Effects of spatial, temporal, and spectral resolutions on the estimation of wheat and barley leaf area index using multi- and hyper-spectral data (case study: Karaj, Iran)

Afrasiabian, Yasamin; Noory, Hamideh; Mokhtari, Ali; Nikoo, Maryam Razavi; Pourshakouri, Farrokh; Haghighatmehr, Parisa

doi:10.1007/s11119-020-09749-9

Cited by 15 publications

(11 citation statements)

References 49 publications

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“…Out of the variables calculated from multispectral data, besides normalized reflectance and texture variables, VIs proved their high value for crop DM and LAI estimation as was shown in other studies [37,38,45]. Among them, the red, green, blue vegetation index (RGBVI) developed by Bendig et al [37] for barley DM estimation proved to be one of the most important variables for prediction in this study for DM as well as for LAI.…”

Section: Model Performancesupporting

confidence: 67%

“…Population density is known to vary significantly in correlation to distance from tree row [16,44]. LAI could be accurately predicted from VIs calculated from RS multispectral data [45], although, similar to DM, height information is expected to improve predictions. Due to the multicollinear nature of reflectance variables, where reflectance values of adjacent spectral bands are strongly correlated, multivariate statistics were employed to model DM and LAI.…”

Section: Introductionmentioning

confidence: 99%

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Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

et al. 2021

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Agroforestry systems (AFS) can provide positive ecosystem services while at the same time stabilizing yields under increasingly common drought conditions. The effect of distance to trees in alley cropping AFS on yield-related crop parameters has predominantly been studied using point data from transects. Unmanned aerial vehicles (UAVs) offer a novel possibility to map plant traits with high spatial resolution and coverage. In the present study, UAV-borne red, green, blue (RGB) and multispectral imagery was utilized for the prediction of whole crop dry biomass yield (DM) and leaf area index (LAI) of barley at three different conventionally managed silvoarable alley cropping agroforestry sites located in Germany. DM and LAI were modelled using random forest regression models with good accuracies (DM: R² 0.62, nRMSEp 14.9%, LAI: R² 0.92, nRMSEp 7.1%). Important variables for prediction included normalized reflectance, vegetation indices, texture and plant height. Maps were produced from model predictions for spatial analysis, showing significant effects of distance to trees on DM and LAI. Spatial patterns differed greatly between the sampled sites and suggested management and soil effects overriding tree effects across large portions of 96 m wide crop alleys, thus questioning alleged impacts of AFS tree rows on yield distribution in intensively managed barley populations. Models based on UAV-borne imagery proved to be a valuable novel tool for prediction of DM and LAI at high accuracies, revealing spatial variability in AFS with high spatial resolution and coverage.

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Section: Model Performancesupporting

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

et al. 2021

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“…Similarly, [ 10 ] reported that maize LAI estimation accuracy did not significantly differ between data with two different spectral resolutions and two different modelling methods (LR vs machine learning regression). In contrast, [ 54 ] detailed that narrow band VIs derived from hyperspectral data models yielded 20% higher R 2 values than multispectral data models for wheat and barley LAI estimation.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters

Wijesingha

Dayananda

Wachendorf

et al. 2021

Sensors

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Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study’s outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation.

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“…Rapid and accurate estimation of these two parameters can provide a strong basis for the timely formulation of management measures for young tea plantations (Li B. et al, 2020). However, the traditional crop growth assessment method is based on destructive sampling, which is to manually collect data samples in the field, or use field measuring instruments to evaluate crops (Freeman et al, 2007;Yue et al, 2018;Afrasiabian et al, 2020). Although this method is accurate, it is destructive, labor-intensive, time-consuming, and not operationally feasible for large-scale spatial and temporal measurements (Wang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Using Unmanned Aerial Vehicle-Based Multispectral Image Data to Monitor the Growth of Intercropping Crops in Tea Plantation

et al. 2022

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Aboveground biomass (AGB) and leaf area index (LAI) are important indicators to measure crop growth and development. Rapid estimation of AGB and LAI is of great significance for monitoring crop growth and agricultural site-specific management decision-making. As a fast and non-destructive detection method, unmanned aerial vehicle (UAV)-based imaging technologies provide a new way for crop growth monitoring. This study is aimed at exploring the feasibility of estimating AGB and LAI of mung bean and red bean in tea plantations by using UAV multispectral image data. The spectral parameters with high correlation with growth parameters were selected using correlation analysis. It was found that the red and near-infrared bands were sensitive bands for LAI and AGB. In addition, this study compared the performance of five machine learning methods in estimating AGB and LAI. The results showed that the support vector machine (SVM) and backpropagation neural network (BPNN) models, which can simulate non-linear relationships, had higher accuracy in estimating AGB and LAI compared with simple linear regression (LR), stepwise multiple linear regression (SMLR), and partial least-squares regression (PLSR) models. Moreover, the SVM models were better than other models in terms of fitting, consistency, and estimation accuracy, which provides higher performance for AGB (red bean: R2 = 0.811, root-mean-square error (RMSE) = 0.137 kg/m2, normalized RMSE (NRMSE) = 0.134; mung bean: R2 = 0.751, RMSE = 0.078 kg/m2, NRMSE = 0.100) and LAI (red bean: R2 = 0.649, RMSE = 0.36, NRMSE = 0.123; mung bean: R2 = 0.706, RMSE = 0.225, NRMSE = 0.081) estimation. Therefore, the crop growth parameters can be estimated quickly and accurately using the models established by combining the crop spectral information obtained by the UAV multispectral system using the SVM method. The results of this study provide valuable practical guidelines for site-specific tea plantations and the improvement of their ecological and environmental benefits.

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Effects of spatial, temporal, and spectral resolutions on the estimation of wheat and barley leaf area index using multi- and hyper-spectral data (case study: Karaj, Iran)

Cited by 15 publications

References 49 publications

Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters

Using Unmanned Aerial Vehicle-Based Multispectral Image Data to Monitor the Growth of Intercropping Crops in Tea Plantation

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