Leaf Area Index Estimation Model Research Articles

The fusion of vegetation indices increases the accuracy of cotton leaf area prediction.

Rapid and accurate estimation of leaf area index (LAI) is of great significance for the precision agriculture because LAI is an important parameter to evaluate crop canopy structure and growth status. In this study, 20 vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods extreme learning machine (ELM), random forest (RF), back propagation (BP), multivariable linear regression (MLR), support vector machine (SVM)], and the optimal modeling strategy (RF) was selected. Finally, the vegetation indices with a high correlation with LAI were fused to construct the VI-fusion RF model, to explore the potential of multi-vegetation index fusion in the estimation of cotton LAI. The RF model had the highest estimation accuracy among the LAI estimation models, and the estimation accuracy of models constructed by fusing multiple VIs was higher than that of models constructed based on single VIs. Among the multi-VI fusion models, the RF model constructed based on the fusion of seven vegetation indices (MNDSI, SRI, GRVI, REP, CIred-edge, MSR, and NVI) had the highest estimation accuracy, with coefficient of determination (R2), rootmean square error (RMSE), normalized rootmean square error (NRMSE), and mean absolute error (MAE) of 0.90, 0.50, 0.14, and 0.26, respectively. Appropriate fusion of vegetation indices can include more spectral features in modeling and significantly improve the cotton LAI estimation accuracy. This study will provide a technical reference for improving the cotton LAI estimation accuracy, and the proposed method has great potential for crop growth monitoring applications.

Enhanced Leaf Area Index Estimation in Rice by Integrating UAV-Based Multi-Source Data

The monitoring of crop growth, particularly the estimation of Leaf Area Index (LAI) using optical remote sensing techniques, has been a continuous area of research. However, it has become a challenge to accurately and rapidly interpret the spatial variation of LAI under nitrogen stress. To tackle these issues, this study aimed to explore the potential for precise LAI estimation by integrating multiple features, such as average spectral reflectance (ASR), vegetation index, and textures, obtained through an unmanned aerial vehicle (UAV). The study employed the partial least squares method (PLS), extreme learning machine (ELM), random forest (RF), and support vector machine (SVM) to build the LAI estimation model under nitrogen stress. The findings of this study revealed the following: (i) texture features generally exhibited greater sensitivity to LAI compared to ASR and VIs. (ii) Utilizing a multi-source feature fusion strategy enhanced the model’s accuracy in predicting LAI compared to using a single feature. The best RP2 and RMSEP of the estimated LAI were 0.78 and 0.49, respectively, achieved by RF through the combination of ASR, VIs, and textures. (iii) Among the four machine learning algorithms, RF and SVM displayed strong potential in estimating LAI of rice crops under nitrogen stress. The RP2 of the estimated LAI using ASR + VIs + texture, in descending order, were 0.78, 0.73, 0.67, and 0.62, attained by RF, SVM, PLS, and ELM, respectively. This study analyzed the spatial variation of LAI in rice using remote sensing techniques, providing a crucial theoretical foundation for crop management in the field.

Unmanned aerial vehicle digital image and hyperspectral data for estimating the comparison of leaf area index and biomass of potato at different growth stages

