Accuracy Of Leaf Area Index Estimation Research Articles

New three red-edge vegetation index (VI3RE) for crop seasonal LAI prediction using Sentinel-2 data

Leaf area index (LAI) serves as a pivotal parameter in crop monitoring, significantly impacting agricultural applications. Empirical models are one of the commonly used methods for estimating LAI, they are often dependent on vegetation indices (VIs), predominantly derived from low-to-moderate spatial resolution satellite sensors. A critical limitation of these VIs is their tendency to saturate at elevated LAI values. Additionally, the interplay between chlorophyll content (Cab), LAI, as well as average leaf inclination angle (ALA), particularly in reflectance spectra from red to red-edge regions, has been underexplored in past research. Based on Sentinel-2 satellite, with three red-edge bands and enhanced spatio-temporal resolution, this study introduced a new three red-edge vegetation index (VI3RE), comprising NDVI3RE and CI3RE, which leverages the unique spectral characteristics of these bands and related differential response to LAI and Cab variations. We investigated VI3RE’s efficacy through a threefold approach: firstly, by conducting sensitivity analyses using noise equivalent (NE)ΔLAI ((NE) ΔLAI=1.37 and 1.59 for NDVI3RE and CI3RE; (NE)ΔLAI = 1.75 – 3.25 for other VIs) and extended Fourier amplitude sensitivity test (EFAST) (the contribution of LAI: FOI > 30 % and TOI > 40 % for VI3RE; FOI ∼ 17 % − 28 % and TOI ∼ 25 % − 34 % for other VIs, except for NDVI with TOI ∼ 50 %), we demonstrated VI3RE’s heightened sensitivity to LAI and its improved capability for seasonal LAI estimation compared to other VIs. Secondly, by comprehensively considering the simplicity, interpretability, AIC and R2 values of various models, the linear regression model was selected for analyzing the relationships between LAI and various VIs. We established that VI3RE, particularly NDVI3RE, exhibits a robust correlation with LAI, thereby enhancing LAI estimation accuracy (R2 = 0.72, RMSE = 1.39). Lastly, we applied the LAI-VI regression models to generate crop LAI imageries throughout the growing season, subsequently validating by field measured LAI data. The results affirmed VI3RE’s superior performance in seasonal LAI estimation of crops, notably during peak growth and sowing phases. We conclude that the newly formulated VI3RE offers a universal, highly precise, and reliable model for LAI prediction across various crop types and phenological stages. However, the broader application of VI3RE may be limited, due to the three red-edge bands are not commonly found in most satellite platforms. In addition, some other aspects are not considered in this study, such as phenological information, meteorological factors, sampling strategies, etc., which should be meticulously considered in the future studies.

Inversion of winter wheat leaf area index from UAV multispectral images: classical vs. deep learning approaches.

Precise and timely leaf area index (LAI) estimation for winter wheat is crucial for precision agriculture. The emergence of high-resolution unmanned aerial vehicle (UAV) data and machine learning techniques offers a revolutionary approach for fine-scale estimation of wheat LAI at the low cost. While machine learning has proven valuable for LAI estimation, there are still model limitations and variations that impede accurate and efficient LAI inversion. This study explores the potential of classical machine learning models and deep learning model for estimating winter wheat LAI using multispectral images acquired by drones. Initially, the texture features and vegetation indices served as inputs for the partial least squares regression (PLSR) model and random forest (RF) model. Then, the ground-measured LAI data were combined to invert winter wheat LAI. In contrast, this study also employed a convolutional neural network (CNN) model that solely utilizes the cropped original image for LAI estimation. The results show that vegetation indices outperform the texture features in terms of correlation analysis with LAI and estimation accuracy. However, the highest accuracy is achieved by combining both vegetation indices and texture features to invert LAI in both conventional machine learning methods. Among the three models, the CNN approach yielded the highest LAI estimation accuracy (R 2 = 0.83), followed by the RF model (R 2 = 0.82), with the PLSR model exhibited the lowest accuracy (R 2 = 0.78). The spatial distribution and values of the estimated results for the RF and CNN models are similar, whereas the PLSR model differs significantly from the first two models. This study achieves rapid and accurate winter wheat LAI estimation using classical machine learning and deep learning methods. The findings can serve as a reference for real-time wheat growth monitoring and field management practices.

A knowledge guided deep learning framework for underground natural gas micro-leaks detection from hyperspectral imagery

Detecting natural gas (NG) micro-leaks is critical for NG transport and storage. In the event of underground NG leaks in vegetation covered areas, a priori knowledge derived from analyzing vegetation growth mechanisms can effectively describe the degree of vegetation response to NG stress, which is of theoretical importance for the quantitative detection of NG leaks. This study proposed a knowledge-guided deep learning framework and use it for underground NG leaks detection, in which an image and spectrum combined multi-branching deep learning (DL) network (Img-Spec-PGE model) was used to estimate the leaf area index (LAI) of NG-stressed plants, and an improved physical model for describing plant NG stress mechanisms was proposed, then assimilating the SAFY-NG model with LAI as an intermediate variable to obtain an accurate NG stress factor (Ka). The results showed that the Img-Spec-PGE model with the NIR-RE-R spectral bands' combination had the highest LAI estimation accuracy (R2 = 0.867, RMSE = 0.312) among the three designed DL models. The assimilated SAFY-NG model can accurately simulate the wheat LAI growth curve under NG leaks, and the calibration results of environmental parameters for NG leaks (leak start time and leak point depth) were consistent with the realistic experimental settings. Ka is sensitive to NG leaks in all experimental periods (p < 0.05). Enabling distinction between leak and non-leak scenarios as early as 1 day after NG leaks. 30 days after NG leaks, Ka can distinguish between 1 L min−1 and 1.5 L min−1 gas flow. The proposed approach provides a reference for detecting micro-leaks in NG transit and storage.

Influence of Topography on UAV LiDAR-Based LAI Estimation in Subtropical Mountainous Secondary Broadleaf Forests

The leaf area index (LAI) serves as a crucial metric in quantifying the structure and density of vegetation canopies, playing an instrumental role in determining vegetation productivity, nutrient and water utilization, and carbon balance dynamics. In subtropical montane forests, the pronounced spatial heterogeneity combined with undulating terrain introduces significant challenges for the optical remote sensing inversion accuracy of LAI, thereby complicating the process of ground validation data collection. The emergence of UAV LiDAR offers an innovative monitoring methodology for canopy LAI inversion in these terrains. This study assesses the implications of altitudinal variations on the attributes of UAV LiDAR point clouds, such as point density, beam footprint, and off-nadir scan angle, and their subsequent ramifications for LAI estimation accuracy. Our findings underscore that with increased altitude, both the average off-nadir scan angle and point density exhibit an ascending trend, while the beam footprint showcases a distinct negative correlation, with a correlation coefficient (R) reaching 0.7. In contrast to parallel flight paths, LAI estimates derived from intersecting flight paths demonstrate superior precision, denoted by R2 = 0.70, RMSE = 0.75, and bias = 0.42. Notably, LAI estimation discrepancies intensify from upper slope positions to middle positions and further to lower ones, amplifying with the steepness of the gradient. Alterations in point cloud attributes induced by the terrain, particularly the off-nadir scan angle and beam footprint, emerge as critical influencers on the precision of LAI estimations. Strategies encompassing refined flight path intervals or multi-directional point cloud data acquisition are proposed to bolster the accuracy of canopy structural parameter estimations in montane landscapes.

Integrating Spectral, Textural, and Morphological Data for Potato LAI Estimation from UAV Images

The Leaf Area Index (LAI) is a crucial indicator of crop photosynthetic potential, which is of great significance in farmland monitoring and precision management. This study aimed to predict potato plant LAI for potato plant growth monitoring, integrating spectral, textural, and morphological data through UAV images and machine learning. A new texture index named VITs was established by fusing multi-channel information. Vegetation growth features (Vis and plant height Hdsm) and texture features (TIs and VITs) were obtained from drone digital images. Various feature combinations (VIs, VIs + TIs, VIs + VITs, VIs + VITs + Hdsm) in three growth stages were adopted to monitor potato plant LAI using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), random forest (RF), and eXtreme gradient boosting (XGBoost), so as to find the best feature combinations and machine learning method. The performance of the newly built VITs was tested. Compared with traditional TIs, the estimation accuracy was obviously improved for all the growth stages and methods, especially in the tuber-growth stage using the RF method with 13.6% of R2 increase. The performance of Hdsm was verified by including it either as one input feature or not. Results showed that Hdsm could raise LAI estimation accuracy in every growth stage, whichever method is used. The most significant improvement appeared in the tuber-formation stage using SVR, with an 11.3% increase of R2. Considering both the feature combinations and the monitoring methods, the combination of VIs + VITs + Hdsm achieved the best results for all the growth stages and simulation methods. The best fitting of LAI in tuber-formation, tuber-growth, and starch-accumulation stages had an R2 of 0.92, 0.83, and 0.93, respectively, using the XGBoost method. This study showed that the combination of different features enhanced the simulation of LAI for multiple growth stages of potato plants by improving the monitoring accuracy. The method presented in this study can provide important references for potato plant growth monitoring.

