Soil & Tillage Research 138 (2014) 26–34

Review

Remote sensing of crop residue and tillage practices: Presentcapabilities and future prospects

Baojuan Zheng a,*, James B. Campbell b, Guy Serbin c, John M. Galbraith d

a School of Geographical Sciences and Urban Planning, Arizona State University, Coor Hall, 5th Floor, Tempe, AZ 85281, USAb Department of Geography, Virginia Tech, 115 Major Williams Hall, Blacksburg, VA 24061, USAc Spatial Analysis Unit, Teagasc Ashtown Research Centre, Ashtown, Dublin 15, Irelandd Department of Crop & Soil Environmental Sciences, Virginia Tech, 239 Smyth Hall, Blacksburg, VA 24061, USA

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.1. Tillage practices and crop residue cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.2. Temporal dimensions to tillage assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3. Optical remote sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.1. Regression-based approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.1.1. Tillage indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.1.2. Optical remote sensing platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.2. Data mining approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.3. Image preprocessing: atmospheric corrections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4. Radar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.1. Critical variables significant for tillage assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.1.1. Experimental Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.1.2. Wavelength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.1.3. Incidence angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.1.4. Polarization and row direction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.1.5. Roughness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.1.6. Residue type and condition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.2. Platforms for orbital SAR tillage survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5. Challenges and future possibilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

5.1. The critical role of revisit interval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

5.2. STARFM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

A R T I C L E I N F O

Article history:

Received 29 August 2013

Received in revised form 3 December 2013

Accepted 12 December 2013

Keywords:

Remote sensing

Tillage

Crop residue

Agriculture

Review

Environment

A B S T R A C T

Sustainable agricultural management is essential not only to maintain productivity of current farmlands,

but also to conserve natural environments. Records of agricultural activities are required to assist rapid

assessment of agricultural lands, and thus, designation of management plans and policies. By the 1980s,

when unfavorable environmental impacts of conventional tillage practices were widely recognized,

agronomists introduced conservation tillage to benefit soils and agricultural environments, and soon

began programs to monitor adoption of conservation tillage practices. The role of remote sensing in

acquiring this information has been increasing because remote sensing technologies can provide the

broad scope and the ability to collect sequential imagery to estimate trends and patterns of adoption of

alternative tillage practices. This review encompasses comparisons of remote sensing techniques with

more conventional methods for surveying and estimating tillage status, applications of remote sensing

technologies, data processing and analysis, validation and field data collection, impacts of terrain,

spectral and spatial resolution, timing and temporal detail, and prospects of future instruments.

� 2014 Elsevier B.V. All rights reserved.

Contents lists available at ScienceDirect

Soil & Tillage Research

jou r nal h o mep age: w ww.els evier . co m/lo c ate /s t i l l

* Corresponding author. Tel.: +1 646 750 7087.

E-mail address: bzheng11@asu.edu (B. Zheng).

0167-1987/$ – see front matter � 2014 Elsevier B.V. All rights reserved.

http://dx.doi.org/10.1016/j.still.2013.12.009

B. Zheng et al. / Soil & Tillage Research 138 (2014) 26–34 27

5.3. Role of local soils and terrain. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.4. Local and regional tillage assessment models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.5. Status of current remote sensing systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.6. The context for operational tillage assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

1. Introduction

Global population growth and increasing demands for food,products, and energy create significant pressures on the environ-ment (Kiers et al., 2008). Worldwide, the FAO (2011) estimates thatapproximately one billion people are undernourished, andextreme climatic events, such as the severe and widespreaddrought conditions in the United States (US) in 2012, illustrate thevulnerability of our current food security system. Humans faceimmense challenges to feed the world’s population and tosimultaneously maintain and improve environmental conditions(Foley et al., 2011). Although we have successfully increased foodproduction and reduced hunger, agricultural activities have alsocaused substantial environmental issues, such as increased CO2

and other greenhouse gas emission, soil degradation, biodiversityloss, and water degradation due to excessive nutrient leaching(Foley et al., 2011). Thus, sustainable agricultural management, i.e.,the ability to maintain or increase current yields without furtherenvironmental degradation, plays an important role in addressingthe world’s challenges to improve food security and environmentalconservation. Crop residue management, one of agriculture’s mostimportant conservation measures, has been deployed in manycountries around the world to reduce soil erosion, labor input, fuelconsumption, and to enhance water use efficiency and soil fertility(Derpsch et al., 2010). Monitoring crop residue managementbenefits both crop production and environmental sustainabilitybecause it permits evaluation of management practices and assistsdesignation of effective sustainable management plans andpolicies.

No-till cultivation is practiced in almost every country in theworld, but the United States is one of the few countries that surveystillage practices (Derpsch et al., 2010). However, the US nationalsurvey program was discontinued after 2004, and at present onlylimited numbers of counties have volunteered to continueacquiring tillage data (CTIC, 2013). Currently, acquisition of tillagedata relies on manual field-data collection, survey responses, andagricultural censuses, but it is extremely difficult to acquire thedata systematically and continuously over large areas using thesemethods. Alternatively, remote sensing techniques have thepotential to survey tillage practices inexpensively and efficientlyin a systematic, timely, and cost-effective manner. Remote sensingoffers two technologies that have potential for this task: (1) opticalsystems that passively collect reflected solar radiation in theoptical and mid-infrared regions to record spectra of signatures ofcellulose in plant debris, as well as to detect soil surfacedisturbances, and (2) microwave sensors that actively illuminatethe landscape with microwave radiation, then receive the back-scattered radiation that are sensitive to roughness of soil surfaceand plant debris, and to moisture status at varied depths (Campbelland Wynne, 2011).

Although numerous studies have examined applications ofremote sensing to survey tillage practices, none of the proposedstrategies have been adopted to map tillage practices at broadscales. One possible reason is that mapping techniques are still intheir developmental stages. Each new technique requires rigoroustesting and validation before operational implementation can beconsidered. In addition, issues such as the lack of satelliteobservations and ground validation, remotely sensed data quality,

and effects of soil variation also prevent broad-scale implementa-tion of tillage mapping. Thus, the objective of this paper is toprovide a comprehensive review of the latest progress in tillagemapping. This paper first provides background information pertainto tillage (Section 2), reviews different methodologies according tosensor technologies: optical (Section 3) and radar (Section 4), andthen discusses challenges and potential directions for futureresearch in tillage monitoring (Section 5).

2. Background

2.1. Tillage practices and crop residue cover

Tillage practices disturb soils in different ways, leavingvarying amounts of crop residues on the soil surface (Hugginsand Reganold, 2008), but they can be classified into two broadcategories. Tillage practices that leave 30% or more crop residuecover after tillage and planting are defined as conservationtillage, while those leaving less than 30% crop residue are non-conservation tillage (CTIC, 2002). Conservation tillage includesno-till (zero-till/strip-till), ridge-till, and mulch-till. Non-con-servation tillage consists of reduced-till (15–30% crop residueremaining) and conventional-till (intensive-till) (<15% cropresidue remaining). No-till/strip-till usually disturbs <30% ofrow width (CTIC, 2002; USDA-NRCS, 2006). Because causeand effect relationships exist between types of tillage practicesand crop residue cover, we can apply remote sensing toobserve the soil surface to identify different types of tillagepractices.

2.2. Temporal dimensions to tillage assessment

Although local crop calendars, local management practices, andlocal weather form integral components of tillage assessment atany scale, it is at the broad scales of regional assessment that theymust be explicitly considered to effectively apply remote sensinganalysis to the tillage assessment task. Farmers consider theresponses of each of their fields to local weather, soil conditions,and crop types as they prepare for planting, so timing of tillage willvary from place to place across the landscape, creating a mosaic ofdifferent tillage conditions as planting operations mature atdifferent times.

Tillage assessment requires collection of data from immenseareas within a short time. For example, planted corn area for the UShas been estimated at 97.4 million acres for the 2013 growingseason (USDA-NASS, 2013a). Even at the county scale, a completetillage survey requires assessment of thousands of individual fieldswithin a short interval at the start of the growing season, betweenthe beginning of preparation of soil for planting, and the lateremergence of the new crop, an interval of less than eight weeks inmost instances for the US Corn Belt within the Midwestern UnitedStates. Within this interval, the task requires acquisition of severalsequential images of the surveyed area. Ideally, the imagesequence must begin before tillage is applied, and end after theend of planting season.

Despite a wide awareness of variation in tillage application andplanting dates from field to field, the idea to incorporate temporaldimensions into tillage mapping had not been implemented until

B. Zheng et al. / Soil & Tillage Research 138 (2014) 26–3428

recently by Watts et al. (2011). Timing is very important for tillagedata acquisitions. In situ acquisition of tillage data should bealways conducted at the end of planting season to ensure that themajority of agricultural fields have been planted. However, mostcrops have emerged at the end of the planting season, and theemerged plants weaken our ability to observe tillage patterns fromabove using remote sensing imagery, because green vegetation canconfound crop residue signals (Daughtry et al., 2005; Serbin et al.,2009a; Zheng et al., 2012) and fully emerged crops can concealevidence of the tillage practices. Several early studies (Gowda et al.,2001; Daughtry et al., 2005; Sullivan et al., 2008; Thoma et al.,2004) circumvented this issue by choosing fields that are tilledclose to the image acquisition dates or excluding fields that haveevidence of green vegetation. While it guaranteed success of theirstudies, a single image method or single date can only record anincomplete picture of the tillage pattern. Therefore, success intillage assessment is not dependent on picking the right timing toacquire the tillage information, but on utilizing multi-temporalimagery to provide a full picture of tillage patterns for a region(Zheng et al., 2012).

3. Optical remote sensing

Optical remote sensing sensors record reflective radiation in thevisible, near infrared, and shortwave infrared bands (Fig. 1). Wecan differentiate different materials/objects and monitor biophys-ical properties of the earth’s environment because differentmaterials absorb and reflect differently at different wavelengths.According to the number of bands used in the system, opticalremote sensing can be divided into multispectral and hyperspec-tral imaging systems (Fig. 1). While multispectral imagery consistsof several bands, hyperspectral imagery consists of more thandozens of bands.

Fig. 1. General overview of s

3.1. Regression-based approaches

3.1.1. Tillage indices

Crop residue (non-photosynthetic vegetation) and soils havesimilar spectra, but crop residue has a unique absorption featurenear 2100 nm associated with cellulose and lignin (Daughtry,2001). This absorption feature forms the basis for differentiation ofcrop residue from soils using optical remote sensing imagery.Consequently, the absorption feature forms the basis for devisingseveral tillage indices to magnify the crop residue signal, whilesuppressing spectral signals from soils and green vegetation. Wesummarize tillage indices from recent studies into three categoriesaccording to the types of sensors: hyperspectral, AdvancedSpaceborne Thermal Emission and Reflection Radiometer (ASTER),and Landsat-based tillage indices (Table 1). The Cellulose Absorp-tion Index (CAI), one of the hyperspectral tillage indices, is mostsensitive to crop residue (Serbin et al., 2009a), followed by ASTERand Landsat-based tillage indices because ASTER and Landsatspectral bands are wider and therefore less sensitive to thepresence of residue than hyperspectral bands. In general, tillageindices calculated based upon the 2100 nm cellulose absorptionregion (i.e., ASTER bands 6 & 7 and Landsat band 7) outperformthose that are not based upon cellulose absorption (Serbin et al.,2009a). The Shortwave Infrared Normalized Difference ResidueIndex (SINDRI) is the best ASTER tillage index, while NormalizedDifference Tillage Index (NDTI) is the best Landsat-based tillageindex (Serbin et al., 2009a,b).

Although tillage indices were designed in a way that maximizesour ability to detect various amounts of crop residue on the ground,they are subject to the influence of emerging green vegetation andof variation in the soil background. The magnitudes of theseinfluences vary depending on the spectral resolutions of thesensors. Tillage indices with wider spectral bands have higherlevels of sensitivity to green vegetation and soil variation. As a

atellite remote sensing.

Table 1Summary of satellite optical remote sensing of crop residue cover and tillage practices.

Sensor Tillage indices Formula Description References

AVIRIS CAI 100 � [0.5(R2030 + R2210) � R2100] R2030 and R2210 are the reflectances

of the shoulders at 2030 nm and 2210 nm,

R2100 is at the center of the absorption

Daughtry et al. (2005)

Hyperion Daughtry et al. (2006)

ASTER LCA 100(2 � B6 � B5 � B8) B5, B6, B7, B8: ASTER shortwave infrared bands 5, 6, 7, and 8 Daughtry et al. (2005)

SINDRI (B6 � B7)/(B6 + B7) Serbin et al. (2009b)

Landsat TM

and ETM+

STI B5/B7 B2, B4, B5, B7: Landsat bands 2, 4, 5, and 7 van Deventer et al. (1997)

NDTI (B5 � B7)/(B5 + B7) van Deventer et al. (1997)

Modified Crop

Residue Cover

(B5 � B2)/(B5 + B2) Sullivan et al. (2006)

NDI5; NDI7 (B4 � B5)/(B4 + B5);

(B4 � B7)/(B4 + B7)

McNairn and Protz (1993)

ALI NDTI (B5 � B7)/(B5 + B7) B5 and B7 Galloza et al. (2013)

MODIS None

B. Zheng et al. / Soil & Tillage Research 138 (2014) 26–34 29

result, Landsat-based tillage indices can be easily confounded bythe presence of green vegetation and by variation in soil color andsoil moisture (Zheng et al., 2013a). Although CAI and SINDRI areless sensitive to green vegetation, previous research suggestedremoval of pixels if the Normalized Difference Vegetation Index(NDVI) is greater than 0.30 (Daughtry et al., 2005; Serbin et al.,2009a). Daughtry and Hunt (2008) found that increases in soilwater content decrease CAI values under laboratory conditions.Wet soil conditions did not significantly bias estimation of cropresidue cover using CAI and SINDRI derived from airborne andsatellite remote sensing imagery (Serbin et al., 2009b), however,wet soils caused underestimation of crop residue cover usingLandsat NDTI (Zheng et al., 2013a). Despite the report by Serbinet al. (2009a) that CAI values of soils increase from negative to zeroas organic carbon increases, the effect of soil organic carbon onLandsat NDTI is still unreported. Serbin et al. (2009a) conducted acomprehensive study on the effect of soil properties on remotesensing of crop residue cover. However, incorporation of soilinformation to improve crop residue estimation is still challengingdue to the lack of spatial information describing local variations ofsurface soil properties. Several soil-adjusted tillage indices werealso developed to minimize the effects of soil background, such asCrop Residue Index Multiband (CRIM) (Biard and Baret, 1997) andModified Soil Adjusted Corn Residue Index (MSACRI) (Bannariet al., 2000), but they were all developed based upon lab-measuredspectra and their performance has not been tested on satellite data.

3.1.2. Optical remote sensing platforms

Sensors with moderate spatial resolution that are able tomeasure reflective energy near the 2100 nm spectral region haveshown their ability to map crop residue cover (Daughtry et al.,2006; Serbin et al., 2009b; Zheng et al., 2012). The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and EO-1 Hyperion imagesare effective in estimating crop residue (Daughtry et al., 2005,2006). The CAI values of AVIRIS and Hyperion were linearly relatedto crop residue with R2 > 0.77 for calibration (Daughtry et al., 2005,2006). While the correlation between CAI and crop residue isstrong, the very limited spatial and temporal coverages of AVIRISand Hyperion hyperspectral imagery prevents broad-scale appli-cation. Alternatively, spaceborne multispectral imagery providesextended coverage of the Earth repetitively with minimal costs.

Current satellite multispectral sensors that provide spectralbands covering the cellulose absorption spectral region includeModerate Resolution Imaging Spectroradiometer (MODIS), Ad-vanced Land Imager (ALI), ASTER, Landsat 4 and 5 ThematicMapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+), andLandsat 8 Operational Land Imager (OLI). In addition to consider-ation of spectral regions, identification of images with appropriate

spectral, spatial, and temporal resolutions is required for cropresidue mapping. MODIS imagery is not suitable for mapping cropresidues because its coarse spatial resolution (500 m for bands 5and 7) causes mixed pixel problems for field-scale analysis. ASTERdata have narrower spectral bands compared to Landsat imagery.Thus, the ASTER SINDRI is less sensitive to the effects of greenvegetation (Serbin et al., 2009b) than Landsat indices. However, theASTER SWIR sensor is no longer functioning due to detector failurein April 2008 (NASA/JPL, 2011), so ASTER imagery is no longercapable of crop residue cover estimation. ALI and Landsat TM/ETM+/OLI are all Landsat-type instruments. However, the ALIsensor is only activated to acquire specific scenes upon request andhas a very small footprint. Therefore, the limited spatial andtemporal coverage of ALI imagery constrains our ability to mapcrop residue at large scales.

