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Computers and Electronics in Agriculture 76 (2011) 105–118

Contents lists available at ScienceDirect

Computers and Electronics in Agriculture

journa l homepage: www.e lsev ier .com/ locate /compag

riginal paper

evelopment of an integrated Cropland and Soil Data Management system forropping system applications

ubin Yang ∗, Lloyd Ted Wilson, Jing Wang, Xiaobao Liexas A&M University System, AgriLife Research & Extension Center, 1509 Aggie Drive, Beaumont, TX 77713, United States

r t i c l e i n f o

rticle history:eceived 20 July 2010eceived in revised form 4 January 2011ccepted 24 January 2011

eywords:ropland Data LayerSURGOoil dataeographic Information System (GIS)ap service

a b s t r a c t

Most cropping system models and decision support tools are structured for site-specific (i.e. field- orpoint-based) simulation and analysis. As the need grows for analyses on crop production and managementat local, county, state, national, and even global scales, cropping system models and decision support toolsare increasingly structured to provide the capability for area-wide simulation and analysis at a range ofspatial scales. A major challenge is the development of a data management system that can providedynamic access to large volumes of geo-referenced data needed by such applications. The objective ofthis paper is to present a methodology to develop a Cropland and Soil Data Management system that iscapable of automatic data consolidation and integration, and can provide dynamic access to the integrateddata by cropping system applications. The Cropland Data Management component of the system is basedon the Cropland Data Layer (CDL) products from the USDA National Agricultural Statistics Service andis implemented with seven program modules: Data Requester, Data Fetcher, Data Parser, Geodatabase

atabase management system

ropping system Builder, Map Service Builder, Map Cache Generator, and Cropland Map Viewer. The Soil Data Managementcomponent is based on the Soil Survey Geographic (SSURGO) database from the USDA Natural ResourcesConservation Service and is implemented with six program modules: Data Requester, Data Fetcher, DataParser, Database Builder, Soil Map Generator, and Soil Map Viewer. The approaches and methodologypresented in the paper can serve as a reference for those who are interested in developing integrated

ions.

cropping system applicat

. Introduction

Systems integration and analysis plays an increasing role in ournderstanding of cropping system performance and in our ability toredict cropping system responses to changing environments. Overhe past several decades, we have seen tremendous progress inystems integration, as exemplified by the development and refine-ent of numerous cropping system models and decision support

ools, such as GOSSYM (Baker et al., 1983; McKinion et al., 1989;odges et al., 1998), EPIC and APEX (Williams et al., 1989, 2000;assman et al., 2005), CENTRURY (Parton et al., 1994), CropSyst

Stockle et al., 1994, 2003), APSIM (McCown et al., 1995; Wangt al., 2002; Keating et al., 2003), RicePSM (Wu and Wilson, 1998),SSAT (Jones et al., 2003), AgClimate (Fraisse et al., 2006; Paz et al.,009), and SEAMLESS (van Ittersum et al., 2008).

Most cropping system models and decision support tools aretructured for site-specific (i.e. field- or point-based) simulationnd analysis (Priya and Shibasaki, 2001; Liu et al., 2007; Yangt al., 2010b). As the need grows for analyses on crop production

∗ Corresponding author. Tel.: +1 409 752 2741; fax: +1 409 752 5560.E-mail address: yyang@aesrg.tamu.edu (Y. Yang).

168-1699/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.compag.2011.01.012

© 2011 Elsevier B.V. All rights reserved.

and management at local, county, state, national, and even globalscales, such tools are increasingly structured to provide the capa-bility for analyses at a range of spatial scales (Calixte et al., 1992;Hartkamp et al., 1999; Graham et al., 2000; Rao et al., 2000; Priyaand Shibasaki, 2001; Ines et al., 2002; Tan and Shibasaki, 2003; Liuet al., 2007; Liu, 2009; Gijsman et al., 2007; Shi et al., 2008; Beccaliet al., 2009; Zhang et al., 2009). This can be achieved by using rep-resentative sites or grid cells within a target region to account forspatial variability in crop production due to different weather andsoil conditions and production practices (Priya and Shibasaki, 2001;Tan and Shibasaki, 2003; Liu et al., 2007; Tao et al., 2009; Wilkenset al., 2009).

Providing the capability for analyses at multiple spatial scalesrequires a major increase in the volume of data supporting theapplications (Liu, 2009). A major challenge is retrieving and consol-idating scattered data into an integrated data management systemthat can be dynamically accessed by different applications (Fenget al., 2009; Yang et al., 2010b). Priya and Shibasaki (2001) used a

stochastic simulation method to generate fine resolution weather,soil and digital elevation data from coarse resolution data for grid-based national crop yield simulation in India. Tan and Shibasaki(2003) used a standard Geographic Information System (GIS) pack-age to manage various data layers such as soil, landform, and

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limate, and relied on either ASCII or binary data format for dataxchange between the crop model Erosion Productivity Impact Cal-ulator (EPIC) and the GIS package. Gijsman et al. (2007) describedethods to convert a public-domain soils database into a format

hat contains information typically needed as inputs to biophysicalnd ecosystem models. Schmierer et al. (2007) converted a SSURGOoils database into a simple soils database for use in portable com-uter and web-based GIS applications. Liu et al. (2007) stored GISaster data sets such as digital elevation, soil, climate, land use,rrigation and fertilizer in ArcGIS 9 and used an Input Data Transla-ion Module to generate grid-based text files as inputs to the EPIC

odel. Beaudette and O’Geen (2009) developed a framework toonstruct a web-based interface to STATSGO and SSURGO, whichan support sophisticated data storage, querying, map composition,ata presentation, and contextual links to related materials. Butost of these applications focus on either GIS-model integration orIS-web integration without sufficiently addressing the problem ofata consolidation.

