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

Zheng, Boyuan; Campbell, James B.; Beurs, Kirsten M. de

doi:10.1016/j.rse.2011.09.016

Cited by 102 publications

(74 citation statements)

References 21 publications

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“…NDTI and STI were calculated from bands six (1560-1660 nm) and seven (2100-2300 nm) of the Landsat-8 OLI image data, and the NDTI-MRC and STI-MRC regressions yielded R 2 values of 0.84 and 0.78, respectively (Table 3). Our results were consistent with those of Zheng et al [18,19] and Galloza et al [11], indicating that it is feasible to estimate MRC using NDTI or STI from Landsat-8 OLI images. The NDI7 values were calculated from bands five (845-885 nm) and seven of the Landsat-8 OLI imagery data.…”

Section: Discussionsupporting

confidence: 93%

“…Murray and Williams [39] suggested that a broad absorption feature near 2100 nm is also evident in the reflectance spectra of all dry crop residues, which is probably associated with lignin and cellulose in crop residue. Thus, spectral information at 1450-1960 nm and near 2100 nm is highly correlated with CRC and can be used to estimate CRC [4,10,14,16,18,19]. NDTI and STI were calculated from bands six (1560-1660 nm) and seven (2100-2300 nm) of the Landsat-8 OLI image data, and the NDTI-MRC and STI-MRC regressions yielded R 2 values of 0.84 and 0.78, respectively (Table 3).…”

Section: Discussionmentioning

confidence: 99%

“…Pacheco and McNairn [7] estimated CRC based on SPOT and Landsat-5 TM data, and good consistency between the predicted and measured CRC was obtained using the spectral unmixing method. Zheng et al [18,19] The textural feature method can be used to extract textural features through image processing techniques and to analyze texture either quantitatively or qualitatively [21]. Image texture analysis involves measuring heterogeneity in the tonal values of pixels within a defined area of an image [22], and this method has been used to estimate parameters of vegetative structure [23,24].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

Jin

Wen

et al. 2015

Remote Sensing

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Abstract:The application of crop residue has become increasingly important for providing a barrier against water and wind erosion and improving soil organic matter content, infiltration, evaporation, temperature, and soil structure. The objectives of this work were to: (i) estimate maize residue cover (MRC) from Landsat-8 OLI images using seven vegetation indices (VIs) and eight textural features; and (ii) compare the VI method, textural feature method, and combination method (integration of textural features and spectral information) for estimating MRC with partial least squares regression (PLSR). The results showed that the normalized difference tillage index (NDTI), simple tillage index (STI), normalized difference index 7 (NDI7), and shortwave red normalized difference index (SRNDI) were significantly correlated with MRC. The MRC model based on NDTI outperformed (R 2 = 0.84 and RMSE = 12.33%) the models based on the other VIs. Band3mean, Band4mean, and Band5mean were highly correlated with MRC. The regression between Band3mean and MRC was stronger (R 2 = 0.71 and RMSE = 15.21%) than those between MRC and the other textural features. The MRC estimation accuracy using the combination method (R 2 = 0.96 and RMSE = 8.11%) was better than that based on only the VI (R 2 = 0.88 and RMSE = 11.34%) or textural feature (R 2 = 0.90 and RMSE = 9.82%) methods. The results suggest that the combination method can be used to estimate MRC on a regional scale. OPEN ACCESSRemote Sens. 2015, 7 14560

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

Jin

Wen

et al. 2015

Remote Sensing

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“…Fortunately, some of these constraints can be reduced due to the recent availability of Sentinel-1 and 2 images together with Landsat 8, and the potential capability of synthetizing Landsat images at the MODIS temporal resolution using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) [149]. Zheng et al [150] developed a multitemporal approach to minimize the confounding effects of green vegetation. Using this approach, the authors reported a R 2 of 0.89 between the minNDTI (the minimum NDTI from a NDTI time series) and crop residue cover in central Indiana, U.S.…”

Section: Soil Tillagementioning

confidence: 99%

Remote Sensing and Cropping Practices: A Review

et al. 2018

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For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

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“…However, some arable lands were cultivated only once in one year. Multi-temporal images can compensate for cropland classification errors from harvesting or an absence of planting and capture agricultural changes during the crop's life [65]. Multi-temporal images also make full use of phenological differences in different classes.…”

Section: Data and Pre-processingmentioning

confidence: 99%

Landsat-Based Land Cover Change in the Beijing-Tianjin-Tangshan Urban Agglomeration in 1990, 2000 and 2010

Yang

Sun

2017

IJGI

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Rapid urbanization dramatically changes the local environment. A hybrid classification method is designed and applied to multi-temporal Landsat images and ancillary data to obtain land cover change datasets. A support vector machine (SVM) classifier is used to classify multi-temporal Landsat Enhanced Thematic Mapper Plus (ETM+) images that were collected in 2000 at the pixel level. These images are also segmented with the mean shift method. The impervious surface is refined based on a combination of the segmented objects and the SVM classification results. The changed areas in 1990 and 2010 are determined by comparing the Thematic Mapper (TM) and ETM+ images via the re-weighted multivariate alteration detection transformation method. The TM images that were masked as changed areas in 1990 and 2000 are input into the SVM classifier. Land cover maps for 1990 and 2010 are produced by combining the unchanged area in 2000 with the new classes of the changed areas in 1990 and 2010. Land cover change has continuously accelerated since 1990. Remarkably, arable land decreased, while the impervious surface area significantly increased.

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

Cited by 102 publications

References 21 publications

Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

Remote Sensing and Cropping Practices: A Review

Landsat-Based Land Cover Change in the Beijing-Tianjin-Tangshan Urban Agglomeration in 1990, 2000 and 2010

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