SATELLITE REMOTE SENSING FOR DOMESTIC CROP REPORTING IN THEUNITED STATES AND CANADA:A LOOK TO THE FUTURE

LA TfL~D~TECTION SPATIALE, OUTIL NATIONAL D'INFORMATION SURLES CULTURES AU CANADA ET AUX ~TATS-UNIS: PERSPECTIVES D'AVENIR

Robert A. Ryerson,l Richard S. sigman,2Ronald J. Brownl

ABSTRACT

The benefits of foreign crop monitoring through remote sensing have been well docu-mented and well researched through programs such as LACIE and AGRISTARS. Although there arefewer benefits to be realized in domestic crop monitoring, they are still significant, whilethe technical problems of implementation are somewhat reduced compared to foreign monitoring.As a result of the efforts of various agencies in solving the technical problems there havebeen successes in domestic crop reporting with remote sensing in the United States since 1978,with successes in Canada being more recent.

As a first step towards looking to the future this paper addresses, in tabular form,the commonalities and differences in the approaches taken to remote sensing for domestic cropreporting the United States and Canada. From this table, other sources, and the authors'experience, the focus is then shifted to what the future of operational domestic crop reportingwith satellite data is likely to require and what this implies in terms of Research and Develop-ment. The kinds of problems addressed include among others, central vs distributed analysis,satellite requirements, data volumes and delivery times, integration with other informationsystems, and yield prediction.

,/RESUME

Les avantages du contrale des cultures etrangeres par teledetection sont largementreleves dans la documentation publiee et font l'objet d'amples recherches par Ie biais deprogrammes tels Ie LACIE et l'AGRISTARS. Bien que peu nombreux, les avantages a retirer d'uncontrale des cultures nationales sont encoure importants, et les problemes techniques de miseen oeuvre sont reduits comparativement a ceux du contrale etranger. Les efforts deployes pardiverses agences pour resoudre ces problemes ont connu un certain succes aux Etats-Unis depuis1978, les succes du Canada etapt plus recents.

En guise de premiere etape de l'exploration de l'avenir, la presente communicationexpose, sous la forme de tableaux, les similitudes et les differences des methodes de contra Iedes cultures par teledetection employees au Canada et aux Etats-Unis. En puisant dans cestableaux, dans d'autres sources et dans leur propre experience, les auteurs placent ensuitel'accent sur les imperatifs vraisemblables du contrale operationnel des cultures par satelliteet traduisent ces imperatifs en termes de consequences en matiere de recherche et de develop-pement. Les types de problemes dont il est question sont notamment: centralisation etdecentralisation de l'analyse, caracteristiques necessaires des satellites, volumes des donneeset delais de livraison, unification a d'autres systemes d'information et prevision desrendements.

lCanada Centre for Remote Sensing, 2464 Sheffield Road, Ottawa, Ontario. KIA OY72Economics and Statistics Service, USDA Washington, D.C.

KEYWORDS/MOTS-CLES: LACIE, AGRISTARS, SATELLITE-SENSING, CROP-YIELD, LANDSAT, CANADA, USA,DATA-PROCESSING.

Presented at the 7th Canadian Symposium on Remote Sensing, Winnipeg, Manitoba.September 8-11, 1981.

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1. INTRODUCTION

This paper provides a look at thefut ure of sa te lli te based domest i(' (' ropmonitoring in the United States and Canadafrom the perspe('tive of what developmentshave o('{'urred in the past, what the technicalproblems are likely to be in the future, andwhat options will likely be considered withinthe next decade or so.

2. THE PAST

The benefits of crop monitoring throughsatellite remote sensing have been estimatedto be millions of dollars for both the UnitedStates (Osterhoudt, 1978) and Canada (Clough,1974). Although these studies haveidenti fied most of the potential benefits inthe area of foreign crop monitoring, somes i gni fi('a nt bene fi ts for domest i(' cropreporting have also been identified. Theseand similar benefit studies have led to thesupport for the necessary Rand D in bothcountries to develop satellite-based foreignand domestic crop reporting methods.

