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

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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: ARGENTINA

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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

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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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SENSOR SPECIFICATION SHEETS

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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!

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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: