Estimation of Water Interception of Winter Wheat Canopy Under Sprinkler Irrigation Using UAV Image Data

Abstract. The establishment of commercial forestry plantations in natural grassland vegetation, results in increased transpiration and interception which in turn, results in a streamflow reduction. Methods to quantify this impact typically require LAI as an input into the various equations and process models that are applied. The use of remote sensing technology as a tool to estimate leaf area index (LAI) for use in estimating canopy interception is described in this paper. Remote sensing provides a potential solution to effectively monitor the spatial and temporal variability of LAI. This is illustrated using Hyperion hyperspectral imagery and three vegetation indices, namely the normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI) and Vogelmann index 1 to estimate LAI in a catchment afforested with Eucalyptus, Pinus and Acacia genera in the KwaZulu-Natal midlands of South Africa. Of the three vegetation indices used in this study, it was found that the Vogelmann index 1 was the most robust index with an R2 and root mean square error (RMSE) values of 0.7 and 0.3 respectively. However, both NDVI and SAVI could be used to estimate the LAI of 12 year old Pinus patula accurately. If the interception component is to be quantified independently, estimates of maximum storage capacity and canopy interception are required. Thus, the spatial distribution of LAI in the catchment is used to estimate maximum canopy storage capacity in the study area.

This study evaluates the effectiveness of Sentinel-2 satellite imagery in assessing carbon sequestration in mango orchards using multiple vegetation indices (VIs). We established 49 quadrat sampling plots (40*40 m) across diverse mango orchards in Phitsanulok Province, Thailand to collect ground truth data for aboveground carbon storage estimation. Twelve vegetation indices were analyzed, including Normalized difference vegetation index (NDVI), Normalized difference red edge index (NDRE), Enhanced Vegetation Index (EVI), Green normalized difference vegetation index (GNDVI), Green Chlorophyll Index (Clgreen), Ratio Vegetation Index (RVI), Triangular vegetation index (TVI), Modified Triangular vegetation index (TVI-2), Angular Vegetation Index (AVI), Normalized Pigment Chlorophyll Ratio Index (NPCRI), and Modified Simple Ratio Index (MSRI) as well as Chlorophyll Index Red Edge (CIRE). These indices demonstrated significant correlations with field-measured biomass (R2 ranging from 0.04 to 0.80). The Random Forest (RF) ensemble model, optimized with 400 trees (ntree) and 20 variables at each split (mtry), integrated these indices to predict carbon storage. The model achieved exceptional accuracy (R2=0.97) with a Root Mean Square Error (RMSE) of 1.57 ton C ha-1 and Mean Absolute Error (MAE) of 1.05 ton C ha-1. Feature importance analysis revealed that f (NDVI), f (NDRE), f (TVI-2), and f (GNDVI) contributed 0.283, 0.160, 0.377, and 0.181, respectively, to the model's predictive power. Cross-validation using a 70:30 training–testing split confirmed the model's robustness. The developed model enables efficient monitoring of carbon sequestration of mango plantations, with the mean carbon sequestration calculated to be 40.6 ton C ha-1 (σ=42.19, n=49) and wide range of carbon sequestration values (4.13 to 218.6 ton C ha-1). This methodology provides a cost-effective, scalable approach for carbon seqesstration assessment in fruit tree plantations, supporting both sustainable agricultural practices and carbon credit initiatives in the agricultural sector.

