Zheng_2024_Remote-Sensing-of-Environment(4)

Availability of practical and reliable methods for monitoring prac-tice of tillage will reduce uncertainty in ecosystem models and permit identi ?cation of areas at risk for soil erosion and nutrient losses.Re-mote sensing is an ef ?cient and cost-effective way to obtain informa-tion concerning CRC/tillage practices.Mapping tillage practices using single-date images could be problematic unless the area has very nar-row window of planting dates.Watts et al.(2011)showed that incor-porating high temporal datasets can improve mapping accuracy of conservation tillage.Our study supports the ?ndings by Watts et al.(2011)that temporal resolution plays a signi ?cant role in mapping CRC/tillage practices accurately.Multi-temporal analyses (minNDTI and PC methods)are able to classify tillage categories and predict CRC along a continuum more accurately.Time-series of Landsat imag-ery have the potential to map CRC at broad scales and ?ll the tempo-ral data gaps in the observation of tillage practices.The Landsat Data Continuity Mission (LDCM)will provide the opportunity for continu-ously mapping the Earth's continental surface.The Hyperspectral In-frared Imager (HyspIRI)mission will provide another opportunity for mapping crop residue with the global coverage and 60meter spa-tial resolution.However,a 19-day revisit time of HyspIRI may not be short enough to provide two to three cloud-free images during plant-ing season.Future remote sensing platforms should consider im-provement of temporal resolution for crop residue detection.

Table 4

Error matrix for three residue cover classes using simple linear regression for all dataset.

Classi ?cation data

Reference data CRC b 30%

30%b CRC b 70%CRC >70%Total User accuracy CRC b 30%

190019100%30%b CRC b 70%31321872%CRC >70%01252696%

Total

22142763

Producer's accuracy

86%

93%

93%

Overall accuracy:90%;Kappa coef ?cient:85%.Bold data are the number of pixels correctly assigned to each class.

C R C %

Percentage Change %

Fig.8.The correlation between crop residue cover (CRC)and percentage change (PC)of NDTI (n =63).Green dots:correctly classi ?ed as CRC >70%(PC b 40%);blue dots:correctly classi ?ed as 30%b CRC b 70%(40%b PC b 70%);orange dots:correctly classi ?ed as CRC b 30%(PC >70%);red dots:misclassi ?cation.(For interpretation of the refer-ences to color in this ?gure legend,the reader is referred to the web version of this article.)

Table 5

Error matrix for three residue cover classes using the percentage change method.Classi ?cation data

Reference data CRC b 30%

30%b CRC b 70%CRC >70%Total User accuracy CRC b 30%

210021100%30%b CRC b 70%11231675%CRC >70%02242692%

Total

22142763

Producer's accuracy

95%

86%

89%

Overall accuracy:90%;Kappa coef ?cient:85%.Bold data are the number of pixels correctly assigned to each class.

Table 6

Summary of Landsat 5TM and 7ETM+scenes available for Central Indiana.Year Image acquisition date

Total images ξ201030-Mar ?,15-Apr ?,9-May,25-May,10-Jun 520094-Apr,12-Apr ?,22-May,23-Jun 4200730-Mar,15-Apr,1-May,2-Jun

420064-Apr ?,28-Apr,6-May ?,22-May ?,30-May 5200524-Mar,9-Apr,25-Apr,11-May,27-May 5200414-Apr ?,8-May,1-Jun ?,

3200312-Apr ?,28-Apr ?,6-May,22-May,23-Jun 5200225-Apr ?,3-May,20-Jun

3200121-Mar ?,14-Apr,30-Apr,8-May ?,9-Jun ?,17-Jun 6200026-Mar,27-Apr,13-May,29-May,6-Jun ?51999

24-Mar,25-Apr,11-May,27-May

4

*Images from Landsat 7ETM+archive.ξ

Total:total number of images;the average cloud cover of all the images is 7.43%.

182 B.Zheng et al./Remote Sensing of Environment 117(2012)177–183

Acknowledgements

This project was funded by the Graduate Research Development Program(GRDP)at Virginia Tech and Virginia Tech Geography Department's Sidman P.Poole Scholarship.The authors would like to thank the remote sensing team from IUPUI and Kai Wang for assist-ing data collection.

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