From these efforts, a number ofsuccesses have resulted in the area ofdomestic crop reporting. The first successin the United States came in 1978 (Hanus('hak~ aI, 1980) when results using satellitedata were incorporated into official cropestimates for ('orn and soybeans in Iowa. Thesu('('ess in Canada ('ame later (Ryerson ~ aI,1981), when satellite data were used togenerate an a('reage estimate for potatoes inNew Brunswick before the offi('ial estimatewas released.

Table compares the two approa('heswhich resulted in these successes. Theevolution of the methods has been di fferent,partly as a result of the crops and regionsstudied, size of organization, data produ('tsavailable, and hardware used. Table 1 istherefore organized by major topics of('omparison: crops and regions, data used,corollary data, field data, users, approo('h,and state of use. It should be noted thatthe statisti('al approa('h of a regressionestimator used in Canada was patterned afterthat used in the United States. The fullapproaches are outlined in the various papersIi s ted at the end of the pape r.

Given the variety of approachesadopted, a wide range of past experience isavailable from which to identify problemareas whi('h are pe('uliar to ('ertainapproa('hes and those which are ('ommon toboth.

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3. TECHNICALISSUES

3.1 lntrodu('tion

The major techni('al issues in usingsatellite remote sensing for domestic ('ropmonitoring which must be addressed in thefuture in(' lude crop separability, yielddetermination, data availablility, andanalysis methods. Some policy relatedquestions which could affe('t the nature oftechnical problems are also dis('ussed below.

3.2 Separability

Despite the impression sometimes giventhat any given temperate crop can be separatedfrom all surrounding ('rops with Landsat MSSdata, this is so only if certain ('onditionsabout absen(~e of confusion crops, and date ofimagery with respe('t to growing season (orcrop's growth stage) are met. Thesecondit ions are rarely met. For example,potatoes were easily differentiated from othercrops in New Brunswick (Ryerson ~~, 1981),but in southern Alberta, with corn and sugarbeets as ('onfusion crops, the separation wasless distinct. (Ryerson and Shaw, 1981).

The problem of separability of target('rops from their surrounding confusionbackgruunds has led to an in(' reased interestin acquiring detailed ground spectralmeasurements in Canada and further analyzingexisting spectral data sets in the U.S. Theseare useful before new satellite systems arelaunched (Ahern et aI, 1980, Staenz et aI,1980, Tucker et aI, 1978,1979) and before oras new large programs are begun (Brown et aI,19HO) •

Indications are that existing andplanned systems will not provide separationson a routine basis for all crops in allregions. The study of existing data on ('ropspectra indicate that finer spatial/spectralresolutions will not lead to a generalsolution to the separability problem, althoughit will improve the situation for ('ertain('onfusions in certain areas. In general, inthe author I s opi ni on, mult i temporal data aremore valuable than finer spatial, spectral orradiometric resolution. Thus the repeat cyclea nd data availability are key elements of thecrop separability problem. For a ('rop whi('hcan only be separated from surrounding ('ropsduring a one-week window, it is highlyunlikely that any existing or plannedsatellite ('ould reliably provide data at thecorrect growth stage, especially if theseeding was delayed or spread out over severalweeks for any of the region's crops.

Future planning must consider theproblem of regional spectral I spatialconfusions and the consequent need forresearch using ground spectral data,supporting aircraft simulations of satellitedata, as well as special classificationmethods and cloud-area estimation procedures(see Section 3.5 below). Regardless of howsimple any problem may at first appear, usersshould always be ready for the possibilitythat the crop of interest may not beseparable from its background orsurroundings.

3.3 Yield Determination

Much of the Large Area Crop InventoryExperiment (LACIE) and Agriculture andResources Inventory Through Aerospace RemoteSensing (AgRISTARS) Rand D involves foreignand domestic yield determinations from acombination of meteorological and Landsatdata. Although some remarkable success hasbeen obtained, there are still major problemsrequiring solutions.