Abstract Leaf Area Index (LAI) and biomass (BIO) are essential agronomic parameters that reflect the growth of potatoes and are related to their biomass. Their precise estimation is capable of monitoring crop growth, guiding field management, and optimizing planting spatial patterns. Traditional potato leaf area indexing and biomass estimation primarily rely on field sampling surveys. This method is low in efficiency, high in cost, and limited by the number of samples. It cannot accurately reflect potato growth and meet the real-time estimation needs of large areas. Compared to the use of satellite remote sensing data (RSD) for estimating LAI and biomass, research on estimating these two phenotypic parameters using crewless aerial vehicle (UAV) RSD is relatively immature. Research on estimating crop growth index parameters by remote sensing primarily focuses on data obtained from specific types of sensors, targeting specific growth stages to compare and analyze the accuracy of different methods. However, there are few estimates of the impact of optimizing the best data types and optimal growth stage for LAI and biomass estimation by comparing and analyzing different sensor data and different growth stages. Multi-sensor integration technology has made it possible to study different crop phenotype information and estimate the best data type and optimal growth stage in crop phenotypic data estimation, establishing it as a new hot spot in the field. This paper integrates high-definition digital cameras and imaging hyperspectrometers on the UAV platform to obtain digital images and hyperspectral data simultaneously, along with ground-measured potato leaf area index and biomass data. Using the partial least squares regression (PLSR), random forest (RF), support vector machine (SVM), and backpropagation (BP) neural network methods, we got digital images and hyperspectral data from different stages of growth, put together a digital image index and a vegetation index, and looked at how they related to LAI and BIO. Then, we chose the index that had the strongest correlation. To establish LAI and biomass estimation models at various growth phases, this paper compared and analyzed the estimation impacts of various data types and models at various growth phases. It then selected the best data types for LAI estimation and biomass estimation at different growth stages, as well as the best growth phases for LAI and biomass estimation. The outcomes indicated that when potato LAI was estimated, the mean values of R 2 and RMSE of the four estimation models were 0.75 and 0.30 Kg/mu at the tuber growth stage, respectively, and the estimation effect was the best, indicating that this was the best growth phase for LAI estimation. The average values of R 2 and RMSE in the LAI estimation model using the hyperspectral vegetation index were 0.73 and 0.33 Kg/mu, respectively, indicating that hyperspectral data was the best data type for LAI estimation. When potato biomass was estimated, the mean values of R 2 and RMSE of the four methods were 0.67 and 15.25 Kg/mu, respectively, at the tuber growth stage, which were better than other growth phases, demonstrating that this was the best growth phase for biomass estimation. The average values of R 2 and RMSE of the biomass estimation model using the hyperspectral vegetation index were 0.67 and 20.08 Kg/mu, respectively, indicating that the hyperspectral data was the best data type for biomass estimation. The average values of R 2 of the LAI and biomass estimation model at the maturity stage were only 0.56 and 0.36, both of which indicated poor estimation effects. Our study can serve as a guide to selecting the most effective method for estimating parameters for essential indexes in crop growth monitoring.

Estimation of grain filling rate of winter wheat using leaf chlorophyll and LAI extracted from UAV images

The grain filling rate (GFR) plays a pivotal role in assessing winter wheat grain development and guiding crop variety selection and cultivation strategies. Presently, GFR monitoring primarily relies on destructive sampling methods, which are impractical for swift assessments in extensive agricultural fields. Our study introduces a novel approach for estimating winter wheat grain filling rate (GFR) using leaf chlorophyll content (LCC) and Leaf Area Index (LAI) extracted from UAV imagery. Initially, we established a quantitative relationship between GFR and LAI as well as LCC in winter wheat. Subsequently, UAV multispectral imagery was employed to track the time-series dynamics of LAI and LCC during the grain-filling stage. Ultimately, we constructed an estimation model for winter wheat GFR and achieved plot-scale GFR mapping through UAV-based imaging technology. To identify sensitive vegetation index combinations for LAI and chlorophyll content (SPAD), we employed adaptive re-weighted sampling and continuous projection algorithms, respectively. Following this, we employed partial least squares and random forest algorithms to develop LAI and SPAD estimation models, which were then compared to full-band regression. Finally, GFR estimation was based on the conversion models of LAI and SPAD to GFR. Our findings revealed that, despite variations in water supply, there were no significant differences in GFR trends among the irrigated treatments. However, in the nitrogenous fertilizer application treatments, GFR initially declined with increased nitrogenous fertilizer supply, reaching its peak between 21 and 25 days post-flowering, during which period the differences in GFR among the various nitrogenous fertilizer application treatments were not significant. As the reproductive period advanced, higher nitrogenous fertilizer treatments maintained a higher GFR for a more extended period and experienced a slower decline compared to the lower nitrogenous fertilizer treatments. LAI validation yielded an R2 of 0.84, a root mean square error (RMSE) of 0.39, and a normalized root mean square error (NRMSE) of 10.9%. SPAD validation resulted in R2, RMSE, and NRMSE values of 0.79%, 1.30%, and 12.5%, respectively. The GFR estimation achieved an R2 of 0.77, an RMSE of 0.32 mg/d, and an NRMSE of 11.3%. Mapping the GFR distribution in the field, coupled with water and fertilizer management, supports UAVs in monitoring crop growth and refining field-level water and fertilizer decisions.

