Zheng_2024_Remote-Sensing-of-Environment(2)

soil and residue lines at point M

Biard&Baret,1997

Simple tillage index(STI)B5/B7B2:Landsat TM/ETM+band2;B4:

TM/ETM+band4;B5:TM/ETM+

band5;B7:TM/ETM+band7;Van Deventer et al.,1997

NDTI(B5?B7)/(B5+B7)

Modi?ed CRC(B5?B2)/(B5+B2)Sullivan et al.(2006)

NDI5;NDI7(B4?B5)/(B4+B5);

(B4?B7)/(B4+B7)

McNairn and Protz(1993) Hyperion CAI0.5(R2.0+R2.2)?R2.1R2.0and R2.2:the re?ectance on the

shoulders at2021nm and2213nm

Daughtry et al.(2006)

ASTER LCA100(2×B6?B5?B8)B5,B6,B7,B8:ASTER shortwave

infrared bands5,6,7,and8Daughtry et al.(2005)

SINDRI(B6?B7)/(B6+B7)Serbin et al.

(2009a) Fig.1.Pictures of agricultural?elds:before tillage(A),after tillage/planting with no or little vegetation(B&C),and crop emergence(D).

178 B.Zheng et al./Remote Sensing of Environment117(2012)177–183

is only active when requested.ASTER's shortwave infrared(SWIR)de-tector failed in April2008(NASA,2011).Thus,subsequently,ASTER im-agery is not capable of CRC mapping.MODIS revisits the same area daily;however,MODIS data have coarse spatial resolution(500m in SWIR bands),so may experience mixed pixel problems for many agri-cultural landscapes.

Landsat5TM and Landsat7ETM+imagery currently provide the best available imagery for mapping CRC,not only because their short-wave infrared(SWIR)Band7(2080–2350nm)is sensitive to crop residue,but also because they provide moderate spatial resolution (30m)and an eight-day revisit rate using both Landsat5and7.

3.Study area and data

3.1.Study site

This study was conducted in Central Indiana(Fig.2),one of the most signi?cant agro-ecoregions within the Eastern Corn Belt Plains of the United States.Locations of?eld data for this study are shown in Fig.2.

Central Indiana is an extensive agricultural region with?at topog-raphy.This landscape is drained by long,shallow,streams occupying sinuous valleys.Agricultural lands often have drainage ditches and channelized streams to promote soil drainage in?at,poorly drained, areas.The principal crops are maize(Zea mays)and soybeans(Glycine max).Most of the soils of this region are Al?sols,Inceptisols,and Mol-lisols(Major Land Resource Area[MLRA]111A).Soil erosion rates in this region are from7.5to4.1tons per acre from1982to2007 (USDA-NRCS,2007).

3.2.Field measurements

CRC was measured using a line transect method(Morrison et al., 1993)from May13to May26in2010.We used a50-foot(15.24m) measuring tape which can be easily pided into100parts with0.5-foot intervals shown as red markings.At each sampling site,the tape was stretched diagonally across the rows(NRCS,1992)and the number of markings intersecting crop residue was counted.Then we measured off diagonal and counted the number of markings intersecting crop res-idue again.Percent cover was calculated by the average of the two counted numbers of the markings.In addition,we used a Garmin eTrex GPS unit(positional accuracy of b15m)to record the location of each measurement,acquired photographs,and made notes for each sampling site.We measured a total of72?elds using the line transect method,among which44?elds were planted with corn and28?elds with soybean in2009with the con?rmation of a cropland data layer (58bd8d2b482fb4daa58d4bea/research/Cropland/SARS1a.htm).We found that17of the28soybean?elds displayed a mixture of corn and soybean residue.

3.3.Remotely sensed data

Five Landsat images(Path21/Row32)acquired on March30 (ETM+7),April15(ETM+7),May9(TM5),May25(TM5),and June10(TM5)in2010were atmospherically corrected to surface re?ectance using the Landsat Ecosystem Disturbance Adaptive Proces-sing System(LEDAPS)(Masek et al.,2006).Images acquired on May9 and25are partially covered by clouds and cloud shadows.The Landsat 7ETM+images are scan line corrector(SLC)-off and have data gaps. Serbin et al.(2009b)compared several Landsat-based tillage indices and found that the Normalized Difference Tillage index(NDTI)was the best for separating crop residue and soil.Thus,we generated NDTI layers for each surface re?ectance image and stacked the images into

a time-series of NDTI image.

