AN RPC EVALUATION OF NASA REMOTE SENSING
INPUTS AND MODEL DERIVED DATA
FOR REGIONAL CROP YIELD PREDICTION MODELING
Charles O’ Hara
Preeti Mali
Bijay Srestha
Geo Resources Institute
Mississippi State University
May 17, 2006
NASA RPC PDR
David Lewis
Bob Ryan
Institute for Technology
Development and SSAI
Stennis Space Center
May 17, 2006
GENERAL OVERVIEW
• RPC Evaluation of Soybean Yield Modeling
• Regional Level Prediction
• Integration of Remote Sensing
• Advantages, Disadvantages and Tradeoffs
• Best Possible Solution
NASA RPC PDR
CROP YIELD MODELS
Crop (Soybean) Yield Prediction
• Crop models have been used for predicting crop yield
before harvest.
• These pre-harvest crop yield estimations also help in
regional and global crop prices and trade policies.
Integration of Remote Sensing Data to Crop Yield
Remote Sensing based methods have been used to
provide inputs to a number of crop prediction models.
NASA RPC PDR
RPC: INTEGRATING BASELINE & FUTURE
SENSORS DATA FOR CROP YIELD PREDICTION
Sensors in Current Use
Moderate Resolution Imaging Spectro-radiometer (MODIS)
Advanced Very High Resolution Radiometer (AVHRR)
Both have large Swath Width and High Temporal Resolution
RPC Evaluation: Implement a baseline configuration of the Sinclair Model
for selected soybean production areas in Brazil with current remote
sensing data streams and compare results against results derived from
model outputs using synthetic VIIRS and modeled LIS as data inputs.
Include a well-devised ground data collection campaign, collaboration
with USDA FAS for data sharing and exchange, participation of Dr. Tom
Sinclair as the model owner, programmers to integrate the model, and
researchers who will conduct tests and evaluations of results.
NASA RPC PDR
CROP MODEL SUITABILITY FOR
REGIONAL YIELD PREDICTION
• Regression based empirical methods
• Montieth based models
• Mechanistic or agro-meteorological based methods
The agro-meteorological based crop yield prediction
method provides a good scope in regional yield
predictions using remote sensing.
The variables in these methods are mostly obtained
from meteorological stations, derived from remote
sensing data sources, or computed by models; thus,
they provide global or regional coverage and enhanced
regional model applicability.
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STUDY AREA: ARGENTINA
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STUDY AREA DETAILS: ARGENTINA
Study Area Details:
MODIS 10 x 10 tiles
are shown for areas
to be considered.
Field areas are shown
from previous NASA/
ITD/USDA FAS work
as well as the fields
selected by Dr. Louis
Salado and Dr. Tom
Sinclair for field data
campaign.
NASA RPC PDR
COMPLETED NASA RESEARCH IN
STUDY AREA FOR USDA FAS
Can NASA Research contribute to the foreign crop type assessment
performed by the USDA Foreign Agriculture Service (FAS) Crop Assessment
Estimates Crop Condition Data Retrieval and Evaluation (CADRE)?
#
82
N
#
#
83
84
W
E
S
Landsat Path 228
Other Provinces
Cordoba
200
0
200 400 600 800 Miles
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CROP TYPES IN PROJECT
Crop
# Samples
_______________________________
Corn
140
Forest
40
Pasture (Cultivated)
100
Pasture (Natural)
100
Soybeans
150
Urban
40
Water
40
Wetland
40
Wheat
150
Sorghum
<30
Peanuts
<30
_________________________________
Total
NASA RPC PDR
~800 = total samples to be collected
FIELD DATA COLLECTION
METHODOLOGY
Example of ground truth equipment and digital sampling forms created for this study
NASA RPC PDR
DATA PRE-PROCESSING FLOW
• RED
• QC
•
•
•
Make QC Mask
•
Make Buffer Mask
Make Look Angle Mask
•
NIR
Clip to Bounds
Apply Median Filter
•
Set Bad Pixels to -2
•
Create NDVI
•
Set Background to 0
•
•
Save as ESRI Grid
Apply Masks
•
Import to Imagine
•
Creation of daily NDVI datasets to be used for the hypertemporal composite
NASA RPC PDR
STEPS FOR GENERATION OF A
MODIS-BASED NDVI
Download MOD09 HDF
Resample
Export to GeoTIF
Generate Daily NDVI
Composite NDVI
NASA RPC PDR
HYPERTEMPORAL NDVI PLOTS
FOR 4 MAIN CROPS
Sep
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Oct
Nov
2004
Dec
|
Jan
Feb
2005
Mar
April
CONCLUSIONS
• Moving window compositing produced dataset for good classification results
• Masks and filters applied significantly reduced anomalous and noisy pixels
• The NDVI profiles of the hypertemporal dataset were separable for the corn,
soybean, wheat, forest, other ag, and non-agriculture classes.