Combining Texture, Color, and Vegetation Index from Unmanned Aerial Vehicle Multispectral Images to Estimate Winter Wheat Leaf Area Index during the Vegetative Growth Stage

Leaf Area Index (LAI) is a fundamental indicator of plant growth status in agronomy and environmental research. With the rapid development of drone technology, the estimation of crop LAI based on drone imagery and vegetation indices is becoming increasingly popular. However, there is still a lack of detailed research on the feasibility of using image texture to estimate LAI and the impact of combining texture indices with vegetation indices on LAI estimation accuracy. In this study, two key growth stages of winter wheat (i.e., the stages of green-up and jointing) were selected, and LAI was calculated using digital hemispherical photography. The feasibility of predicting winter wheat LAI was explored under three conditions: vegetation index, texture index, and a combination of vegetation index and texture index, at flight heights of 20 m and 40 m. Two feature selection methods (Lasso and recursive feature elimination) were combined with four machine learning regression models (multiple linear regression, random forest, support vector machine, and backpropagation neural network). The results showed that during the vegetative growth stage of winter wheat, the model combining texture information with vegetation indices performed better than the models using vegetation indices alone or texture information alone. Among them, the best prediction result based on vegetation index was RFECV-MLR at a flight height of 40 m (R2 = 0.8943, RMSE = 0.4139, RRMSE = 0.1304, RPD = 3.0763); the best prediction result based on texture index was RFECV-RF at a flight height of 40 m (R2 = 0.8894, RMSE = 0.4236, RRMSE = 0.1335, RPD = 3.0063); and the best prediction result combining texture and index was RFECV-RF at a flight height of 40 m (R2 = 0.9210, RMSE = 0.3579, RRMSE = 0.1128, RPD = 3.5575). The results of this study demonstrate that combining vegetation indices and texture from multispectral drone imagery can improve the accuracy of LAI estimation during the vegetative growth stage of winter wheat. In addition, selecting a flight height of 40 m can improve efficiency in large-scale agricultural field monitoring, as this study showed that drone data at flight heights of 20 m and 40 m did not significantly affect model accuracy.

Enhancing Leaf Area Index Estimation with MODIS BRDF Data by Optimizing Directional Observations and Integrating PROSAIL and Ross–Li Models

The Leaf Area Index (LAI) is a crucial vegetation parameter for climate and ecological models. Reflectance anisotropy contains valuable supplementary information for the retrieval of properties of an observed target surface. Previous studies have utilized multi-angular reflectance data and physically based Bidirectional Reflectance Distribution Function (BRDF) models with detailed vegetation structure descriptions for LAI estimation. However, the optimal selection of viewing angles for improved inversion results has received limited attention. By optimizing directional observations and integrating the PROSAIL and Ross–Li models, this study aims to enhance LAI estimation from MODIS BRDF data. A dataset of 20,000 vegetation parameter combinations was utilized to identify the directions in which the PROSAIL model exhibits higher sensitivity to LAI changes and better consistency with the Ross–Li BRDF models. The results reveal significant variations in the sensitivity of the PROSAIL model to LAI changes and its consistency with the Ross–Li model over the viewing hemisphere. In the red band, directions with high sensitivity to LAI changes and strong model consistency are mainly found at smaller solar and viewing zenith angles. In the near-infrared band, these directions are distributed at positions with larger solar and viewing zenith angles. Validation using field measurements and LAI maps demonstrates that the proposed method achieves comparable accuracy to an algorithm utilizing 397 viewing angles by utilizing reflectance data from only 30 directions. Moreover, there is a significant improvement in computational efficiency. The accuracy of LAI estimation obtained from simulated multi-angle data is relatively high for LAI values below 3.5 when compared with the MODIS LAI product from two tiles. Additionally, there is also a slight improvement in the results when the LAI exceeds 4.5. Overall, our results highlight the potential of utilizing multi-angular reflectance in specific directions for vegetation parameter inversion, showcasing the promise of this method for large-scale LAI estimation.