PDF HTML阅读 XML下载 导出引用 引用提醒 1988-2013年基于BP神经网络的植被叶面积指数遥感定量反演 DOI: 10.5846/stxb201703290547 作者: 作者单位: 南京林业大学,南京林业大学,南京林业大学,南京林业大学,南京林业大学,南京林业大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划资助项目（2017YFC0505505）；国家自然科学基金项目（31200534）；江苏高校优势学科建设工程资助项目；2014年江苏省普通高校学术学位研究生科研创新计划项目 Quantitative inversion of long sequential leaf area index using remote sensing based on BP neural network from 1988 to 2013 Author: Affiliation: Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:叶面积指数（Leaf Area Index，LAI）高度综合了植被水平覆盖状况和垂直结构，以及枯枝落叶层厚薄和地下生物量多少，是植被影响土壤侵蚀的主要方面。区域尺度的时间序列叶面积指数揭示了区域土壤侵蚀的演化过程。因此，及时准确地掌握区域尺度上长时间序列的植被LAI，对研究土壤侵蚀动态变化与植被的关系至关重要。选择南京市1988-2013年10期遥感影像，基于反向传播（Back Propagation，BP）神经网络构建LAI反演模型，进行了长时间序列的叶面积指数反演。结合2009和2010年LAI实测值，验证与探讨了该模型的评价精度与适应性。结果表明：（1）该模型拟合度较高，2009和2010年平均相对误差、均方根误差、相关系数分别是0.2395和0.2174，0.2962和0.2581，0.7713和0.6844，各项精度评价指标均较好；（2）统计分析去除耕地后全市LAI变化，低植被覆盖（LAI<2）面积不断增加，高植被覆盖区（LAI>3）面积先减少后增加，耕地面积不断减少，符合南京市的发展变化规律；（3）主城区LAI年际变化与其他学者得到的南京市植被盖度变化趋势一致，反演结果的时序性较高。本文提出的基于反向传播神经网络模型反演长时间序列LAI是可行的，为区域尺度土壤侵蚀定量遥感监测提供新途径。 Abstract:Leaf Area Index(LAI) can reflect the horizontal coverage, vertical structure of vegetation, the thickness of the litter layer and the amount of underground biomass, which is the main aspect of vegetation affecting soil erosion. It is very important to monitor the changes in the amount of soil erosion, for useful information to guide the planning of soil and water conservation, protect the soil and water resources and control the soil erosion. Therefore, the method by which we obtain high quality and long sequential LAI at a regional scale is very important for analyzing the relationship between the dynamic changes in soil erosion and vegetation. Previous studies showed that the neural network had an incomparable superiority in terms of complex, nonlinear data fitting and pattern recognition, and had been successfully applied to inverse the LAI in Nanjing based on the multi-spectral remote sensing data derived from the Landsat 8 Operational Land Imager(OLI), four types of vegetation indices(Normalized Difference Vegetation Index, NDVI; Ratio Vegetation Index, RVI; Soil-adjusted Vegetation Index, SAVI; Modified Soil adjusted Vegetation Index, MSAVI), and measured LAI data. The results showed that the accuracy of retrieval was good. In this paper, we used the Back Propagation(BP) neural network model to inverse the LAI in Nanjing during 1988-2013 based on the data derived from Landsat 8 OLI and Landsat 5 Thematic Mapper(TM). Based on the measured values of LAI in 2009 and 2010, the evaluation accuracy and adaptability of the model were verified and discussed. The results showed that:(1) The model had a fitting of higher degree, and average relative errors(MAPE), root mean square errors(RMSE), and correlation coefficients(R) in 2009 and 2010 of 0.2395 and 0.2174, 0.2962 and 0.2581, and 0.7713 and 0.6844, respectively. Each accuracy evaluation index was good.(2) Following statistical analysis, we found that the low vegetation coverage area(LAI < 2) exhibited an increasing trend, the high vegetation coverage area(LAI > 3) presented a first decreasing and then increasing trend, while the cultivated land area decreased with the rapid development in Nanjing.(3) To analyze accurately the LAI, we extracted the LAI in the main urban area, and found that there was a relatively high inversion value, and the inter-annual change in LAI was consistent with the change in vegetation coverage in Nanjing reported by other studies. Therefore, we could see that the BP neural network model had a high accuracy for the time series LAI inversion. It provides a new way for quantitative remote sensing monitoring of regional soil erosion. Moreover, because of other potential limiting factors, such as the errors produced by the BP neural network model, the large area of the inversion area, the complexity of vegetation types and community structure, etc., the inversion accuracy of LAI through remote sensing still needs to be explored, and the inversion method improved. We will try to establish a multi-angle LAI inversion method to construct the coupling model of LAI and soil erosion or quantitative fusion of multi-source remote sensing images in the future study. 参考文献 相似文献 引证文献

