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Detection of peanut leaf spots disease using canopy hyperspectral reflectance
摘要： Leaf spot is one of the most destructive diseases, which has a significant impact on the peanut production. Detecting leaf spot via spectral measurement and analysis is a possible alternative to traditional methods in detecting the spatial distribution of this disease. In this study, we identified sensitive bands and derived hyperspectral vegetation index specific to leaf spot detection. Hyperspectral canopy reflectance spectra of peanut cultivars susceptibilities to leaf spot were measured at two experimental sites in 2017. The normalized difference spectral index (NDSI) was derived based on their correlation with disease index (DI) in the leaf spectrum between 325 nm and 1075 nm. The results showed that canopy spectral reflectance decreased significantly in the near-infrared regions (NIR) as DI increased (r < -0.90). The spectral index for detecting leaf spot in peanut were LSI: (NDSI (R938, R761)) with R2 values of up to 0.68 for the regression model. The high fit between the observed and estimated values indicates that the DI detecting model based on the index could be used in peanut leaf spot detection in the absence of other stresses causing unhealthy symptoms. The results of this study show that it will provide a reliable, effective and accurate method for detecting leaf spot diseases in peanut through the analysis of hyperspectral data in the future.
关键词： Vegetation index,Disease index,Arachis hypogaea L.,Canopy hyperspectral reflectance
更新于2025-09-23 15:23:52
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Assessment of red-edge vegetation indices for crop leaf area index estimation
摘要： This study explores the potential of vegetation indices (VIs) for crop leaf area index (LAI) estimation, with a focus on comparing red-edge reflectance based (RE-based) and the visible reflectance based (VIS-based) VIs. Seven VIs were derived from multi-temporal RapidEye images to correlate with LAI of two crop species having contrasting leaf structures and canopy architectures: spring wheat (a monocot) and canola (a dicot) in northern Ontario, Canada. The relationship between LAI and the selected VIs (LAI-VI) was characterized using a semi-empirical model. The Markov Chain Monte Carlo (MCMC) sampling method was used to estimate the model parameters, including the extinction coefficient (KVI) and VI value for dense green canopy (VI∞). Results showed that crop-specific regression models were much closer to a generic regression model using the RE-based VIs than using the VIS-based VIs. Furthermore, the joint posterior probability distribution of the KVI and VI∞ of the RE-based VIs tended to converge for the two crops. This suggests that the RE-based VIs are not as sensitive to canopy structure, e.g., the average leaf angle (ALA), as the VIS-based VIs. This is also demonstrated by the sensitivity analyses using both PROSAIL simulations and field measurements. Hence, the RE-based VIs can be used to develop a more generic LAI estimation algorithm for different crops. Further studies are required to assess the impact of soil reflectance and other factors, such as illumination-target-viewing geometries and atmospheric conditions, on LAI retrieval.
关键词： Sensitivity analysis,Crops,RapidEye,Leaf area index,red-edge,Vegetation index
更新于2025-09-23 15:23:52
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FVI—A Floating Vegetation Index Formed with Three Near-IR Channels in the 1.0–1.24 μm Spectral Range for the Detection of Vegetation Floating over Water Surfaces
摘要： Through the analysis of hyperspectral imaging data collected over water surfaces covered by floating vegetation, such as Sargassum and algae, we observed that the spectra commonly contain a reflectance peak centered near 1.07 μm. This peak results from the competing effects between the well-known vegetation reflectance plateau in the 0.81–1.3 μm spectral range and the absorption effects above 0.75 μm by liquid water within the vegetation and in the surrounding water bodies. In this article, we propose a new index, namely the floating vegetation index (FVI), for the hyperspectral remote sensing of vegetation over surface layers of oceans and inland lakes. In the formulation of the FVI, one channel centered near 1.0 μm and another 1.24 μm are used to form a linear baseline. The reflectance value of the third channel centered at the 1.07-μm reflectance peak above the baseline is defined as the FVI. Hyperspectral imaging data acquired with the AVIRIS (Airborne Visible Infrared Imaging Spectrometer) instrument over the Gulf of Mexico and over salt ponds near Moffett Field in southern portions of the San Francisco Bay were used to demonstrate the success in detecting Sargassum and floating algae with this index. It is expected that the use of this index for the global detection of floating vegetation from hyperspectral imaging data to be acquired with future satellite sensors will result in improved detection and therefore enhanced capability in estimating primary production, a measure of how much carbon is fixed per unit area per day by oceans and inland lakes.