The integrated Agricultural Information and Management Sys-em (iAIMS) is developed to address the challenge of both dataonsolidation and integration (Wilson et al., 2007a; Yang et al.,007, 2010a,b,c). iAIMS consists of foundation class climatic, soil,ropland, and road network databases, which serve as a foundationor applications that address different aspects of cropping systemerformance and management. The objective of this paper is toresent a methodology to develop an integrated Cropland and Soilata Management system that is capable of automatic data con-

olidation and integration, and can provide dynamic access to thentegrated data by cropping system applications. An earlier papern iAIMS Climatic Data by Yang et al. (2010b) focuses on consolida-ion and integration of mostly site-specific data, while this paperocuses on consolidation and integration of GIS data.

. Cropland and Soil Data sources

.1. Cropland data sources

There are several land cover and cropland data sets in the.S. that are available for integration with cropping system appli-ations: National Land Cover Data Set 1992 (i.e. NLCD 1992)Vogelmann et al., 2001), National Land Cover Database 2001 (i.e.LCD 2001) (Homer et al., 2004, 2007), and Cropland Data Layer

CDL) products (NASS, 2010). NLCD 1992 has 21 classes of landover while NLCD 2001 has 16 classes of land cover, both hav-ng a spatial resolution of 30 m (Vogelmann et al., 2001; Homert al., 2007). Neither NLCD 1992 nor NLCD 2001 identifies specificropland types. The Cropland Data Layer (CDL) products containrop specific digitized data layers with statewide categorizationf ortho-rectified mosaicked images of cropland distribution. TheDL data include agricultural and non-agricultural land cover cate-ories. The major agricultural land cover includes rice, corn, cotton,lfalfa, sorghum, soybeans, sugarcane, winter wheat, peanuts, sun-owers, potatoes, apples, peaches, etc. The non-agricultural landover includes woodland, shrubland, urban, wetland, water, andarren.

The CDL prior to 2006 was based mainly on data from theandsat TM/ETM (Thematic Mapper/Enhanced Thematic Mapper)atellite (NASS, 2010). The only available ground truth data washrough the NASS June Area Survey (JAS). The JAS data was col-ected by field enumerators so it was fairly accurate but was limited

n coverage due to cost and time constraints. Non-agricultural landover was based solely on the interpretation of the image analystNASS, 2010). Beginning in 2006, CDL has been based on imageryrom the Advanced Wide Field Sensor (AWiFS) on the Resourcesat-satellite. It has a resolution of 56 m, or 0.77 acres. NASS uses USDA

in Agriculture 76 (2011) 105–118

Farm Service Agency (FSA) Common Land Unit (CLU) data to trainthe classifier in the agricultural domain and the U.S. Geological Sur-vey (USGS) NLCD 2001 in the non-agricultural domain. Generally,the dominant agricultural crop types are classified with accura-cies ranging from mid 80% to mid 90% (NASS, 2010). But, Homeret al. (2007) point out that the estimated accuracy is based oncross-validation results, and represents only first-order estimateof data quality, and should not be considered a formal accuracyassessment.

The most recent CDL products are available for downloadthrough the NASS CDL website (NASS, 2010). The downloads aretypically in the form of a single zip file and contain the CDL imageryin GeoTIFF (.tif) and ERDAS Imagine (.img) file formats projectedin Universal Transverse Mercator (UTM) coordinate system, alongwith the metadata, which includes accuracy assessments. CDL datacan also be downloaded from the USDA Natural Resources Conser-vation Service (NRCS) Geospatial Data Gateway (NRCS, 2010a).

The CDL imagery in GeoTIFF (.tif) and ERDAS Imagine (.img) for-mat requires users to have GIS capability to access the data, suchas ESRI ArcReader and ENVI software (NASS, 2010). This greatlyrestricts the access and use of cropland data. Furthermore, data inthe original GeoTIFF and ERDAS format does not allow easy integra-tion with cropping system applications that require dynamic accessto site-specific crop information.

2.2. Soil data sources

Country-wide soil data sets in the U.S. are available for inte-gration with cropping system applications from the NationalSoil Geographic (NATSGO) database, the State Soil Geographic(STATSGO) database, and the Soil Survey Geographic (SSURGO)database (NRCS, 1995, 2010a,b). The Soil Interpretations Records(SIR) in each database contain physical and chemical soil propertiesfor approximately 18,000 soil series recognized in the U.S. (NRCS,1995).