• Best classification method from those tested was Minimum Distance classifier
• The overall accuracy was improved using this classifier by separating the
soybean class into two classes for single and double-cropped soybeans
• An overall classification accuracy of 69% was achieved
NASA RPC PDR
FUTURE WORK
• Investigate a classification system that combines the Growing Degree Days and
Minimum Distance into a rules-based classifier (or decision tree classification system)
in order to raise the overall accuracy achieved.
• Develop a weighting rule for the data layers in a decision tree classification scheme
• Use more sample sites in order to separate the pasture and other-agriculture
classes
• Identify sample sizes by crop distribution and acreage
• To reduce noise, expanding the buffer mask to include a two and possibly three
pixel buffer away from identified cloud or ‘bad’ pixels.
• Refine methods for integrating results with crop yield prediction models.
NASA RPC PDR
SINCLAIR MODEL
SINCLAIR MODEL
• Semi-mechanistic model
Named after Thomas Sinclair (University of Florida)
• Used by USDA/FAS PECAD for regional soybean estimations
Basic model inputs are based on the following relationships
(Speath & Sinclair, 1987):
• Leaf emergence as a function of temperature
• Leaf area index as a function of leaf number and plant population
• Interception of solar radiation as a function of leaf area
• Biomass accumulation proportional to intercepted radiation
• Seed yield proportional to biomass
NASA RPC PDR
INPUTS TO THE MODEL
Fraction of
Transpirable
Soil Water
Temperature
Leaf Area
Index
Plant growth
rate
Temperature
Plant Leaf Area
as a function of
Plant growth rate
Precipitation
LAI (Leaf Area
Index) = PLA *
Plant Population
Soil Moisture
Fraction of
Intercepted Radiation
based on LAI
Planting Date
Efficiency of solar
radiation in Biomass
assumption
Soil
Water
Precipitation
Seed Yield
Solar
Radiation
Photoperiod
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Incident
Solar
Radiation
Daily
Nitrogen
Fixation
Daily photosynthetic
Biomass Production
Daily
Vegetative
Biomass
Calculate
Vegetative
growth
Calculate
Seed Growth
rate based on
Harvest Index
Calculate Daily
Nitrogen Budget for
vegetative growth
and Seed growth
MODEL INPUTS
LEAF AREA INDEX
Sinclair Model simulation
Planting Date
Temperature
DIPI (Daily
Increase in
Plastochron
index)
PLA
(Plant Leaf Area)
LAI = PLA *
Plant Population
Soil Water
BASELINE SPATIAL SUBSTITUTE
NOAA-AVHRR (NOAA-Advanced Very High Resolution Radiometer)
MODIS (Moderate Resolution Imaging Spectro-radiometer)
RPC EVALUATION
NASA LIS – Temperature & Soil Moisture (NASA Model)
Visible/Infrared Imager/Radiometer Suite (VIIRS) – Synthetic
NASA RPC PDR
BASELINE MODEL INPUTS
REMOTE SENSING BASED LAI
AVHRR (1km):
NDVI ~ LAI relationship
MODIS (250m):
EVI ~ LAI relationship
MODIS LAI ( MOD 15 LAI : 1km)
NASA RPC PDR
MODEL INPUTS
METEOROLOGICAL DATA
LOCAL GROUND
INPUT
DATA
STATIONS
SOURCES
Data
Temperature,
Precipitation, Solar
Radiation,
Resolution Needs interpolation
Temporal
Hourly, Daily, Weekly
cycle
Coverage
NASA RPC PDR
Depends upon
countries