关键词： Sargassum,sensors,remote sensing,hyperspectrum,vegetation index,algae
更新于2025-09-23 15:22:29
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A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle
摘要： Unmanned aerial vehicle (UAV)-based remote sensing (RS) possesses the significant advantage of being able to efficiently collect images for precision agricultural applications. Although numerous methods have been proposed to monitor crop nitrogen (N) status in recent decades, just how to utilize an appropriate modeling algorithm to estimate crop leaf N content (LNC) remains poorly understood, especially based on UAV multispectral imagery. A comparative assessment of different modeling algorithms (i.e., simple and non-parametric modeling algorithms alongside the physical model retrieval method) for winter wheat LNC estimation is presented in this study. Experiments were conducted over two consecutive years and involved different winter wheat varieties, N rates, and planting densities. A five-band multispectral camera (i.e., 490 nm, 550 nm, 671 nm, 700 nm, and 800 nm) was mounted on a UAV to acquire canopy images across five critical growth stages. The results of this study showed that the best-performing vegetation index (VI) was the modified renormalized difference VI (RDVI), which had a determination coefficient (R2) of 0.73 and a root mean square error (RMSE) of 0.38. This method was also characterized by a high processing speed (0.03 s) for model calibration and validation. Among the 13 non-parametric modeling algorithms evaluated here, the random forest (RF) approach performed best, characterized by R2 and RMSE values of 0.79 and 0.33, respectively. This method also had the advantage of full optical spectrum utilization and enabled flexible, non-linear fitting with a fast processing speed (2.3 s). Compared to the other two methods assessed here, the use of a look up table (LUT)-based radiative transfer model (RTM) remained challenging with regard to LNC estimation because of low prediction accuracy (i.e., an R2 value of 0.62 and an RMSE value of 0.46) and slow processing speed. The RF approach is a fast and accurate technique for N estimation based on UAV multispectral imagery.
关键词： UAV,multispectral imagery,radiative transfer model,LNC,vegetation index,non-parametric regression
更新于2025-09-23 15:22:29
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Rela??es empíricas entre características dendrométricas da Caatinga brasileira e dados TM Landsat 5
摘要： The objective of this work was to adjust models to estimate dendrometric characteristics of the Brazilian dry tropical forest (Caatinga) from Landsat 5 TM sensor data. Measures for tree diameter and height were taken in 60 inventory plots (400 m2), in two municipalities of the state of Sergipe, Brazil. Basal area and wood volume were estimated using an allometric equation and form factor (f = 0.9). Explanatory variables were taken from the TM sensor, after radiometric and geometric correction, having considered, in the analysis, six spectral bands, with 30 m spatial resolution, besides the indexes of simple ratio (SR), of normalized difference vegetation (NDVI), and of soil-adjusted vegetation (Savi). To choose the best explanatory variables, the coefficient of determination (R2), the root mean square error (RMSE), and the Bayesian information criterion (BIC) were considered. The basal area per hectare did not show a significant correlation with any of the explanatory variables used. The best models were adjusted to tree mean height per plot (R2 = 0.4; RMSE = 13%) and to wood volume per hectare (R2 = 0.6; RMSE = 42%). The metrics derived from the Landsat 5 TM sensor have great potential to explain variation in the mean height of trees and in the wood volume per hectare, in remaining areas of the tropical dry forest in the Brazilian Northeast.
关键词： Savi,REDD,vegetation index,reducing emissions,remote sensing,NDVI
更新于2025-09-23 15:22:29
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Method for Mapping Rice Fields in Complex Landscape Areas Based on Pre-Trained Convolutional Neural Network from HJ-1 A/B Data
摘要： Accurate and timely information about rice planting areas is essential for crop yield estimation, global climate change and agricultural resource management. In this study, we present a novel pixel-level classi?cation approach that uses convolutional neural network (CNN) model to extract the features of enhanced vegetation index (EVI) time series curve for classi?cation. The goal is to explore the practicability of deep learning techniques for rice recognition in complex landscape regions, where rice is easily confused with the surroundings, by using mid-resolution remote sensing images. A transfer learning strategy is utilized to ?ne tune a pre-trained CNN model and obtain the temporal features of the EVI curve. Support vector machine (SVM), a traditional machine learning approach, is also implemented in the experiment. Finally, we evaluate the accuracy of the two models. Results show that our model performs better than SVM, with the overall accuracies being 93.60% and 91.05%, respectively. Therefore, this technique is appropriate for estimating rice planting areas in southern China on the basis of a pre-trained CNN model by using time series data. And more opportunity and potential can be found for crop classi?cation by remote sensing and deep learning technique in the future study.