There are numerous studies examining Landsat TM/ETM+imagery’s capability to map crop residue and to differentiate tillagepractices. Landsat imagery draws a significant amount of scientificattention because it is freely available and provides a long-termsynoptic view of the Earth. Early studies were able to differentiateconventional tillage from conservation tillage using logistic regres-sion on NDTI (van Deventer et al., 1997; Gowda et al., 2001) or usingmultiple linear regression on several spectral bands (Thoma et al.,2004). However, Landsat NDTI data failed to estimate crop residuecover using single Landsat images (Daughtry et al., 2006). Such poorperformance is largely because Landsat band 7 is spectrally toocoarsely defined (2080–2350 nm) to separate cellulose absorptionsignals from those of green vegetation (Serbin et al., 2009a). Due todifferent timings of tillage and planting, field surface conditions varyfrom field to field. A one-time snapshot of agricultural lands isunable to show correct tillage status for all fields due to the effects ofgreen vegetation (Zheng et al., 2012). One way to address thevegetation confounding issue is to utilize spectral unmixingtechniques (Pacheco and McNairn, 2010). Although Pacheco andMcNairn (2010) were able to estimate crop residue cover byapplying spectral unmixing approaches to mixed pixels of residueand soils, it is unclear how well the technique could unmix a mixedspectrum of vegetation, soils, and crop residue using multispectraldata. Another method is to detect recently tilled surfaces usingmulti-temporal techniques (Zheng et al., 2012). The multi-temporalapproach, designated as minNDTI (Zheng et al., 2012), simplyextracts minimum NDTI values from time-series NDTI spectralprofiles. The resulting minNDTI image eliminates spectral effects ofgreen vegetation, analogous to a maximum-value MODIS NDVIcomposite image which screens out cloud-contaminated pixels.Zheng et al. (2012) found that minNDTI was strongly correlated withcrop residue cover with R2 of 0.89 in Central Indiana. However, lowercorrelations (R2 of 0.66–0.89) between minNDTI and crop residue

B. Zheng et al. / Soil & Tillage Research 138 (2014) 26–3430

were observed when the technique was applied to four additionallocations in Illinois, Iowa, Northern Indiana, and Maryland (Zhenget al., 2013a). The less satisfactory performance of minNDTI whenapplied to a broader region was ascribed to inability of NDTI toaccount for soil variations due to the broadness of Landsat spectralbands (Zheng et al., 2013a). Nevertheless, the minNDTI approachsignificantly improves the effectiveness of Landsat imagery toestimate crop residue cover and to classify tillage categories (Zhenget al., 2012).

3.2. Data mining approaches

In addition to the regression-based approaches which examinethe physical relationship between tillage indices and crop residue,other researchers explored data mining methodologies (Brick-lemyer et al., 2006; Sudheer et al., 2010; Watts et al., 2009, 2011).Data mining techniques are able to quantify nonlinear relation-ships, and require no human knowledge of underlying physicalrelationships between spectral data and the objects of interest.Several data-driven models have been applied to Landsat imageryto classify two broad tillage categories, including Artificial NeuralNetwork (ANN) (Sudheer et al., 2010), Classification Tree Analysis(CTA) (Bricklemyer et al., 2006; Watts et al., 2009, 2011), andsupport vector machine (Samui et al., 2012). These studiesreported mixed results. Samui et al. (2012) reported superiorityof support vector machine over logistic regression models. Sudheeret al. (2010) and Watts et al. (2009, 2011) yielded good resultsusing CTA. However, Bricklemyer et al. (2006) failed to detect tilledfarms on a June 26 Landsat ETM+ image. The poor performancereported by Bricklemyer et al. (2006) is likely due to the presence ofcrop canopy on some surveyed farms. Thus, with the presence ofvarious amount of green vegetation on fields, data-driven basedmodels might fail to differentiate different tillage practices usingsingle-date images, unless a region has an unusually short timewindow of tilling and planting activities. In addition, crop canopiescan completely block the view of crop residue for some agriculturalfields if imagery was acquired in the late planting season. Wattset al. (2011) successfully improved tillage classification accuracyby incorporating high temporal resolution data into a randomforest classifier, showing that local differences in times of tillageand planting in response to variations in terrain and climaterequire incorporation of multi-date images into the data-drivenapproaches. There are several advantages of CTA and randomforest classifier: (1) able to handle both numerical and categoricaldata; (2) require little data preparation; (3) work well with largedatasets; (4) use a white box model. In contrast, ANN is a ‘‘blackbox’’ approach. Because it is hard to interpret the resulting modelsfrom ANN, the models often cannot be generalized well to newdata. However, there are limitations to CTA and random forests.Locally optimized models from CTA and random forests might notbe generalized well to other locations, and overfitting can createerrors in the analysis.

3.3. Image preprocessing: atmospheric corrections

Generally, image classification using a single-date image doesnot require atmospheric correction because it is reasonable toassume that atmospheric condition is homogeneous within eachscene. In contrast, multi-temporal analyses often require atmo-spheric correction. Early studies of tillage mapping have used darkobject (e.g., shadows and water bodies) subtraction (Chavez, 1988)and multiple-date empirical radiometric normalization (Jensen,1996) to preprocess Landsat imagery. However, these twomethods often fail to provide reliable, consistent surface reflec-tance data when the analysis requires several images acquired atdifferent dates. In recent years, the Landsat Science Team has been

making efforts to provide higher-level Landsat data products.Landsat data users have access to the top of atmospheric (TOA)correction products from Web-enabled Landsat Data (WELD) (Royet al., 2010) and to Landsat Ecosystem Disturbance AdaptiveProcessing System (LEDAPS) (Masek et al., 2006), a preprocessingtool using the MODIS/6S approach, to produce TOA and surfacereflectance Landsat TM/ETM+ data. LEDAPS has been proven to be areliable tool. Users are now able to order Landsat surfacereflectance data directly from EarthExplorer (http://earthexplor-er.usgs.gov/) and USGS ESPA (EROS Science Processing Architec-ture) ordering interface (https://espa.cr.usgs.gov) withoutinstalling the LEDAPS tool (USGS, 2013), which can benefit landsurface change studies and future operational implementation oftillage mapping.