Monitoring leaf area index of the sown mixture pasture through UAV multispectral image and texture characteristics

Leaf area index (LAI) is an important phenotypic trait closely related to photosynthesis, respiration, and water utilization. In recent years, unmanned aerial vehicles (UAVs) multispectral capabilities enable the acquisition of spectral information from visible light to near infrared, facilitating vegetation growth monitoring. This study aims to explore the methodology of combining vegetation indices, color indices, texture information, and ecological factors based on UAV multispectral images to enhance the accuracy of the sown mixture pasture LAI estimation. A field experiment involving 13 mixed sowing combinations of alfalfa (Medicago sativa L.), tall fescue (Festuca elata Keng ex E. Alexeev) and plantain (Plantaga lanceolata L.) was conducted out. Multiple linear regression, Bagging algorithm, support vector machine (SVM), random forest algorithm (RF), KNN algorithm, and back propagation neural network (BP) were used to construct the LAI prediction model. The results showed that combining vegetation index (VI) + color index (CI) + normalized difference texture index (NDTI), and ecological factors (EF) could enhance the accuracy of LAI estimation. The sensitive characteristic combinations for alfalfa, tall fescue, and plantain were found to be NDRE (Normalized difference red-edge index) + NGBDI (Normalized green–blue difference index) + (B) MEA-(G) HOM (Blue band Mean - Green band Homogeneity) + DTR (Daily temperature difference), MSR (Modified simple ratio) + NGRDI (Normalized green–red difference index) + (G) COR-(R) VAR (Green band Correlation – Red band Variance) + DTR (Daily temperature difference)), and MSR (Modified simple ratio) + ExG (Excess green) + (G) SEM-(G) MEA (Green band Second-order moment – Green band Mean) + DTR (Daily temperature difference), respectively. RF exhibited superior prediction capability, further enhancing the accuracy of forage LAI prediction. The alfalfa, tall fescue, and plantain obtained coefficient of determination (R2) of 0.83, 0.79 and 0.79, root mean squared error (RMSE) of 0.50, 0.58 and 0.70, and mean absolute error (MAE) of 0.36, 0.45 and 0.55, respectively. These findings provide valuable insights for the estimation of leaf area index of the sown mixture pasture through UAV multispectral images and texture characteristics.

Quantifying effect of maize tassels on LAI estimation based on multispectral imagery and machine learning methods

A reliable method to estimate the leaf area index (LAI) in a field environment is crucial for precise monitoring of crop-growth status. Currently, the crop canopy information has been widely used to estimate LAI using remote sensing methods. Many studies regard canopy tassels and leaves as integrated objects, no systematic study has yet investigated how tassels affect the accuracy of LAI estimates. Moreover, the estimation accuracy and generalization ability of the number of selected vegetation indices was seldom evaluated. Therefore, this study used deep learning segmentation methods to quantify how maize tassels affect LAI estimates and to evaluate how the number of variables affects LAI estimates. The results showed that the multispectral dataset segmentation tassels had the highest accuracy when using the VGG-encoded U-Net model (class pixel accuracy, CPA = 89.53 %; mean intersection over union, MIoU = 85.97 %). The segmentation accuracy first was increased and then decreased with tassel growth. By quantifying the contribution of tassels to the vegetation index, tassels most strongly affect the modified nonlinear vegetation index (MNLI) constructed from the canopy spectral information. Moreover, removing the tassels in images could significantly improve the accuracy of LAI estimates using the gradient-boosting decision tree method (GBDT). The estimation method obtained the highest accuracy when using nine vegetation indices to estimate the LAI (R2 = 0.816, RMSE = 0.399, rRMSE = 7.4 %). Overall, the proposed method improves the accuracy of LAI estimates, which provides crucial technical support for monitoring the LAI of maize.