Incremental learning for crop growth parameters estimation and nitrogen diagnosis from hyperspectral data

Nitrogen is an essential nutrient in crop growth cycle and directly affects the photosynthesis of crops. The leaf chlorophyll content (LCC) and leaf area index (LAI) are important for characterizing photosynthetic capacity of crops and are critical indicators for diagnosing nitrogen status of crops. Hyperspectral remote sensing technology provided a means to achieve crop LCC and LAI estimation. However, the redundancy of spectral data and canopy structure effect can cause poor robustness of the estimating models, further hindering the development and application of estimating models across different crop species. In this study, a method based on incremental learning was proposed for the simultaneous estimation of LCC and LAI, and for nitrogen diagnosis across crops. First, for the spectral dataset generated by the PROSAIL model, a deep neural network was used to construct LCC and LAI estimation models (called DNNCA model). Secondly, for the hyperspectral data collected from field crops (soybean, canola and wheat), incremental learning using regularization (LwF algorithm) was used to update the DNNCA model parameters. Finally, the dilution curve model based on the LCC-LAI anisotropic growth relationship was developed to assess crop nitrogen status. The results showed that: (1) The constrained bi-objective optimizated DNNCA model can consider the interactive effect of LAI and LCC on spectral reflectance, and achieved reliable estimation on PROSAIL simulation data set (LAI:R2 = 0.82, RMSE = 0.77 m2/m2; LCC:R2 = 0.91, RMSE = 6.4 ug/cm2). (2) By incremental learning, DNNCA model has continuous learning capability and stable estimation on cross-crop (canola, soybean and wheat) field-measured data (LAI:R2 = 0.64–0.82, RMSE = 0.58–1.02 m2/m2; LCC:R2 = 0.56–0.82, RMSE = 3.9–10.5 ug/cm2). (3) The relationship between the NNILCC and the NNILNC was significant. The NNILCC derived from the anisotropy relationship between crop LAI and LCC was an effective tool for crop nitrogen status diagnosis. (4) The process of crop LAI, LCC and NNILCC reflected the effect of water and nitrogen supply on crop growth. The appropriate water-nitrogen treatments contributed to LAI increase and LCC accumulation. The study demonstrated that hyperspectral remote sensing technology combined with incremental learning is an effective method for cross-crop growth monitoring and nitrogen diagnosis. These results provide a reference and basis for filed water-nitrogen supply and management.

A deep transfer learning framework for mapping high spatiotemporal resolution LAI

Leaf area index (LAI) is an important variable for characterizing vegetation structure. Contemporary satellite-based LAI products with moderate spatial resolution, such as those derived from the MODIS observations, offer unique opportunities for large-scale monitoring but are insufficient for resolving heterogeneous landscapes. Although high-resolution satellite observations can derive detailed LAI maps, the low revisit frequency and the presence of cloud cover disrupt the temporal continuity of these high-resolution data, thereby leading to the fact that most current LAI inversion models for high spatial resolution data are based on single pixel and date without fully utilizing temporal information. Moreover, LAI estimation models trained solely using satellite products or simulations are also impeded by the inconsistencies between field and satellite LAI exist owing to local atmospheric, soil, and canopy conditions, while in-situ LAI measurements are sparse and insufficient for large-scale missions. To address these challenges, this study proposed a new framework based on deep transfer learning, which includes three key features that contribute to its high performance. Firstly, a Bi-directional Long Short-Term Memory (Bi-LSTM) model is pre-trained using MODIS reflectance and MODIS LAI products to capture the general non-linear relationship between reflectance and LAI and incorporate temporal dependencies as prior information to reduce uncertainties associated with ill-posed inversion problems and noises. Secondly, this pre-trained Bi-LSTM is transferred from satellite to field by fine-tuning with sparse in-situ LAI measurements to overcome issues arising from local inconsistencies. Thirdly, reconstructed Landsat time-series images via fusing MODIS and Landsat reflectance images are used as inputs of the Bi-LSTM to generate high-quality Landsat LAI products at both high spatial and temporal resolutions. To validate the proposed approach, field LAI measurements were collected at nine locations across the contiguous U.S. from 2000 to 2018, including three land cover types: croplands, grasslands, and forest. Quantitative assessments demonstrate that the Bi-LSTM outperforms three benchmarks, including a PROSAIL-based Look-Up Table (LUT) method, a random forest-based LAI retrieval and MODIS LAI (MCD15A3H), exhibiting lower RMSE and higher R2 in most cases. Additionally, the Bi-LSTM predictions yield lower random fluctuations than estimations from the LUT·, random forest and MODIS LAI, indicating the higher robustness of the proposed framework. The findings of this study highlight the value of transfer learning in estimation of vegetation biophysical parameters, which involves pre-training using sufficient existing satellite products to produce a generalized model and transferring knowledge from in-situ measurements to bridge gaps between satellite and field. By leveraging advanced transfer learning techniques and multi-source and multi-scale data, the proposed framework enables the production of long-term LAI maps at fine resolutions, facilitating downstream applications in regions characterized by high spatial heterogeneity.