4. Radar

Imaging radars create images of the Earth by broadcastingmicrowave energy toward the earth from aircraft or satelliteplatforms, then receiving the backscattered radiation to form map-like imagery of the landscape (Fig. 1). Current imaging radars arebased upon synthetic aperture radar (SAR) technology, which isespecially effective in acquiring imagery at fine spatial scales.Although broadly considered, specific imaging radars are designedto use microwave radiation at frequencies between 0.3 GHz and30 GHz, corresponding to wavelengths within the interval of 1 cm–1 m, specific systems usually under consideration for agriculturalapplications operate at X-band (8–12 GHz), C-band (4–8 GHz), orL-band (1–2 GHz). Most SARs operate at center frequencies of9.65 GHz for X-band, 5.40 GHz for C-band, and 1.27 GHz for L-band. For our present discussion, SAR, microwave, and radar referhere to imaging radars currently used for earth observation.

Whereas optical systems basically convey information aboutbiological properties of crop residue, SAR imagery conveysinformation about the physical structure of crop residue, the soilsurface, and its moisture status. SAR systems have the advantagesof all-weather capabilities, fine spatial resolution, and capabilitiesfor both daytime and nighttime acquisitions. As active remotesensing systems, specifics of the transmitted microwave radiationare known with precision, so differences observed in the receivedbackscatter can be analyzed in detail. As a result, researchersexamine not only the strength of the backscatter, but also multiplevariables derived from polarization and wavelength. Backscatteredmicrowave energy is often reported as the normalized backscat-tering coefficient (s8).

4.1. Critical variables significant for tillage assessment

In operation, each instrument is characterized by specificwavelengths, incidence angles, look directions, and polarizations,as well as interactions between the microwave signal and thelandscape (penetration, depolarization, brightness), and charac-teristics of the landscape (including surface roughness, moisture,row direction, residue type, residue size, and residue moisturecontent). Together, these varied data create complex multivariatesystems that must be carefully analyzed to determine their abilityto assess tillage status. The brief review below highlights some ofthe issues that pertain to applications of active microwave remotesensing for tillage assessment. McNairn and Brisco (2004) andAdams et al. (2013) provide comprehensive reviews of currentresearch.

4.1.1. Experimental Context

Research exploring the effectiveness of microwave radiation fortillage assessment relies principally upon field experiments usingtruck-mounted scatterometers, with antennas mounted on movable

B. Zheng et al. / Soil & Tillage Research 138 (2014) 26–34 31

booms. In this context, researchers have the ability to alter theorientation of the antenna, and to change wavelength andpolarization as needed to conduct the experiment. Typicalexperimental plots measure perhaps 40 m � 40 m in size, usuallypositioned in an agricultural setting, specifically in situations thatpermit alteration of tillage and moisture conditions as needed tomeet research objectives. Brisco et al. (1991) have confirmed theeffectiveness field-based experiments for understanding behavior ofremotely acquired microwave data.

McNairn et al. (2001) report that their scatterometer resultsconfirm earlier experiments that microwave backscatter issensitive to the presence of crop residue. However, microwavedata are also sensitive to a multiplicity of sensor characteristics,including wavelength, incidence angle, polarization, and lookdirection. In addition, SAR data are also sensitive to soil surfaceconditions (i.e., soil moisture and surface roughness) (Bruckleret al., 1988). Backscattered SAR signals are mainly affected byslope, surface roughness, crop residue, and dielectric propertiesassociated with soil moisture (Fung, 1994; McNairn et al., 2002).Early studies found that various tillage treatments caused differentsoil surface roughness, and hence influenced radar backscattering(Brisco et al., 1991; McNairn et al., 1996). SAR scattering increaseswith increases in surface roughness. Thus, backscatter (s8) valuesof rough surfaces are higher than those of smooth surfaces (Moranet al., 2002; Hadria et al., 2009).

4.1.2. Wavelength

The effectiveness of SAR systems for tillage assessment isclosely linked to wavelength of the instrument; signals transmit-ted at longer wavelengths will tend to penetrate deeper into thesoil surface, and thereby are less sensitive to the presence of cropresidue. As a result, the relatively short wavelengths of X- and C-band SAR systems are likely to be effective for tillage assessment.

4.1.3. Incidence angle

Baghdadi et al. (2002, 2008) found that radar signals are moresensitive to surface roughness observed at higher incidence angles.Because signal penetration increases at steeper observation angles(i.e., lower incidence angles), in principal, observation at higherincidence angles tends to reduce signal penetration and to increasesensitivity to the presence of surface residue (McNairn et al., 1996).