The manual and time-consuming nature of current agronomic technology monitoring of fertilizer and irrigation requirements, the possibility of overusing fertilizer and water, the size of cassava plantations, and the scarcity of human resources are among its drawbacks. Efforts to increase the yield of cassava plants &gt; 40 tons per ha include monitoring fertilization approach or treatment, as well as water stress or drought using UAVs and deep learning. The novel aspect of this research is the creation of a monitoring model for the irrigation and fertilizer to support sustainable cassava production. This study emphasizes the use of Unnamed Aerial Vehicle (UAV) imagery for evaluating the irrigation and fertilization status of cassava crops. The UAV is processed by building an orthomosaic, labeling, extracting features, and Convolutional Neural Network (CNN) modeling. The outcomes are then analyzed to determine the requirements for air pressure and fertilization. Important new information on the application of UAV technology, multispectral imaging, thermal imaging, among the vegetation indices are the Soil-Adjusted Vegetation Index (SAVI), Leaf Color Index (LCI), Leaf Area Index (LAI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Green Normalized Difference Vegetation Index (GNDVI).

Accurate estimation of Miscanthus and Switchgrass biomass production is essential for optimizing bioenergy crop management and land use planning. Remote sensing supports energy crop cultivation by monitoring agronomic parameters and estimating biomass production, helping optimize yields and efficiency. This study utilizes six years of Sentinel-2 imagery to assess biomass yield using four vegetation indices: NDVI, the modified NDVI (NDVI-NIR), the modified Normalized Difference Red Edge Index (NDRE), and the Soil Adjusted Vegetation Index (SAVI). Continuous monthly indices were derived throughout the growing seasons, utilizing Google Earth Engine to process Sentinel-2 images on a monthly basis. Multiple regression models, based on consecutive VI values, were used to assess the VI–yield correlation, with R² as a performance metric, and subsequently, yearly biomass yields were analyzed to develop predictive models. Results revealed differences between crops and variable correlations between VIs and biomass yield.

Accurate assessment of the physicochemical parameters of rice is critical for increasing agricultural production and ensuring food security. The leaf area index (LAI), leaf chlorophyll content (expressed as the soil plant analysis development (SPAD)), and canopy chlorophyll content (CCC) of the rice at single and multiple periods were estimated using abundance correction indicators by integrating the normalized difference red edge index (NDRE) and refined abundance information, with the goal of developing a high-precision and unified estimation model (AC-NDRE) of the physicochemical parameters of rice that can be adapted to various temporal and spatial scales. The results showed that while not significant, the accuracy of predicting the LAI, SPAD, and CCC of rice using the NDREgreen with the soil background removed was better than that of the NDRE model. The NDREgreen yielded the highest coefficients of determination (R 2) of 0.69, 0.71, and 0.70, root mean square errors (RMSEs) of 2.35, 2.19, and 97.61, relative RMSEs (RRMSEs) of 31.87%, 5.61%, and 33.06%, respectively. Furthermore, severe instability was observed in the accuracy of the NDREgreen model on both the spatial and temporal scales. The AC-NDRE-I, which is based on strong light, and the AC-NDRE-II, which is based on moderate light, exhibited evident advantages in estimating the LAI/CCC and SPAD, respectively. The optimal LAI, SPAD, and CCC estimation accuracies based on the AC-NDRE were R 2 values of 0.83, 0.74, and 0.82, RMSEs of 1.73, 2.06, and 76.41, RRMSEs of 23.49%, 5.29%, and 25.88%, respectively. The AC-NDRE approach achieved a stable performance under complicated circumstances. The conclusions of this study indicated that the AC-NDRE-based method for estimating the LAI, SPAD, and CCC of rice could effectively address the issues of a limited model estimation accuracy caused by soil background, NDRE saturation during the middle to late growth stages of rice, and shaded leaves.