The central problems in deriving yielddata can be summarized:

3.4 Data Availability

Data availability and conti.nuity dependupon cycle frequency, cloud cover, satellitefailures, grouM processing failures aM sheervolume of data. Users will not invest in thetechnology to use the data if they have nogua ra ntees tha t access wi11 be cont i nuous andat a cost they can afford.

The cycle frequency, or the delaybetween one satellite pass over the area andthe next, has been the subject of a variety ofstudies. With less frequent coverage,multi-date analysis for crop separation orchange detection is not possible. Asfrequency decreases even more, there is anincreased probability that no image will beobtained because of cloud cover. For thisreason, satellites like SPOT that looksideways to adjacent cloud free satellitetracks may solve much of this problem.However such a satellite will introduceserious geometric distortion and require asophisticated decision process for allocatingcoverage. Moreover, high frequency coverageof one area must be at the expense of lesscoverage of the adjacent ones.

3. Yield models relyi ng onmeteorological data use datacollected from coarse networks.

No single satellite available or beingplanned can provide the information necessaryfor accurate yield forecasting.

2. Yield models now in use areimprecise and tend not to besensitive to dras tic changes fromnormal conditions nor do theycontain a feedback mechanism.

4. Regression estimators such as areused for crop area estimates havebeen applied to yield estimationfor some crops in the USA, with buta marginal gain in precision.There has been even less success inthe case of foreign crops.

With the present satellites long pasttheir design life and now exhibiting someproblems, a major fear is lack of data becauseof satellite failure. This is already aproblem with 1981 Canadian work since theground segment which includes the DigitalImage Correction System (DICS) (Guertin et aI,1979) cannot use the degraded Landsat III dataas input. (See 3.5 below).

Although less dramatic than satellitefailures, ground processing failures canresult in delays in delivery or evendestruction of data which have been acquired.

Experience in the United States in 1978during the summer crop season indicates thatwith one Landsat satellite with an eighteenday repeat cycle, less than 50% of Iowa wascovered. With two Landsats having nine daycoverage in 1978, eighty-five percent of thestate was cloud free at some time in thecritical period (Hanuschak et aI, 1980).Although further consideration must be paid tothe periodicity of movement of frontal weathersystems across North America, experiencesuggests that a fi ve or six day repeat cyclewould provide coverage of most areas duringthe key data acquisition windows for cropdiscrimination. In the meantime, stopgapmethods have been developed to estimate cropareas under localized clouds. (Ryerson et aI,1981; Hanuschak,1976).

record informationthe above ground

Only for certainconsistently bevarious biomass

Satellite data(primarily) ongreen vegetation.crops can thisrelated throughindices to yield.

1.

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For most Landsat receiving stations there isnot sufficient redundancy built in toguarantee continuous production under veryhigh demand. Si nce delays or losses of datato date as a result of ground segmentfailures have not been high, there may be atendancy to become complacent. With theturnaround times and volumes of data requiredfor domestic crop estimation, interruptionsin data production would lead to failure ofcrop estimation procedures now in place.

As noted above, turnaround times arecritical for domestic crop reporting. Slowerturnaround times would be acceptable for rawCCTs (say 20 days) if assura nee of imageavailability could be given to plan dataanalysis and information flows. The elapsedtime between satellite pass and data deliverydepends on the volume of data requested forthe same time frame, the data's resolution,and the type of processing requested.Guaranteed fast delivery (24 hours) from CCRSof standard computer tapes is possible forCanadian data at a cost of $690 per scene(three times the normal cost). Delivery fromthe U.S. sys tern usually requi res four to sixweeks at $200. Geometrically corrected DICSproducts costing $600 for about one quarterof a Landsat scene require up to 10 days toproduce in the rush mode. The system is nowclose to saturation and continued fastturnaround cannot be guaranteed until newprocessi ng equipment now bei ng des igned is onstream.

for the sa te 11ite based proc ed ures • Thesegment boundaries do not change, althoughabout twenty per cent of the segments arerotated out of the sample each year. However,in some regions field boundaries do changefrom year to year.