4.1.4. Polarization and row direction

Crop residue cover has stronger correlations with cross-polarized backscatter than co-polarized backscatter (McNairnet al., 2001). Cross-polarized backscatter has the additional benefitof reduced sensitivity to radar look direction effects (Brisco et al.,1991; McNairn and Brisco, 2004), because, higher co-polarizedbackscatter (HH and VV) values are observed when row directionsperpendicular to the look direction rather than parallel to the lookdirection (Beaudoin et al., 1990). Brisco et al.’s (1991) study, at L, C,and Ku bands, found that row directions within 10–158 ofperpendicular can impact radar backscatter by several decibels,creating artifacts that lead to differences in backscatter withinfields with the same crop (McNairn and Brisco, 2004). These effectsare often visible as angular patterns (known as ‘‘bow–tie’’ effects)arising from changes in row direction as harvesting or tillageequipment traverses fields. Such bow–tie effects are strongest inlike-polarized mode, but not present in cross-polarized mode (asobserved in C-band aerial SAR data). Agricultural fields with littleor no residue cover are generally dominated by surface scattering,while no-till fields are dominated by multiple and volumescattering (McNairn et al., 2002). In addition to cross-polarizedlinear backscatter, McNairn et al. (2002) found that crop residuecover was also significantly correlated with two polarimetricparameters: co-polarized circular backscatter and pedestal height,

which are associated with volume scattering and multiplescattering. However, crop residue cover only accounted for about40% of variance in these radar parameters, while surface roughnessexplained about 60% of variance in all seven radar parametersexamined by McNairn et al. (2002). Given findings from theseprevious studies, estimation of crop residue cover using micro-wave responses alone is difficult.

4.1.5. Roughness

Brisco et al. (1991) found that differences in soil surfaceroughness caused by use of alternative tillage implements hadsignificant effects on s8. The magnitude of the change in s8 wassimilar to the difference in s8 of parallel versus perpendicular rowaspect angles for like polarized data, although the cross-polarizedchannels were less sensitive to row direction. Thus, polarizationratios may be useful for evaluating row direction influences. Theyrecommend further research to understand effects of soil type, soilmoisture, and row azimuth angles between 0 and 908 on s8.

4.1.6. Residue type and condition

McNairn et al. (2001), in field experiments, investigated thesensitivity of C- and L-band data to variations in residue type andmoisture content. Both corn and barley residues were found toretain high levels of moisture (60%, and 40–50%, respectively),which varied considerably in response to wetting and dryingevents. Stronger backscatter was associated with higher moisturelevels, especially with respect to moist corn residue observed by C-band cross-polarized data at shallow incidence angles. Cross-polarized backscatter was found to be sensitive to both corn andbarley residue, and generally insensitive to variations in lookdirection and row orientation.

Although many of McNairn et al. (2001) findings supported thepotential role of satellite SAR observation of tillage status, theywere careful to highlight effects of temporal and spatial variabilitypresent within the agricultural landscape under operationalconditions: ‘‘. . . results presented in this paper confirm that radarbackscatter is sensitive to crop residue. However conditions onagricultural fields are complex, with soil and residue character-istics changing temporally, varying across fields, and varying fromone field to the next. Although these scatterometer results areencouraging, care must be taken in extrapolating results fromcontrolled experiments to operationally mapping exercises’’ (p.257). They specifically highlighted the likelihood of confusionbetween rough tillage surfaces and high residue cover, especiallyfor finer residues. Their concerns represent another example of thephenomena encountered by Zheng et al.’s (2013a) study of opticalimagery, when they found that local terrain conditions outside therange of those encountered in their pilot studies complicated theirapplication of the minNDTI technique. It seems likely that studiesin microwave realm, with a large number of variables, willencounter similar issues as they extend applications fromcontrolled experiments to encounter the full range of landscapeconditions necessary for operational applications.

4.2. Platforms for orbital SAR tillage survey

Of the numerous SAR satellite systems, each with distinctivecharacteristics (Table 2), only a few are suitable as potentialsystems for broad-scale tillage assessment. For systematic surveyof tillage status, practicality mandates that imaged areas should belarge enough to permit survey at regional scales within short timeintervals. For this discussion, we suggest that imaged areasperhaps 100 km � 100 km in size might form an arbitrary, butuseful, standard. (For comparison, we note that this area is smallerthan that of a Landsat scene (170 km N-S � 183 km E-W.) Becausecurrent strategies for tillage assessment require acquisition of

Table 2Characteristics of selected synthetic aperture radar (SAR) satellite systems.

System Dates of service Wavelength/

frequency band

Polarization Spatial

resolution (m)

Swath width/

frame size (km)

Revisit

interval

Incidence

angles (8)

ERS-1 1991–2000 C VV 25 102 Varied 32–38

JERS-1 1992–1998 L HH 18 75 44 days 20–26

SIR-C 1994 L, C, X Full 30 15–90 N/A Multiple

RADARSAT-1 1995–2013 C HH 8–100 45–500 24 days 10–60

ERS-2 1995–2011 C VV 25 102 35 days 19–26

ENVISAT-ASAR 2002–2010 C Dual 30/150 100/400 35 days 15–45

ALOS-PALSAR 2006–2011 L Full 10/100 30–360 46 days 7.9–60

RADARSAT-2 2007–present C Full 9–100 25–170 24 days 49–60

TerraSAR-Xa 2007–present X Full 16 100 2.5 days 15–60

COSMO-SkyMed Constellation 2007–present X Dual 30 100 1–15 days 25–50

SAR-Lupe (5 satellites) 2006–present X Full <1 8 � 60a 11 h Multiple

TanDEM-X 2010–present X Full 18 100–150 2.5 days 20–65

RADARSAT Constellation 2018 C Full 30 125 24 h 21–47

Note: This table lists selected systems with simplified details because some systems offer several acquisition modes that prevent concise summarization. When possible, the

configurations most likely to be suitable for tillage assessment have been listed. Please refer to Campbell and Salomonson (2010) for a more complete list. L-, C-, and X-bands

denote systems operating in the 1–2, 4–8, and 8–12 GHz frequency bands, respectively. V and H denote vertical and horizontal polarizations respectively for the transmitted

and received signals; dual means that either two co-polarized or one co- and one cross-polarized modes were received in an acquisition.a Stripmap modes.

