Zheng_2024_Remote-Sensing-of-Environment

Remote sensing of crop residue cover using multi-temporal Landsat imagery

Baojuan Zheng a ,?,James B.Campbell a ,Kirsten M.de Beurs a ,b

a Department of Geography,Virginia Tech,115Major Williams Hall,Blacksburg,VA 24061,USA

b

Department of Geography and Environmental Sustainability,The University of Oklahoma,100E.Boyd Street,Norman,OK,USA

a b s t r a c t

a r t i c l e i n f o Article history:

Received 5August 2011

Received in revised form 26September 2011Accepted 29September 2011

Available online 4November 2011Keywords:Landsat

Remote sensing Crop residue Tillage

Normalized difference tillage index Multi-temporal

Tillage practices,which have direct impacts on soil and water quality,have changed dramatically during the past several decades.Tillage information is one of the important inputs for environmental modeling,but the availability of this information is still limited spatially and temporally.Previous studies have encountered dif-?culties in de ?ning reliable correlations between crop residue cover (CRC)and Landsat-based tillage indices because they neglected the signi ?cance of the timing of tillage implementation.This study explores relation-ships between temporal changes of agricultural surfaces and the normalized difference tillage index (NDTI)in Central Indiana.We found that minimum NDTI (minNDTI)values extracted from multi-temporal NDTI pro ?les reliably indicate the surface status when tillage or planting occurred.Simple linear regression reveals a coef ?cient of determination (R 2)of 0.89between CRC and minNDTI for calibration.In addition,a percent-age change (PC)method was tested for classifying CRC into three categories (CRC b 30%;30%b CRC b 70%;CRC >70%).Both the minNDTI and PC methods resulted in overall classi ?cation accuracies of >90%,produ-cer's accuracies of 83–100%,and user's accuracies of 75–100%.Our results indicated that both Landsat TM and ETM+imagery are capable of mapping CRC,however,multi-temporal Landsat imagery is required.To establish a capability for crop residue mapping,designers of future remote sensing platforms should consider increasing temporal resolution.

?2011Elsevier Inc.All rights reserved.

1.Introduction

Agricultural best management practices,such as conservation till-age and cover crops,have been adopted widely in recent years.The bene ?ts of conservation tillage are substantial,including improve-ment of soil and water quality,reduction of soil erosion,and maximi-zation of agricultural water use ef ?ciency (USDA-NRCS,2001).Reliable and systematic site-speci ?c conservation tillage data do not currently exist,but would form an important resource supporting the evaluation of the effectiveness of these practices.

Non-conservation tillage (intensive/conventional and reduced till-age)leaves less than 30%crop residue cover (CRC),while conservation tillage leaves more than 30%CRC (CTIC,2010).Current CRC data are not surveyed systematically and vary from one location to another.The USDA Natural Resources Conservation Service (NRCS)collects CRC data visually using a line-transect method (Morrison et al.,1993).The Conservation Technology Information Center (CTIC)provides assess-ments of conservation tillage practices,but collects data using annual roadside surveys of crop residue levels,which is subjective.Its tillage data are available at county,state,and regional levels.The county-level data were recently aggregated to 8-digit Hydrologic Unit (HU)wa-tersheds (Baker,2011).The National Agricultural Statistics Service (NASS)data relies on survey respondents and is only available at state

and county level.These inventory data are either too coarse (i.e.,they cannot provide ?eld level detail,nor report within-?eld spatial variabil-ity),or are inconsistent,adding more uncertainties in the environmen-tal modeling process.The spatial and temporal gaps in these inventory data restrict our ability to simulate the impact of crop management on water quality or carbon sequestration at broad scales (Jarecki et al.,2005;Saseendran et al.,2007).Thus,there is a strong need to develop methods to monitor agricultural practices over large areas,over time,using consistent methods.Multispectral remote sensing offers an op-portunity to systematically obtain information describing crop residues ef ?ciently and objectively over broad areas.

Early attempts to use remote sensing techniques for mapping CRC can be traced back to 1975(Gausman et al.,1975).Since then,the po-tential of remote sensing of crop residue has been investigated both in the laboratory and in the ?eld (Biard &Baret,1997;Daughtry,2001;Daughtry et al.,1995;Sullivan et al.,2007,2006).Remote sens-ing tillage indices,such as the crop residue index multiband (CRIM)(Biard &Baret,1997),the cellulose absorption index (CAI)(Daughtry,2001),and crop residue cover index (Sullivan et al.,2006)are designed in the laboratory to amplify the differences in the spectral signals between crop residues and soils (Table 1).Most tillage indices are based on the cellulose and lignin absorption features near 2100nm.Researchers have applied these tillage indices (Table 1)to airborne (Daughtry et al.,2005)and satellite remote sensing imagery (Daughtry et al.,2006;Gowda et al.,2003;Serbin et al.,2009a;Sullivan et al.,2008;Thoma et al.,2004;van Deventer et al.,1997).

Remote Sensing of Environment 117(2012)177–183

?Corresponding author.Tel.:+16467507087.E-mail address:baojuan5@58bd8d2b482fb4daa58d4bea (B.

Zheng).

0034-4257/$–see front matter ?2011Elsevier Inc.All rights reserved.doi:

10.1016/j.rse.2011.09.016

Contents lists available at SciVerse ScienceDirect

Remote Sensing of Environment

j o u r n a l h om e p a g e :ww w.e l s e v i e r.c o m /l o c a t e /r s e

These previous methods neglect an important factor—the timing of tillage or planting,which can vary greatly from?eld to?eld within even small regions.Three different surface conditions can coexist in a single image during the planting season(Fig.1):before tillage/planting(A), after tillage/planting with no or little vegetation(B&C),and crop emer-gence(D).Most?elds are under condition A at the early planting season and in condition D at the end of the planting season.If there are agricul-tural?elds tilled after an image was acquired,the previous methods would wrongfully designate these?elds as no-till.If crops have emerged,the green vegetation is likely to confound the residue cover estimation(Daughtry et al.,2005).Therefore,the methods previously outlined(i.e.,single image methods)could be problematic in predicting CRC and cannot be applied broadly.Watts et al.(2009)suggested that the use of higher temporal datasets might better capture surface distur-bances in minimum tillage?elds.Although Watts et al.(2011)pro-duced better classi?cation accuracy using a?ve-date Landsat model,a physical relationship between Landsat data and tillage categories was not de?ned.Instead they generated classi?cation models with the Ran-dom Forest classi?er.The objective of this study is to reveal the impor-tant role of temporal changes in CRC mapping,and to present a simple and objective method to map CRC using multi-temporal Landsat imagery.

2.Remote sensing imagery for crop residue detection

Accurate mapping of CRC not only requires remotely sensed data with spectral and spatial detail,but also with high temporal resolu-tion.Based on crop residue's unique absorption features near 2100nm(Daughtry,2001),past and current satellite remote sensing platforms capable of mapping CRC include Landsat5TM and7ETM+, EO-1Hyperion,the Advanced Spaceborne Thermal Emission and Re-?ection Radiometer(ASTER),and the Moderate Resolution Imaging Spectroradiometer(MODIS).Hyperion imagery,with a narrow swath width(7.5km),has low temporal coverage because its sensor

Table1

Satellite-based tillage indices.

Sensor Tillage index Formula Description Reference

Landsat CRIM SM/SR SM:distance from point M to the

soil line;SR:distance between

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