Improving estimation of maize leaf area index by combining of UAV-based multispectral and thermal infrared data: The potential of new texture index

Crop leaf area index (LAI) is one of the important indicators to evaluate crop growth and guide field management, and can be used to predict crop yield. Spectral and thermal information extracted by multispectral (MS) and thermal infrared (TIR) sensors mounted on an unmanned aerial vehicle (UAV) can be used for LAI estimation. Image texture is sensitive to the changes in crop surface grayscale or color characteristics, and can be combined with spectral and thermal information to estimate the LAI. But single texture metric has limitations in LAI estimation. Therefore, the purpose of this study is to construct new texture indices based on texture metrics extracted from MS and TIR images, and combined spectral and thermal information to enhance the estimation accuracy of maize LAI. Three replicates of maize experiments under different irrigation treatments were conducted in 2020. The MS and TIR sensors were mounted on a UAV to acquire maize canopy images during critical growth stages and acquire field LAI value of samples synchronously. The LAI estimation models were established using MS data, TIR data, as well as their combination. These models were constructed by Back Propagation Neural Network (BPNN), Partial least squares regression (PLSR), and Random Forest Regression (RFR). Finally, the performance of LAI estimation models was evaluated by the coefficient of determination (R2), root mean square error (RMSE) and relative root mean square error (rRMSE). Results shown that: (i) Among the eight kinds of texture metrics extracted from MS and TIR images, the texture metric mean (MEA) has the best performance. Compared with single texture metrics, texture indices constructed by different metrics has stronger correlation with LAI. (ii) Adding texture indices to estimation models significantly improved model accuracy, especially multispectral three-texture index(MS-TTI) has higher LAI estimation potential than thermal infrared three-texture index(TIR-TTI). (iii) Compared with the use of MS or TIR data alone, the estimation model constructed by combining MS data and TIR data have better performance. The best estimation model obtained by the RFR method (R2 = 0.862, RMSE = 0.246 and rRMSE = 10.20 %) further improved the LAI estimation of maize, with R2 increasing by 6.55 % and 14.48 %, respectively. In conclusion, the combination of MS and TIR data can effectively improve the estimation accuracy of maize LAI, and also provide a feasible method for monitoring crop growth.

Leaf Area Index Inversion of Spartina alterniflora Using UAV Hyperspectral Data Based on Multiple Optimized Machine Learning Algorithms

The leaf area index (LAI) is an essential biophysical parameter for describing the vegetation canopy structure and predicting its growth and productivity. Using unmanned aerial vehicle (UAV) hyperspectral imagery to accurately estimate the LAI is of great significance for Spartina alterniflora (S. alterniflora) growth status monitoring. In this study, UAV hyperspectral imagery and the LAI of S. alterniflora during the flourishing growth period were acquired. The hyperspectral data were preprocessed with Savitzky–Golay (SG) smoothing, and the first derivative (FD) and the second derivative (SD) spectral transformations of the data were then carried out. Then, using the band combination index (BCI) method, the characteristic bands related to the LAI were extracted from the hyperspectral image data obtained with the UAV, and spectral indices (SIs) were constructed through the characteristic bands. Finally, three machine learning (ML) regression methods—optimized support vector regression (OSVR), optimized random forest regression (ORFR), and optimized extreme gradient boosting regression (OXGBoostR)—were used to establish LAI estimation models. The results showed the following: (1) the three ML methods accurately predicted the LAI, and the optimal model was provided by the ORFR method, with an R2 of 0.85, an RMSE of 0.19, and an RPD of 4.33; (2) the combination of FD SIs improved the model accuracy, with the R2 value improving by 41.7%; (3) the band combinations screened using the BCI method were mainly concentrated in the red and near-infrared bands; (4) the higher LAI was distributed on the seaward side of the study area, while the lower LAI was located at the junction between the S. alterniflora and the tidal flat. This study serves as both theoretical and technological support for research on the LAI of S. alterniflora and as a solid foundation for the use of UAV remote sensing technologies in the supervisory control of S. alterniflora.