B. Zheng et al. / Soil & Tillage Research 138 (2014) 26–3432

several scenes during the planting season, tillage assessmentrequires a revisit time of perhaps two weeks or less. From results offield research programs mentioned previously, an operational SARsystem should, at a minimum, require full polarization, and useshorter wavelengths, likely C- or X-band. From field research,effective assessment of tillage status would favor systems thatpermit observation at large incidence angles.

The older systems in Table 2 seem unsuitable for tillageassessment because of their long revisit times, or in someinstances, other qualities. Some of the more recent systems,specifically, TerraSAR-X/TanDEM-X, COSMO_SkyMed constella-tion, and the planned ESA Sentinel-1 and RADARSAT constellation,appear to have system parameters (wavelength, polarization,revisit time, and size of imaged area) that would permit their usefor tillage assessment missions. Still uncertain at this time are anunderstanding of full effects of phenomena such as row direction,incidence angle, and moisture levels of crop residues, which couldinfluence effectiveness of these systems for tillage assessment.

5. Challenges and future possibilities

Broad-scale mapping of tillage practices is still challengedbecause of several major issues: (1) varied timing of soilpreparation and planting, (2) confounding issues caused by soilvariation and green vegetation, (3) relatively low revisit rates ofmoderate-spatial resolution imagery (such as Landsat and ASTER),(4) defining appropriate spatial scales for applying local andregional models, and (5) future availability of satellite systemswith capabilities for broad-scale tillage assessment.

5.1. The critical role of revisit interval

Agricultural land surfaces change so rapidly due to soil andresidue management, and crop growth, that the ability of a sensorto frequently revisit the same location is critical for monitoringagricultural activities, crop growth, and prediction of crop yields.While Zheng et al.’s (2012) minNDTI multi-temporal techniquecould eliminate green vegetation effects with an adequatenumber of Landsat observations, the eight-day revisit rate ofcombined Landsat 8 OLI and 7 ETM+ cannot guarantee a cloud-freeimage every two weeks, especially in tropical regions or otherareas that have persistent cloud cover. Crop progress andcondition reports from agricultural agencies provide informationof local crop and field conditions, which can assist selection ofappropriate satellite observations according to the timing of

planting and crop emergence (Zheng et al., 2013a). Landsat 7ETM+ has experienced data gap issues since 2003 because of thescan line corrector (SLC) off issue (USGS, 2010), which makesimplementation of the minNDTI technique more difficult. Theremote sensing community has made significant efforts to fillLandsat 7 SLC-off data gaps. The Landsat 5 TM stopped operatingin November 2011, and was decommissioned in May 2013. Thesuccessful launch of Landsat 8 in February 2013 restoresapplicability of the minNDTI technique, but Landsat 7 ETM+imagery is still required to boost the number of acquisitions.Zheng et al. (2013b) incorporated a multi-scale segmentationmethod specifically tailored to fill missing NDTI values in Landsat7 ETM+ SLC-off imagery to facilitate broad-scale tillage mapping.They presented county tillage maps with information of threetillage categories, i.e., non-conservation tillage (<30% cropresidue), conservation tillage (30–70% residue), conservationtillage-no till (>70% residue) (Zheng et al., 2013b). Cloud- andcloud shadow-contaminated pixels in a time series image willdecrease the number of observations for those pixel locations.Consequently, estimation of tillage status for the cloud-contami-nated pixels could be less accurate. Providing a quality assessmentmap with indications of how many times each pixel iscontaminated by clouds and cloud shadows is necessary toinform users about the quality of a tillage map at pixel level.

5.2. STARFM

The Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM) (Gao et al., 2006), which produces fused Landsat imageswith 30-m spatial resolution at MODIS temporal frequency, couldbe potentially useful to enhance temporal resolution for tillagemapping. However, the coefficients of determination (R2)between actual and STARFM synthetic data were reported torange between 0.00 and 0.99 among different bands for the samelocation in Watts et al.’s (2011) study. Inconsistencies in STARFM’sability to predict surface reflectance data among different bandscould introduce additional errors. As such, the potential toincorporate STARFM into minNDTI technique to improve ourability to map tillage practices currently remains unknown andresearch is required for further investigation. Nevertheless, futureimprovement of data fusion techniques, e.g., the ESTARFM (Zhuet al., 2010), and higher quality of Landsat 8 and the EuropeanSpace Agency (ESA) Sentinel-2 data could potentially provide dataoptimized in both temporal and spatial resolutions for tillageassessment.

B. Zheng et al. / Soil & Tillage Research 138 (2014) 26–34 33

5.3. Role of local soils and terrain

Zheng et al. (2013a) highlighted the influences of local soil andterrain upon the NDTI values, and McNairn et al. (2001) also notedthat conditions on agricultural fields are so complex, with soil andresidue characteristics changing temporally and spatially, that caremust be taken in extrapolating results from controlled experi-ments to operational mapping exercises. Tillage assessment effortscan benefit from further examination of such effects, andinvestigations to apply information from existing soil, terrain,and elevation data to identify where and when such effects mightinfluence NDTI values, and possibly provide a basis for reducingeffects upon NDTI values.