GOES
Satellite
Systems
Precipitation
METEOSAT,
TRMM
METEOSAT:
Precipitation, Thermal
AVHRR,
MODIS
Land Surface
Temperature
TRMM:Precipitation
4 km
Daily
2.5-5km
Daily
METEOSAT:
North and
Europe/Africa/Indian
South America
Ocean
TRMM: Tropics
MODIS : 1km
AVHRR LAC: 1km
Daily
Global
MODEL INPUTS
INPUT DATA
SOURCES
INTEGRATED DATA SOURCES
NCDC ( National
USAF-AGRMET
NASA-LIS (Land
Climatic Data
(Agriculture
Information System)
RPC INPUT
Center)
Meteorology model)
Source
Ground Met Stations
Data
Temperature,
Precipitation, Solar
Radiation,
Resolution
Needs interpolation
Temporal cycle
Hourly, Daily, Weekly
Coverage
United States
NASA RPC PDR
High-performance land
Integrated, Interpolated surface modeling and
and Assimilated dataset data assimilation
system
Precipitation,
Temperature, Soil
Precipitation,
Temperature, Soil
Temperature, Soil
Moisture, EvapoMoisture etc
transpiration etc
½ degree ( ~ 40 km)
1 km
3 hourly,Daily
Global
Daily
Global
MODEL INPUTS
OTHER INPUTS
Day length: Calculated based on Latitude and Day
of year
Planting date: Important variable usually estimated
from local knowledge and crop reports
NASA RPC PDR
MODEL INPUTS
PLANTING DATE ESTIMATION
Improved through remote sensing
Zonal function
Temporal NDVI
Phenology curve
Temporal NDVI
cube
Detect onset of
greenness
Develop refined
estimation of crop
planting date
NASA RPC PDR
RPC CHALLENGES
* Critical RPC Items
Baseline Configuration
Challenges
Mitigation Solutions
Spatial Variability
Use of sensors and products with comparable
spatial resolution
* Include synthetic VIIRS for RPC comparison
Temporal Issues
Use of temporal computational solutions such as
temporal map algebra
Dataset Adaptability issues: Temporal,
Spatial, Geometric, Radiometric
Use of integrated systems such as *NASA-LIS
Model Manipulation challenges
Involve the model developer into the process (Dr.
Thomas Sinclair)
Validation challenges
Field campaign with local experts to collect critical
field data
NASA RPC PDR
NPOESS VIIRS
• In 2008, the National Polar-orbiting Operational Environmental
Satellite System (NPOESS) Visible Infrared Imager Radiometer
Suite (VIIRS) instrument will be launched into 1330, 1730, and 2130
local-time ascending-node sun-synchronous polar orbits.
• VIIRS will replace three different currently operating sensors:
– The Defense Meteorological Satellite Program (DMSP) Operational
Line-scan System (OLS),
– The NOAA Polar-orbiting Operational Environmental Satellite (POES)
Advanced Very High Resolution Radiometer (AVHRR), and
– The NASA Earth Observing System (EOS Terra and Aqua) Moderate
Resolution Imaging Spectroradiometer (MODIS).
NASA RPC PDR
VIIRS SIMULATION
• VIIRS will have a ground sample distance (GSD) ranging
from 371 m by 387 m at nadir to 800 m by 800 m at the
edge of the scan
• Since the MODIS red-band and NIR-band reflectances
have a GSD of 250 m at nadir, simulations of the types of
NDVI images to be expected from the VIIRS sensor can
be created from MODIS data
• Temporal VIIRS simulations, such as near-daily NDVI time
series plots and temporally-processed image videos, can
be created using the TSPT.