Leaf area index estimation under wheat powdery mildew stress by integrating UAV‑based spectral, textural and structural features

The infection of powdery mildew changes the internal physiological activity and external morphology of winter wheat, resulting in damage to crop health. Leaf area index (LAI) is an important agronomic parameter to assess crop growth. However, how to accurately and quickly interpret the spatial variation of LAI under powdery mildew stress has become a difficult problem. To detect the spatial variation and improve the estimation accuracy of winter wheat LAI under powdery mildew stress, unmanned aerial vehicle (UAV) multispectral remote sensing technique was used to continuously observe winter wheat infected with diseases in the field. The artificial inoculation experiment of powdery mildew was carried out in the experimental base of the Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Xinxiang, China in 2022. The occurrence of powdery mildew changed the canopy spectral reflectance and morphology of winter wheat. Parameters of vegetation indices (VIs), structure (Str) and texture (Tex) features were used to quantify the spectral reflectance and morphology of winter wheat canopy. Sensitivity analysis of different types of features and LAI and crop disease index (DI) was conducted to obtain specific features responding to LAI and DI. Partial least squares method (PLSR) was used to construct LAI estimation model under powdery mildew stress. The results of this study showed that (i) in contrast to crop height, crop coverage and canopy volume maintained consistency with the time change of LAI, which could be utilized to assess LAI variation under powdery mildew stress. (ii) In general, Tex features were more sensitive to LAI and DI than the Str and VIs. (iii) The R2 and RMSE of LAI estimated under powdery mildew stress by Tex, Str and VIs were 0.56, 0.50, 0.45 and 0.32, 0.35, and 0.36, respectively. The combination of VIs, Str, and Tex provided the highest estimation accuracy (R2 = 0.71, RMSE = 0.26). Compared with VIs, Str, Tex, VIs + Str, VIs + Tex, Str + Tex, the R2 of winter wheat LAI estimation under powdery mildew stress was increased by 58%, 42%, 27%, 22%, 15%, and 8%, respectively. This study analyzed the spatial variation of LAI of winter wheat under powdery mildew stress using remote sensing technique, which provided an important theoretical basis for crop disease control in the field.

High-quality images and data augmentation based on inverse projection transformation significantly improve the estimation accuracy of biomass and leaf area index

Estimating above-ground biomass (AGB) and leaf area index (LAI) is crucial for determining crop developmental status and estimating grain yield, thus facilitating high-throughput phenotyping of maize (Zea Mays L.). Recently, unmanned aerial vehicle (UAV) have been widely used to monitor crop growth and estimate LAI and AGB. However, few studies have reported on the quantitative analysis of improved accuracy in biomass and leaf area index estimation using undistorted images obtained through inverse projection transformation algorithms. Additionally, there is limited research on predicting biomass and LAI during various growth stages by replacing traditional data augmentation methods with images captured from different azimuth and zenith angles. The results showed that when using undistorted image at single solar zenith angle of 0°, the accuracy of AGB and LAI estimation was improved by 20.3% and 7.3%, respectively, compared to the models estimated from orthoimages. The undistorted images with various azimuth and zenith angles were 9–15 times larger than the orthomosaic image dataset, and could be used as a new training data that is similar to the original data but with variations. The AGB and LAI estimation model constructed using the data augmentation method achieved better performance with higher average R2 (0.98). Model performance was increased by 40% and 16.7% for estimating AGB and by 19.3% and 11.3% for estimating LAI based on undistorted images, respectively, in comparison to models based on orthophotos and undistorted images due to the increased diversity and quantity of training data. Canopy structural including canopy coverage and plant height, characterized by 96.9%, was found to be the most reliable indication for estimating AGB and LAI, while other RGB sensor-derived feature combinations contributed only 3.1% to the estimation. Spectral features took the second place, and the textural features was the weakest. In summary, the data processing framework using data augmentation based on various azimuth and zenith angles' images can improve the performance of crop growth estimation and provide a mechanism for precision agriculture under field conditions.

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.

A High Spatiotemporal Enhancement Method of Forest Vegetation Leaf Area Index Based on Landsat8 OLI and GF-1 WFV Data