The USA approach requires digitizationof boundaries of both the segments and fieldsafter ground data collection. Thecanadianapproach takes advantage of the availabilityof geometrically corrected fast-turnarounddata. Segment boundaries can be digitizedprior to the eurre nt seas on I s imageacquisition. When new data are acquired, itis possible to register old and new data andthe segment boundaries. Since the fieldboundaries need not be digitized for use inobtai ni ng training data, as in the USAapproach. throughput is potentially fastersi nee less manpower is requi red duri ng thepeak estimation season. The gain isespecia lly important in areas where segmentsare complex, and after the initial year whenonly 20% of all segments must be located andstored. There is a further gain to berealized if the data are to be integrated intoa place-related information system formultiple use. The use of the geometricallycorrected DICS data makes multiple data use aviable possibility. Raw data (P tape, in theUSA) however, must go through significantadditional processing if they are to becombined temporally or with other data formultiple use.

Are multiple uses to beIf so, do they requiremulti temporal satelliteor satellite data andda ta ?

With continued success in domestic cropreporting, it can be expected that thedemands on current systems will outstriptheir capabilities to produce and analyzedata within the next five years. Increasedda ta volume a nd age of equipment will onlyserve to exacerbate the problem.

3.5 Methods of Segment Handling andClassification

The questions whichto evaluate whether orcorrected data should befollowing:

mustnotused

be addressedgeomet rica lly

include the

considered?merging ofdata and Isome other

There a re two cent ra 1 di ffe re ncesbetween the Canada and USA approaches todomestic crop monitoring as outlined in Table1 which bear further scrutiny. These are theuse of geometrically corrected vs uncorrectedLandsat data with respect to handling ofsegments, and the classification methodsused.

In both the USA and Canada, samplesegments of from one to three square miles insize are ennumerated on the ground byinterviewers. In the traditional method,crop areas obtained from these segments areexpanded to generate crop area estimates forla rger regions. These same segments are used

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2. What are the true operational datacosts likely to be for eachproduct?

3. Given these costs, whichalternative is most c:ost effectivegiven number of segments, segmentshape and ease of location,classi fica tion system used a nd userrequi rements?

The second major difference is in thetype of classification algorithm used. In theUSA a semi-automatic Maximum LikelihoodDecision Rule (MLDR) Classifier on a generalpurpose main frame computer is used. This

classifier assumes a Gaussian distribution ofvalues being classified. Training data arebased on field labels which are located fromthe digitized segment maps and subsequentlyverified on line printer outputs. In Canadathe simpler parallelepiped classifier on theCCRS Image Anlaysis System (CIAS)(Goodenough, 1979) is used. Training dataare manually selected by an analyst who usesa cursor on a video display of the data.Results are displayed in real time and can bemodified by adding or deleting trainingpixels from fields known from segment maps tobe those of interest (Ryerson et aI, 1981).In the Canadian work, thus far involvingestimates for single crops such as potatoesand canola, the parallelepiped has provenmore accurate for acreage estimates than theMLDR. This may be attributable to theinteractive capabilities associated with theparallelepiped as compared to the MLDR. Itis assumed that for some separations in thefuture, more sophisticated classificationmethods will be required in the Canadianwork.

A central question on classificationmethods arises: is the technology adva needenough to allow batch processing using MLDRfor crop estimation without a video displayand the human interaction made possible withthe simple and fast parallelpiped classifier?There is disagreement on this issue on bothsides of the border.

3.6 Innovations in Processing andIntegration of Geo-Coded Data

There are two potential developmentswhich may be considered as viable additionsto the methodology. The first of these isthe possible use of a distributed imageprocessing network feeding into a centraldata bank. This is receiving some attentionin both countries. The second innovation isthe possible integration of external databases to improve an estimate's accuracy orutility. There are three alternatives fordomestic crop reporting data processessing:centralized, local or a hybrid of the two.

Cent ral processing has a number ofadvantages: an identical approach for allregions, justification for a larger singlesystem (likely at less cost than for a numberof smaller systems) and hence a higherpotential for automation of labor intensivetasks, more efficient use of budgets and thepossibility for continued R&D, aconcentration of staff in one location (thusless reliance on a few trained individuals)which can focus on problems of a national

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nature requiring timely solutions, and acentral repository which would be able to feedinto national decision making more easily thana number of local centers.

