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

Jin, Xiuliang; Ma, Jianhang; Wen, Zhidan; Song, Kaishan

doi:10.3390/rs71114559

Cited by 45 publications

(38 citation statements)

References 46 publications

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“…In addition, the results show that MLR has a poor performance when using multi-collinearity data to estimate AGB, which confirms the results of a previous study [87]; PLSR performs best in three conventional regression techniques for tackling the multi-collinearity problem, which confirms the results of a previous study [87]. Thus, PLSR is a useful tool that can be used to estimate several response variables simultaneously, while accounting for multi-collinearity variables [88].…”

Section: Analysis and Selection Of Vegetation Indexessupporting

confidence: 81%

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

et al. 2018

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Above-ground biomass (AGB) provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index) and eight statistical regression techniques: artificial neural network (ANN), multivariable linear regression (MLR), decision-tree regression (DT), boosted binary regression tree (BBRT), partial least squares regression (PLSR), random forest regression (RF), support vector machine regression (SVM), and principal component regression (PCR), which are used to analyze hyperspectral data acquired by using a field spectrophotometer. The vegetation indexes (VIs) determined from the spectra were first used to train regression techniques for modeling and validation to select the best VI input, and then summed with white Gaussian noise to study how remote sensing errors affect the regression techniques. Next, the VIs were divided into groups of different sizes by using various sampling methods for modeling and validation to test the stability of the techniques. Finally, the AGB was estimated by using a leave-one-out cross validation with these powerful techniques. The results of the study demonstrate that, of the eight techniques investigated, PLSR and MLR perform best in terms of stability and are most suitable when high-accuracy and stable estimates are required from relatively few samples. In addition, RF is extremely robust against noise and is best suited to deal with repeated observations involving remote-sensing data (i.e., data affected by atmosphere, clouds, observation times, and/or sensor noise). Finally, the leave-one-out cross-validation method indicates that PLSR provides the highest accuracy (R 2 = 0.89, RMSE = 1.20 t/ha, MAE = 0.90 t/ha, NRMSE = 0.07, CV (RMSE) = 0.18); thus, PLSR is best suited for works requiring high-accuracy estimation models. The results indicate that all these techniques provide impressive accuracy. The comparison and analysis provided herein thus reveals the advantages and disadvantages of the ANN, MLR, DT, BBRT, PLSR, RF, SVM, and PCR techniques and can help researchers to build efficient AGB-estimation models.

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Section: Analysis and Selection Of Vegetation Indexessupporting

confidence: 81%

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

et al. 2018

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“…Previous studies have proposed numerous indices to estimate CRC. Table 1 lists several promising spectral-based indices for CRC, such as the simple tillage index (STI) [32], the SRNDI [34], the NDSVI [33], DFI [30], the normalized difference tillage index (NDTI) [32], and the hyperspectral CAI [20]. The hyperspectral SINDRI is calculated from the: (i) hyperspectral bands 2210 and 2260 nm [36], (ii) ASTER SWIR bands 6 and 7 [36], and (iii) from the currently functional Worldview-3 SWIR bands 6 and 7 [14].…”