UAV multispectral images for accurate estimation of the maize LAI considering the effect of soil background

The high proportion of soil background pixels in UAV remote sensing images is an important reason for the uncertainty of high-precision leaf area index (LAI) estimation at early growth stages of crops. Although the traditional method of removing soil pixels from images based on canopy coverage (CC) eliminates pure soil pixels, it can cause spectral saturation at the early stages and therefore affect the accuracy of LAI estimation. In this study, a new method called reduced soil contribution (CS) was constructed to improve the accuracy of LAI estimation. This method can be improved by introducing a quantitative method to account for the contribution of soil information, which can be used to correct the calculation of vegetation indices and eliminate soil interference in maize LAI estimation. A six-rotor UAV equipped with a multispectral camera was used to collect field image data. Experimental plots with different maize breeding varieties were laid out to carefully evaluate the accuracy of the estimation model using UAV multispectral images collected at different growth stages. The performance of four estimation models, a light gradient boosting machine, gradient-boosting decision tree, random forest regression model and extreme gradient boosting, for LAI estimation was evaluated. The CS-based approach significantly improved the accuracy of the LAI model estimation, reducing the rRMSE by 1.89% for a single growing season compared to the traditional method. On average, the rRMSE for all growth stages decreased by 3.5%, demonstrating its effectiveness in improving maize LAI estimation accuracy. Randomness in error measured by Moran’s I metrics showed that the GBDT (gradient-boosting decision trees) model based on the CS method showed less spatial aggregation. These results showed that the CS model can effectively reduce the influence of soil on the estimation of the maize LAI and improve the LAI estimation accuracy compared with the direct removal of soil background pixels from an image.

Research on rice leaf area index estimation based on fusion of texture and spectral information

Leaf Area Index (LAI) is one of the indicators used to measure the growth status of rice fields. Rapid, accurate, and large-scale monitoring of LAI plays an important role in ensuring stable grain yield increase. In recent years, the spectral saturation problem and the parameter adjustment problem of machine learning algorithms have become the main limitations to improve the accuracy of LAI estimation. High-resolution Unmanned Aerial Vehicles (UAVs) images contain not only rich spectral information, but also texture information reflecting the crop canopy structure. Therefore, in order to fully understand the role of spectral information and texture information fusion in rice LAI estimation, this study used the hyperspectral sensor carried by the UAVs to obtain the spectral images of rice canopy of different varieties and different growth stages. Rice canopy reflectance and 8 basic texture features based on Gray-level Co-occurrence Matrix (GLCM) were extracted from hyperspectral images to calculate vegetation indexs (VIs) and combined texture features. Normalized difference texture index (NDTI), Non-linear texture index (NLTI), Enhanced vegetation texture index (EVTI), and Modified triangular texture index (MTTI) were calculated using two and three GLMC-based texture features to explore the effect of combinations of different basic texture features on LAI sensitivity. Two rice LAI estimation models were developed for single spectral indicators and combined with texture indicators, respectively. The results show that: (1) After preprocessing and feature band screening, the optimal spectral band, vegetation index, and trilateral parameters were obtained. When the combined spectral parameters (SP) of the three were used as the only input to the model, R2 showed an increasing trend throughout the growth period. The best results were achieved using the support vector regression (SVR) combined with the pelican optimization algorithm (POA) in the pre jointing stage: R2= 0.839, RMSE = 0.107, and MAPE = 7.02%. (2) When texture information based on hyperspectral images was incorporated into the model input, the results showed that the models based on spectral indicators combined with texture measurements were all superior to those using spectral indicators alone, with the best model having a coefficient of determination: R2 = 0.917, RMSE = 0.078, and MAPE = 4.19%, which has promising applications in crop growth index detection. This technology can quickly and effectively monitor the growth status of crops in the field, providing a theoretical basis for estimating crop yield in the later stage.

Influence of Structure and Texture Feature on Retrieval of Ramie Leaf Area Index

Leaf area index (LAI), as one of the important crop field phenotypes, plays a crucial role in evaluating crop growth and yield. The remote sensing method for crop parameters monitoring makes it possible to obtain crop LAI in a large area, efficiently and non-destructively. Fusion of spectral features, structure features, and texture features obtained by remote sensing images can effectively improve LAI estimation performance, but the impact of crop structure and texture feature differences on LAI remote sensing monitoring remains unclear. In this study, ramie was classified according to the differences of canopy structure, and then the LAI inversion performance of ramie was evaluated under different datasets to determine the contribution of structure features (canopy coverage, plant height) and texture features derived from remote sensing images to LAI estimation. Finally, multi-feature remote sensing indexes were integrated to construct the optimal ramie LAI estimation model. The results showed that structure features and texture features had an effect on ramie LAI inversion performance, and there were significant differences in LAI estimation accuracy among different data sets. A single structure feature or texture feature could not significantly optimize LAI inversion performance, while multi-feature fusion could effectively improve LAI estimation accuracy (R2 = 0.776, RMSE = 0.740). This study provides a robust, practical, and low-cost method for ramie LAI estimation, and provides a reference for crop LAI inversion research based on structural features and texture features.