Soybean, a globally important food and oil crop, requires accurate estimation of above-ground biomass (AGB) to optimize management and prevent yield loss. Despite the availability of various remote sensing methods, systematic research on effectively integrating canopy height (CH) and spectral information for improved AGB estimation remains insufficient. This study addresses this gap using drone data. Three CH utilization approaches were tested: (1) simple combination of CH and spectral vegetation indices (VIs), (2) fusion of CH and VI, and (3) integration of CH, VI, and growing-degree days (GDDs). The results indicate that adding CH always enhances AGB estimation which is based only on VIs, with the fusion approach outperforming simple combination. Incorporating GDD further improved AGB estimation for highly accurate CH data, with the best model achieving a root mean square error (RMSE) of 87.52 ± 5.88 g/m2 and a mean relative error (MRE) of 28.59 ± 1.99%. However, for the multispectral data with low CH accuracy, the VIs + GDD fusion (RMSE = 92.94 ± 6.84 g/m2, MRE = 30.08 ± 2.29%) surpassed CH + VIs + GDD (RMSE = 97.99 ± 6.71 g/m2, MRE = 31.41 ± 2.56%). The findings highlight the role of CH accuracy in AGB estimation and validate the value of growth-stage information in robust modeling. Future research should prioritize the refining of CH prediction and the optimization of composite variable construction to promote the application of this approach in agricultural monitoring.

Hazelnut (Corylus avellana L.) cultivation in Italy is facing constraints related to climate change, causing decreases in production as a consequence of summer droughts and late spring heatwaves. This two-year study (2024–2025, i.e., Y1 and Y2) evaluated the effectiveness of two biostimulant protocols on the eco-physiological and productive performance of a hazelnut orchard (cv ‘Tonda Gentile Romana’) in Central Italy. Treatment A included a mixture of formulations (silicon, Ecklonia maxima and microalgae), while Treatment B featured an Ecklonia maxima-containing biostimulant. Data-gathering combined ground-level measurements and remote-sensing technologies, which allowed for the extraction and assessment of vegetation indexes such as the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Red Edge Index (NDRE) and the Normalized Difference Moisture Index (NDMI). Treatments A and B successfully maintained higher chlorophyll content; this beneficial effect was validated by the NDVI, but the NDRE might have suffered from soil interference due to its high sensitivity. The NDMI was positively influenced by both treatments. Treatment A brought to a remarkable production increase in both seasons, especially in Y1 with 7.75 kg plant−1 (+40% vs. Control) and without negatively affecting the shell/nut ratio. These findings suggest that biostimulants could represent an effective strategy for improving productivity and enhancing abiotic stress resilience in hazelnut cultivation.

The prediction of soil moisture conditions using multispectral data from unmanned aerial vehicles (UAVs) has advantages over ground measurements in terms of costs and monitoring range. However, the prediction accuracy for moisture conditions using spectral data alone is low. In this study, relationships between water deficits and phenotypic characteristics in oats were evaluated and used to develop a UAV multispectral-based water prediction model. The vegetation indices NDRE (Normalized Difference Red Edge), CIG (Chlorophyll Index), and MCARI (Modified Chlorophyll Absorption in Reflectance Index) were highly correlated with oat yield. Based on a multipath analysis in the structural equation modeling framework, irrigation (p &lt; 0.01), leaf area index (LAI) (p &lt; 0.001), and SPAD (p &lt; 0.001) had direct positive effects on NDRE. Three distinct machine learning approaches—linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to establish predictive models between the Normalized Difference Red Edge Index (NDRE) and soil water content (SWC). The linear regression model showed moderate correlation (R2 = 0.533). Machine learning approaches demonstrated markedly superior performance (RF: R2 = 0.828; ANN: R2 = 0.810). Nonlinear machine learning algorithms (RF and ANN) significantly outperform conventional linear regression in estimating SWC from spectral vegetation indices.

