charles o’ hara preeti mali bijay srestha geo resources institute mississippi state university
DESCRIPTION
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. David Lewis Bob Ryan - PowerPoint PPT PresentationTRANSCRIPT
NASA RPC PDR
AN RPC EVALUATION OF NASA REMOTE SENSING INPUTS AND MODEL DERIVED DATA
FOR REGIONAL CROP YIELD PREDICTION MODELING
Charles O’ HaraPreeti Mali
Bijay SresthaGeo Resources Institute
Mississippi State UniversityMay 17, 2006
David LewisBob Ryan
Institute for Technology Development and SSAIStennis Space Center
May 17, 2006
NASA RPC PDR
• RPC Evaluation of Soybean Yield Modeling
• Regional Level Prediction
• Integration of Remote Sensing
• Advantages, Disadvantages and Tradeoffs
• Best Possible Solution
GENERAL OVERVIEW
NASA RPC PDR
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.
CROP YIELD MODELS
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.
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RPC: INTEGRATING BASELINE & FUTURE SENSORS DATA FOR CROP YIELD PREDICTION
Sensors in Current UseModerate 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.
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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 DETAILS: ARGENTINA
Study Area Details:
MODIS 10 x 10 tilesare shown for areasto be considered.
Field areas are shownfrom previous NASA/ITD/USDA FAS workas well as the fieldsselected by Dr. LouisSalado and Dr. TomSinclair for field datacampaign.
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200 0 200 400 600 800 Miles
CordobaOther ProvincesLandsat Path 228
N
EW
S
#
83
#
82
#
84
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)?
COMPLETED NASA RESEARCH IN STUDY AREA FOR USDA FAS
NASA RPC PDR
Crop # Samples _______________________________Corn 140Forest 40Pasture (Cultivated) 100Pasture (Natural) 100Soybeans 150Urban 40Water 40Wetland 40Wheat 150Sorghum <30Peanuts <30_________________________________
Total ~800 = total samples to be collected
CROP TYPES IN PROJECT
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FIELD DATA COLLECTION METHODOLOGY
Example of ground truth equipment and digital sampling forms created for this study
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DATA PRE-PROCESSING FLOW
Creation of daily NDVI datasets to be used for the hypertemporal composite
• NIR• QC • RED
• Clip to Bounds
• Make QC Mask
• Create NDVI
• Apply Masks
• Set Bad Pixels to -2
• Set Background to 0
• Save as ESRI Grid
• Import to Imagine
• Apply Median Filter
• Make Buffer Mask
• Make Look Angle Mask
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STEPS FOR GENERATION OF A MODIS-BASED NDVI
Download MOD09 HDF
Generate Daily NDVI
Resample
Export to GeoTIF
Composite NDVI
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Sep Oct Nov Dec | Jan Feb Mar April
2004 2005
HYPERTEMPORAL NDVI PLOTS FOR 4 MAIN CROPS
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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
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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.
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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
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INPUTS TO THE MODEL
Leaf Area Index
Temperature
Precipitation
Soil Moisture
Planting Date
Solar Radiation
Photoperiod
Temperature
Plant growth rate
Plant Leaf Area as a function of
Plant growth rate
LAI (Leaf Area Index) = PLA *
Plant Population
Fraction of Intercepted Radiation
based on LAI
Soil Water
Fraction of Transpirable
Soil Water
Efficiency of solar radiation in Biomass
assumption
Daily photosynthetic Biomass Production
Incident Solar
Radiation
Daily VegetativeBiomass
Calculate Daily Nitrogen Budget for vegetative growth and Seed growth
Calculate Vegetative
growth
Calculate Seed Growth rate based on Harvest Index
Daily Nitrogen Fixation
Seed Yield
Precipitation
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MODEL INPUTS
LEAF AREA INDEX
Sinclair Model simulation
NOAA-AVHRR (NOAA-Advanced Very High Resolution Radiometer)MODIS (Moderate Resolution Imaging Spectro-radiometer)
NASA LIS – Temperature & Soil Moisture (NASA Model)Visible/Infrared Imager/Radiometer Suite (VIIRS) – Synthetic
BASELINE SPATIAL SUBSTITUTE
Planting Date
Soil Water
Temperature
DIPI (Daily Increase in Plastochron
index)
PLA (Plant Leaf Area)
LAI = PLA * Plant Population
RPC EVALUATION
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BASELINE MODEL INPUTS
REMOTE SENSING BASED LAI
AVHRR (1km):
NDVI ~ LAI relationship
MODIS (250m):
EVI ~ LAI relationship
MODIS LAI ( MOD 15 LAI : 1km)
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MODEL INPUTS
METEOROLOGICAL DATA
INPUT DATA
SOURCES
LOCAL GROUND
STATIONSGOES Satellite Systems
METEOSAT,
TRMM
AVHRR,
MODIS
DataTemperature, Precipitation, Solar Radiation,
Precipitation
METEOSAT: Precipitation, Thermal
TRMM:Precipitation
Land Surface Temperature
Resolution Needs interpolation 4 km 2.5-5kmMODIS : 1km
AVHRR LAC: 1km
Temporal cycle
Hourly, Daily, Weekly DailyDaily
Daily
CoverageDepends upon countries
North and South America
METEOSAT: Europe/Africa/Indian Ocean
TRMM: Tropics
Global
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MODEL INPUTS
INPUT DATA
SOURCES
NCDC ( National Climatic Data
Center)
USAF-AGRMET (Agriculture
Meteorology model)
NASA-LIS (Land Information System)
RPC INPUT
Source Ground Met StationsIntegrated, Interpolated and Assimilated dataset
High-performance land surface modeling and data assimilation system