Retrieval of Mangrove leaf area index and its response to typhoon based on WorldView-3 image

In recent decades, mangroves have become one of the world's most threatened and vulnerable ecosystems due to human disturbance and climate change. Remote sensing techniques have helped to obtain biophysical parameters of forests on a large scale. However, to our knowledge, mangrove leaf area index (LAI) inversion using texture features and its scale dependency is rarely discussed. Whether the dynamics of remotely sensed LAI could reveal the responses of mangrove species to typhoon disturbance remain unknown. In this study, we classified mangrove species, estimated the LAI of each species based on high-resolution WorldView-3 (WV-3) images, and evaluated the performance of different object-based classifiers and the effects of texture features and scale dependency on the LAI model. We characterized typhoon disturbance on different species using interannual LAI variation. Random forest (RF) has the highest accuracy in mangrove species classification, with an overall accuracy of 82.29% after post-classification, compared with naive Bayes (NB), classification and regression trees (CART), k-nearest neighbor (KNN), and support vector machines (SVM). Texture features significantly increased the accuracy of LAI estimates, and the optimal prediction result was generated at a 2-m resampling rate (R2 = 0.52; MSE = 0.56). The mean LAIs of B. gymnorrhiza and S. apetala assemblages were significantly higher than those of K. obovata, S. caseolaris, and A. marina. LAI variation demonstrates a disturbance pattern caused by Typhoon Mangkhut. This typhoon had the greatest impact on K. obovata, while S. apetala and S. caseolaris showed good resistance to wind damage. We concluded that RF was the best classifier for mapping mangrove species distribution using object-based classification algorithms with WV-3 image. The texture features play a key role in constructing the estimation model of mangrove LAI with scale dependency.

Improving Leaf Area Index Estimation With Chlorophyll Insensitive Multispectral Red-Edge Vegetation Indices

As an essential vegetation biophysical trait that determines the plant's structure and photosynthetic capacity, characterizing of leaf area index (LAI) is important for vegetation growth and health monitoring. The empirical models based on vegetation indices (VIs) from remote sensing images is an effective method for deriving LAI. However, due to the coupled impacts of LAI and leaf chlorophyll content (LCC) on canopy reflectance and saturation effect, most VIs cannot achieve a good accuracy of LAI estimation. The remotely sensed red-edge reflectance can provide valuable information to delineate the LAI, therefore a series of leaf chlorophyll insensitive red-edge VIs by using the Sentinel-2 and GF-6 multispectral images are developed in this work to improve the LAI estimation accuracy. The potentials of reflecting LAI variations and sensitivity to LCC changes for each Sentinel-2 and GF-6 red-edge band are comprehensively analyzed based on the PROSAIL model to select the optimal band in VIs design. The proposed VIs are then evaluated in multiple ways, including with PROSAIL simulated datasets, ground measured LAI with canopy spectra and real satellite images. The evaluation results based on field LAI measurements indicate that the proposed red-edge VIs can effectively improve crop LAI estimation accuracy with the best regression coefficient ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.81 for Sentinel-2 and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.65 for GF-6) among all comparative VIs. Our work showcases incorporating red-edge bands with suitable formula is promising for improving VI-based LAI retrieval, and they offer a practicable solution to fast achieve decameter LAI maps by using the Sentinel-2 or GF-6 images.

Improved Estimation of Leaf Area Index by Reducing Leaf Chlorophyll Content and Saturation Effects Based on Red-Edge Bands

