
– Use a HUC12 watershed spatial framework with
approximately 125 random flowpaths per HUC12
• 2,500 HUC12 watersheds in the current domain
– Each approximately 100 km2
– Over 280,000 flowpaths modeled daily
Daily Erosion Project

4. NEXRAD
Precipitation
LiDAR
Elevation
gSSURGO
Soils
Field-scale
Land-use &
Management
Runoff
Detachment
Delivery
Soil Moisture
Water Erosion
Prediction Project
Mechanistic Model

5. • The WEPP mechanistic erosion model needs tillage
information to simulate the residue burial and
decomposition process
– Less measurement intensive than providing temporal
estimates of residue mass and surface coverage
– Better guides residue mass and coverage estimates than
extrapolating from last measurements
• Tillage information is not readily available
– High repeat sources do not have enough resolution
– High resolution sources do not have enough repeat
• We are attempting to estimate tillage practices from
field mean residue cover remaining at planting
Daily Erosion Project

6. DEP - Land Use & Management
• Estimates are made for
agricultural land parcels
greater than 15 acres
• Field boundaries are
derived from 2008 available
USDA FSA CLUs
• Manually edited to reflect
single crop boundary from
a NASS Cropland Data Layer
– IA, IL 2009
– KS, WI 2014
– MN, MO 2016

7. Each field polygon has
• Majority land cover for
previous 8 years
– Example shows only 2010-
2015 imagery
– Assigned a major rotation
+
DEP - Land Use & Management
2013
20112010
2012
20152014

8. USDA/ARS Agricultural Conservation Planning Framework
(ACPF) and the Daily Erosion Project
• The DEMs, crop rotation, and soils data used in the Daily Erosion
Project are derived from the Agricultural Conservation Planning
Framework (ACPF) database
– Tomer et al., USDA/ARS National Laboratory for Agriculture and the Environment (NLAE)
• ACPF database combines with the ACPF Toolbox to do field-level
conservation planning
– Saturated Buffers, Nutrient Removal Wetlands, WASCOBs, etc.
• The ACPF (Midwestern USA only) is available at the website:
http://acpf4watersheds.org
• Summary results for ACPF containing 2017 CDL crop fraction after
removing duplicates on border buffers
– 3.598 million boundaries in 258 HUC8s
– 1.767 million agricultural fields greater than 15 acres in size

9. • We attempt to estimate
residue cover by
observing reflectance in
the visible and infrared
• Residue has slightly
different characteristics
than soil or vegetation
– Hyperspectral sensors
can better target
cellulose and lignin
signals in SWIR but
these lack areal
coverage and
repeatability needed
Daily Erosion Project

10. • We thus use SWIR
information from the
Landsat TM, ETM+, and
OLI sensors as well as
Sentinel 2 sensors
• The Normalized
Difference Tillage Index
(NDTI; next slide) or
other custom SWIR
ratios can correlate well
with remaining residue
cover/tillage intensity
DEP Management – Residue Cover

11. Post Fall Residue Polygons NDTI = Band 5 – Band 7
Band 5 + Band 7
DEP Management – Residue Cover

12. • We analyze all Landsat
and Sentinel 2 scenes for
acceptable imagery
– Filter out water, snow,
clouds, and cloud
shadows
• This gives us the
maximum amount of
observations per year
– Landsat every 7/9 days
– Sentinel 2 every 5 days
• Utilize Minimum NDTI
value for the year
DEP Management – Residue Cover

13. • This method requires gathering thousands of
residue counts at ground sample points
– Resulting in multiple groups using multiple methods
of surveying, often as part of other duties
• Minnesota soil residue cover estimates courtesy of Leif
Olmanson and David Mulla at UM
• Iowa data as part of Iowa Nutrient Research Center project
and various residue survey projects
• Nebraska data as part of NE NRCS estimation program
• Kansas data as part of a KSU residue survey project
• Estimates are then converted back to tillage
Daily Erosion Project

15. • Minnesota Board of Water and Soil Resources is
required by law to estimate sheet, rill, and wind
erosion across the state every year
• The Daily Erosion Project is helping generate
these estimates for Minnesota BWSR after being
given residue cover estimates for the state
• The University of Minnesota has been collecting
residue cover ground truth samples at thousands
of locations across the state each year starting in
2016
Daily Erosion Project

16. Sentinel 2
Crop Residue
May 5, 2016
Courtesy of Leif
Olmanson, PhD

17. Sentinel 2 Crop Residue May 5, 2016 based on 2015 CDL
Corn

18. Sentinel 2 Crop Residue May 5, 2016 based on 2015 CLDL
Soybeans

19. Daily Erosion Project
2015 and 2016 Residue Cover was collected at locations across
the state as part of an INRC grant. Analysis was done by
photographic interpretation.
Soil moisture conditions were favorable across the sampling
domain and emergence was early in some fields.