NASA RPC PDR
Synthetic VIIRS for RPC Evaluation –
Bob Ryan
• MODIS data will be collected for the study area for the
period from 2005 to 2007.
• VIIRS data will be simulated for specific desired time
intervals
• IRS ResourceSat 1 AWiFS image data are in active use
by the USDA FAS for crop monitoring and acreage
estimation.
• AWiFS image data provides an opportunity to create
simulated products for comparison to actual MODIS
products as well as to the synthetic VIIRS products to
perform preliminary validation and uncertainty
quantification of the synthetic products.
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SENSOR SPECIFICATION SHEETS
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Scale Issues, Synthetic Product
Validation, and Uncertainty Analysis
Selecting large fields as study sites
with areas that include semicontinuous features enables crop
characteristics to be measured by a
plurality of image pixels by
operational sensors. Synthetic
image products with reduced spatial
resolution will be produced that
provide pixels that still remain within
the boundaries of the selected study
sites.
A set of images with significantly
higher spatial resolution and similar
spectral characteristics will be
employed to test the results of the
data simulation and develop
preliminary quantification of
uncertainty.
NASA RPC PDR
PRELIMINARY VIIRS NDVI SIMULATION
SHEELY FARM CROP FIELDS
MODIS 250 m GSD NDVI
NASA RPC PDR
VIIRS 400 m GSD NDVI
PRELIMINARY VIIRS NDVI SIMULATION
SHEELY FARM COTTON FIELD, 2003
MODIS NDVI Time Series
NASA RPC PDR
VIIRS NDVI Time Series
PRELIMINARY VIIRS NDVI SIMULATION
SHEELY FARM GARLIC FIELD, 2003
MODIS NDVI Time Series
NASA RPC PDR
VIIRS NDVI Time Series
VIIRS PIXEL AGGREGATION
• VIIRS uses a pixel aggregation technique whereby three
pixels are aggregated in-scan from nadir to a sensor
zenith angle (SZA) of 31.71°, two pixels are aggregated
in-scan at SZA’s from 31.71° to 47.87°, and no
aggregation occurs beyond an SZA of 47.87°.
• Due to this technique, although VIIRS has a larger GSD
than MODIS at nadir, it has a smaller in-scan GSD at
large SZA.
NASA RPC PDR
RESOLUTION VS SCAN ANGLE
Source: Dr. Robert E Murphy,
NPP Project Scientist, NASA GSFC
NASA RPC PDR
Synthetic VIIRS Data Product Validation
IRS (Indian Remote Sensing) RESOURCESAT-1
RESOURCESAT-1 Orbit and Coverage Details
RESOURCESAT-1 was launched into a sun-synchronous orbit at an altitude of 817 km
following the current IRS 1C ground track. The RESOURCESAT-1 satellite was launched
October 17, 2003 with a design life of 5 years.
Orbits/cycle
Semi-major axis
Altitude
Inclination
Eccentricity
Number of orbits/day
Orbit Period
Repetivity
Distance between adjacent paths
Distance between successive ground
tracks
Ground trace velocity
Equatorial crosing time
NASA RPC PDR
341
7195.11
817 km
98.69 degrees
0.001
14.2083
101.35 minutes
5-24 days
117.5 km
2,820 km
6.65 km/sec
10.30 ± 5 min A.M. (at descending node)
Synthetic VIIRS Data Product Validation
AWiFS Characteristics
Advanced Wide Field Sensor (AWiFS)
The Advanced Wide Field Sensor
(AWiFS) with twin cameras has a 56
meter NADIR resolution with a 700 km
combined swath and a five day revisit
time. To cover such a wide swath,the
AWiFS camera is split into two separate
electro-optic modules (AWiFS-A and
AWiFS-B) tilted by 11.94 degrees with
respect to each other.