The NATSGO database is formed based on the boundariesof the major land resource areas (MLRA) and regions, with theMLRA boundaries developed primarily from state general soil maps(NRCS, 1995). The NATSGO database is used primarily for nationaland regional resource appraisal and planning.

The STATSGO map data are generalized from county generalsoil maps, covering multi-county, state, multi-state, and regionalareas. The map data is overlaid onto the U.S. Geological Survey’s1:250,000-scale topographic quadrangle series (NRCS, 1995). Thesmallest mapped area is about 1500 acres (Watermeier, 2004). EachSTATSGO map unit is linked to the SIR attribute database. TheSTATSGO database was designed primarily for regional, multistate,river basin, state, and multicounty resource planning, management,and monitoring, and its data is not sufficiently detailed to makeinterpretations at a county level (NRCS, 1995). STATSGO’s spatialand tabular data were revised and updated in 2006. It has beenrenamed as the U.S. General Soil Map (STATSGO2) (NRCS, 2010a,b).

The SSURGO map data are derived from detailed soil surveymaps at scales between 1:12,000 and 1:63,360. The soil map unitsare linked to attributes in the Map Unit Interpretations Record(MUIR) relational database, which includes over 25 physical andchemical soil properties and interpretations. The SSURGO databaseprovides the most detailed level of information and is designed pri-marily for farm and ranch, landowner/user, township, county, orparish natural resource planning and management (NRCS, 1995).The SSURGO data are available in 3 formats: (1) U.S. Geological

Survey (USGS) Digital Line Graph Optional format (also referredto as DLG-3, with file extension .dlg), (2) ESRI ArcView Shape FileFormat (with file extension .shp), and (3) Arc Interchange for-mat with file extension .e00. Most county soil survey data havebeen converted to digital shape files that can be used by Geo-

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raphic Information Systems (GISs). NRCS has made the countyatabases and related shape files available through Soil Data Marthttp://soildatamart.nrcs.usda.gov/) and Geospatial Data Gatewayhttp://datagateway.nrcs.usda.gov/).

To retrieve the database information, a Microsoft Access soilsatabase and the spatial and tabular files specific for each countyust be downloaded to a local computer and decompressed. Thendata loading program must be run to associate these files to theatabase. The result is a county-specific database that containseveral hundred megabytes of data and about 50 tables with com-licated relationships (Schmierer et al., 2007). Data downloadingnd loading is a very time consuming process if one wants to getounty survey data for all counties in the U.S. for spatial simulationnd analysis. Also the spatial information and soil property infor-ation are stored separately (spatial data in shape file and property

ata in tabular table) and cannot be easily accessed by simulationrograms.

Information in SSURGO database can be viewed by directlyxamining the tabular data or using a GIS program such as ESRIrcView, but neither is practical for general users. NRCS pro-ides Soil Data Viewer (http://soildataviewer.nrcs.usda.gov/) as anxtension to ESRI ArcMap that allows a user to create soil-basedhematic maps. The application can also be run independent of ESRIrcMap, but output is then limited to a tabular report. But even with

he Soil Data Viewer, users still need to go through the data down-oading and loading process for each county survey area and needo have the skills to work with GIS-based software. The complex-ty associated with the processing and presentation of the SSURGOatabase is a major hindrance to its wide-spread use (Schmierert al., 2007; Beaudette and O’Geen, 2009).

. Cropland Data Management

Our Cropland Data component of the management systemocuses only on the CDL data, since it is the only data sets thatrovide crop-specific land distribution data. Major tasks include1) Develop program modules that can provide automatic request-ng, fetching, parsing, consolidation, and integration of croplandata from the NRCS Geospatial Data Gateway and allow fast andynamic access to its underlying crop information by applicationrograms, and (2) Develop a web-based cropland data viewer thatllows fast and dynamic data display at a range of spatial scalesor a single crop or any combinations of crops. Major requirementsor the system include (1) Minimal page and data requests, and

inimal network traffic to the server that provides the croplandata, (2) Automatic requesting, fetching, parsing, consolidation, andatabase integration, (3) Automatic detection and recovery fromower outages, server and network downtime, and (4) Automaticrocessing of newly added cropland data by state and year.

Development of the Cropland Data component involves threeey technologies: GIS, Database Management System, and Webevelopment Platform. We use the ESRI ArcGIS 9.3 Products (ESRI,009a) as our GIS integration platform, Microsoft SQL Server 2008s our database management system, and Microsoft Visual Stu-io 2008 and ESRI ArcGIS Web Application Developer FrameworkADF) as our web development platform.

.1. Data organization

The Cropland Data Management component is built through

coherent integration of GIS, database, and web development

echnologies. Five types of data are stored and managed: originaletched cropland data, cropland data that have been decompressed,ropland data in ESRI map document format, cropland data in theeodatabase, and cropland data in the map cache. The map docu-

in Agriculture 76 (2011) 105–118 107

ment, geodatabase, and map cache are needed to prepare data fordynamic integration with cropping system applications.

