Estimation of Winter Wheat Residue Coverage Using Optical and SAR Remote Sensing Images

Cai, Wei; Zhao, Shuang; Wang, Yamei; Peng, Fanchen; Heo, Joon; Duan, Zheng

doi:10.3390/rs11101163

Cited by 19 publications

(17 citation statements)

References 41 publications

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“…Previous studies indicated that the saturation issue of optical remote sensing can affect the performances of statistical regression models in estimating crop biophysical parameters [73,74]. To overcome the saturation problems inherent in the remotely sensed optical measurements, a multisource remote sensing fusion is proposed [30,73].…”

Section: Phenotypic Variation and Relationship With Yieldmentioning

confidence: 99%

Phenotypic Traits Estimation and Preliminary Yield Assessment in Different Phenophases of Wheat Breeding Experiment Based on UAV Multispectral Images

et al. 2022

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The utility of unmanned aerial vehicles (UAV) imagery in retrieving phenotypic data to support plant breeding research has been a topic of increasing interest in recent years. The advantages of image-based phenotyping are related to the high spatial and temporal resolution of the retrieved data and the non-destructive and rapid method of data acquisition. This study trains parametric and nonparametric regression models to retrieve leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fractional vegetation cover (fCover), leaf chlorophyll content (LCC), canopy chlorophyll content (CCC), and grain yield (GY) of winter durum wheat breeding experiment from four-bands UAV images. A ground dataset, collected during two field campaigns and complemented with data from a previous study, is used for model development. The dataset is split at random into two parts, one for training and one for testing the models. The tested parametric models use the vegetation index formula and parametric functions. The tested nonparametric models are partial least square regression (PLSR), random forest regression (RFR), support vector regression (SVR), kernel ridge regression (KRR), and Gaussian processes regression (GPR). The retrieved biophysical variables along with traditional phenotypic traits (plant height, yield, and tillering) are analysed for detection of genetic diversity, proximity, and similarity in the studied genotypes. Analysis of variance (ANOVA), Duncan’s multiple range test, correlation analysis, and principal component analysis (PCA) are performed with the phenotypic traits. The parametric and nonparametric models show close results for GY retrieval, with parametric models indicating slightly higher accuracy (R2 = 0.49; MRSE = 0.58 kg/plot; rRMSE = 6.1%). However, the nonparametric model, GPR, computes per pixel uncertainty estimation, making it more appealing for operational use. Furthermore, our results demonstrate that grain filling was better than flowering phenological stage to predict GY. The nonparametric models show better results for biophysical variables retrieval, with GPR presenting the highest prediction performance. Nonetheless, robust models are found only for LAI (R2 = 0.48; MRSE = 0.64; rRMSE = 13.5%) and LCC (R2 = 0.49; MRSE = 31.57 mg m−2; rRMSE = 6.4%) and therefore these are the only remotely sensed phenotypic traits included in the statistical analysis for preliminary assessment of wheat productivity. The results from ANOVA and PCA illustrate that the retrieved remotely sensed phenotypic traits are a valuable addition to the traditional phenotypic traits for plant breeding studies. We believe that these preliminary results could speed up crop improvement programs; however, stronger interdisciplinary research is still needed, as well as uncertainty estimation of the remotely sensed phenotypic traits.

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Section: Phenotypic Variation and Relationship With Yieldmentioning

confidence: 99%

Phenotypic Traits Estimation and Preliminary Yield Assessment in Different Phenophases of Wheat Breeding Experiment Based on UAV Multispectral Images

et al. 2022

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“…Zhou et al [8] proposed a deep learning algorithm to detect the ground straw coverage under conservation tillage by using UAV low-altitude remote sensing images. Cai et al [9] used optical and SAR remote sensing images to estimate Winter Wheat residue coverage. Memon et al [10] assessed wheat straw cover in a rice-wheat cropping system by using Landsat Satellite Dat.…”

