Mapping Crop Residue by Combining Landsat and WorldView-3 Satellite Imagery

Abstract

A unique, multi-tiered approach was applied to map crop-residue cover on the Eastern Shore of the Chesapeake Bay, United States. Field measurements of crop-residue cover were used to calibrate residue mapping using shortwave infrared (SWIR) indices derived from WorldView-3 imagery for a 12-km × 12-km footprint. The resulting map was then used to calibrate and subsequently classify crop residue mapping using Landsat imagery at a larger spatial resolution and extent. This manuscript describes how the method was applied and presents results in the form of crop-residue cover maps, validation statistics, and quantification of conservation tillage implementation in the agricultural landscape. Overall accuracy for maps derived from Landsat 7 and Landsat 8 were comparable at roughly 92% (+/− 10%). Tillage class-specific accuracy was also strong and ranged from 75% to 99%. The approach, which employed a 12-band image stack of six tillage spectral indices and six individual Landsat bands, was shown to be adaptable to variable soil-moisture conditions—under dry conditions (Landsat 7, 14 May 2015) the majority of predictive power was attributed to SWIR indices, and under wet conditions (Landsat 8, 22 May 2015) single band reflectance values were more effective at explaining variability in residue cover. Summary statistics of resulting tillage class occurrence matched closely with conservation tillage implementation totals reported by Maryland and Delaware to the Chesapeake Bay Program. This hybrid method combining WorldView-3 and Landsat imagery sources shows promise for monitoring progress in the adoption of conservation tillage practices and for describing crop-residue outcomes associated with a variety of agricultural management practices.

Retrospective tillage differentiation using the Landsat‐5 TM archive with discriminant analysis

“…We believe that the amount of rainfall on the scene acquisition day (±1 d) might have caused indifference in the classification accuracy (no rain observed during 2006, 2007, and 2011; 8.12 mm on 2008, and cumulative 7.84 mm on 1-3 June in 2010, respectively). The results were comparable to the performance of crop residue cover map based on the Landsat-7 and 8 with the changes in the moisture conditions (Hively, Shermeyer, Lamb, Daughtry, & Quemada, 2019). Even for the tillage indices NDTI that we used for both the DA and LR models in our study and Hively et al (2019) studies, NDTI performed better to classify NT on no-rainfall events than on CT. For example, under high moisture conditions, there is easy separation of bare tilled soils with significant crop residue (Seeley et al, 1983).…”

“…() studies, NDTI performed better to classify NT on no‐rainfall events than on CT. For example, under high moisture conditions, there is easy separation of bare tilled soils with significant crop residue (Seeley et al., ). In high moisture conditions in CT, the bare soil decreases the reflectance in Bands 4, 5, and 7 (Table ), whereas in NT conditions, there is an increase in the reflectance of all the three bands allowing for easy separation of soil and crop residue cover (Hively et al., ). These results were clearly evident in our study (Table ), with the same result for CT with a no‐rain event.…”

Evaluation of SWIR Crop Residue Bands for the Landsat Next Mission

“…Quemada and Daughtry (2016) obtained similar findings in a field-based study assessing NPV cover prediction performance for established indices and finding the more accurate applications of NDTI are generally limited to settings where residue is fresh, soil and residue moisture contents are below 25%, and GV cover is minimal [32]. The influence of moisture on broadband NPV index variability can make inter-image calibration and NPV timeseries analyses particularly challenging [28,33].…”

“…These studies have largely utilized Landsat and Sentinel-2 imagery, leveraging spectrally broad contrasts between SWIR bands at~1600 nm and~2200 nm to separate NPV and soil. The most effective Landsat-based NPV index has generally been found to be the Normalized Difference Tillage Index (NDTI) (Gelder et al, 2009, R 2 = 0.67 [27]; Jin et al, 2015 [22], R 2 = 0.84; Najafi et al, 2019, R 2 = 0.76 [23]; Hively et al, 2019, R 2 = 0.84 [28], where R 2 refers to goodness of fit for the correlation between the index and percent NPV cover): NDTI = (R 1610 − R 2200 )/(R 1610 + R 2200 )…”

A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods

“…These methods are time-consuming and labor-intensive, thus making systematic and continuous quantification of CRC over large areas difficult [5,6]. Alternatively, remote sensing is an efficient technique to acquire CRC spatially and temporally in a rapid, accurate, and objective manner [7,8]. Remote sensing techniques used to estimate CRC can be classified into empirical regression based on crop residue indices (CRIs), classification [9,10], spectral unmixing [11], and spectral angle methods [12,13].…”