The original cropland data is in zip file format. The decom-pressed data is in GeoTIFF (.tif) and ERDAS Imagine (.img) format,along with files for color legends and metadata. The original anddecompressed files are stored in folders and subfolders, organizedfollowing the hierarchical structure of country, state/province, andyear for quick reference and access. The geodatabase is stored inMicrosoft SQL Server along with ESRI ArcSDE (Spatial DatabaseEngine) server. To allow flexibility in distributing data storage tomultiple disks and servers and to maximize data access load andspeed, cropland data for each country is designed to be stored ina separate database (even though only the U.S. cropland data hasbeen integrated).

3.2. System architecture and program modules

The cropland data building process is divided into seven dis-tinct phases: data request, data fetching, data parsing, geodatabasebuilding, map service building, map cache generation, and dataviewer development. The seven phases are mapped to the cor-responding program modules: Data Requester, Data Fetcher, DataParser, Geodatabase Builder, Map Service Builder, Map Cache Gener-ator, and Map Viewer (Fig. 1). Each module was developed usingMicrosoft Visual Studio 2008 and the C# .NET language, and theArcGIS ADF. To provide the capability for automatic data process-ing and status checking, the state of each activity is recorded andupdated in a Microsoft SQL 2008 database.

3.2.1. Data RequesterThe NRCS Geospatial Data Gateway provides current and his-

toric cropland data products for individual states and years. It hasseveral options for data search and download (FTP or media in CDor DVD). To avoid manual manipulation of CD or DVD data, we usethe FTP download option. The basic request steps include (1) Selectstate and year, (2) Search the Geospatial Data Gateway for dataavailability, and (3) Submit request (Fig. 1).

The Data Requester is responsible for processing the web pages toidentify available data and submit requests. The entire request pro-cess includes multiple requests and responses due to the dynamicnature of page contents: status checking, data selection (state,product type, and format), and request submission (Fig. 1). TheStatus Checker is responsible for checking the status database toidentify the states and years that lack cropland data. The Web PageParser processes each requested page and makes the next requestbased on the contents in the requested page. The Request Submis-sion is the last step of a single request process and results in thedata being queued on the data source server. The source serversends an email notification containing an FTP download link whenthe requested data becomes available. The status of each request isrecorded in an SQL Server 2008 database.

3.2.2. Data FetcherThe Data Fetcher is responsible for fetching cropland data that is

made available by the data source based on the request from DataRequester. It consists of several program routines (Fig. 1). The EmailMonitor tracks incoming emails from the data source by regularlychecking the local email server. The Email Content Parser parsesthe contents of the email and identifies the FTP link and file namefor data download. The FTP Data Fetcher fetches the cropland data

from the FTP server. The File Writer saves the original data file inZip format to a drive on the server with organized folders for quickaccess and updates the status of the request. The Email Monitor is amail client developed using Microsoft .Net Sockets library. The FTPData Fetcher is developed using Microsoft .Net Web Client library.

108 Y. Yang et al. / Computers and Electronics in Agriculture 76 (2011) 105–118

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A specific data request to the Geospatial Data Gateway is queuedn its server. There is usually a delay of several minutes or evenours before the request is processed and a notification emailent, depending on the queue length and the amount of dataequested. A synchronous request-fetch approach would be ratherime-consuming if one wants to fetch large number of historic dataets. A pipelined asynchronous request-fetch approach is used inur program (Yang et al., 2010b). A separate process is createdor the Data Requester and the Data Fetcher, respectively. The Dataequester sends out data requests at predefined intervals. The Dataetcher monitors incoming emails and fetches data when available.tatus of each data request and data fetching are stored in an SQLerver database, which are updated by both the Data Requester andhe Data Fetcher. Lost data requests are sent again after a predefinednterval (Yang et al., 2010b).

.2.3. Data ParserThe original cropland data downloaded from the Geospatial

ata Gateway is in a zip file format and must be decompressed.he Data Parser is responsible for decompressing the original datale and saving the data to the server running the Data Parser. Itonsists of several program routines: The Status Checker checkshe last file that has been decompressed and saved, and continueshe parsing process from where it is left last time. The File Fetcheretches original file stored in the server drive. The File Decompressorecompresses the original file. The File Writer saves the decom-ressed file(s) to the server with organized folders for quick accessnd updates the status of the data parsing. The File Decompressor isased on the Sharp Zip Library by Reilly (2010), which can handleip, GZip, Tar, and BZip2, and is written in C# for the .NET platform.

.2.4. Geodatabase BuilderNASS provides CDL imagery in GeoTIFF and ERDAS Imagine for-

at for each state and each year with each crop represented by aifferent color. Although the decompressed cropland imagery can

e directly displayed and analyzed using GIS desktop applicationssuch as ESRI ArcReader) for coverage of a single or multiple crop-and types, the embedded crop information is not easily accessibley application programs. Information in the CDL imagery needso be integrated into a geodatabase that allows storing, querying,

opland Data Management modules.

and manipulating geographic information and spatial data. TheGeodatabase Builder is responsible for automatically building thecropland geodatabase based on the CDL imagery. The geodatabasebuilding process includes feature class building and feature classimporting. The corresponding program modules are Feature ClassBuilder and Feature Class Importer (Fig. 1).