Leaf area index (LAI) serves as a critical indicator for evaluating crop growth and guiding field management practices. While spectral information (vegetation indices and texture features) extracted from multispectral sensors mounted on unmanned aerial vehicles (UAVs) holds promise for LAI estimation, the limitations of single-texture features necessitate further exploration. Therefore, this study conducted field experiments over two consecutive years (2021-2022) to collect winter oilseed rape LAI ground truth data and corresponding UAV multispectral imagery. Vegetation indices were constructed, and canopy texture features were extracted. Subsequently, a correlation matrix method was employed to establish novel randomized combinations of three-dimensional texture indices. By analyzing the correlations between these parameters and winter oilseed rape LAI, variables with significant correlations (p < 0.05) were selected as model inputs. These variables were then partitioned into distinct combinations and input into three machine learning models-Support Vector Machine (SVM), Backpropagation Neural Network (BPNN), and Extreme Gradient Boosting (XGBoost)-to estimate winter oilseed rape LAI. The results demonstrated that the majority of vegetation indices and texture features exhibited significant correlations with LAI (p < 0.05). All randomized texture index combinations also showed strong correlations with LAI (p < 0.05). Notably, the three-dimensional texture index NDTTI exhibited the highest correlation with LAI (R = 0.725), derived from the spatial combination of DIS5, VAR5, and VAR3. Integrating vegetation indices, texture features, and three-dimensional texture indices as inputs into the XGBoost model yielded the highest estimation accuracy. The validation set achieved a determination coefficient (R2) of 0.882, a root mean square error (RMSE) of 0.204 cm2cm-2, and a mean relative error (MRE) of 6.498%. This study provides an effective methodology for UAV-based multispectral monitoring of winter oilseed rape LAI and offers scientific and technical support for precision agriculture management practices.

BackgroundCross-validation techniques in genetic evaluations encounter limitations due to the unobservable nature of breeding values and the challenge of validating estimated breeding values (EBVs) against pre-corrected phenotypes, challenges which the Linear Regression (LR) method addresses as an alternative. Furthermore, beef cattle genetic evaluation programs confront challenges with connectedness among herds and pedigree errors. The objective of this work was to evaluate the LR method's performance under pedigree errors and weak connectedness typical in beef cattle genetic evaluations, through simulation.MethodsWe simulated a beef cattle population resembling the Argentinean Brangus, including a quantitative trait selected over six pseudo-generations with a heritability of 0.4. This study considered various scenarios, including: 25% and 40% pedigree errors (PE-25 and PE-40), weak and strong connectedness among herds (WCO and SCO, respectively), and a benchmark scenario (BEN) with complete pedigree and optimal herd connections.ResultsOver six pseudo-generations of selection, genetic gain was simulated to be under- and over-estimated in PE-40 and WCO, respectively, contrary to the BEN scenario which was unbiased. In genetic evaluations with PE-25 and PE-40, true biases of − 0.13 and − 0.18 genetic standard deviations were simulated, respectively. In the BEN scenario, the LR method accurately estimated bias, however, in PE-25 and PE-40 scenarios, it overestimated biases by 0.17 and 0.25 genetic standard deviations, respectively. In herds facing WCO, significant true bias due to confounding environmental and genetic effects was simulated, and the corresponding LR statistic failed to accurately estimate the magnitude and direction of this bias. On average, true dispersion values were close to one for BEN, PE-40, SCO and WCO, showing no significant inflation or deflation, and the values were accurately estimated by LR. However, PE-25 exhibited inflation of EBVs and was slightly underestimated by LR. Accuracies and reliabilities showed good agreement between true and LR estimated values for the scenarios evaluated.ConclusionsThe LR method demonstrated limitations in identifying biases induced by incomplete pedigrees, including scenarios with as much as 40% pedigree errors, or lack of connectedness, but it was effective in assessing dispersion, and population accuracies and reliabilities even in the challenging scenarios addressed.

The World Health Organization on March 11, 2020, declared Coronavirus Disease 2019 (Covid-19) a pandemic. Covid-19 is a disease caused by a new type of coronavirus, namely Sars-CoV-2, which affects the respiratory system. Until now, positive confirmed cases of Covid-19 in Indonesia are still occurring every day. This study aims to predict the addition of Covid-19 cases in Indonesia. The data is sourced from the public API page covid19.go.id in the form of an additional number of Covid-19 cases in Indonesia by 122 lines of data. Predictions are made using linear regression and polynomial regression methods as comparisons. Evaluation of the linear regression method obtains a value of R2 = 0.57, while the polynomial regression method obtains a value of R2 = 0.84. Based on these evaluations, the polynomial regression method yields better results than the linear regression method. The prediction of Covid-19 cases in Indonesia from January to March 2022 using the polynomial regression methods predicts that the addition of Covid-19 cases will rise again.

