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

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 difficulties in defining reliable correlations between crop residue cover (CRC) and Landsat-based tillage indices because they neglected the significance of the timing of tillage implementation. This study explores relationships 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 profiles reliably indicate the surface status when tillage or planting occurred. Simple linear regression reveals a coefficient of determination (R2) of 0.89 between CRC and minNDTI for calibration. In addition, a percentage change (PC) method was tested for classifying CRC into three categories (CRC < 30%; 30% < CRC < 70%; CRC > 70%). Both the minNDTI and PC methods resulted in overall classification accuracies of > 90%, producer'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.

► We used multi-temporal Landsat TM and ETM+ imagery to map crop residue cover (CRC).
► We developed two multi-temporal methods: minimum NDTI and percentage change.
► They improved our ability to predict CRC more accurately.
► They are simple and can be applied to archived imagery over broad regions.
► Temporal resolution is an important factor in mapping CRC/tillage practices.