Local processing also has advantages:centers would be closer to the problem withmore local knowledge and the opportunity tofield check inconsistent segments identifiedduri ng analysis, a regionally tailoredindividual approach could be used, localproblems of a pressing nature (eg. a localizeddrought over several counties) could be dealtwith more easily, local (state or provin<'e)data bases may be tapped more easily, localdata could likely be made available forearlier estimate dates than would be the casewith a central facility, and the lessspecialized analysis system could serve otherlocal users in the state or province when notrequired for agriculture to reduce thesystem's cost to agriculture.

A third, and perhaps the best approachin this area, is to use a central host systemwith a series of local processing centers with"intelligent" image analysis systems asterminals which could also stand alone. Suchan approach combines many of the advantages ofeach approach.

Another future development which shouldbe considered is the integration of ancillaryinformation into analyses to maximize theutility of the satellite data for decisionmaking. Within the next decade this wouldinvolve the use of geo-coded data bases orplace-related information systems as well asdigital terrain models (DTM). Theidenti fication of crops, their locations, andchanges from previous years could bythemGel ves form a use ful part of the data basefor assessing crop rotations, irrigationchanges, water use, and their affects on suchlocalized problems as soil erosion and soilsalinity. When combined with soils data,slope and cadastral information, the data basecould become a powerful extension tool forprovinces, states or counties. Lookingfurther into the future, the use of avideotext system by farmers could provide themwi th a new management tool.

There are a number of excitingpossibilities in methods for the future, butoptimism must be tempered with the realizationthat there are problems in methodology, dataavailability and data costs which must beanswered if domestic crop reporting is tobecome truly operational. In addition, thereare policy decisions required which willaffect how (and if) the technology will bedeveloped.

3.7 Policy and Economics Questions

The following five policy and economicsquestions largely determine where thetechnology will go in the next decade.

First, as noted in Section 3.4, theremust be continuity of data for users to feelcomforta ble with the tec hnology for somethi ngas important as crop reporting. At present,continuity of the MSS or Thematic Mapper (TM)is not assured into the 1990's. An importantpart of continuity is the requirement of aclear understanding of pricing for variousproducts. Above a certain price, there willbe changes in methodology and beyond afurther threshold there will be resistanceand possibly rejection of the technology.

Another area of concern in policy iswhether governments (Federal, state/province)or industry will be in control of thesatellite data delivery technology, and withwhat effect? It is assumed that the futureanalysis of the data would be no differentthan now, governments, industry anduniversities would all have the capability todo certain types of analyses.

A third area of policy concern is thenature of the problem which is presentlybeing addressed through the use of satellitedata. Satellite data are being used toestimate crop areas for specific regionsselected before the current crop season.This approach, while yielding increasedprecision (for fixed sample size) over theuse of ground data alone, essentiallyduplicates the objectives of the groundsurvey. If satellite data costs risesubstantially, the possibility exists thatthese data may not be competitive on a purelyeconomie basis. This is particularly so whenone considers the current labour intensivebudget structures of ground surveys vs thecapital costs required for statistical datacollection agencies to use satellite data.There is, however, one area to whichsatellite technology can uniquely contribute

the monitoring of singular or episodicevents which can cause major perturbations inplanting, harvest and yield. With properorganization and flexibility it should bepossible to apply the same general principlesused in the LACIE, AgRISTARS and in theForeign Agricultural Service of USDA tomonitor critical areas on an ad hoc basis.Decisions may be required in this area,depending on future data product costs,timeliness of data availability andinstability of both weather and markets forcrops.

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The fourth area for policy discussionis that of central vs local vs a hybrid of thetwo for image processing, the ramifications ofwhich were discussed in the previous section.The decision as to which approach is best willbe a hard one involving complex technical andpolitical questions. Today all data arecollected regionally. All data in Canada andpart in the USAare sent to a central facilityfor processing. However results are oftenreturned to the region (province or state) foruse in local decision making.

