Using Hyperspectral Crop Residue Angle Index to Estimate Maize and Winter-Wheat Residue Cover: A Laboratory Study

Yue, Jibo; Tian, Qingjiu; Dong, Xinyu; Xu, Kaijian; Zhou, Chengquan

doi:10.3390/rs11070807

Cited by 24 publications

(10 citation statements)

References 51 publications

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“…The limited accuracy of broadband approaches results from several factors including issues with inter-image calibration, background soil moisture conditions, and interference from low levels of green vegetation. Yue et al, (2019) conducted a laboratory-based study assessing the NPV cover prediction performance of broadband indices, narrowband indices, and narrowband spectral angle mapping approaches, finding that broadband indices were most susceptible to errors in NPV cover prediction when moisture conditions varied [31]. 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].…”

Section: Crop Residue Measurement Using Broadband Multispectral Indicesmentioning

confidence: 99%

“…Narrowband SWIR indices have consistently demonstrated improved accuracy in mapping crop residue compared to broadband approaches like the NDTI [9][10][11]16,28,[31][32][33][34][35]. Furthermore, the narrowband SWIR indices that measure ligno-cellulose absorption features are far less influenced by the presence of green vegetation than are broadband indices [9][10][11] and are also less influenced by moisture variability and more amenable to correction for moisture content using water ratio indices [9,32,33].…”

Section: Narrowband Swir Indices Measuring 2100 Nm and 2300 Nm Ligno-cellulose Absorption Featuresmentioning

confidence: 99%

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Evaluation of SWIR Crop Residue Bands for the Landsat Next Mission

et al. 2021

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This research reports the findings of a Landsat Next expert review panel that evaluated the use of narrow shortwave infrared (SWIR) reflectance bands to measure ligno-cellulose absorption features centered near 2100 and 2300 nm, with the objective of measuring and mapping non-photosynthetic vegetation (NPV), crop residue cover, and the adoption of conservation tillage practices within agricultural landscapes. Results could also apply to detection of NPV in pasture, grazing lands, and non-agricultural settings. Currently, there are no satellite data sources that provide narrowband or hyperspectral SWIR imagery at sufficient volume to map NPV at a regional scale. The Landsat Next mission, currently under design and expected to launch in the late 2020’s, provides the opportunity for achieving increased SWIR sampling and spectral resolution with the adoption of new sensor technology. This study employed hyperspectral data collected from 916 agricultural field locations with varying fractional NPV, fractional green vegetation, and surface moisture contents. These spectra were processed to generate narrow bands with centers at 2040, 2100, 2210, 2260, and 2230 nm, at various bandwidths, that were subsequently used to derive 13 NPV spectral indices from each spectrum. For crop residues with minimal green vegetation cover, two-band indices derived from 2210 and 2260 nm bands were top performers for measuring NPV (R2 = 0.81, RMSE = 0.13) using bandwidths of 30 to 50 nm, and the addition of a third band at 2100 nm increased resistance to atmospheric correction residuals and improved mission continuity with Landsat 8 Operational Land Imager Band 7. For prediction of NPV over a full range of green vegetation cover, the Cellulose Absorption Index, derived from 2040, 2100, and 2210 nm bands, was top performer (R2 = 0.77, RMSE = 0.17), but required a narrow (≤20 nm) bandwidth at 2040 nm to avoid interference from atmospheric carbon dioxide absorption. In comparison, broadband NPV indices utilizing Landsat 8 bands centered at 1610 and 2200 nm performed poorly in measuring fractional NPV (R2 = 0.44), with significantly increased interference from green vegetation.