The leaf area index (LAI) is a crucial parameter for analyzing terrestrial ecosystem carbon cycles and global climate change. Obtaining high spatiotemporal resolution forest stand vegetation LAI products over large areas is essential for an accurate understanding of forest ecosystems. This study takes the northwestern part of the Inner Mongolia Autonomous Region (the northern section of the Greater Khingan Mountains) in northern China as the research area. It also generates the LAI time series product of the 8-day and 30 m forest stand vegetation growth period from 2013 to 2017 (from the 121st to the 305th day of each year). The Simulated Annealing-Back Propagation Neural Network (SA-BPNN) model was used to estimate LAI from Landsat8 OLI, and the multi-period GaoFen-1 WideField-View satellite images (GF-1 WFV) and the spatiotemporal adaptive reflectance fusion mode (STARFM) was used to predict high spatiotemporal resolution LAI by combining inversion LAI and Global LAnd Surface Satellite-derived vegetation LAI (GLASS LAI) products. The results showed the following: (1) The SA-BPNN estimation model has relatively high accuracy, with R2 = 0.75 and RMSE = 0.38 for the 2013 LAI estimation model, and R2 = 0.74 and RMSE = 0.17 for the 2016 LAI estimation model. (2) The fused 30 m LAI product has a good correlation with the LAI verification of the measured sample site (R2 = 0.8775) and a high similarity with the GLASS LAI product. (3) The fused 30 m LAI product has a high similarity with the GLASS LAI product, and compared with the GLASS LAI interannual trend line, it accords with the growth trend of plants in the seasons. This study provides a theoretical and technical reference for forest stand vegetation growth period LAI spatiotemporal fusion research based on high-score data, and has an important role in exploring vegetation primary productivity and carbon cycle changes in the future.

Evaluating the Canopy Chlorophyll Density of Maize at the Whole Growth Stage Based on Multi-Scale UAV Image Feature Fusion and Machine Learning Methods

Maize is one of the main grain reserve crops, which directly affects the food security of the country. It is extremely important to evaluate the growth status of maize in a timely and accurate manner. Canopy Chlorophyll Density (CCD) is closely related to crop health status. A timely and accurate estimation of CCD is helpful for managers to take measures to avoid yield loss. Thus, many methods have been developed to estimate CCD with remote sensing data. However, the relationship between the CCD and the features used in these CCD estimation methods at different growth stages is unclear. In addition, the CCD was directly estimated from remote sensing data in most previous studies. If the CCD can be accurately estimated from the estimation results of Leaf Chlorophyll Density (LCD) and Leaf Area Index (LAI) remains to be explored. In this study, Random Forest (RF), Support Vector Machines (SVM), and Multivariable Linear Regression (MLR) were used to develop CCD, LCD, and LAI estimation models by integrating multiple features derived from unmanned aerial vehicle (UAV) multispectral images. Firstly, the performances of the RF, SVM, and MLR trained over spectral features (including vegetation indices and band reflectance; dataset I), texture features (dataset II), wavelet coefficient features (dataset III), and multiple features (dataset IV, including all the above datasets) were analyzed, respectively. Secondly, the CCDP was calculated from the estimated LCD and estimated LAI, and then the CCD was estimated based on multiple features and the CCDP was compared. The results show that the correlation between CCD and different features is significantly different at every growth stage. The RF model trained over dataset IV yielded the best performance for the estimation of LCD, LAI, and CCD (R2 values were 0.91, 0.97, and 0.97, and RMSE values were 6.59 μg/cm2, 0.35, and 24.85 μg/cm2). The CCD directly estimated from dataset IV is slightly closer to the ground truth CCD than the CCDP (R2 = 0.96, RMSE = 26.85 μg/cm2) calculated from LCD and LAI. The results indicated that the CCD of maize can be accurately estimated from multiple multispectral image features at the whole growth stage, and both CCD estimation strategies can be used to estimate the CCD accurately. This study provides a new reference for accurate CCD evaluation in precision agriculture.

Development of the grass LAI and CCC remote sensing-based models and their transferability using sentinel-2 data in heterogeneous grasslands

ABSTRACT Estimation of biophysical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC) at high spatiotemporal resolution is important for managing natural and heterogeneous environments. However, accurate estimation of biophysical variables particularly over heterogeneous environments remains a challenge. The objective of the study was to develop locally parameterized grass LAI and CCC empirical models using the Sentinel-2 variables combined with the Stepwise multiple linear regression (SMLR) and Random forest (RF) at the Golden Gate Highlands National Park (GGHNP) and Marakele National Park (MNP) in South Africa. Results showed that in MNP, SMLR yielded better LAI estimation with root mean squared error (RMSE) of 0.67 m2.m−2 and mean adjusted error (MAE) of 0.54, explaining 48% of LAI variability, when bands and indices are combined. In contrast, RF gave better CCC estimation i.e. RMSE and MAE of 17.08 µg.cm−2 and 13.18 respectively, explaining about 40% of CCC variability with Sentinel-2 bands only. In GGHNP, the RF models provided the best estimates of both LAI and CCC compared to SMLR models. Furthermore, the CCC and LAI estimation models of GGHNP showed improved model accuracies when 50% and 75% of the MNP field samples were transferred to the GGHNP models. In contrast, the CCC and LAI estimation models of MNP showed a decline in model performance across all scenarios where the GGHNP field samples were transferred to the MNP models. These findings have significant implications for the development of locally parameterized types of models that can provide improved and consistent site-specific accurate estimates of grass biophysical parameters over heterogeneous environments.