Leaf area index (LAI) is an important indicator for monitoring vegetation growth and estimating crop yields. The empirical-based model using vegetation indices (VIs) is an effective method for LAI estimation at the regional scale. However, due to the complexity of canopy radiation interaction processes, the leaf chlorophyll content ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C_ab</i> ) and saturation effects on canopy reflectance restrict the accuracy of VI-based LAI retrieval. To address these limitations, we propose a novel chlorophyll-insensitive vegetation index (CIVI) using red, red-edge and near-infrared bands to improve regional LAI mapping. The CIVI was developed based on the sensitivity analysis of red-edge band reflectance to LAI and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C_ab</i> using the simulation dataset from the PROSAIL model. Then, the performance of CIVI was carefully evaluated from two aspects: the sensitivity of VI to LAI and other parameters, and the accuracy of LAI estimates using different VIs over homogeneous (cropland and grassland) and non-homogeneous (forest) biome canopies. The results suggested that CIVI can capture LAI variations well while remaining insensitive to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C_ab</i> variations. Additionally, the sensitivity of CIVI to other vegetation biochemical and biophysical parameters did not increase significantly compared to that of other VIs. Furthermore, CIVI exhibited the best performance of LAI retrievals over both homogeneous (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.938, RMSE=0.447 and rRMSE=21.3%) and non-homogenous (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.635, RMSE=0.693 and rRMSE=14.0%) canopies among all selected VIs, especially for the high LAI. Our results indicated that the developed CIVI incorporating red-edge bands with a suitable formula can effectively reduce the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C_ab</i> and saturation effects, which is promising for improving VI-based LAI estimation.

Enhanced Leaf Area Index Estimation With CROP-DualGAN Network

Quantitative estimation of regional leaf area index (LAI) is an important basis for large-scale crop growth monitoring and yield estimation. With the development of deep learning, theoretically, the use of neural networks can effectively improve the accuracy of LAI estimation, but sufficient training samples are often required due to a large number of network parameters. In an actual regional LAI quantitative estimation, there are only a few samples, which is difficult to train in networks. Therefore, a crop dual-learning generative adversarial network (CROP-DualGAN) was proposed in this article for data enhancement of small samples to estimate regional LAI. The method uses dual learning to generate hyperspectral reflectance and corresponding LAI, including two groups of generative adversarial networks, in which the generator is used to generate data that conforms to the distribution of the training set, and the discriminator is used to judge the true or false generated samples. The generators and discriminators are constantly optimized in the confrontation so that the distribution of generated data is closer to that of training samples. In single crop type experiments, 30 training samples with enhanced in VGG16 achieved the R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of cereal, maize and rape seed as 0.921, 0.990 and 0.956, and in SSLLAI-Net achieved the R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of cereal, maize and rape seed as 0.971, 0.991 and 0.962. In multiple crop types experiments, the result is lower than individual crop estimation, but higher than that of without enhancement. Finally, non-parametric test is used to prove that most improvement in LAI estimation is significant, and the accuracy won’t decrease when improvement is not significant. In all, proposed method is universal and can effectively help benchmark models to improve regional LAI estimation accuracy with neural networks.

Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography.

The technology of cotton defoliation is essential for mechanical cotton harvesting. Agricultural unmanned aerial vehicle (UAV) spraying has the advantages of low cost, high efficiency and no mechanical damage to cotton and has been favored and widely used by cotton planters in China. However, there are also some problems of low cotton defoliation rates and high impurity rates caused by unclear spraying amounts of cotton defoliants. The chemical rate recommendation and application should be based upon crop canopy volume rather than on land area. Plant height and leaf area index (LAI) is directly connected to plant canopy structure. Accurate dynamic monitoring of plant height and LAI provides important information for evaluating cotton growth and production. The traditional method to obtain plant height and LAI was s a time-consuming and labor-intensive task. It is very difficult and unrealistic to use the traditional measurement method to make the temporal and spatial variation map of plant height and LAI of large cotton fields. With the application of UAV in agriculture, remote sensing by UAV is currently regarded as an effective technology for monitoring and estimating plant height and LAI. In this paper, we used UAV RGB photos to build dense point clouds to estimate cotton plant height and LAI following cotton defoliant spraying. The results indicate that the proposed method was able to dynamically monitor the changes in the LAI of cotton at different times. At 3 days after defoliant spraying, the correlation between the plant height estimated based on the constructed dense point cloud and the measured plant height was strong, with [Formula: see text] and RMSE values of 0.962 and 0.913, respectively. At 10 days after defoliant spraying, the correlation became weaker over time, with [Formula: see text] and RMSE values of 0.018 and 0.027, respectively. Comparing the actual manually measured LAI with the estimated LAI based on the dense point cloud, the [Formula: see text] and RMSE were 0.872 and 0.814 and 0.132 and 0.173 at 3 and 10 days after defoliant spraying, respectively. Dense point cloud construction based on UAV remote sensing is a potential alternative to plant height and LAI estimation. The accuracy of LAI estimation can be improved by considering both plant height and planting density.