AWiFS specifications
IGFOV
56m (nadir)
70m (at field edge)
Spectral Bands
B2: 0.52-0.59
B3: 0.62-0.68
B4: 0.77-0.86
B5: 1.55-1.70
Swath
370 km each head
740 km (combined)
Integration time 9.96 msec
Quantization10 bits
No. of gains1
NASA RPC PDR
NASA AWiFS Characterize/Validation Activities
Some additional input may be provided here by NASA about their
efforts to characterize and validate calibrate reflectance products
from AWiFS data sources.
NASA RPC PDR
CONCEPTUAL REPRESENTATION
Compile MODIS data for area of interest
and temporal range defined. Create
synthetic VIIRS data to match the area
and temporal range of the MODIS data.
NASA RPC PDR
Running NDVI for
Daily Model Runs
Desired Event Based
Product for Critical
Phenological Development
RPC IMPLEMENTATION OF PARALLEL TEMPORAL MAP
ALGEBRA FOR RAPID DATA PRODUCT DEVELOPMENT
TMA is the temporal extension to conventional map algebra.
Treats time series of imagery as three dimensional data set.
XY plane represent Earth’s surface.
Z dimension represents time.
X
Y
Y
Image n
Image 1
Time
X
NASA RPC PDR
Z
TMA PARALLEL PROCESSING
Bijay Srestha – MS Thesis
• Global or regional coverage requires large volume of
satellite data.
• Need for intensive computing to integrate and process
large datasets.
• Parallel processing is the decomposition of a large problem
into smaller problems that can be solved simultaneously to
provide faster execution time.
• Many spatial programs are inherently parallel.
• Parallel processing can provide leap in performance.
NASA RPC PDR
TMA Parallel Processing
Temporal Cube
Block
Distribution &
Processing
1
Temporal Composite
NASA RPC PDR
2
n
……………………………………
MODIS or Synthetic VIIRS
Pre-processing
Surface reflectance day 1
NDVI day 1
Surface reflectance day 2
NDVI day 2
…
…
Surface reflectance day N
NDVI day N
Surf.Refl. Quality day 1
QMask day 1
Surf. Refl. Quality day 2
QMask day 2
QMask day N
Geolocation Angles day 1
view zenith angle day 1
Geolocation Angles day 1
view zenith angle day 2
…
…
NASA RPC PDR
Quality Mask cube
…
…
Surf. Refl. Quality day N
Geolocation Angles day N
NDVI cube
view zenith angle day N
View zenith
angle cube
Input to
Compositing
Algorithm
TMA Compositing
NDVI Cube
View Angle Cube
Surface reflectance Quality Cube
Model based
constraints to
create masked
NDVI
Masking
Masked NDVI Cube
TMA Operations
NDVI Composite
NASA RPC PDR
Experimental Results
High quality temporal
composites may be
efficiently created for
custom products and
desired temporal and
geographic ranges of
interest!
NASA RPC PDR
Implementation of
parallel TMA abilities
in the RPC will enable
the rapid generation of
custom temporal
composites of real and
simulated data sources
and enable rapid use of
desired products in
evaluations!
CONCLUSIONS
• More research is needed for validating LAI-based inputs from
remote sensing for agricultural modeling purposes.
• A single sensor does not provide sufficient information to meet the
needs for modeling regional agricultural systems, therefore
integrated systems such as NASA-LIS are necessary to address
spatial, temporal and adaptability issues.
• NASA–LIS provides up to 1km resolution, enhancing compatibility
with other inputs of comparable resolution.
• Employing a set of synthetic VIIRS data products will enable the
evaluation to consider the sensitivity of the model to the
characteristics of the data streams from the future NASA sensor.
• Agricultural yield prediction requires multi-temporal analysis and
implementation of solutions such as temporal map algebra offers
opportunity to implement robust solutions.
NASA RPC PDR
PDR Questions and Discussion Items
RPC Experimental Design:
Baseline and Future Data Assimilation Plan:
Strength of RPC Team:
Adequacy of Field Data Campaign and Local Knowledge Expertise:
Identification of Pathway to ISS:
NASA RPC PDR