The Feature Class Builder generates a shape file based on theGeoTIFF imagery for each state and year. The attribute table forthe GeoTIFF imagery includes object ID, RGB color values (red,green, and blue), and classification (i.e. land cover type). The shapefile contains individual polygons associated with individual cropfields. It allows fast access of field crop type and polygon data.The original attribute table for the shape file converted from theCDL imagery includes only object ID for polygon and code for croptype. For simulation and analysis purpose, information on field area,county name, soil matching key, and the centroid of each polygonis needed. The Feature Class Builder also automatically expands theoriginal attribute table to include these additional attributes. Thecounty name is identified by comparing a field polygon with the U.S.county boundary shape file from ESRI (ESRI, 2009b). The centroidis obtained by querying each shape object of the shape file usingADF APIs. The centroid associated with a specific field in the shapefile is then used to query the iAIMS soil database (Yang et al., 2007)to retrieve the soil matching key, which allows quick access of thesoil information contained in the soil database (see Section 4). Oneshape file is created for each state and each year. The Feature ClassImporter adds each shape file as a feature class to the ArcSDE geo-database. The updated attribute table is also automatically addedduring the process.

3.2.5. Map Service BuilderTo provide the capability to dynamically view the CDL cropland

data via the web, it is necessary to build cropland map servicesto deliver dynamic map contents to the web users. Although themap service building process can be accomplished manually with

ESRI’s ArcCatalog Server Tools, it is tedious and time consuming.The Map Services Builder is developed to automate the map servicesgeneration process.

The CDL imagery is provided for each state and year with eachavailable cropland cover represented by a different color. To pro-

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ide the capability to selectively display maps for any single cropover or any combinations of crop covers, it is necessary to create aeparate map service for each crop cover. Map display for multipleropland cover can then be achieved by overlaying layers of individ-al crop maps. Although a map service can be created for each stateased on the state’s CDL imagery, a service would have to be cre-ted for each state-crop-year combination, significantly increasinghe number of services to be managed. Our approach was to createmap service for the entire conterminous United States for each

rop-year combination. A map document template (.mxd) file withTM projection (WGS 1984) is first created for the conterminousnited States. The CDL imagery for a state and year is then added asdata layer to the map template to produce a composite imagery

or the conterminous United States. The program then selectivelyisplays the imagery for a specific crop based on its color attributend saves the composite crop imagery as a map document (.mxd).ne MXD file is created for each crop cover for each year and thenublished as a map service of the ArcGIS Server. The Map Serviceuilder automatically creates a national-scale map document anddds the map document to ArcGIS server as a service (Fig. 1). Theap Doc Builder is a program routine that creates map documents

nd the Map Service Loader is a program routine that adds each mapocument to the ArcGIS Server as a map service.

.2.6. Map Cache GeneratorAlthough map display can be dynamically provided via map ser-

ices, map delivery to the web would be very slow since mapseed to be generated on the fly. A map cache represents a snapshotf a map in a predefined resolution and spatial scope. With mapaching, the map server can draw the entire map at several differ-nt scales, and stores copies of the map images. The server can thenistribute these images upon map requests. It is much quicker forhe ArcGIS Server to access a cached image than to draw the mapt each request (ESRI, 2009a). Another benefit of caching is that themount of detail in the image does not noticeably affect the speedhe server can distribute the copy (ESRI, 2009a). Map caching issed to allow fast navigation of the cropland map images. Althoughhe Server Tools in ESRI ArcCatalog can be used to manually defineache scales and tiling scheme during the map cache creation pro-ess, to do so is time-consuming. Map Cache Builder is developedo automatically generate a total of 10 cache scales for each cropover with the highest resolution at 1:64,000 (≈17 m) and the low-st resolution at 1:32,000,000 (≈8500 m). A fused cache is used toenerate cache tiles for the entire conterminous U.S. The cache istored in the server cache directory for quick access during mapiewing.

.2.7. Web-based Cropland Map ViewerThe web-based Copland Map Viewer is designed to shield users

rom the complexity of directly accessing map data using GIS pro-rams. It uses the map services and map cache for quick mapelivery and navigation over the web. The Cropland Map Viewerllows users to view cached maps at different spatial resolutionsnd extent. It is developed using Microsoft Visual Studio 2008 andhe ArcGIS Web ADF for the Microsoft .NET Framework. The ADFncludes a set of web controls, classes, frameworks, and APIs thatre used to build the web application. The Toolbar Builder is respon-ible for building the map navigation toolbar (extent, zoom, pan,tc.) with customized menu items and actions. The Floating Paneluilder is responsible for building the Map Contents panel with cus-omized menus and actions. The Resource Manager is responsible for

pecifying map data source, creating data layers as map resourcetems, updating Floating Panel content, and managing data layerisplay or overlay. The Request Manager is responsible for process-

ng the map request, rendering and delivering the map to the weblient for display.

in Agriculture 76 (2011) 105–118 109

The web-based Cropland Map Viewer can be accessed athttp://beaumont.tamu.edu/CroplandData/ (Wilson et al., 2010).Fig. 2 shows a screen shot of the major cropland distribution forthe conterminous United States, while Fig. 3 shows an expandedview of the major cropland distribution for the area west of Hous-ton, Texas. Currently only 2009 cropland data is included in the mapviewer since it provides the most comprehensive coverage of theconterminous United States. The program is designed to provideusers easy navigation of data at different spatial scales. The toolbaron the upper center allows users to zoom in, zoom out, pan, andreturn to full extent of the map view. Users can also use the mouse(hold and drag) to specify a rectangular area for display with auto-matic zooming to the extent as defined by the selected area. TheMap Content selector on the right side allows users to selectivelydisplay the distribution map of a single crop or any combination ofcrops.