Non-destructive monitoring of maize LAI by fusing UAV spectral and textural features.

Leaf area index (LAI) is an essential indicator for crop growth monitoring and yield prediction. Real-time, non-destructive, and accurate monitoring of crop LAI is of great significance for intelligent decision-making on crop fertilization, irrigation, as well as for predicting and warning grain productivity. This study aims to investigate the feasibility of using spectral and texture features from unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning modeling methods to achieve maize LAI estimation. In this study, remote sensing monitoring of maize LAI was carried out based on a UAV high-throughput phenotyping platform using different varieties of maize as the research target. Firstly, the spectral parameters and texture features were extracted from the UAV multispectral images, and the Normalized Difference Texture Index (NDTI), Difference Texture Index (DTI) and Ratio Texture Index (RTI) were constructed by linear calculation of texture features. Then, the correlation between LAI and spectral parameters, texture features and texture indices were analyzed, and the image features with strong correlation were screened out. Finally, combined with machine learning method, LAI estimation models of different types of input variables were constructed, and the effect of image features combination on LAI estimation was evaluated. The results revealed that the vegetation indices based on the red (650 nm), red-edge (705 nm) and NIR (842 nm) bands had high correlation coefficients with LAI. The correlation between the linearly transformed texture features and LAI was significantly improved. Besides, machine learning models combining spectral and texture features have the best performance. Support Vector Machine (SVM) models of vegetation and texture indices are the best in terms of fit, stability and estimation accuracy (R2 = 0.813, RMSE = 0.297, RPD = 2.084). The results of this study were conducive to improving the efficiency of maize variety selection and provide some reference for UAV high-throughput phenotyping technology for fine crop management at the field plot scale. The results give evidence of the breeding efficiency of maize varieties and provide a certain reference for UAV high-throughput phenotypic technology in crop management at the field scale.

A Comparison of Different Remote Sensors for Ramie Leaf Area Index Estimation

Leaf area index (LAI) is an important parameter indicating crop growth. At present, spectral technology has developed into a popular means for LAI monitoring, which can provide accurate estimation results by constructing a model with crop spectral information and a ground LAI value. Spectral range and data type may affect the performance of the model, but few studies have compared the estimation accuracy of crop LAI using different spectral sensors, especially in ramie. In this study, we compared the potential to estimate the ramie LAI of a hyperspectral sensor with a multispectral sensor. A handheld hyperspectral sensor and an airborne multispectral sensor were used to collect spectral data from multiple growth stages, and then four machine learning algorithms were used to construct the LAI estimation model. The results showed that the R2 of the hyperspectral model was 0.702, while the R2 of the multispectral model was 0.816. The cropped hyperspectral data was less sensitive to LAI than the multispectral data with the same spectral band, due to the result of radiation area and data type. The accuracy of the ramie LAI estimation model constructed with all stage data was improved (R2 = 0.828). In conclusion, the airborne multi-spectrometer is more suitable for monitoring ramie LAI in the field.

Retrieving leaf area index of rubber plantation in Hainan Island using empirical and neural network models with Landsat images