The last policy item presented dealswith the user involvement and technologytransfer. The decision has already been madein the United States and Canada on the timingand degree of user involvement. A disc ussionis included here for those in other disciplineareas or other countries beginning their work.The question is how should users be involvedand how should technology transfer beeffected. The answers for both are ..from thestart". The key element is the schedule foradoption, training and assumption of budgetaryresponsibilities. All such plans should befixed in advance during the planning sessionto specific milestones according to a definitetimetable. There must be both management andworking level commmitment before beginning.The planning exercise must be thorough eventhough this may be a long and arduous task asterms are defined and re-defined and mutualunderstanding is reached among people of quitedifferent technical backgrounds. The remotesensing technical specialist must workdi rec tly wi th the end user. Resea rch orie ntedindividuals in the user area are not the sameas the user, the real producer ;-i;nalyst ofcrop statistics. Efforts should be made toincorporate remote sensing into existingprocedures not to replace them.Demonstrations must be carefully planned. Theinitial funding is usually primarily from thetechnology oriented agency. Clear guidelineson what constitutes success should bespecified. Corollary data, such as aerialphotography, are often use ful for thispurpose.

3.8 Problem Summary

Although technical difficulties doremain major advances have already been madein crop separation, yield predic tion, dataavailability, methodology and in developinguser awareness. The infrastructure needed tohandle both technical and policy relatedproblems which can impede development indomestic crop reporting is developing. Thefact that a large number of people (includingend users of the data) have been involved and

In addition to new users and generaltopics, there will be changes in cropsassessed. There will be increased attentionto crops whose production is volatile andregional persper:tives on problemidentification and solution.

Although inventories performed bygovernmental or quasi governmental agencieswill continue to be based on states, provinresand (perhaps) smaller r:rop districts ofparticular interest, the players will ('hange.There will be increased activity by state andprovincial agencies, drawing heavily onFederal/Industrial techni<:al capabilities.Furthermore, there will likely be continuedrapid development of the high technologyindustry selling image analysis systems withspecialized agriculturally oriented softwareand hardware. In addition to systemsdevelopment there will also be the developmentof a service industry offering not just dataanalysis facilities, but information orprojections based on traditional as well asremote sensing data - much like those sellingweather / crop forecasting systems today.With lower image analysis systems costs in thefuture, it can also be expected that the graintrade, processors, smaller marketing boards,farmers associa tions, etc. may buy processi ngcapabilities, and will most certainly buyinformation.

have gained valuable experience bodes wellfor the future solution of many of theproblems. Given this experience, a betterunderstanding of the nature of problems ispossible, and identifying options for remotesensing satellite based domestic cropreporting which may be considered in thefuture can be much more realistic than it wasonly four or five years ago.

4. OPTIONSFOR THE FUTURE

4.1 Introduction

We now have a history to which we <:anlook back and from which we can project tothe future to assess the implications of thevarious issues discussed to this point.Before the space remote sensing programreturned the first satellite images, man hadonly an imprecise idea of what the earthwould look li ke from space. Although therewere a variety of simulations, none could,wi th any certai nty, be viewed as realis tic.Even the first Gemini (NASA, 1967) and Apollo9 images of agricultural areas from 126 n.mi(Col well et aI, 1971) did not prepa re us forthe exceptional detail available from 565miles above the earth's surface from Landsat.

Now, however, we have experience withdigital satellite data and have even beenable to accurately simulate Landsat MSSdigital data from an airborne flight atapproximately the same time on the same dayas the satellite pass (Ahern et aI, 1980).Given the accuracy of such simulations, theopportunity for predi<:ting the future utilityof specific sensors for specific problems isgreatly enhanced (Sigman and Craig, 1981).