4. Soil Data Management

Our Soil Data component of the management system focusesonly on the SSURGO database since it provides the most detailedsoil information. Major tasks include (1) Develop program modulesthat can provide automatic requesting, fetching, parsing, consoli-dation, and integration of soil data from NRCS Soil Data Mart and (2)Develop a web-based soil map viewer that allows fast map displayof soil information at a range of spatial scales. Major requirementsfor the system and associated technologies are similar to thosedescribed for the Cropland Data.

4.1. Soil data organization

The Soil Data component is built through a coherent integrationof the GIS, database, and web development technologies. Four typesof data are stored and managed: the original fetched soil data, soildata that have been decompressed, soil data in SQL databases, andsoil data in pre-generated soil maps.

The original soil data downloaded from NRCS Soil Data Mart isin zip file format for each survey area for a state. The unzipped filesare expanded to a tabular and a spatial data folder. Both the originalzipped and the unzipped files are stored in folders following thehierarchical structure of country, state, and survey area for quickreference and access. The tabular data are then saved to a MicrosoftSQL 2008 database. A customized version of the SSURGO databaseis built combining the tabular data with the polygon data from theshape file.

4.2. System architecture and program modules

The entire soil data building process is divided into six dis-tinctive phases: data request, data fetching, data parsing, databasebuilding, soil map generation, and soil map viewer development,which are mapped to its corresponding program modules: DataRequester, Data Fetcher, Data Parser, Database Builder, Map Gener-ator, and Map Viewer (Fig. 4). All program modules are developedusing Microsoft Visual Studio 2008 and the C# .NET language.

4.2.1. Data Requester, Data Fetcher, and Data ParserAlthough soil data can be downloaded from either Geospatial

Data Gateway (NRCS, 2010a) or Soil Data Mart (NRCS, 2010b), thisdescription applies only to data download from Soil Data Mart.

Data downloading from Geospatial Data Gateway is already cov-ered in the Cropland Data Management component in the previoussection. The NRCS Soil Data Mart provides up-to-date soil data forsurvey areas in each state. Users are guided through a number ofdata selection pages before a data request is submitted and queued.

110 Y. Yang et al. / Computers and Electronics in Agriculture 76 (2011) 105–118

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each state (e.g. ShapeVector TX for Texas). Names for tables andtheir columns were based on SSURGO database where appropri-ate (NRCS, 2010b). The purpose of a separate ShapeVector table foreach state is to provide fast data access. Table CHorizon contains key

Table 1Data mapping between SSURGO and customized soil database.

Data source SSURGO data Customized database tables

Tabular data mapunit MapUnitcomponent Componentchorizon CHorizonchtexturegrp CHTextureGrpchtexture CHTexture

Spatial data Attribute data (.dbf) ShapeProjection data (.proj) ProjectionSpatial index data (.sbn) Shape

Fig. 2. Zoomed-out view of the cropland distribution for the con

The Data Requester is responsible for processing the web pageso identify available data and submit a data request. The entireequest process includes multiple requests and responses due tohe dynamic nature of page contents: (1) Selection of a state, county,r survey area, (2) Data class selection (tabular, spatial, or bothabular and spatial), (3) Template database selection (Microsoftffice Access versions), and (4) Request submission. The Web Pagearser processes each requested page and makes the next requestased on the content in the requested page. The request submis-ion involves the entry of notification email for data downloadingia FTP. It is the last step of a single request process and results in theequest being queued on the data source server. The source serverends an email notification when the requested data becomes avail-ble. The status of each request is recorded in an SQL Server 2008atabase. The soil Data Fetcher and Data Parser modules involve rou-ines similar to those described for the Cropland Data Managementomponent (see Sections 3.2.2 and 3.2.3).

.2.2. Database BuilderOnce the Microsoft Access template soils database, and the

patial and tabular files are downloaded to the local computernd decompressed for a specific survey area, a data loading pro-ram must be run to associate these files to the database. Theesult is a county-specific database that ranges from 200 to 300

egabytes in size (Schmierer et al., 2007). The spatial data can-

ot be used until it has been imported into a GIS application (e.g.SRI ArcView, ESRI ArcInfo). Once the soil tabular data has beenmported into a database, and the soil spatial data into a GIS appli-ation, using the soil data in a GIS application is still a huge challenge

nous United Sates (screen shot from the Cropland Map Viewer).