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Section: Crop Residue Measurement Using Broadband Multispectral Indicesmentioning

confidence: 99%

Section: Narrowband Swir Indices Measuring 2100 Nm and 2300 Nm Ligno-cellulose Absorption Featuresmentioning

confidence: 99%

Evaluation of SWIR Crop Residue Bands for the Landsat Next Mission

et al. 2021

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“…The crop residue cover estimation and the conservative tillage monitoring based on remote sensing data have become a topic of significant interest to researchers [10,11]. In the past few decades, a series of methods have been proposed for estimating local and regional crop residue cover using remote sensing data, including the linear spectral unmixing [12], the spectral index [13,14], and the triangle space technique [15]. However, remote sensing techniques to estimate crop residue cover has been limited by the variations of moisture in the crop residue and soil [16].…”

Section: Introductionmentioning

confidence: 99%

“…The cellulose and lignin absorption features are attenuated as moisture content increases, which decreases the accuracy of crop residue cover estimation using existing spectral index methods [10]. The problem of the triangle space technique lies in the difficulty of acquiring hyperspectral images, which limits the application of this method for estimating crop residue cover on a large-scale [15]. These studies are all about crop residue cover estimation using remote sensing data, disregarding the residue cover type (i.e., residue covering directly, stalk-stubble breaking, stubble standing, etc.).…”

Section: Introductionmentioning

confidence: 99%

Corn Residue Covered Area Mapping with a Deep Learning Method Using Chinese GF-1 B/D High Resolution Remote Sensing Images

et al. 2021

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Black soil is one of the most productive soils with high organic matter content. Crop residue covering is important for protecting black soil from alleviating soil erosion and increasing soil organic carbon. Mapping crop residue covered areas accurately using remote sensing images can monitor the protection of black soil in regional areas. Considering the inhomogeneity and randomness, resulting from human management difference, the high spatial resolution Chinese GF-1 B/D image and developed MSCU-net+C deep learning method are used to mapping corn residue covered area (CRCA) in this study. The developed MSCU-net+C is joined by a multiscale convolution group (MSCG), the global loss function, and Convolutional Block Attention Module (CBAM) based on U-net and the full connected conditional random field (FCCRF). The effectiveness of the proposed MSCU-net+C is validated by the ablation experiment and comparison experiment for mapping CRCA in Lishu County, Jilin Province, China. The accuracy assessment results show that the developed MSCU-net+C improve the CRCA classification accuracy from IOUAVG = 0.8604 and KappaAVG = 0.8864 to IOUAVG = 0.9081 and KappaAVG = 0.9258 compared with U-net. Our developed and other deep semantic segmentation networks (MU-net, GU-net, MSCU-net, SegNet, and Dlv3+) improve the classification accuracy of IOUAVG/KappaAVG with 0.0091/0.0058, 0.0133/0.0091, 0.044/0.0345, 0.0104/0.0069, and 0.0107/0.0072 compared with U-net, respectively. The classification accuracies of IOUAVG/KappaAVG of traditional machine learning methods, including support vector machine (SVM) and neural network (NN), are 0.576/0.5526 and 0.6417/0.6482, respectively. These results reveal that the developed MSCU-net+C can be used to map CRCA for monitoring black soil protection.

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“…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]. The most widely used among these methods is empirical regression constructed from a linear or nonlinear relationship between CRC and the CRIs, also known as "the CRI technique".…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an R2cv of 0.63 and RMSEcv of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of R2cv = 0.66 and RMSEcv = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an R2cv = 0.61 and RMSEcv = 6.415%. The estimation was improved by an SVR model with the same input predictors (R2cv = 0.67, RMSEcv = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with R2cv = 0.69 and RMSEcv = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC.

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Using Hyperspectral Crop Residue Angle Index to Estimate Maize and Winter-Wheat Residue Cover: A Laboratory Study

Cited by 24 publications

References 51 publications

Evaluation of SWIR Crop Residue Bands for the Landsat Next Mission

Evaluation of SWIR Crop Residue Bands for the Landsat Next Mission

Corn Residue Covered Area Mapping with a Deep Learning Method Using Chinese GF-1 B/D High Resolution Remote Sensing Images

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

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