5.4. Local and regional tillage assessment models

An empirical model developed based upon local conditionsoften will not transfer well to other locations, as reported byZheng et al. (2013a), who found that locally developed modelsperformed better than a ‘universal’ model. The performance oflocal models could vary from one location to another, or from yearto year, in response to local weather, soil, and terrain conditions.Zheng et al. (2013a) also found that abundant rainfall during theplanting season has negative effects on crop residue coverestimation. Thus, a single local model might not be able topredict crop residue cover accurately over time for the region.Field observations may be required to calibrate the model everyyear to account for the effects of soil variation. Alternatively, localsoil-adjusted tillage indices might be able to minimize the effectsof soil background. Because the soil-adjusted tillage indices weredesigned under controlled conditions (Biard and Baret, 1997;Bannari et al., 2000), future efforts can be devoted to test thecapability of these indices on satellite data. Galloza et al. (2013)found that ALI data have better capability to discriminate cropresidue from soils than Landsat TM data and ascribed the betterperformance to ALI’s pushbroom design and capability to operatewithout saturation over the full range of albedos with 12 bitdynamic range. Their results indicate that Landsat 8’s OLI willhave improved capability to estimate crop residue (Galloza et al.,2013).

5.5. Status of current remote sensing systems

The recent launch of the Landsat 8 and retirement of Landsat 5TM continues satellite coverage with two instruments providingNDTI capabilities – Landsat 7 (TM7) and Landsat 8 (OLI). These twoLandsat instruments are in orbits for 16-day coverage cycles,together providing an 8-day observation cycle. Landsat 8 OLI’sspectral channels pairs, OLI’s bands 6 (SWIR-1) (1570–1650 nm)and 7 (SWIR-2) (2110–2290 nm) and TM’s bands 5 (1550–1750 nm) and 7 (2080–2350 nm), provide compatible spectralchannels for purposes of calculation of NDTI. Zheng et al. (2013b)presented and validated the multi-scale segmentation strategy forcorrecting Landsat 7’s SLC-off imagery in a manner that preservesfield-by-field NDTI integrity, thereby providing the capability for8-day repeat NDTI observations. At the time of this writing,research has yet to investigate applications of OLI imagery forNDTI, but evidence from analysis of ALI imagery (Galloza et al.,2013) suggests that it should enhance our ability to accuratelyassess crop residue cover. The 24-day revisit intervals forRADARSAT-2 (and for the now-inoperative Envisat) illustrate thedifficulties in making a validated algorithm for microwave tillageassessment operational, due to the necessity for multipleobservations during planting season. In contrast, the frequentrevisit intervals of systems such as TerraSAR-X and the ESA’sSentinel-1 (X- and C-bands, respectively), in combination with

microwave’s cloud penetrating capabilities, would greatly increasethe opportunities for acquiring sequential imagery.

5.6. The context for operational tillage assessment

Operational implementation of systematic tillage assessmentrequires not only a validated algorithm, and systematic acquisitionof imagery, as outlined above, but supporting geospatial datasystems, and field data collection programs. For example, Zhenget al.’s (2013b) implementation of broad-scale tillage assessmentrelied upon the USDA Cropland Data Layer (USDA-NASS, 2013b) toisolate cropland by masking forest, urban, and water from theanalysis, and to select corn and soybean cropland for analysis.Although this information could be acquired from other sources,their use illustrates that any broad-scale operational tillageassessment program cannot stand as isolated efforts, but willdepend upon supporting data systems, including, conceivably, datafrom other satellite systems to provide ancillary data to supportthe robustness of the tillage analysis.

Operational implementation of remote sensing systems fortillage assessment would also require systematic field datacollection campaigns on an annual basis to provide: (a) regionalcalibration data (as discussed by Zheng et al., 2013a,b) for calibrationof each year’s analysis, and (b) validation data for retrospectiveassessment of place-to-place accuracy of each year’s survey results.Zheng et al. (2013a) highlighted impacts of local variations in terrain,moisture, and soil color upon effectiveness of the minNDTItechnique – operational tillage assessment therefore would benefitfrom soil background data to highlight regions where localconditions might reduce effectiveness of minNDTI estimates. Witha multi-year record of operational experience, assessment usingfield data collection programs might permit estimation of localconfidence intervals for tillage estimates. Field data collectionefforts, using validated survey protocols (Hill, 2013), and coordinat-ed regionally to coincide with local tillage and planting calendars,would best be conducted by state-level efforts such as the CTIC (CTIC,2013) programs, calling upon state-based USDA-NRCS staff and theknowledgeable local volunteers that have contributed to CTICsuccesses, or upon comparable institutions in other countries.

The ability to monitor tillage practices will be greatly improvedin the near future, as more high quality satellite data will beavailable to us: the ESA Sentinel-2 constellation, a Landsat-typesensor with a 5-day equatorial revisit rate; the planned ESAEnvironmental Mapping and Analysis Program (EnMAP); and theproposed NASA Hyperspectral Infrared Imager (HyspIRI) with 19-day revisit time. These data, together with Landsat 8, will permitthe use of multiple sensors to provide frequent observations tocapture rapid changes of agricultural lands. Multispectral andhyperspectral data acquired concurrently could be combined toestimate crop residue at the multispectral spatial extent withimproved accuracy (Galloza et al., 2013). Future studies willinvolve multi-sensor multi-date image fusion. Inter-sensor cali-bration of tillage indices, therefore, will be required. Non-parametric classifiers are also attractive when multisource dataare utilized in a classification. Combination of optical and radardata could potentially improve mapping accuracies of tillagepractices.

This review has focused upon conditions prevailing largely inNorth America, and more specifically, the United States’ Corn Belt,with respect to issues such as crops, crop residue, tillage systems,supporting data systems, terrain, and soils. Therefore, althoughtillage assessment has significance through the world, specifics ofimplementation in other regions have yet to be fully investigated.We believe, however, that this review has identified key issues asunderstood at present as they likely apply to tillage assessmentconsidered broadly.

B. Zheng et al. / Soil & Tillage Research 138 (2014) 26–3434

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