(NRCS, 2007; Schmierer et al., 2007; Beaudette and O’Geen, 2009).Similar to the approach by Schmierer et al. (2007), we simpli-fied the SSURGO database to tables containing information mostrelevant to cropping system simulation and analysis. Schmiereret al. (2007) condensed the original SSURGO database to only 4tables, which reduced the size of the database to about one-tenthof the original size. We developed a customized database with10 major tables (Table 1 and Fig. 5) plus a ShapeVector table for

Spatial index data (.sbx) ShapeShape File (.shp) Shape, ShapeVector State

Others StateSurveyAreaStateSurveyArea

Y. Yang et al. / Computers and Electronics in Agriculture 76 (2011) 105–118 111

Fig. 3. Zoomed-in view of the cropland distribution for an area west of Houston, Texas, USA (screen shot from the Cropland Map Viewer).

Fig. 4. Schematic representation of the Soil Data Management modules.

112 Y. Yang et al. / Computers and Electronics in Agriculture 76 (2011) 105–118

l data

sssc

aasdpbdLpt

The soil attributes for display include soil texture, organic mat-ter, and soil pH at 4 different soil depths (0–10, 10–20, 20–50, and

Fig. 5. Schema of the customized soi

oil physical and chemical properties needed for cropping systemimulation, including soil texture (sand, clay, and silt), bulk den-ity, organic matter, phosphorous, available water content, waterarrying capacity, and soil pH.

SSURGO DB Builder is a program module that automatically cre-tes a database for the tabular data from SSURGO with only tabless identified in Table 1. Customized DB Builder builds a customizedoil database based on data in the tabular data tables and spatialata files. The Projection table is built based on information in therojection file (.proj). The Shape and ShapeVector tables are builtased on information in the attribute data (.dbf), the spatial indexata (.sbn or .sbx), and the shape file (.shp). ArcView Shape File

ibrary (Pickard, 2007) is used to read spatial data (shape and itsolygon shape vectors) from the shape file. Database Writer writeshe data to corresponding tables in the customized soil database.

base based on the SSURGO database.

4.2.3. Soil Map GeneratorTo shield users from the complexity of the soil database, a map

generation program was developed to automatically generate soilattribute maps for different soil properties and at different soildepths with display scales at national, state, and county levels. Asoil map generation database is used to store information related tothe map generation (Fig. 6). The database is designed to store infor-mation at the continent, country, region, state, district, and countylevels, though only country, state, and county levels are used forsoil map generation for the U.S.

>50 cm). These depths are chosen arbitrarily for display purpose,but they do provide sufficient detail by depth for many crop modelsto simulate root growth and water uptake. Furthermore, applica-

Y. Yang et al. / Computers and Electronics in Agriculture 76 (2011) 105–118 113

il Ma

tloemdtspaecSu

Fig. 6. Schema of the So

ion programs can access information from the soil database forayers as defined in the original SSURGO database. Since the depthsf different soil horizons vary among different map units, a lin-ar interpolation algorithm weighted by layer depth was used toap values for different soil properties to the 4 “standardized” soil

epths. Three hierarchical levels of soil maps are generated: Coun-ry, State, and County. A color map is created for each attribute type,oil layer, and spatial level combination. The Map Database Builderopulates the database tables for each attribute type, soil layer,

nd spatial level combination. The Map Generator creates maps forach attribute type, soil layer, and spatial level combination. Theounty level map is built based on polygon information from thehapeVector table in the customized soil database for all its mapnits. A state level map is built as a mosaicked map of all the coun-

p Generation database.

ties, while a country level map is built as a mosaicked map of all thestates. A country level map takes the longest time to build becauseit combines all the map units for the entire country. All createdmaps are stored on the server with folders following the hierarchi-cal structure of the spatial scope. An entry is created in the databaseto record the image path needed to retrieve a map for display onweb browsers.

Since SSURGO doesn’t contain soil color data, Beaudette andO’Geen (2009) extracted color data from the Official SeriesDescriptions (OSD) (NRCS, 2010d) database, converted it intoRGB triplets, and presented their soil maps in Munsell color

notation. We used an arbitrary color scheme for soil maps asso-ciated with different soil properties (type, organic matter, and pHvalue).

114 Y. Yang et al. / Computers and Electronics in Agriculture 76 (2011) 105–118

U.S. (

4

oftfMwaml

5C

i(dtt

Fig. 7. Soil texture map at top 10 cm for

.2.4. Web-based Soil Map ViewerThe Soil Map Viewer is a web-based program that provides

nline access to soil maps at the country, state, and county levelsor major soil properties at four different layers. The Map Loca-or retrieves the location of the map (country, state, or county)rom the map database to be displayed based on user request. The

ap Displayer, prepares the map page and delivers the map to theeb browser. The web-based Soil Map Viewer can be accessed

t http://beaumont.tamu.edu/SoilData/. It provides soil attributeaps for selected soil horizons at the country, state and county

evels (Figs. 7–9).