4.3 Availability of Data

Since availabilityultimately within the controldecision making pror:ess,statements about the futureregard to data availability.

of data isof the political

only limi ted<:an be made with

Although the future options arediscussed here with some confidence, it isstill diffi<:ult to predict future politicaldecisions - especially those based on thatarcane art of economics. The balance of thisser:tion therefore attempts to put aside allbut the technical issues to arrive at animage, albeit fuzzy, of where domestic r:ropmonitoring is likely to be in the next decadein terms of topical problems addressed,availability of data, the technology likelyto be used, operational methods and Rand D.

4.2 Topical Problems for the Future

Until now, remote sensi ng for domesticcrop monitoring has focused on crop areaestimation. In the future it will alsoinclude monitoring of singular or episodicevents in an early warning mode.

36

It can be assumed that there will bemore redundancy built into the ground segment,ground processing will be mur:h faster andplanning for future satellites will be basedmore on ground spectroscopy and airbornesimulations. The shuttle will make itpossible to repair and extend the useful lifeof remote sensing satellites.

4.4 Likely Technology for the Future

As a result of requirements to maintaincont i nui ty of da ta and to keep da ta rates aslow as possible, some of the present satellitetechnology may still be useful well into the1990's. However, the ground segment willchange dramatically and will become muchfaster with higher throughput rates.

The MSS Technology will still be usefulfor much of the work in the U.S. Great Plains

5. SUMMARYANDCONCLUSION

These contemplated future remotese nsi ng ca pabi li ties in the a rea of domes t ircrop statistics should also benefit othertypes of inventory needs. Joint datacollection, facility sharing, and multi-userprocessing streams by inventory specialistsfrom diverse disciplines are expected.

regional or national. Maps of cropdistributions and changes in distributions inhard copy format or as data in a geo-database, will be used for applications as diverseas transportation route and site selection anderosion control planning. Maps of changes indistribution may be useful tools for marketingof crops, equipment sales, planning cropstorage, processing and shipment, irrigationdistrict development, etc.

It is likely that the same type ofstatistical procedure now used with satellitedata, combined with meteorological satellitedata and corollary data from geocoded-databases will provide the basis of future work.The major changes will corne in theorganizations using imagery and applying theresults, and in the hardware interactionbetween local and central facilities. Aswell, there will be a broadening ofapplication to include monitoring singularevents.

a most excitingthe technology's

In short, it will betime for those involved indevelopment and application.

In the past few years domestic cropreporting using satellite data has gone fromR&Dto demonstration use in the United Statesand Canada. In the nex t decade, moreexperience, advances in methods, new hardwareand wider demonstration of the technologyshould lead to a more mature data collectioninfrastructure incorporating image processinghardware manufacturers, a service industry,agribusiness and governments, barringpolitical level decisions to delay or stop thesatellite remote sensing program, to curtaildata availability, or to excessively increasethe charges.

Geometrically corrected imagery willlikely be used for all work whichincorporates segments since it is expectedthat such data will be a routine product athigh throughput rates as the technologyimproves. For applications requiring rapidturnaround, say twenty four to forty eighthours, uncorrected data could be used.

Actual processi ng methodology andorganization will be refined considerably inthe next few yea rs. It is expec ted that anetwork for image processing will evolve. Acentral facility supported (likely) by theagencies responsible for domestic cropestimates (one facility in each country) willhost a number of intelligent stations. Therewill obviously be more concerns with securityas precision and timeliness improve andexternal access is possible.

and the Canadian Prairies, as well as forsome local studies elsewhe re. Both the SPOTand TM systems will be useful for finerspatial resolution, and together willcontribute to more frequent coverages. The TMwill also have an important role to play inmaki ng fi ne spect ral sepa rations.

The whole area estimation procedureincluding segment location, training setselection and classification will become moreautomated. The first step towards automateddigitizing and pixel labelling has alreadybeen taken (Ozga and Sigman, 1981). Althoughmore automation will be incorporated, it isexpected that the video display will remainan important tool for verification and realtime class modification in the man-machineinteractive approach which has long been acornerstone of Canadian work in this field(Economy et aI, 1974, Mosher et aI, 197H).With the power and elegance of the newclassi fiers, they may gradually replace theparallelpiped classifier in Canada if theybecome more interactive or under more directcontrol of the analyst. Although one of theobjectives of the R&Deffort is to remove asmuch of man's subjertivity as possible fromanalyses, it is proba ble that someagricultural commodity analysts will stillvalue and retain this subjectivity.