20. Daily Erosion Project
High NDVI
(emerged crops)
High NDVI
(emerged crops)

21. Daily Erosion Project
High NDVI
(emerged crops)
High NDVI
(emerged crops)

22. Daily Erosion Project
2014 and 2017 Residue Cover was collected at locations across
the state as part of work by watershed coordinators or other
watershed projects. Analysis was done by visual estimation.
Soil moisture conditions were wet in 2014, especially in potholes
and normal in 2017. Emergence was also early in some fields.

23. Daily Erosion Project
Ultra Low NDTI
(likely ponded)
Ultra Low NDTI
(likely ponded)

24. Daily Erosion Project

25. DEP Residue Cover Future
• Finish analyzing Iowa residue cover surveys
– Remove high vegetation fraction fields from correlation analysis
• NDVI threshold-based filtering
– Remove inundated portions of fields from correlation analysis
• The USGS Landsat team has recently provided a new product to automate this
– Develop final residue cover/NDTI correlation curve
– Automate high vegetation and inundation removal/correction
– Generate
• Analyze Nebraska and Kansas residue cover surveys
• Investigate alternative methods to define the tillage
index/residue cover relationship
– Hyperspectral analysis

26. DEP Questions?
• Acknowledgements:
– ISU Agronomy Department Endowment
– IDEP1, IDEP2, DEP
– USDA Agricultural Research Service NLAE
– IDEP1, IDEP2, DEP
– USDA NRCS
– Preliminary Management Work, ACPF Expansion
– Environmental Defense Fund
– Preliminary ACPF Work
– Iowa DOT and Iowa Institute for Hydraulic Research (IIHR)
– DEM Enforcement
– North Central Regional Water Network
– ACPF Expansion in Nebraska, Missouri, Wisconsin, and Minnesota
DEP Web Pages
http://dailyerosion.org/map/
http://dailyerosion.org/

27. DEP Questions?
• Acknowledgements:
– Iowa Nutrient Research Center
– Cover Crops/Residue Cover Mapping
– Conservation Practice Mapping
– Stacked Scenarios for Phosphorus
– Union of Concerned Scientists
– Erosion Scenarios
– Minnesota BWSR and DNR
– Minnesota DEM Enforcement and ACPF/DEP Expansion
– Nebraska NRCS
– Nebraska DEM Enforcement and ACPF/DEP Expansion
– US Department of Housing and Urban Development FEMA
– Flood Reduction Scenarios

29. DEP Sampling Scheme
HUC12 Estimate – Mean of all flowpaths below
(Each HUC12 approx. 10000 ha)
HUC12 Sub-catchment (Stratified sample)
(Each sub-catchment approx. 100 ha)
(Approximately 125 flowpaths per HUC12)
Flowpath (1 Random sample w/i sub-catchment)
(Approximately 10-100 m long)
(Only in dispersed flow)

30. HUC12s & Sub-catchments
HUC 12 Boundary
(approx. 10000 ha)
Subcatchment
Boundary
(approx. 100 ha)
Subcatchments are defined by the
Peuker-Douglas constant drop stream
Algorithm in TAU-DEM

31. Sub-catchments & Flowpaths
Subcatchment Boundary
Flowpath
(Not modeled)
Flowpath
(Modeled)
1 random flowpath per subcatchment
Must be on agricultural land
Approximately 10-100 m long
Only modeled in sheet/rill flow

32. What’s It Like In the Field?
We model the yellow flowpath (sheet and rill)
The black flowpath (concentrated flow) is
currently not modeled

33. Flowpaths & Land Use
FB070802030106
GenLU
Corn/Soybeans
C/S with Continuous Corn
Pasture
Conservation Rotation
Mixed Agriculture
Continuous Corn
Extended Rotation
Flood-prone Cropland
Forest
LT 15ac
UnAssigned
Water/wetland

34. Flowpath Parameterization
Land Use Soils

35. DEP Hillslope Profiles
• WEPP OFEs break at Land-use and Soils boundaries
• Slope estimated for each OFE from DEM
OFE 2
OFE 3
OFE 1
OFE 4

36. Sentinel 2
Crop Residue
May 5, 2016
Courtesy of Leif
Olmanson, PhD

37. Sentinel 2
Crop Residue
May 5, 2016
With
agroecoregions

38. Minnesota Tillage/Residue Combos
Mulch Level Soybean Corn
No-Till 0.25 0.70
Very High 0.15 0.45
High 0.10 0.30
Medium 0.05 0.15
Low 0.02 0.05
Moldboard 0.00 0.02