NASA RPC PDR 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 Institute for Technology Development and SSAI Stennis Space Center May 17, 2006
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Charles O’ Hara Preeti Mali Bijay Srestha Geo Resources Institute Mississippi State University
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 Presentation
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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
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.
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.
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
• 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
• 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.
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
• 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).
• 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.
• 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.
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.
• 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.
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)
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.
Some additional input may be provided here by NASA about their efforts to characterize and validate calibrate reflectance products from AWiFS data sources.
• 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.
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!
• 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.