Statistical estimates will continue tobe important, but two newer products, database compatible results and maps, will beused increasingly. Depending upon the use,the data base could be local, regional, ornational. It is expected that satellite database updates will also be inrorporated intotheir specific information collection plansby agribusiness interests be they local,

6. ACKNOWLEDGEMENTS

The authors wish to acknowledge theconstrurtive criticism of a number ofcolleagues on early drafts of this paper.Drs. Strome and Ahern of CCRS deserveparticular mention.

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38

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Table 1

Comparison of USA-Canada DomesticCrop Monitoring Using Satellite Data

Item

Crops and Regions

USA CANADA

-regions selected

-selection of regionbased on

-crops

Data Used

Landsat MultispectralScanner (MSS)

Delivery time afterLandsat Pass

Corollary Data

Field Spectroscopy

Current-yearairborne imagery

entire states(1981: Kansas, Iowa,Oklahoma, Missouri)

10 criteria, such asuser needs and interests,previous R&D, land area,cloudiness, % of nationalcrop total

major crops; e.g.winter wheat, corn,soybeans, potatoes.

Full frame EROS P-tape(resampled to 79M pixel)

6 to 8 weeks(spring 1981)

Via review ofresearch literature.None by user.

airborne imageryfor R&D/demonstrationmode only

39

small province/cropreporting districts

user need, crop separabilitybased on field spectralmeasurements

potatoes and canola/rapeseed(a major oilseed)

calibrated (Cal 3) Sin xX

geometric correction, oneC CT per 4 (1:50,000) NTSmaps re-sampled to SOMpixels (See Guertin,~.!.aI, 1979)

< 10 days to analysis centre

Full range of above-canopyspectral measurements.

airborne imagery for R&D/demonstration mode only

Table 1 Continued

Item

Field Data

User Involvement

Methodology

Segment Handling

Classif icationAlgorithm

StatisticalMethods

Output

USA

by segment(nominally 0.5 or 1.0sq. miles in cultivatedstrata). On (non-current) air photo allfields in segment aredelineated. Crop typesand areas for all fieldsrecorded from farminterviews.

work is done bya group within theuser agency (USDA/SRS).Procedures developed byresearch unit withoperational stateoffices taking overmore of the necessarysteps each year.

-after current-yeardata received. allfield boundariesare digitized

-Maximum Likelihood(MLDR). Digitizedfield boundaries fortraining data locationverified on line-printer output.

-Regression

-crop area estimates.some work has been donewith Objective YieldSurvey data in theregression model.

40

CANADA

by segment (variable sizefor potatoes. 3 sq. milesfor canola/rapeseed. Onan air photo the crop ofinterest and confusioncrops and their areas arerecorded from farminterviews. For canola/rapeseed all crops arerecorded. crop stage ofgrowth is reported to theanalysis centre by localstaff.

-remote sensing portion isdone by an agency (CCRS)external to the user agency(Statistics Canada).-the user agency is beingtrained to do all work in-house.

-before current-year dataare received segments areoutlined for subsequentoverlay on new data

-parallelpiped. MLDR may beapplicable to other problems.A video display is used tolocate training data and verifyresults.

-Ratio and Regression

-crop area estimates

Table 1 Continued

Item

Date of Output

Software

Hardware

Satellite Data

Professional StaffIn Agencies

USA

winter wheat: Nov. 1spring-planted crops:Dec. 1 (Final estimates)

in place (continualenhancement)

interactive processingon DEClO via commercialtime-sharing service.Full-scene classificationon NASA CRAY I.

-has own program

Applications: 8 PY(person years) R&D: 7 PY(Funded through Agristars)

41

CANADA

late August early September(Second or interim areaestimate date)

some software under development

-relies on US or other programs

5 PY from over 15 people.(Includes rangeland).