关键词： mapping rice ?elds,convolutional neural network,time series of vegetation index,complex landscape,transfer learning
更新于2025-09-23 15:21:01
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Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf-Tissue Nutrients in Fields Managed with Straw Removal
摘要： The total or partial removal of sugarcane (Saccharum spp. L.) straw for bioenergy production may deplete soil quality and consequently affect negatively crop yield. Plants with lower yield potential may present lower concentration of leaf-tissue nutrients, which in turn changes light reflectance of canopy in different wavelengths. Therefore, vegetation indexes, such as the normalized difference vegetation index (NDVI) associated with concentration of leaf-tissue nutrients could be a useful tool for monitoring potential sugarcane yield changes under straw management. Two sites in S?o Paulo state, Brazil were utilized to evaluate the potential of NDVI for monitoring sugarcane yield changes imposed by different straw removal rates. The treatments were established with 0%, 25%, 50%, and 100% straw removal. The data used for the NDVI calculation was obtained using satellite images (CBERS-4) and hyperspectral sensor (FieldSpec Spectroradiometer, Malvern Panalytical, Almelo, Netherlands). Besides sugarcane yield, the concentration of the leaf-tissue nutrients (N, P, K, Ca, and S) were also determined. The NDVI efficiently predicted sugarcane yield under different rates of straw removal, with the highest performance achieved with NDVI derived from satellite images than hyperspectral sensor. In addition, leaf-tissue N and P concentrations were also important parameters to compose the prediction models of sugarcane yield. A prediction model approach based on data of NDVI and leaf-tissue nutrient concentrations may help the Brazilian sugarcane sector to monitor crop yield changes in areas intensively managed for bioenergy production.
关键词： vegetation index,satellite images,yield monitoring,hyperspectral sensor,crop residue management,remote sensing
更新于2025-09-19 17:15:36
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Evaluation of OptRx <sup>TM</sup> Active Optical Sensor to Monitor Soybean Response to Nitrogen Inputs
摘要： BACKGROUND: Active optical crop sensors have been gaining importance to determine in-season nitrogen (N) fertilization requirements for on-the-go variable rate applications. Although most of these active in-field crop sensors have been evaluated in maize (Zea mays L.) and wheat (Triticum aestivum L.emend.Thell.), these sensors have not been evaluated in soybean [Glycine max (L.)Merr.] production systems in North Dakota (ND), USA. Recent research from both South Dakota and ND, USA indicate that in-season N application in soybean can increase soybean yield under certain conditions. RESULTS: The study revealed that OptRxTM sensor reading did not show any significant differences from early to midway through the growing season. The NDRE (Normalized Difference Red Edge index) data collected towards the end of the growing season showed significantly higher values for some of the N treatments as compared to others in both years. The NDRE values were strongly correlated to grain yield for both years under tiled (r = 0.923) and non-tiled (r = 0.901) drainage conditions. Certain soybean varieties displayed significantly higher NDRE values over both years. The three varieties tested across years, under both tiled and non-tiled conditions, showed a significant linear relationship between late August NDRE values and yield (R2=0.85 for tiled and R2=0.81 for non-tiled). CONCLUSION: In this research, the study results show that the OptRxTM sensor has the potential to work for soybean as well, though later in the crop growing season. Further investigation is needed to confirm the use of OptRx? sensor for variable rate in-season N applications in soybeans.
关键词： OptRx TM sensor,Vegetation Index,Soybeans,Nitrogen
更新于2025-09-11 14:15:04
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In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features
摘要： The work focuses on developing a classification tree approach for in-season crop mapping during early summer, by integrating optical (Landsat 8 OLI) and X-band SAR (COSMO-SkyMed) data acquired over a test site in Northern Italy. The approach is based on a classification tree scheme fed with a set of synoptic seasonal features (minimum, maximum and average, computed over the multi-temporal datasets) derived from vegetation and soil condition proxies for optical (three spectral indices) and X-band SAR (backscatter) data. Best performing input features were selected based on crop type separability and preliminary classification tests. The final outputs are crop maps identifying seven crop types, delivered during the early growing season (mid-July). Validation was carried out for two seasons (2013 and 2014), achieving overall accuracy greater than 86%. Results highlighted the contribution of the X-band backscatter (σ°) in improving mapping accuracy and promoting the transferability of the algorithm over a different year, when compared to using only optical features.
关键词： Red Green Ratio Index (RGRI),Normalized Difference Flood Index (NDFI),COSMO-SkyMed,Random Forest,Enhanced Vegetation Index (EVI),multi-temporal,summer crops,Landsat 8 OLI,rule-based classification,agriculture
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Estimation of Leaf Area Index with Various Vegetation Indices from Gaofen-5 Band Reflectances
摘要： This paper attempted to retrieve leaf area index (LAI) from Gaofen (GF)-5 satellite simulation data using 6 common used vegetation indices. The canopy reflectances from 0.4-2.5μm were simulated from the combination of vegetation leaf model PROSPECT and four-stream scattering by arbitrarily inclined leaves (4SAIL) model. GF-5 satellite spectral response functions (SRF) were used to calculate the band reflectances in visible and near infrared regions. Polynomial regression was used to establish the relationships between the vegetation indices and LAI, and coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the relationships. The results showed that DVI among those indices is the best to retrieve LAI from GF-5 data with R2 of 0.964. The results also showed that the retrieval accuracy can be as high as 0.338.
关键词： Leaf area index (LAI),vegetation index,canopy reflectances,GF-5
更新于2025-09-10 09:29:36