Higher Leaf Area Index Values Research Articles

Effects of tree density variations on outdoor heritage conservation: Numerical study of an ancient brick city wall with four orientations

Trees possess the potential to safeguard outdoor cultural heritage through the regulation of microclimates in their surroundings. The Leaf Area Index (LAI), a collective measure of the foliar portion within vegetation structure, reflects the regulatory ability of trees. In this study, numerical simulations were performed to compare the hygrothermal conditions of the Nanjing City Wall facing four orientations under the influence of evergreen trees with four LAI values, while also assessing the potential risks of salt weathering and algal growth. The results were as follows: 1) under the protective canopy of trees with identical LAI, the west and east walls experienced the highest temperature fluctuations, followed by the south and north sides. Wall surface temperature fluctuation was negatively correlated with LAI; 2) except for LAI = 1.5, the north wall had the least water content fluctuation under equivalent LAI levels. Water content fluctuation generally exhibited a decreasing trend with rising LAI; 3) at the same LAI level, the south wall presented the highest risk of salt weathering (including efflorescence and subflorescence), whereas the north wall faced the lowest risk. The risk of salt weathering decreased with an increase in LAI; 4) When shielded by trees with identical LAI, the north wall exhibited the greatest risk of algal growth, while the east wall faced least the risk. Higher LAI values were associated with an increased risk of algal growth on the wall surface. These results could provide a theoretical basis for large outdoor immovable heritage conservation and plant management around heritage sites.

Mapping Crop Leaf Area Index and Canopy Chlorophyll Content Using UAV Multispectral Imagery: Impacts of Illuminations and Distribution of Input Variables

Leaf area index (LAI) and canopy chlorophyll content (CCC) are important indicators that describe the growth status and nitrogen deficiencies of crops. Several studies have been performed to estimate LAI and CCC using multispectral cameras onboard an unmanned airborne vehicle (UAV) system. However, the impacts of illuminations during UAV flight and problems of how to invert still need more investigation. UAV flights with a multispectral camera were performed under clear (diffuse ratio 0) and cloudy illumination conditions (diffuse ratio 1) over rapeseed, wheat and sunflower (only clear) fields. One-dimension radiative transfer model PROSAIL was run twice to generate a clear-sky model and a cloudy-sky model, respectively. The LAI and CCC of flights under a clear sky were inverted from the clear-sky model, and the flights under cloudy conditions were inverted from both clear-sky and cloudy-sky models to compare the results. Moreover, three Look-Up-Tables (LUT) were built with same input variables but different distributions of LAI. Results showed that LAI from uniform dense LUT had better correspondence with ground measurements for all crops (R2 = 0.51~0.69). The illumination condition had little impact on small to medium LAI (LAI < 5) and CCC. However, the inversion of imageries during cloudy sky conditions from the clear-sky model led to an overestimation of high LAI values.

Is stunting in children under five associated with the state of vegetation in the Democratic Republic of the Congo? Secondary analysis of Demographic Health Survey data and the satellite-derived leaf area index

BackgroundThe prevalence of stunting in the Democratic Republic of the Congo (DRC) is one of the highest globally. However, only a few studies have attempted to measure the association between stunting and vegetation, which is an important food source. The leaf area index (LAI) is an excellent measure for the vegetation state. ObjectiveThis paper intended to measure the association between the LAI and stunting among children under five years of age in the DRC. Its aim was to better understand the boundary conditions of stunting and explore potential links to climate and environmental change. MethodsThis paper adopts a secondary data analysis approach. We used data on 5241 children from the DRC Demographic Health Survey (DHS) 2013–2014, which was collected from a nationally representative cross-sectional survey. We used the satellite-derived LAI as a measure for the state of vegetation and created a 10-km buffer to extract each DHS cluster centroid’s corresponding mean leaf-area value. We used a generalised mixed-effect logistic regression to measure the association between LAI and stunting, adjusting the model for mother’s education, occupation and birth interval, as well as child’s age and national wealth quintile. A height-for-age Z-score (HAZ) was calculated and classified according to WHO guidelines. ResultsChildren in communities surrounded by high LAI values have lower odds of being stunted (OR [odds ratio] = 0.63; 95% CI [confidence interval] = 0.47–0.86) than those exposed to low LAI values. The association still holds when the exposure is analysed as a continuous variable (OR = 0.84; 95% CI = 0.74–0.95).When stratified in rural and urban areas, a significant association was only observed in rural areas (OR = 0.6; 95% CI = 0.39–0.81), but not in urban areas (OR = 0.9; 95% CI = 0.5−0.5). Furthermore, the study showed that these associations were robust to LAI buffer variations under 25 km. ConclusionsGood vegetation conditions have a protective effect against stunting in children under five years of age. Further advanced study designs are needed to confirm these findings.

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.

Effect of Irrigation System and Irrigation Interval on Seed Yield and Water Productivity of two Soybeans (Glycine max L. Merr.) Cultivars under Drip and Furrow Irrigation in Eastern Sudan

Water stress is one of the limiting factors of soybean yield in eastern Sudan. The objective of this study was to obtain basic information on soybean cultivation using two irrigation systems in eastern Sudan. This study examined the leaf area index, yield and irrigation water productivity (IWP) response using two soybean cultivars: Sudan1 (V1) and Sudan2 (V2) under drip (DS1) and furrow (DS2) irrigation systems. The experiment was carried out in the summer seasons of 2016 and 2017. Three irrigation intervals, every 4, 8 and 12 days designated as W1, W2 and W3, respectively, were used. A randomized complete block design was used as strip-split plot arrangement with three replications. The data were statistically analyzed using STATISTICS 10; mean separation was computed using LSD. The results showed that cultivar Sudan1 gave higher leaf area index values under four days irrigation interval particularly with furrow irrigation system. Four days irrigation interval treatment increased seeds yield by more than 2.5 % relative to 8 days irrigation interval and 34 % relative to 12 days irrigation interval, as average for both seasons. Drip irrigation system significantly (P ≤ 0.05) increased IWP compared to furrow irrigation system. Highest water productivity was obtained under four days irrigation interval. Eight days irrigation interval significantly (P ≤ 0.05) inhibited the positive effect of irrigation system on seed yield and IWP. The study concludes that IWP for drip irrigation, in both seasons, was 20.5 % higher than furrow irrigation. However, mean seed yield for furrow system, in both seasons, was 25.6% higher than drip irrigation system.

Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR

Infestations of Tomicus spp. have caused the deaths of millions of Yunnan pine forests in Southwest China; consequently, accurate monitoring methods are required to assess the damage caused by these pest insects at an early stage. Considering the limited sensitivity of optical reflectance on the early stage of beetle stress, the potential of thermal infrared (TIR) can be exploited for monitoring forest health on the basis of the change of canopy surface temperature (CST). However, few studies have investigated the impact of the leaf area index (LAI) on the accuracy of TIR data-based SDR assessments. Therefore, the current study used unmanned airborne vehicle (UAV)-based TIR and light detection and ranging (LiDAR) data to assess the capacity of determining the potential for using TIR data for determining SDR under different LAI conditions. The feasibility of using TIR for monitoring SDRs at the tree level and plot scales were analyzed using the relationship between SDR and canopy temperature. Results revealed that: (1) prediction accuracy of SDR from CST is promising at high LAI values and decreases quickly with LAI, and is higher at the single tree scale (R2 = 0.7890) than at the plot scale (R2 = 0.5532); (2) at either single tree or plot scale, a significant negative correlation can be found between CST and LAI (−0.9121 at tree scale and −0.5902 at plot scale); (3) LAI affects the transmission paths of sunlight and sensor, which mainly disturbs the relationship between CST and SDR. This article evaluated the high possibility of using TIR data to monitor SDRs at both tree and plot levels and assessed the negative impact of a low LAI (&lt;1) on the relationship between temperature and SDR. Accordingly, when measuring forest health using TIR data, additional data sources are required to eliminate the negative impact of low LAIs and to improve the monitoring accuracy.

Relationship between Canopy Structure and Community Structure of the Understory Trees in a Beech Forest in Japan

Understory trees occupy a spatially heterogeneous light environment owing to light interception by patchily distributed canopy leaves. We examined the spatial distribution of canopy leaves and the spatial structure of the understory tree community (height &lt; 5 m) and their relationships in a beech forest in Nagano, Japan. We measured the canopy leaf area index (LAI) at 10 m intervals (n = 81) in a permanent research plot (1 ha). We established a circular subplot centered on each LAI measurement point, and determined the species composition and the aboveground net primary production of wood (ANPPW) of the understory tree community by using tree size data from an open database in the Monitoring Sites 1000 project. There was a significant negative correlation between canopy LAI and the ANPPW of understory trees and a significant positive correlation between the ANPPW of understory and understory tree density. The dominant species of understory trees differed between subplots with high and low LAI values. Our results suggest that niche differentiation allows trees in the understory community to make use of various light conditions, thereby enhancing the primary productivity of the entire community.

Estimating LAI From Winter Wheat Using UAV Data and CNNs

With the advent of high-resolution unmanned aerial vehicle (UAV) data and advancing methods of deep learning, new opportunities have emerged in remote sensing to assess biophysical plant parameters. In this study, we investigated the potential of UAV-borne RGB data and convolutional neural networks (CNNs) to estimate the leaf area index (LAI) of winter wheat during two cropping seasons. In this context, spectral RGB and geometric plant information based on a normalized surface model (nDSM) were used as input variables. The results of the study demonstrated the suitability of optical UAV data and CNNs for LAI estimation of winter wheat at different growth stages and under various lightning conditions. The combination of RGB data and plant structures provided the best overall prediction accuracy ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r^{2} = 0.83$ </tex-math></inline-formula> ) compared to the models with only one input source (RGB: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r^{2} = 0.58$ </tex-math></inline-formula> , nDSM: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r^{2} = 0.75$ </tex-math></inline-formula> ). Especially the estimation of low and high LAI values was improved using the complementary image information. Moreover, the results showed that the CNN models outperformed two classical machine learning (ML) approaches in terms of accuracy.

Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops

We proposed a direct approach to validate hectometric and kilometric resolution leaf area index (LAI) products that involved the scaling up of field-measured LAI via the validation and recalibration of the decametric Sentinel-2 LAI product. We applied it over a test study area of maize crops in northern China using continuous field measurements of LAINet along the year 2019. Sentinel-2 LAI showed an overall accuracy of 0.67 in terms of Root Mean Square Error (RMSE) and it was used, after recalibration, as a benchmark to validate six coarse resolution LAI products: MODIS, Copernicus Global Land Service 1 km Version 2 (called GEOV2) and 300 m (GEOV3), Satellite Application Facility EUMETSAT Polar System (SAF EPS) 1.1 km, Global LAnd Surface Satellite (GLASS) 500 m and Copernicus Climate Change Service (C3S) 1 km V2. GEOV2, GEOV3 and MODIS showed a good agreement with reference LAI in terms of magnitude (RMSE ≤ 0.29) and phenology. SAF EPS (RMSE = 0.68) and C3S V2 (RMSE = 0.41), on the opposite, systematically underestimated high LAI values and showed systematic differences for phenological metrics: a delay of 6 days (d), 20 d and 24 d for the start, peak and the end of growing season, respectively, for SAF EPS and an advance of −4 d, −6 d and −6 d for C3S.

Flow resistance of floodplain vegetation mixtures for modelling river flows

River flows are greatly influenced by floodplain vegetation with implications on hydrological and hydraulic conditions from cross-sectional to river reach scales. Flow models need to reliably reflect changes in the riverine environment, such as vegetation growth associated with altered flow regimes, increased sediment loads and eutrophication. Leaf area index (LAI) based approaches are increasingly used as tools to predict the flow resistance caused by natural vegetation. However, current LAI-based modelling involves uncertainty at low and high vegetation densities and flow velocities due to a lack of research and validation at the outer ranges. The aim of this paper is to investigate the flow resistance for a mixture of flexible floodplain vegetation consisting of woody plants and understory grasses of low to high densities (LAI = 1–5) over a wide range of mean flow velocities (0.05–1.2 m/s) at low relative submergences of 1–2. A novel flume setup was designed by using high-accuracy pressure sensors to measure flow resistance and force sensors to measure plant drag forces. Flow resistance decreased by 35–90% when the submergence H/hv increased from 1 to 2, which is a highly relevant range for floodplain flows. The results provided new evidence that LAI-based modelling of vegetative friction factors can be reliably extended from low to high LAI values for non-submerged vegetation. However, adjustments to existing LAI-based approaches are required for water stages higher than the vegetation height. Furthermore, the friction by the understory grasses cannot be neglected as often assumed in literature especially for cases of low plant densities and low relative submergences.

Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs.

Fusion of Sentinel-2 and PlanetScope time-series data into daily 3 m surface reflectance and wheat LAI monitoring

The dynamics of Leaf Area Index (LAI) from space is key to identify crop types and their phenology over large areas, and to characterize spatial variations within growers’ fields. However, for years remote-sensing applications have been constrained by a trade-off between the spatial and temporal resolutions. This study resolves this limitation. Over the past decade, the number of companies and organizations developing CubeSat constellations has increased. These new satellites make it possible to acquire large image collections at high spatial and temporal resolutions at a relatively low cost. However, the images obtained from CubeSat constellations frequently suffer from inconsistency in the data calibration between the different satellites within the constellation. To overcome these inconsistencies, a new method to fuse a time series of images sourced from two different satellite constellations is proposed, combining the advantages of both (i.e., the temporal, spatial and spectral resolution). This new technique was applied to fuse PlanetScope images with Sentinel-2 images, to create spectrally-consistent daily images of wheat LAI at a 3 m resolution. The daily 3 m LAI estimations were compared with 57 in-situ wheat LAI measurements taken in Australia and Israel. This approach was demonstrated to successfully estimate Green LAI (LAI before the major on-set of leaf senescence) with an R2 of 0.94 and 86% relative accuracy (RMSE of 1.37) throughout the growing season without using any ground calibration. However, both the Sentinel-2 based estimates and the fused Green LAI were underestimated at high LAI values (LAI > 3). To account for this, regression models were developed, improving the relative accuracy of the Green LAI estimations by up to a further 47% (RMSE of 0.35–0.63) in comparison with field measured LAI. The new time series fusion method is an effective tool for continuous daily monitoring of crops at high-resolution over large scales, which opens up a range of new precision agriculture applications. These high spatio-temporal resolution time-series are valuable for monitoring crop growth and health, and can improve the effectiveness of farming practices and enhance yield forecasts at the field and sub-field scales.

Mapping leaf area index in a mixed temperate forest using Fenix airborne hyperspectral data and Gaussian processes regression

Machine learning algorithms, in particular, kernel-based machine learning methods such as Gaussian processes regression (GPR) have shown to be promising alternatives to traditional empirical methods for retrieving vegetation parameters from remotely sensed data. However, the performance of GPR in predicting forest biophysical parameters has hardly been examined using full-spectrum airborne hyperspectral data. The main objective of this study was to evaluate the potential of GPR to estimate forest leaf area index (LAI) using airborne hyperspectral data. To achieve this, field measurements of LAI were collected in the Bavarian Forest National Park (BFNP), Germany, concurrent with the acquisition of the Fenix airborne hyperspectral images (400−2500 nm) in July 2017. The performance of GPR was further compared with three commonly used empirical methods (i.e., narrowband vegetation indices (VIs), partial least square regression (PLSR), and artificial neural network (ANN)). The cross-validated coefficient of determination (Rcv2) and root mean square error (RMSEcv) between the retrieved and field-measured LAI were used to examine the accuracy of the respective methods. Our results showed that using the entire spectral data (400−2500 nm), GPR yielded the most accurate LAI estimation (Rcv2 = 0.67, RMSEcv = 0.53 m2 m−2) compared to the best performing narrowband VIs SAVI2 (Rcv2 = 0.54, RMSEcv = 0.63 m2 m−2), PLSR (Rcv2 = 0.74, RMSEcv = 0.73 m2 m−2) and ANN (Rcv2 = 0.68, RMSEcv = 0.54 m2 m−2). Consequently, when a spectral subset obtained from the analysis of VIs was used as model input, the predictive accuracies were generally improved (GPR RMSEcv = 0.52 m2 m−2; ANN RMSEcv = 0.55 m2 m−2; PLSR RMSEcv = 0.69 m2 m−2), indicating that extracting the most useful information from vast hyperspectral bands is crucial for improving model performance. In general, there was an agreement between measured and estimated LAI using different approaches (p > 0.05). The generated LAI map for BFNP using GPR and the spectral subset endorsed the LAI spatial distribution across the dominant forest classes (e.g., deciduous stands were generally associated with higher LAI values). The accompanying LAI uncertainty map generated by GPR shows that higher uncertainties were observed mainly in the regions with low LAI values (low vegetation cover) and forest areas which were not well represented in the collected sample plots. This study demonstrated the potential of GPR for estimating LAI in forest stands using airborne hyperspectral data. Owing to its capability to generate accurate predictions and associated uncertainty estimates, GPR is evaluated as a promising candidate for operational retrieval applications of vegetation traits.

Validation and inter-comparison of the FY-3B/MERSI LAI product with GLOBMAP and MYD15A2H

ABSTRACT The leaf area index (LAI) describes the structure of vegetation and is a key variable for Earth system process modelling and eco-hydrological models at regional and global scales. The Medium-Resolution Spectral Imager (MERSI) onboard China’s new generation of polar-orbiting meteorological satellite series FengYun-3 (FY-3) can provide continuous and global observations of the land surface. Therefore, it could be a potential data source for global LAI retrieval. In this study, a LAI product was generated from FY-3B/MERSI data using the GLOBCARBON LAI algorithm. Cross-calibration of the FY-3B/MERSI spectral response function with Land Remote-Sensing Satellite (Landsat) Thematic Mapper (TM), allowed to correct the influence of the spectral response function difference on the inversion results. Field measurements of LAI and scale-converted LAI reference images provided by the ImagineS project were used to validate and inter-compare the FY-3B/MERSI LAI product with two widely used medium-resolution LAI products, GLOBMAP LAI product and Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product (MYD15A2 H). The results demonstrate that FY-3B/MERSI LAI has the lowest uncertainty of the three products. The low uncertainty of FY-3B/MERSI LAI in shrub-grass mixed areas (root mean square error, RMSE = 0.07), in part, by the generally underestimated LAI value. For deciduous broadleaf forest, of the three products tested, FY-3B/MERSI LAI is closest to the LAI obtained from the reference image (coefficient of determination, R 2 = 0.70) and yields the lowest uncertainty (RMSE = 0.81). GLOBMAP (R 2 = 0.58), which uses the same algorithm, and surface cover data as FY-3B/MERSI LAI, significantly overestimates the LAI. This overestimation may partly due to the use of a relatively lower clumping index. MYD15A2H shows a relatively weak correlation with the reference data (R 2 = 0.25) and a higher uncertainty (RMSE = 1.45). For mature crops, all three LAI products display systematic underestimation of LAI. FY-3B/MERSI LAI yields the greatest underestimation (about 50%), followed by GLOBMAP (about 35%) and MYD15A2H (about 15%). Our inter-comparison of the three LAI products demonstrates that all have higher correlation for low LAI values. FY-3B/MERSI shows similar capabilities and quality to those of the MODIS sensor with respect to the top of atmosphere observations. However, different atmospheric correction processes may cause large changes in the input Simple Ratio (SR), which in turn will cause bias when retrieving high LAI values. In addition, the temporal and spatial variation of clumping index was insufficiently considered in FY-3B/MERSI LAI product, further improvement is needed by using timely updated and high spatial resolution clumping index data.

Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape

Leaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions, Sentinel 2 (ESA) and Landsat 8 (NASA), provides this opportunity. In this study, we evaluated the leaf area index generated from three methods, namely, existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the SNAP biophysical model, and the THEIA L2A surface reflectance products from Sentinel-2. The intercomparison was conducted over the agricultural scheme in Bekaa (Lebanon) using a large set of in-field LAIs and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. The major studied crops include herbs (e.g., cannabis: Cannabis sativa, mint: Mentha, and others), potato (Solanum tuberosum), and vegetables (e.g., bean: Phaseolus vulgaris, cabbage: Brassica oleracea, carrot: Daucus carota subsp. sativus, and others). Additionally, crop-specific height and above-ground biomass relationships with LAIs were investigated. Results show that of the empirical VI relationships tested, the EVI2-based HLS models statistically performed the best, specifically, the LAI models originally developed for wheat (RMSE:1.27), maize (RMSE:1.34), and row crops (RMSE:1.38). LAI derived through European Space Agency’s (ESA) Sentinel Application Platform (SNAP) biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm (from SNAP biophysical processor) produced an acceptable accuracy level compared to HLS-EVI2 models (RMSE of 1.38) but with significant underestimation at high LAI values. Our findings show that the LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other vegetation indices when all crops were combined, and therefore it can be identified as an index that is best suited for a unified algorithm for crops in semi-arid irrigated regions with heterogeneous landscapes. Furthermore, our analysis shows that the observed height-LAI relationship is crop-specific and essentially linear with an R2 value of 0.82 for potato, 0.79 for wheat, and 0.50 for both cannabis and tobacco. The ability of the linear regression to estimate the fresh and dry above-ground biomass of potato from both observed height and LAI was reasonable, yielding R2: ~0.60.

The Impact of Non-Photosynthetic Vegetation on LAI Estimation by NDVI in Mixed Grassland

Leaf area index (LAI) is widely used for algorithms and modelling in the field of ecology and land surface processes. At a global scale, normalized difference vegetation index (NDVI) products generated by different remote sensing satellites, have provided more than 40 years of time series data for LAI estimation. NDVI saturation issues are reported in agriculture and forest ecosystems at high LAI values, creating a challenge when using NDVI to estimate LAI. However, NDVI saturation is not reported on LAI estimation in grasslands. Previous research implies that non-photosynthetic vegetation (NPV) reduces the accuracy of LAI estimation from NDVI and other vegetation indices. A question arises: is the absence of NDVI saturation in grasslands a result of low LAI value, or is it caused by NPV? This study aims to explore whether there is an NDVI saturation issue in mixed grassland, and how NPV may influence LAI estimation by NDVI. In addition, in-situ measured plant area index (PAI) by sensors that detect light interception through the vegetation canopy (e.g., Li-cor LAI-2000), the most widely used field LAI collection method, might create bias in LAI estimation or validation using NDVI. Thus, this study also aims to quantify the contribution of green vegetation (GV) and NPV on in-situ measured PAI. The results indicate that NDVI saturation (using the portion of NDVI only contributed by GV) exists in grassland at high LAI (LAI threshold is much lower than that reported for other ecosystems in the literature), and that the presence of NPV can override the saturation effects of NDVI used to estimate green LAI. The results also show that GV and NPV in mixed grassland explain, respectively, the 60.33% and 39.67% variation of in-situ measured PAI by LAI-2000.

Mapping Productivity and Essential Biophysical Parameters of Cultivated Tropical Grasslands from Sentinel-2 Imagery

Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextricably linked to dry matter increase and plant support capacity. In recent years, high spectral and spatial resolution remote sensors, e.g., the European Space Agency (ESA)’s Sentinel satellite missions, have become freely available for agricultural science, and have proven to be powerful monitoring tools. The use of vegetation indices has been essential for crop monitoring and biomass estimation models. The objective of this work is to test and demonstrate the applicability of different vegetation indices to estimate the biomass productivity, the foliar nitrogen content (FNC), the plant height and the leaf area index (LAI) of several tropical grasslands species submitted to different nitrogen (N) rates in an experimental area of São Paulo, Brazil. Field reflectance data of Panicum maximum and Urochloa brizantha species’ cultivars were taken and convoluted to the Sentinel-2 satellite bands. Subsequently, different vegetation indices (Normalized Difference Vegetation Index (NDI), Three Band Index (TBI), Difference light Height (DLH), Three Band Dall’Olmo (DO), and Normalized Area Over reflectance Curve (NAOC)) were tested for the experimental grassland areas, and composed of Urochloa decumbens and Urochloa brizantha grass species, which were sampled and destructively analyzed. Our results show the use of different relevant Sentinel-2 bands in the visible (VIS)–near infrared (NIR) regions for the estimation of the different biophysical parameters. The FNC obtained the best correlation for the TBI index combining blue, green and red bands with a determination coefficient (R2) of 0.38 and Root Mean Square Error (RMSE) of 3.4 g kg−1. The estimation of grassland productivity based on red-edge and NIR bands showed a R2 = 0.54 and a RMSE = 1800 kg ha−1. For the LAI, the best index was the NAOC (R2 = 0.57 and RMSE = 1.4 m2 m−2). High values of FNC, productivity and LAI based on different sets of Sentinel-2 bands were consistently obtained for areas under N fertilization.