. Integration of the Cropland and Soil Data Managementomponents with Cropping System Applications

The Cropland and Soil Data Management components presented

n the paper are part of iAIMS. As with the iAIMS Climatic DataYang et al., 2010b), both the cropland and soil databases allowynamic data access from cropping systems applications. Applica-ions that currently use the cropland and/or soil databases includehe Rice Water Conservation Analyzer (RiceWCA: Wilson et al.,

screen shot from the Soil Map Viewer).

2007b) and the Integrated Biomass and Economic Viability Ana-lyzer (BEVA: Yang et al., 2010a). RiceWCA is a web-based toolthat can evaluate field- and regional-level costs, water savings,and yield benefit associated with implementing on-farm conser-vation measures (Wilson et al., 2007b). Soil information for eachfield in the region of interest is retrieved from the soil databasebased on the latitude and longitude of the field. BEVA is a web-based tool that can identify and rank potential biorefinery sitesand develop optimal biomass production and delivery plans, basedon the cropland distribution, crop production potentials, and roadnetwork (Yang et al., 2010a). The climatic and soil databases pro-vide site-specific weather and soil information needed to simulatecrop biomass potential. The cropland database provides the spa-tial distribution of potential land parcels available for bioenergycrop production. The climatic, soil, cropland, and road networkdatabases form the backbone of our cropping systems applica-

tions and play a critical role in furthering our understanding ofcrop responses to diversified environments, in addressing ques-tions related to production of conventional and bioenergy crops,and climate change impacts at local, county, state, national, andglobal scales.

Y. Yang et al. / Computers and Electronics in Agriculture 76 (2011) 105–118 115

xas, U

6

c2vbptefdtpMG2m2

Fig. 8. Soil texture map at top 10 cm for Te

. Discussion

There are several web-based tools available for exploring U.S.ropland and soil information such as Crop Explorer (Kanarek,005; FAS, 2010), Soil Data Viewer (NRCS, 2006), Web Soil Sur-ey (NRCS, 2010c), and Soil-Web (Beaudette and O’Geen, 2009),ut they are not designed to provide dynamic data access to crop-ing system applications, and they do not sufficiently addresshe problem of data consolidation. Our climatic database (Wilsont al., 2007a; Yang et al., 2010b) provides access to weather dataor most countries in the world. Although the cropland and soilatabases are currently limited to the conterminous United States,he integration framework and methodology developed in thisaper provides the capacity to easily expand to other countries.

ajor data sources that are available for expansion include (1)lobal land cover data from the U.S. Geological Survey (USGS,010), (2) European land cover database – Coordination of Infor-ation on the Environment (European Environmental Agency,

010), (3) The Global Land Survey (GLS) collection of Landsat

.S. (screen shot from the Soil Map Viewer).

imagery provided by The Global Land Cover Facility (GLCF) (GLCF,2010), (4) New digital soil map of the world developed by Global-SoilMap.Net (Sanchez et al., 2009), (5) World Soil Information fromthe International Soil Reference and Information Centre (ISRIC,2010), (6) European Soil Portal (http://eusoils.jrc.ec.europa.eu/)from the European Commission – Joint Research Centre – Insti-tute for Environment and Sustainability, and (7) The HarmonizedWorld Soil Database from the Food and Agriculture Organiza-tion of the United Nations (FAO) and the International Institutefor Applied Systems Analysis (IIASA) (FAO et al., 2009; Batjes,2009). Development of an integrated global climatic, cropland andsoil database system that provides dynamic access to underlyingdata would overcome some of the limitations of data availabilityfor cropping system models to perform simulation and analysis

at multiple-country and even global scales. This would greatlyimprove our ability to address the challenges of crop produc-tion and food security and plan for new challenges of climatechange and accelerated natural resources degradation (FAO et al.,2009).

116 Y. Yang et al. / Computers and Electronics in Agriculture 76 (2011) 105–118

nty, T

7

ggldtCmaTsGanDtbo

Fig. 9. Soil texture map at top 10 cm for Fisher Cou

. Conclusion

Although considerable progress has been made toward inte-rating cropping system applications with large volumes ofeo-referenced data, much work remains to be done to stream-ine the process of data consolidation from diversified sources andynamic data integration with GIS and application programs. Inhis paper, we presented a methodology to develop an integratedropland and Soil Data Management system that is capable of auto-atic data consolidation and integration, and can provide dynamic

ccess to the integrated data by cropping system applications.he Cropland Data Management component is implemented witheven program modules: Data Requester, Data Fetcher, Data Parser,eodatabase Builder, Map Service Builder, Map Cache Generator,nd Cropland Map Viewer. The Soil Data Management compo-

ent is implemented with six program modules: Data Requester,ata Fetcher, Data Parser, Database Builder, Soil Map Genera-

or, and Soil Map Viewer. Although both components have beenuilt based on data for the U.S., the approaches and methodol-gy described in this paper can serve as a reference for those who

exas, U.S. (screen shot from the Soil Map Viewer).

are interested in developing integrated cropping system applica-tions.

Acknowledgements

This work was partly supported by funds from Texas A&M Uni-versity System AgriLife Research Cropping Systems Program forFY’08-FY’09 and the Jack B. Wendt Endowed Chair.

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