Section: Traditional Crop Residue Cover Spectral Indicesmentioning

confidence: 99%

“…An SI is a combination of two or more remotely detected reflectance bands. Estimates of CRC based on remote-sensing data are quantified by using SIs, such as the dead fuel index (DFI) [30], the normalized difference index (NDI, NDI5, and NDI7) [31], the normalized difference tillage index [32], the normalized difference senescent vegetation index (NDSVI) [33], the short-wave near-infrared normalized difference residue index (SRNDI) [34], the cellulose absorption index (CAI) [35], the shortwave infrared normalized difference residue index (SINDRI) [36], and lignin cellulose absorption [37]. A linear or exponential empirical CRC-estimation equation can be constructed and applied to remote-sensing data by using SI methods.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Crop residue left in the field after harvest helps to protect against water and wind erosion, increase soil organic matter, and improve soil quality, so a proper estimate of the quantity of crop residue is crucial to optimize tillage and for research into environmental effects. Although remote-sensing-based techniques to estimate crop residue cover (CRC) have proven to be good tools for determining CRC, their application is limited by variations in the moisture of crop residue and soil. In this study, we propose a crop residue angle index (CRAI) to estimate the CRC for four distinct soils with varying soil moisture (SM) content and crop residue moisture (CRM). The current study uses laboratory-based tests ((i) a dry dataset (air-dried soils and crop residues, n = 392); (ii) a wet dataset (wet soils and crop residues, n = 822); (iii) a saturated dataset (saturated soils and crop residues, n = 402); and (iv) all datasets (n = 1616)), which allows us to analysis the soil and crop residue hyperspectral response to varying SM/CRM. The CRAI combines two features that reflect the moisture content in soil and crop residue. The first is the different reflectance of soil and crop residue as a function of moisture in the near-infrared band (833 nm) and short-wave near-infrared band (1670 nm), and the second is different reflectance of soils and crop residues to lignin, cellulose, and moisture in the bands at 2101, 2031, and 2201 nm. The effects of moisture and soil type on the proposed CRAI and selected traditional spectral indices ((i) hyperspectral cellulose absorption index; (ii) hyperspectral shortwave infrared normalized difference residue index; and (iii) selected broad-band spectral indices) were compared by using a laboratory-based dataset. The results show that the SM/CRM significantly affects the broad-band spectral indices and all other spectral indices investigated are less correlated with CRC when using all datasets than when using only the dry, wet, or saturated dataset. Laboratory study suggests that the CRAI is promising for estimating CRC with the four soils and with varying SM/CRM. However, because the CRAI was only validated by a laboratory-based dataset, additional field testing is thus required to verify the use of satellite hyperspectral remote-sensing images for different crops and ecological areas.

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“…They found that bands of 5 and 7 due to covering the region near 2100 nm are suitable for the estimation of the SSR. Earlier studies applied NDTI and STI indices to multispectral Landsat 6, 7, and 8 images [19][20][21] and obtained accurate results.…”

Section: Introductionmentioning

confidence: 99%

“…The lignin cellulose index and the shortwave infrared normalized difference residue index were two other multispectral tillage indices based on advanced spaceborne thermal emission and reflection radiometer data, which are superior to the Landsat-based tillage indices, but not as good as the CAI in terms of mapping and characterizing the SSR [23]. Jin et al [21] increased the accuracy of the detection of SSR by integrating Landsat-8 based tillage indices and gray level co-occurrence matrix (GLCM) textural features.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue

et al. 2019

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Soil degradation, defined as the lowering and loss of soil functions, is becoming a serious problem worldwide and threatens agricultural production and terrestrial ecosystems. The surface residue of crops is one of the most effective erosion control measures and it increases the soil moisture content. In some areas of the world, the management of soil surface residue (SSR) is crucial for increasing soil fertility, maintaining high soil carbon levels, and reducing the degradation of soil due to rain and wind erosion. Standard methods of measuring the residue cover are time and labor intensive, but remote sensing can support the monitoring of conservation tillage practices applied to large fields. We investigated the potential of per-pixel and object-based image analysis (OBIA) for detecting and estimating the coverage of SSRs after tillage and planting practices for agricultural research fields in Iran using tillage indices for Landsat-8 and novel indices for Sentinel-2A. For validation, SSR was measured in the field through line transects at the beginning of the agricultural season (prior to autumn crop planting). Per-pixel approaches for Landsat-8 satellite images using normalized difference tillage index (NDTI) and simple tillage index (STI) yielded coefficient of determination (R2) values of 0.727 and 0.722, respectively. We developed comparable novel indices for Sentinel-2A satellite data that yielded R2 values of 0.760 and 0.759 for NDTI and STI, respectively, which means that the Sentinel data better matched the ground truth data. We tested several OBIA methods and achieved very high overall accuracies of up to 0.948 for Sentinel-2A and 0.891 for Landsat-8 with a membership function method. The OBIA methods clearly outperformed per-pixel approaches in estimating SSR and bear the potential to substitute or complement ground-based techniques.

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

Cited by 45 publications

References 46 publications

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

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

Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue

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