High Nitrogen Use Efficiency (NUE) phenotype identification is a long-time challenge for rice breeding and sustainable agriculture in order to reduce nitrogen (N) fertilizer costs and decrease environmental damages caused by N overapplication. The challenge comes mainly from two aspects: 1) high NUE genetic sources are biologically scarce; 2) from the technique side of view, fewer easy, nondestructive but reliable methodology are available to evaluate plant N variations lively through the entire growth duration (GD). To conquer the challenges, a valuable Honglian type hybrid rice LY9348 is identified as a high NUE rice variety both in Nitrogen Uptake Efficiency (NUpE) and Nitrogen Utilization Efficiency (NUtE) through nitrogen dosage field analysis. Canopy nitrogen content (CNC) is used to illustrate N accumulations and changes in LY9348 by a high-throughput Unmanned Aerial Vehicle Remote Sensing Platform (UAV-RSP) to screen two mixed categories (51 vs. 42 varieties) selected from representative higher NUE indica rice collections. Five Vegetative Indices (VIs) are compared and Normalized Difference Red Edge Index (NDRE) is in the best correlation with CNC (r=0.80). Compared six key developmental stages of rice varieties from transplanting to maturation, the high NUE phenotype of LY9348 is shown as a dy-namic CNC curve, which the N accumulation is moderately high during vegetative developmental stages but distinguishably higher in reproduction developmental stages with a slower reduction rate. CNC curves of different rice varieties are analyzed to construct two non-linear regression models between N%, N% x leaf area index (LAI) with NDRE sepa-rately. Both models are capable to extract the specific high NUE phenotype of LY9348 in time-series with the coefficient of determination (R2) above 0.61 (Model I) and 0.86 (Model II). Parameters influencing the correlation accuracy between NDRE and N% are evaluated to be better by taking the tillering stage data out, separating short and long GD varieties apart for analysis and adding canopy structures such as LAI into consideration. The high NUE phenotype of LY9348 can be traced and reidentified in different years, locations and genetic germplasm groups, therefore, an effective and reliable high-throughput method in assisting high NUE breeding selections is proposed.

Unmanned aircraft vehicles (UAV) are widely used for yield estimations in agricultural production. Many significant improvements have been made towards the usage of hyperspectral and thermal sensors. The practical application of these new techniques meanwhile has been limited by the cost of data collection and the complexities of data processing. The objective of this paper is to evaluate the effectiveness of wheat yield estimations based on integrating vegetation indices (VI), solar radiation and crop height (CH), all of which are characterized by lower cost of data collection and processing. The VIs, solar radiation and CH were calculated based on UAV-based multispectral images obtained from two separate plots in Southern Germany and validated with data from a third plot. We compare the individual and joint predictive performance of different VIs, CH, and solar radiation by contrasting the estimated yield with actual yield based on multiple linear regression and quantile regression. The best predictive power was found for a combined estimation with CH, solar radiation and a Normalized Difference Red-edge Index (R2 = 0.75, RMSE = 0.53). This combined estimation resulted in a 15–20% improvement in the prediction of wheat yield accuracy as compared with utilizing any of the indices separately.

The red edge band is considered as one of the diagnosable characteristics of green plants, but the large-scale remote sensing retrieval of fractional vegetation coverage (FVC) based on the red edge band is still rare. To explore the application of the red edge band in the remote sensing estimation of FVC, this study proposed a new vegetation index (normalized difference red edge index, RENDVI) based on the two red edge bands of Chinese GaoFen-6 satellite (GF-6). The FVC estimated by using three vegetation indices (NDVI, RENDVI1, and RENDVI2) were evaluated based on the field survey FVC obtained in Minqin Basin of Gansu Province. The results showed that there was a good linear correlation between the FVC estimated by GF-6 WFV data and the FVC investigated in the field, and the most reasonable estimation of FVC was obtained based on RENDVI2 model (R 2 = 0.97611 and RMSE = 0.07075). Meanwhile, the impact of three confidence levels (1, 2, and 5%) on FVC was also analyzed in this study. FVC obtained from NDVI and RENDVI2 has the highest accuracy at 2% confidence, while FVC based on RENDVI1 achieved the best accuracy at 5% confidence. It could be concluded that it is feasible and reliable to estimate FVC based on red edge bands, and the GF-6 Wide Field View (WFV) data with high temporal and spatial resolution provide a new data source for remote sensing estimation of FVC.