Integrating an attention-based deep learning framework and the SAFY-V model for winter wheat yield estimation using time series SAR and optical data

Information on the spatial distribution of yields can be obtained over a large area by using remote sensing (RS) data. Combining Synthetic Aperture Radar (SAR), being sensitive to above ground biomass and soil moisture in all weather conditions, and optical data can improve the usability of RS data and provide a basis for pixel-based crop yield estimation (YE). In this study, an Upscaled Convolutional Gated Recurrent Unit model incorporated an attention mechanism (UpSc-AConvGRU model) was proposed to improve the estimation accuracy of the winter wheat growth parameter, Leaf Area Index (LAI). Gap filling the time series of optical data was done with backscatter coefficients, local incidence angles and polarimetric decomposition information from Sentinel-1 SAR imagery. The time series LAI estimated by the UpSc-AConvGRU model and Vegetation Temperature Condition Index (VTCI) retrieved from Sentinel-3 optical imagery were then used as state variables of the SAFY-V model to estimate winter wheat yield. The results showed that the proposed UpSc-AConvGRU model incorporated the Convolutional Block Attention Module (CBAM) can effectively improve the accuracy of LAI estimation, with RMSEs ranging from 0.413 to 0.699 m2 m2 for LAI estimated within main growth stages (MGSs) of winter wheat. The correlation between estimated LAI and Sentinel-3 retrieved LAI was generally higher at irrigated farmland compared to rain-fed farmland. The estimated LAI was closest to Sentinel-3 retrieved LAI at the green-up and late heading-filling stage of winter wheat, followed by the jointing and early heading-filling stage, and finally the milk maturity stage. There was good agreement between the SAFY-V model estimated and field measured winter wheat yields (R2 = 0.546, RMSE = 0.757 t ha−1), and the estimated yields at the pixel scale in the Guanzhong Plain, PR China were satisfactory. This study combined deep learning and crop growth modeling, proposed a new pixel scale winter wheat YE method.

An Improved LAI Estimation Method Incorporating with Growth Characteristics of Field-Grown Wheat

Leaf area index (LAI), which is an important vegetation structure parameter, plays a crucial role in evaluating crop growth and yield. Generally, it is difficult to accurately estimate LAI only using vegetation index in remote sensing (RS), especially for unmanned aerial vehicle (UAV) based RS, as its high-resolution advantage has not been fully utilized. This study aims to propose an improved LAI estimation method that comprehensively considers spectral information and structural information provided by the UAV-based RS to improve the LAI estimation accuracy of field-grown wheat. Specifically, this method introduces the canopy height model (CHM) to compensate for the lack of structural information in LAI estimation, and then takes canopy coverage (CC) as a correction parameter to alleviate the LAI overestimation. Finally, the performance of this method is verified on RGB and multispectral images, respectively. The results show that canopy structure, namely CHM and CC, can significantly improve the accuracy of LAI estimation. Compared with the traditional method, the proposed method improves the accuracy by 22.6% on multispectral images (R2 = 0.72, RMSE = 0.556) and by 43.6% on RGB images (R2 = 0.742, RMSE = 0.534). This study provides a simple and practical method for UAV-based LAI estimation, especially for the application of low-cost RGB sensors in precision agriculture and other fields.

Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index.

Estimating the crop leaf area index (LAI) accurately is very critical in agricultural remote sensing, especially in monitoring crop growth and yield prediction. The development of unmanned aerial vehicles (UAVs) has been significant in recent years and has been extensively applied in agricultural remote sensing (RS). The vegetation index (VI), which reflects spectral information, is a commonly used RS method for estimating LAI. Texture features can reflect the differences in the canopy structure of rice at different growth stages. In this research, a method was developed to improve the accuracy of rice LAI estimation during the whole growing season by combining texture information based on wavelet transform and spectral information derived from the VI. During the whole growth period, we obtained UAV images of two study areas using a 12-band Mini-MCA system and performed corresponding ground measurements. Several VI values were calculated, and the texture analysis was carried out. New indices were constructed by mathematically combining the wavelet texture and spectral information. Compared with the corresponding VIs, the new indices reduced the saturation effect and were less sensitive to the emergence of panicles. The determination coefficient (R2) increased for most VIs used in this study throughout the whole growth period. The results indicated that the estimation accuracy of LAI by combining spectral information and texture information was higher than that of VIs. The method proposed in this study used the spectral and wavelet texture features extracted from UAV images to establish a model of the whole growth period of rice, which was easy to operate and had great potential for large-scale auxiliary rice breeding and field management research.