DataTemperature, Precipitation, Solar Radiation,
Precipitation, Temperature, Soil Temperature, Soil Moisture, Evapo-transpiration etc
Precipitation, Temperature, Soil Moisture etc
Resolution Needs interpolation½ degree ( ~ 40 km)
1 km
Temporal cycle Hourly, Daily, Weekly 3 hourly,DailyDaily
Coverage United StatesGlobal
Global
INTEGRATED DATA SOURCES
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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
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MODEL INPUTS
PLANTING DATE ESTIMATION
Improved through remote sensing
Zonal function
Temporal NDVI cube
Temporal NDVI Phenology curve
Detect onset of greenness
Develop refined estimation of crop
planting date
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RPC CHALLENGES
Baseline Configuration Challenges
Mitigation Solutions
Spatial VariabilityUse 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 challengesInvolve the model developer into the process (Dr. Thomas Sinclair)
Validation challengesField campaign with local experts to collect critical field data
* Critical RPC Items
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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).
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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.
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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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Scale Issues, Synthetic ProductValidation, and Uncertainty Analysis
Selecting large fields as study sites with areas that include semi-continuous 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.
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PRELIMINARY VIIRS NDVI SIMULATION SHEELY FARM CROP FIELDS
MODIS 250 m GSD NDVI VIIRS 400 m GSD NDVI
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PRELIMINARY VIIRS NDVI SIMULATION SHEELY FARM COTTON FIELD, 2003
MODIS NDVI Time Series VIIRS NDVI Time Series
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PRELIMINARY VIIRS NDVI SIMULATION SHEELY FARM GARLIC FIELD, 2003
MODIS NDVI Time Series VIIRS NDVI Time Series
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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.
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Source: Dr. Robert E Murphy, NPP Project Scientist, NASA GSFC
RESOLUTION VS SCAN ANGLE
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Synthetic VIIRS Data Product ValidationIRS (Indian Remote Sensing) RESOURCESAT-1
RESOURCESAT-1 Orbit and Coverage DetailsRESOURCESAT-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 341
Semi-major axis 7195.11
Altitude 817 km
Inclination 98.69 degrees
Eccentricity 0.001
Number of orbits/day 14.2083
Orbit Period 101.35 minutes
Repetivity 5-24 days
Distance between adjacent paths 117.5 km
Distance between successive ground tracks
2,820 km
Ground trace velocity 6.65 km/sec
Equatorial crosing time 10.30 ± 5 min A.M. (at descending node)
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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 specificationsIGFOV
56m (nadir)70m (at field edge)
Spectral BandsB2: 0.52-0.59B3: 0.62-0.68B4: 0.77-0.86B5: 1.55-1.70
Swath370 km each head740 km (combined)
Integration time 9.96 msecQuantization10 bitsNo. of gains1
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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.
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CONCEPTUAL REPRESENTATION
Running NDVI for Daily Model Runs
Desired Event Based Product for Critical
Phenological Development
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.
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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.
XY
Z
Y
X
Time Image 1
Image n
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TMA PARALLEL PROCESSINGBijay 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.
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TMA Parallel Processing
12
n
……………………………………
Block Distribution & Processing
Temporal Cube
Temporal Composite
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MODIS or Synthetic VIIRS Pre-processing
NDVI cube
Surface reflectance day 1
Surface reflectance day 2
Surface reflectance day N
…
NDVI day 1
NDVI day 2
NDVI day N
…
Surf.Refl. Quality day 1
Surf. Refl. Quality day 2
Surf. Refl. Quality day N
…
Quality Mask cube
QMask day 1
…
Geolocation Angles day 1
Geolocation Angles day 1
Geolocation Angles day N
… View zenith angle cube
view zenith angle day 1
…
QMask day 2
QMask day N
view zenith angle day 2
view zenith angle day N
Input to CompositingAlgorithm
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TMA Compositing
NDVI Cube View Angle Cube
Masking
TMA Operations
NDVI Composite
Surface reflectance Quality Cube
Masked NDVI Cube
Model based constraints to create masked NDVI
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Experimental Results
High quality temporalcomposites may be efficiently created forcustom products anddesired temporal andgeographic ranges of interest!
Implementation of parallel TMA abilitiesin the RPC will enablethe rapid generation ofcustom temporal composites of real andsimulated data sourcesand enable rapid use ofdesired products in evaluations!
NASA RPC PDR
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.
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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: