Leaf Area Index Estimation Research Articles

Multivariate regressions coupling colorimetric and textural features derived from UAV-based RGB images can trace spatiotemporal variations of LAI well in a deciduous forest

ABSTRACT The leaf area index (LAI) is a critical structural variable related to a variety of biophysical processes of vegetation, and its efficient and accurate estimation is of importance to understanding ecosystem dynamics. Despite many direct and indirect methods being well developed, in the more recent unmanned aerial vehicle (UAV)-based RGB data have emerged to be a promising alternative source for LAI estimation due to their low-cost, considerable flexibility, and useful applicability. In this study, we have investigated the feasibility of using multivariate regressions combined with different colour indices and textural-based features extracted from the UAV-based RGB images for tracing the dynamical LAI across different phenological stages in a typical temperate deciduous forest. The results demonstrated that the visible atmospherically resistant index (VARI) and red–green ratio index (RGR) were the best when using colour indices (CIs) alone while the difference (D) performed best if using textural information only to estimate the spatiotemporal changes of LAI. More importantly, the multivariate regressions based on the combination of CIs and texture-based features resulted in greater accuracies for LAI estimation, in which the random forest regression (R2 = 0.85, RMSE = 0.56, RPD = 2.52) outperformed other analytical methods. The findings of this study provide a cost-effective yet reliable approach to trace LAI from a commonly and easily obtained data source, with the capability to extend to ever-large scales for quick vegetation monitoring and management.

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.

Retrieving leaf area index from FY-3D MERSI-II data using a sensor-adaptive algorithm

ABSTRACT Leaf area index (LAI), officially listed as one of essential climate variables, quantifies the structure and amount of vegetation and characterizes the interaction between vegetation and climate. The advanced MEdium Resolution Spectral Imager (MERSI-II) onboard FengYun-3D (FY-3D) can provide twice-daily global observations of earth at a spatial resolution of 250 m. Therefore, it has great potential for promoting the improvement of global LAI products and boosting the development of earth system modelling. However, the existing methods are mostly sensor-specific, they can not be directly applied to MERSI-II data for LAI generation. In this paper, we proposed a sensor-adaptive approach for LAI estimation based on MERSI-II observations. This method is composed of an LAI retrieval look-up table based on radiative transfer theory, which was calibrated by a global optimizing algorithm to adapt MERSI-II characteristics, and a backup algorithm training neural networks with MERSI-II Normalized Difference Vegetation Index (NDVI) and Moderate Resolution Imaging Spectroradiometer (MODIS) LAI. We evaluated the MERSI-II LAI retrievals by intercomparison to MODIS products over mainland China and direct validation using ground-based upscaled LAI reference maps. The assessments demonstrate that (1) the derived MERSI-II LAI products agree well with the MODIS benchmarks in both spatial and temporal respects; (2) compared to the MODIS LAI, the MERSI-II LAI retrievals with less gaps show great potential in improving spatiotemporal continuity; (3) validation versus in-situ measurements reveals acceptable accuracy of the MERSI-II LAI products with a R2 of 0.85 and RMSE of 0.82; (4) the proposed parametric optimization strategy could successfully transplant MODIS LAI algorithm to MERSI-II data.

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.

Dynamic emergent leaf area in tidal wetlands: Implications for satellite-derived regional and global blue carbon estimates

The IPCC Special Report on the Ocean and Cryosphere in a Changing Climate highlights the importance of blue carbon in tidal wetlands in combating climate change. In this study, we highlight the uncertainty associated with leaf area index (LAI) estimations in tidal wetlands, specifically salt marshes, a key vegetation parameter for productivity models and Earth System Models (ESM). LAI, derived from satellite reflectance data, is linked to atmospheric carbon exchange and gross primary production (GPP) across vegetative ecosystems. However, estimating salt marsh LAI is challenging because canopy height and density vary across short distances, and tidal flooding alters the atmosphere-exposed leaf area, hereafter called emergent leaf area index (ELAI), at short time scales. Further, in tidal wetlands dominated by species such as Spartina alterniflora, canopy height and density vary across short distances. We present a novel approach for measuring spatiotemporal dynamics in tidal wetland ELAI. We modeled ELAI from vertical LAI profiles and created spatial estimates across tidal periods. We then linked ELAI with eddy covariance carbon (C) fluxes through footprint modeling and revealed correlations between emergent leaf area and C fluxes. Next, we demonstrated that ELAI can be readily estimated across 10-m spatial scales using Sentinel-2 satellite data, even during high tides (R2 = 0.89; NRMSE = 10%). Finally, we showed a common product, MODIS MYD15A2H, underestimated (20%) LAI during dry conditions but overestimated (7–93%) during high flooding. Dynamic ELAI could reduce uncertainties in satellite-derived global GPP products when developing blue carbon budgets for ecosystems threatened by accelerated sea level rise.

Forest, Crop and Grassland Leaf Area Index Estimation Using Remote Sensing: A Review of Current Research Methods, Sensors, Estimation Models and Accomplishments

Leaf area index (LAI) is an important parameter in plant ecophysiology; it can be used to quantify foliage directly and as a measure of the photosynthetic active area and, thus, the area subject to transpiration in vegetation. The aim of this paper was to review work on remote sensing methods of estimating LAI across different forest ecosystems, crops and grasslands in terms of remote sensing platforms, sensors and models. To achieve this aim, scholarly articles with the title or keywords “Leaf Area Index estimation” or “LAI estimation” were searched on Google Scholar and Web of Science with a date range between 2010 and 2020. The study’s results revealed that during the last decade, the use of remote sensing to estimate and map LAI increased for crops and natural forests. However, there is still a need for more research concerning commercial forests and grasslands, as the number of studies remains low. Of the 84 studies related to forests, 60 were related to natural forests and 24 were related to commercial forests. In terms of model types, empirical models were most often used for estimating the LAI of forests, followed by physical models.

Estimation of Leaf Area Index in a Typical Northern Tropical Secondary Monsoon Rainforest by Different Indirect Methods

The leaf area index (LAI) is a crucial indicator for quantifying forest productivity and community ecological processes. Satellite remote sensing can achieve large-scale LAI monitoring, but it needs to be calibrated and validated according to the in situ measurements on the ground. In this study, we attempted to use different indirect methods to measure LAI in a tropical secondary forest. These methods included the LAI-2200 plant canopy analyzer (LAI-2200), Digital Hemispherical Photography (DHP), Tracing Radiation and Architecture of Canopies (TRAC), and Terrestrial Laser Scanning (TLS) (using single-station and multi-station measurements, respectively). Additionally, we tried to correct the measured LAI by obtaining indicators of woody components and clumping effects. The results showed that the LAI of this forest was large, with estimated values of 5.27 ± 1.16, 3.69 ± 0.74, 5.86 ± 1.09, 4.93 ± 1.33, and 3.87 ± 0.89 for LAI-2200, DHP, TRAC, TLS multi-station, and TLS single-station, respectively. There was a significant correlation between the different methods. LAI-2200 was significantly correlated with all other methods (p < 0.01), with the strongest correlation with DHP (r = 0.684). TRAC was significantly correlated with TLS single-station (p < 0.01, r = 0.283). TLS multi-station was significantly correlated with TLS single-station (p < 0.05, r = 0.266). With the multi-station measurement method, TLS could maximize the compensation for measurement bias due to the shadowing effects. In general, the clumping index of this forest was 0.94 ± 0.05, the woody-to-total area ratio was 3.23 ± 2.22%, and the total correction coefficient was 1.03 ± 0.07. After correction, the LAI estimates for all methods were slightly higher than before, but there was no significant difference among them. Based on the performance assessment of existing ground-based methods, we hope to enhance the inter-calibration between methods to improve their estimation accuracy under complex forest conditions and advance the validation of remote sensing inversion of the LAI. Moreover, this study also provided a practical reference to promote the application of LiDAR technology in tropical forests.

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.

Dynamic simulation of leaf area index for the soybean canopy based on 3D reconstruction

Leaf area index (LAI) of the soybean canopy was an important indicator to reflect the growth and development of the soybean plant. However, the traditional LAI measurement in the field was expensive, time-consuming, and challenging to achieve high accuracy. Thus, in this study, a calculation method of LAI for the soybean canopy based on 3D reconstruction was proposed, and the dynamic simulation model of canopy LAI was established. First, northeast soybean varieties of Kangxianchong8 and Dongnong252 were taken as the research objects, and a multi-source image synchronous acquisition platform for soybean canopy based on Kinect 2.0 was constructed to obtain the canopy image data from the V3 to R7 growth periods. Second, the 3D structure of the soybean canopy was reconstructed by conditional filtering and statistical filtering. Third, a soybean LAI estimation method was established by canopy analysis. The determination coefficient R2 between the calculated value and the standard value of LAI was greater than 0.99. Finally, a dynamic simulation model of soybean LAI was established based on the Richards model and the genetic parameters of varieties. The results showed that the accuracy of the dynamic simulation model reached above 0.99, which realized the critical technology of rapid detection and dynamic simulation of LAI for the soybean canopy, and provided quantitative dynamic prediction and technical support for scientific regulation of ecology and morphology for the soybean canopy.

Assessing the Prospects of Remote Sensing Maize Leaf Area Index Using UAV-Derived Multi-Spectral Data in Smallholder Farms across the Growing Season

Maize (Zea Mays) is one of the most valuable food crops in sub-Saharan Africa and is a critical component of local, national and regional economies. Whereas over 50% of maize production in the region is produced by smallholder farmers, spatially explicit information on smallholder farm maize production, which is necessary for optimizing productivity, remains scarce due to a lack of appropriate technologies. Maize leaf area index (LAI) is closely related to and influences its canopy physiological processes, which closely relate to its productivity. Hence, understanding maize LAI is critical in assessing maize crop productivity. Unmanned Aerial Vehicle (UAV) imagery in concert with vegetation indices (VIs) obtained at high spatial resolution provides appropriate technologies for determining maize LAI at a farm scale. Five DJI Matrice 300 UAV images were acquired during the maize growing season, and 57 vegetation indices (VIs) were generated from the derived images. Maize LAI samples were collected across the growing season, a Random Forest (RF) regression ensemble based on UAV spectral data and the collected maize LAI samples was used to estimate maize LAI. The results showed that the optimal stage for estimating maize LAI using UAV-derived VIs in concert with the RF ensemble was during the vegetative stage (V8–V10) with an RMSE of 0.15 and an R2 of 0.91 (RRMSE = 8%). The findings also showed that UAV-derived traditional, red edge-based and new VIs could reliably predict maize LAI across the growing season with an R2 of 0.89–0.93, an RMSE of 0.15–0.65 m2/m2 and an RRMSE of 8.13–19.61%. The blue, red edge and NIR sections of the electromagnetic spectrum were critical in predicting maize LAI. Furthermore, combining traditional, red edge-based and new VIs was useful in attaining high LAI estimation accuracies. These results are a step towards achieving robust, efficient and spatially explicit monitoring frameworks for sub-Saharan African smallholder farm productivity.

Optimization of Lai Estimation Method Based on Smartphones with Fisheye Lens

Leaf area index (LAI) is an important biological factor reflecting vegetation growth and forest ecosystem. LAI can be used to obtain plant health status, carbon cycle, and surrounding ecological environment effectively. In this study, the smartphone was equipped with a fisheye lens, and the optimization method was used to estimate LAI, which was compared with digital hemispherical photography (DHP) to investigate the possibility of the new method for LAI estimation. The hemispherical image was divided into blocks, and the optimized Otsu method was used for algorithm segmentation, which can effectively distinguish vegetation from the sky. Concurrently, when the gap fraction inversion LAI was performed, the linear inversion algorithm was improved based on single-angle inversion, and the LAI was obtained by inversion through the linear fitting of the mul-tiangle gap fraction. The experimental sample was located in Olympic National Forest Park in Beijing. Three coniferous mixed forests and three broadleaved forests were selected from the experimental sample. LAI measurements from smartphones were compared with those from DHP. In the samples for mixed coniferous forests, the values for coefficients of determination R^2 were 0.835, 0.802, and 0.809, and root mean square errors (REMS) were 0.137, 0.120, and 0.147. For the broadleaf forest samples, the values for R² were 0.629, 0.679, and 0.758, and REMS were 0.144, 0.135, and 0.137. The R^2 and RMES for the overall data was 0.810 and 0.134, respectively, and a good agreement between the LAI measurements from the proposed method and those from the DHP supports an accurate estimation. The results show that the use of a fisheye lens on a smartphone can effectively and accurately obtain tree canopy LAI. This provides a fast and effective new method to measure LAI of forest vegetation near the ground, which is of great significance for studying the interaction between plant growth status, ecological environment, and phenological changes.

Leaf area index estimation of pergola-trained vineyards in arid regions using classical and deep learning methods based on UAV-based RGB images

Timely and accurate mapping of leaf area index (LAI) in vineyards plays an important role for management choices in precision agricultural practices. However, only a little work has been done to extract the LAI of pergola-trained vineyards using higher spatial resolution remote sensing data. The main objective of this study was to evaluate the ability of unmanned aerial vehicle (UAV) imageries to estimate the LAI of pergola-trained vineyards using shallow and deep machine learning (ML) methods. Field trials were conducted in different growth seasons in 2021 by collecting 465 LAI samples. Firstly, this study trained five classical shallow ML models and an ensemble learning model by using different spectral and textural indices calculated from UAV imageries, and the most correlated or useful features for LAI estimations in different growth stages were differentiated. Then, due to the classical ML approaches need the arduous computation of multiple indices and feature selection procedures, another ResNet-based convolutional neural network (CNN) model was constructed which can be directly fed by cropped images. Furthermore, this study introduced a new image data augmentation method which is applicable to regression problems. Results indicated that the textural indices performed better than spectral indices, while the combination of them can improve estimation results, and the ensemble learning method showed the best among classical ML models. By choosing the optimal input image size, the CNN model we constructed estimated the LAI most effectively without extracting and selecting the features manually. The proposed image data augmentation method can generate new training images with new labels by mosaicking the original ones, and the CNN model showed improved performance after using this method compared to those using only the original images, or augmented by rotation and flipping methods. This data augmentation method can be applied to other regression models to extract growth parameters of crops using remote sensing data, and we conclude that the UAV imagery and deep learning methods are promising in LAI estimations of pergola-trained vineyards.

Inter-Seasonal Estimation of Grass Water Content Indicators Using Multisource Remotely Sensed Data Metrics and the Cloud-Computing Google Earth Engine Platform

Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m−2 and R2 of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m−2 and R2 of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R2 = 0.86) when compared to the RMSE of 0.03 mm and R2 of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m−2 and R2 of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m−2 and R2 = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m−2 and R2 = 0.91 as compared to the wet season (RMSE = 10.75 g/m−2 and R2 = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients.

LAI estimation through remotely sensed NDVI following hail defoliation in maize (Zea mays L.) using Sentinel-2 and UAV imagery

Extreme events such as hailstorms are a cause for concern in agriculture, leading to both economic and food supply losses. Traditional damage estimation techniques have recently been called into question since damages have rarely been quantified at the large-field scale. Damage-estimation methods used by field inspectors are complex and sometimes subjective and hardly account for damage spatial variability. In this work, a normalized difference vegetation index (NDVI)-based parametric method was applied using both unmanned aerial vehicles (UAV) and Sentinel-2 sensors to estimate the leaf area index (LAI) of maize (Zea mays L.) resulting from simulated hail damage. These methods were then compared to the LAI values generated from the Sentinel-2 Biophysical Processor. A two-year experiment (2020–2021) was conducted during the maize cropping season, with hail events simulated during a range of maize developmental stages (the 8th-leaf, flowering, milky and dough stages) using a 0–40% defoliation gradient of damage intensities performed with the aid of specifically designed prototype machines. The results showed that both sensors were able to accurately estimate LAI in a nonstandard damaged canopy while requiring only the calibration of the extinction coefficient k(vartheta ) in the case of parametric estimations. In this case, the calibration was performed using 2020 data, providing k(vartheta ) values of 0.59 for Sentinel-2 and 0.37 for the UAV sensor. The validation was performed on 2021 data, and showed that the UAV sensor had the best accuracy (R2 of 0.86, root-mean-square error (RMSE) of 0.71). The k(vartheta ) value proved to be sensor-specific, accounting for the NDVI retrieval differences likely caused by the different spatial operational scales of the two sensors. NDVI proved effective in parametrically estimating maize LAI under damaged canopy conditions at different defoliation degrees. The parametric method matched the Sentinel-2 biophysical process-generated LAI well, leading to less underestimations and more accurate LAI retrieval.

Utilization of UAV Remote Sensing in Plant-Based Field Experiments: A Case Study of the Evaluation of LAI in a Small-Scale Sweetcorn Experiment

In crop production, which is largely dependent on environmental conditions, various attempts at environmental or social changes have been highlighted, and many field experiments are needed for them. However, since field experiments in agricultural production are constrained by high labor and time consumption, alternative methods to respond to these constraints are required. In this study, to establish a new method for application to field experiments, we proposed the evaluation of the leaf area index (LAI) of all individual plants in an experimental sweetcorn field using an unmanned aerial vehicle (UAV). Small-scale field experiments were conducted over two years. In the first year, the nitrogen fertilizer level was changed, and the plant density and additional nitrogen fertilizer application time were changed in the next year. Three vegetation indices (VIs), namely, the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), and simple ratio (SR), were validated to quantify the LAI estimation using a UAV for individual plants. For the evaluation of the individual plants, we used a plant-based method, which created all of the plant buffers based on the points of existing plants and the plant distance. To confirm the impact of the method, we additionally demonstrated the relationship between the LAI and yield, the results of statical analyses, and the difference of the center and the border of the field. Among the three VIs, index SR was found the most promising in the estimation of the LAI of the individual sweetcorn plants, providing the strongest correlation of yield with SR. Because a lot of data were obtained using the plant-based method, the statical differences in the LAI and yield were more easily detected for the plant density and fertilizer treatments. Furthermore, interesting differences between the center and the border of the field were found. These results indicate the availability and impact of plant-based evaluations using UAVs in near future field experiments.

Estimation of crop water stress index and leaf area index based on remote sensing data

Abstract Estimation of crop water stress index (CWSI) and leaf area index (LAI) over large-irrigation schemes requires the use of cutting-edge technologies. Combinations of remote sensing techniques with ground-truth data have become available for use at the catchment level. These approaches allow us to estimate actual evapotranspiration and have the capability of monitoring crop water status and saving irrigation water in water-scarce regions. This study was conducted in the eastern Mediterranean Region of Turkiye. Fully distributed CWSI maps were generated and we assessed the relationship between CWSI and LAI for some specific crops in the winter season of 2021. Landsat 7 and 8 data were used and meteorological data were acquired from two stations in the study area. ‘Mapping Evapotranspiration at high Resolution with Internalized Calibration’ methodology was applied to estimate the energy balance components. CWSI maps displayed spatiotemporal changes in tandem with crop-type variations. Consequently, results presented a high correlation (r = 0.95 and r = 0.99 for wheat and lettuce, respectively) between CWSI and LAI, a moderate correlation (r = 0.44) for potatoes in the winter season. Thus, by utilizing remotely sensed data, the CWSI values would be directly estimated without requiring any in situ measurements of the canopy and air temperature over-irrigation scheme.

Assessing the consistency of crop leaf area index derived from seasonal Sentinel-2 and Landsat 8 imagery over Manitoba, Canada

To take full advantage of cloud-free optical remote sensing data for crop leaf area index (LAI) retrieval throughout the growing season, integration of multi-sensor satellite data is increasingly resorted. However, the consistencies of LAI products derived from different satellites using different retrieval approaches should be assessed. This study aimed at understanding the spatiotemporal consistency of crop LAI derived from S2/MSI (Multi Spectral Instrument, Sentinel-2) and L8/OLI (Operational Land Imager, Landsat 8) data using two retrieving approaches, a field-data-driven (FDD) approach driven by field measured LAI and a hybrid approach based on simulations of the PROSAIL radiative transfer model. Results from the two approaches were assessed using field measured LAI for six types of crops in 2016 over Manitoba, one of the major agricultural provinces in the Canadian Prairies. LAI estimates from S2/MSI data obtained unbiased root-mean-square-error (ubRMSE) of 0.39 and 0.51 cm2 cm−2 for the FDD and hybrid approaches respectively. For the L8/OLI, ubRMSE of LAI estimates were 0.82 and 0.92 cm2cm−2 for the FDD and hybrid approaches, respectively. To evaluate the temporal consistency of crop LAI derived from the two satellites, daily crop LAI was reconstructed using the Parametric Double-Hyperbolic Tangent model (PDHT) with the aid of Bayesian statistical approach. Time series crop LAI of the hybrid approach had lower variability of estimated parameters in the PDHT and reconstructed daily LAI compared with that of the FDD approach. Based on the Spatial Efficiency Metric (SPAEF) that is a spatial performance metric for assessing spatial pattern similarity, the hybrid approach obtained relatively strong spatial consistency of LAI derived from the two satellites compared with the FDD approach. The study revealed that hybrid approach can achieve good spatiotemporal consistencies in seasonal LAI estimates from the two satellites combined when large numbers of field measurements are not available.

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.

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.