4.Methods

Multi-temporal Landsat imagery can capture agricultural changes during the spring planting season.Fig.3shows both the changes of Normalized Difference Vegetation Index(NDVI)and NDTI for our study region in Indiana between March30(day89)and June10 (day161)in2010.NDVI and NDTI are positively correlated with

the Fig.2.Locations of sampling sites in Central Indiana.

179 B.Zheng et al./Remote Sensing of Environment117(2012)177–183

green vegetation cover and CRC respectively.The decrease in NDTI values from day 89to day 129(Fig.3)corresponds to the decrease of CRC due to residue weathering and tillage application on the ?eld,while the rebound of NDTI values after day 129is caused by growing vegetation.Thus,NDTI values are affected by greening vege-tation.Fig.4shows how the NDTI values change through time from March 30(day 89)to June 10(day 161)in 2010for three pixels with different levels of CRC.The abrupt change in NDTI value (the diamond dotted line)from day 105(NDTI =0.10)to day 129(NDTI=0.01)(Fig.4)is due to signi ?cant decreases in the amount of CRC (b 30%)caused by non-conservation tillage,while the change in NDTI value was less abrupt (e.g.,changes from 0.14to 0.09)when con-servation tillage (>30%CRC)was applied to the ?elds.The increased NDTI value after day 129(May 9)is caused by growing vegetation.For this speci ?c example,the use of single images acquired on the days 105or 145would result in dif ?culties differentiating conservation from non-conservation tillage.A single image cannot provide reliable assessment of tillage practices because tillage or planting could happen anytime from April to early June in Central Indiana (Table 2),and in the absence of sequential imagery,analysts cannot determine the correct status of a ?eld.4.1.Minimum NDTI

Due to partial cloud cover in some Landsat images and data gaps in Landsat 7ETM+images,samples affected by clouds,cloud shadows,

and missing data were removed from analysis,resulting in 63clean samples.The minimum NDTI (minNDTI)values,representing the closest status of the surface condition right after planting,were chosen from each spectral pro ?le.We applied simple linear regression (SLR)to deter-mine the relationship between minNDTI and ?eld observed CRC.We ?rst sorted our ?eld observation samples by minNDTI values and pid-ed them into calibration (n=31)and test (n=32)datasets by selecting every other sample to ensure representative subsamples.The regression equation from the calibration dataset was then applied to the test data-set.We pided CRC into three categories:CRC b 30%(non-conservation tillage),30%b CRC b 70%,and CRC>70%.Conservation tillage was split into two categories (30%–70%and >70%)to identify ?elds that were likely managed with no-till (CRC>70%).4.2.Percentage change method

In the next step,we applied a percentage change (PC)method to map CRC.We ?rst selected the NDTI values before planting for each sample pixel (NDTI B ).NDTI B was selected according to the following criteria:1)it must be acquired before the minNDTI occurred;and 2)its value should be larger than 0.08because some ?elds may have ex-perienced several tillage operations at different times before planting.Note that the selection criteria for NDTI B may be different for other regions.

The rationale for the PC method is to detect changes of the same pixel from time I (before tillage)to time П(after tillage/planting).The PC is calculated by

NDTI B ?minNDTI eT=NDTI B ?100%

e1T

The magnitude of change in NDTI is different for different tillage types (Fig.4).This method is unique in its ease of use,ability to min-imize effects of soil variation,and to map tillage practices over broad regions.It requires less ?eld validation effort,and can be applied ret-rospectively to archived imagery,as well as those acquired in the future.5.Results

5.1.Minimum NDTI

We found a linear relationship between CRC and minNDTI with a coef ?cient of determination (R 2)of 0.89and root mean square of error (RMSE)of 10.5%for the calibration data (Fig.5).The R 2between measured and predicted CRC is 0.85and RMSE is 12.6%for the test dataset (Fig.6).The slope is 1.05when the intercept was forced to zero.The SLR results in an R 2of 0.87between CRC and minNDTI and RMSE of 11.5%using all 63samples (Fig.7).

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