Section: Introductionmentioning

confidence: 99%

Detection Method of Straw Mulching Unevenness with RGB-D Sensors

et al. 2022

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Returning straw to the field is very important of for the conservation tillage to increase land fertility. It is vital to detect the unevenness of the straw covering to evaluate the performance of no-tillage planter, especially for the ones with returning full amount of straw. In this study, two kinds of RGB-D(Red, Green, Blue-Depth) sensors (RealSense D435i and Kinect v2) were applied to estimate the straw mulching unevenness by detecting the depth of straw coverage. Firstly, the overall structure and working principle of no-tillage planter with returning the full amount of straw was introduced. Secondly, field images were captured with the two kinds of RGB-D sensors after no tillage planter operation. Thirdly, straw covering unevenness computing was carried on a system developed by Matlab. Finally, the correlation analysis was conducted to test for the relationship between the straw covering unevenness by manual and deep sensors, with R (correlation coefficient) of 0.93, RMSE(Root Mean Square Error) of 4.59% and MAPE(Mean of Absolute Percentage Error) of 3.86% with D435i sensor, and with R of 0.915, RMSE of 6.53% and MAPE of 13.85% with Kinect V2, which showed both kinds of RGB-D sensors can acquire the unevenness of straw covering efficiently. The finding can provide a potential way to detect the unevenness of straw coverage and data support for operation evaluation and improvement of no-tillage planter.

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“…However, the spectral or image features obtained from a UAV are saturated in the later stage of crop growth, which leads to poor accuracy in the estimation of crop yield. In order to solve this problem, researchers have attempted to combine spectral and image features; they have found that the vegetation indices (VIs) combined with a textural feature index (a Normalized Differential Texture Index, NDTI) extracted from 550 nm and 800 nm band images obtained by UAV multispectral camera provided better results than using traditional textural features and vegetation indices in a rice AGB estimation model [14]. Previous studies have proved the feasibility of UAV-based textural features and VIs along with their combination in wheat AGB estimation and yield detection.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Aboveground Biomass For Winter Wheat In The Later Growth Stage By Combining Digital Texture And Spectral Analysis

Zheng

Tao

Bao

et al. 2021

Preprint

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Background: Aboveground biomass (AGB) is an important indicator to predict crop yield. Traditional spectral features or image textures have been proposed to estimate the AGB of crops, but they perform poorly in estimation of AGB at high biomass levels. The present study thus evaluated the ability of spectral features, image textures, combinations thereof to estimate winter wheat AGB. Result: The spectral features were obtained from the wheat canopy reflectance spectra of 400–1000 nm including original wavelengths and seven vegetation indices (VIs), then we screened effective wavelengths (EWs) through successive projection algorithm (SPA) and the optimal vegetation index selected by correlation analysis. The image textures features were extracted by gray level co-occurrence matrix including texture features (TEX) and normalized difference texture index (NDTI), then we selected effective variables including the optimal texture subset (OTEXS) and the optimal normalized difference texture index subset (ONDTIS) through the ranking of feature importance of random forest (RF). Linear regression (LR), partial least squares regression (PLS) and random forest (RF) were established to evaluate the relationship between each calculated feature and AGB. The results demonstrate that the ONDTIS with PLS based on validation datasets exhibited better performance in estimating AGB for the post-seedling stage (R2 = 0.75, RMSE = 0.04). Moreover, the combinations of OTEXS and EWs with LR based on validation datasets exhibited the highest prediction accuracy for the post-seedling stage (R2 = 0.78, RMSE = 0.05). Conclusion: The findings show that the combined use of spectral features and image textures can effectively improve the accuracy for AGB estimation especially in post-seeding stage.

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Estimation of Winter Wheat Residue Coverage Using Optical and SAR Remote Sensing Images

Cited by 19 publications

References 41 publications

Phenotypic Traits Estimation and Preliminary Yield Assessment in Different Phenophases of Wheat Breeding Experiment Based on UAV Multispectral Images

Phenotypic Traits Estimation and Preliminary Yield Assessment in Different Phenophases of Wheat Breeding Experiment Based on UAV Multispectral Images

Detection Method of Straw Mulching Unevenness with RGB-D Sensors

Estimation of Aboveground Biomass For Winter Wheat In The Later Growth Stage By Combining Digital Texture And Spectral Analysis

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