The leaf area index (LAI) is an important parameter for describing the growth status and canopy structure of vegetation. The rapid and accurate acquisition of the vegetation or agroforestry LAI has great scientific significance in agroforestry ecological ecosystems research and a very important practical value for guiding agricultural and forestry production. In this study, the typical tropical crops (rubber forest) in Hainan Island were selected as the research area, the empirical and neural network (NN) LAI estimation models of rubber forest were constructed based on satellite remote sensing vegetation indices and the field LAI measurement data, and the spatiotemporal variation was analyzed. The results showed that, compared with normalized difference vegetation index (NDVI), green NDVI (GNDVI), ratio VI (RVI), normalized near-infrared (NNIR), wide dynamic range VI (WDRVI), and normalized difference water index (NDWI), enhanced vegetation index (EVI), soil adjusted vegetation index (SAVI), DVI, renormalized DVI (RDVI), and modified SAVI (MSAVI) have higher correlations with LAI. Among the LAI estimation models of rubber forest based on empirical and artificial NN (ANN) models, the estimation accuracy of ANN achieves the highest value. The linear fitting determination coefficient R2 of the observed and simulated rubber forest LAI was 0.85 (p < 0.001), the root mean square error (RMSE) was 0.15, and the average relative error (RE) was 1.93%. However, there was underestimation in the middle-value area and overestimation in the high- and low-value areas of LAI. Based on remote sensing mapping of the rubber forest LAI, the high LAI values (4.40 to 6.00 m2 m − 2) were mainly distributed in Danzhou and Baisha (west of Hainan Island); the middle LAI values (3.80 to 4.40 m2 m − 2) were mainly located in Chengmai, Tunchang, and Qiongzhong (middle of Hainan Island); and the low LAI values (<3.80 m2 m − 2) were shown primarily on Ding’an, Qionghai, Wanning, Ledong, and Sanya (east and south of Hainan Island). In summary, the remote sensing estimation model for the rubber plantation LAI based on the vegetation index has high accuracy and good values for application.

Estimating maize LAI by exploring deep features of vegetation index map from UAV multispectral images

Visible and near-infrared imaging spectroscopy is an efficient method for non-destructive estimation of crop leaf area index (LAI), in which effective features extraction is a key factor affecting model accuracy. Vegetation index like normalized difference vegetation index (NDVI) is a typical parameter extracted from spectral images for LAI estimation. However, it always faces challenges of values saturation and sensitivity decreasing due to the canopy coverage changes and complex environment influences. Therefore, the study aims to propose a method to explore deep features based on deep learning model (ResNet50) and vegetation index (VI) map, by which contributes to improve the accuracy of maize LAI estimation. In two-years’ experiments of 2020 and 2021, the multi-spectral imaging sensor carried by the unmanned aerial vehicle (UAV) was used to collected remote sensing images of the maize canopy during multi-growth stages under different nitrogen fertilizer treatments, and a total of 792 LAI values were collected. In terms of feature extraction, 10 VIs were extracted based on the spectral images of maize canopy, and comprehensive features (texture and deep features) were extracted based on the VI map. In terms of model construction, partial least squares regression (PLS) models were constructed based on three feature types. The results showed that normalized difference red edge (NDRE) VI was the best indicator for monitoring maize LAI among all the VIs investigated. Meanwhile, LAI multivariate linear model constructed based on texture features extracted from NDRE images had better accuracy. Deep features of NDRE images were further extracted by ResNet50 model. And the maize LAI estimation models based on PLS were constructed with VI, texture features and deep features of two-year data, respectively. The results showed that compared with VI and texture features, the LAI estimation model based on deep features showed optimal accuracy in two-year data ( R 2 = 0.827, greater than 0.781 and 0.780; RMSEP = 0.405, less than 0.455 and 0.455; MAE = 0.320, less than 0.366 and 0.363). In addition, the LAI estimation model based on deep features was successfully evaluated with 2020 and 2021 validation data (2020: R 2 = 0.872, RMSEP = 0.379, MAE = 0.294; 2021: R 2 = 0.733, RMSEP = 0.433, MAE = 0.348). The experimental results demonstrate the effectiveness of the proposed deep feature extraction method based on deep learning model and VI images in LAI estimation, which provides a feasible method for monitoring crop growth information based on UAV platform.

Estimating leaf area index of maize using UAV-based digital imagery and machine learning methods

Leaf area index (LAI) is a fundamental indicator of crop growth status, timely and non-destructive estimation of LAI is of significant importance for precision agriculture. In this study, a multi-rotor UAV platform equipped with CMOS image sensors was used to capture maize canopy information, simultaneously, a total of 264 ground‐measured LAI data were collected during a 2-year field experiment. Linear regression (LR), backpropagation neural network (BPNN), and random forest (RF) algorithms were used to establish LAI estimation models, and their performances were evaluated through 500 repetitions of random sub-sampling, training, and testing. The results showed that RGB-based VIs derived from UAV digital images were strongly related to LAI, and the grain-filling stage (GS) of maize was identified as the optimal period for LAI estimation. The RF model performed best at both whole period and individual growth stages, with the highest R2 (0.71–0.88) and the lowest RMSE (0.12–0.25) on test datasets, followed by the BPNN model and LR models. In addition, a smaller 5–95% interval range of R2 and RMSE was observed in the RF model, which indicated that the RF model has good generalization ability and is able to produce reliable estimation results.