Evaluating the potential of red edge position (REP) of hyperspectral remote sensing data for real time estimation of LAI & chlorophyll content of kinnow mandarin (Citrus reticulata) fruit orchards

Leaf area index (LAI) and chlorophyll content (Chl) content are important indicators of plants health and nutrition status. Hyperspectral remote sensing is useful for the real-time estimation of plants biophysical and biochemical traits. The main objective of the current study was to evaluate the potential of red edge position (REP) extracted from hyperspectral reflectance profiles to estimate the LAI and Chl content of kinnow mandarin fruit orchards in Pakistan. First, we compared methods to extract the REP from hyperspectral reflectance profiles of kinnow mandarin tree canopies. Second, the ordinary least square (OLS) regression was used to investigate relationships of the derived REP with the observed kinnow mandarin traits (LAI and Chl content). Finally, kinnow mandarin traits for non-observed locations were predicted and the validity of the calibrated models was confirmed through the validation dataset. The results showed that kinnow mandarin trees depicted higher spectral response in the red edge region (680–731 nm) with REP shifting towards longer wavelengths with higher values of LAI and Chl content. Among the four algorithms tested for extraction of REP from hyperspectral reflectance, the REP models calibrated with polynomial fitting and the linear extrapolation outperformed to predict LAI and Chl content of kinnow mandarin trees with R2 values equal to 0.93 and 0.90 and RMSE values equal to 0.156% and 10.1% respectively while REP models calibrated with maximum first derivative and polynomial interpolation were less sensitive to predict these traits in kinnow mandarin trees. We conclude that the REP extracted from ground based hyperspectral reflectance can accurately estimate the kinnow mandarin LAI and Chl content and can be effectively used to assess crop health status in a wider range to manage nutritional requirement real-time in the kinnow mandarin fruit orchards. The developed mechanism will help growers for real-time monitoring orchards at different phenological stages and maximize fruit production in a precision agriculture setting.

Application conditions and impact factors for various vegetation indices in constructing the LAI seasonal trajectory over different vegetation types

Abstract Leaf area index (LAI) is a required input for various ecological and crop models. To investigate the application conditions of various vegetation indices (VIs), especially the VIs constructed by red-edge band (VIRE) for estimating LAI, six VIs derived from Medium Resolution Imaging Spectrometer (MERIS) data were used to construct LAI seasonal trajectory for different vegetation types at 15 sites. The PROSAIL model combined with the Extended Fourier Amplitude Sensitivity Test (EFAST) method was adopted to explore the influences and physical basis of canopy biophysical and non-canopy variables on the construction of LAI seasonal trajectory using VIs. For deciduous forests, the normalized difference vegetation index (NDVI) had the highest sensitivity to LAI when LAI 2. For evergreen forests, there were no obvious differences among the sensitivities of six VIs to LAI when LAI 5. For crops, all the VIs had the similar sensitivities at LAI 3. For all three types of vegetation, the VIRE maintained relatively high sensitivity to LAI over the whole range of LAI, especially at high LAI values. The VIs were most affected by chlorophyll content (Cab) and average leaf inclination angle (ALA); their total contribution was about 85%. However, the influence of ALA on VIRE was relatively weak, implying that the VIRE had the potential to establish a universal model for LAI estimation among different vegetation types. Therefore, the optimal VIs over different ranges of LAI were suggested to estimate LAI. In addition, the VIRE should be a preferred choice for estimating LAI to reduce the simulation errors of seasonal LAI, if the RE band is available.

An urban ecohydrological model to quantify the effect of vegetation on urban climate and hydrology (UT&amp;C v1.0)

Abstract. Increasing urbanization is likely to intensify the urban heat island effect, decrease outdoor thermal comfort, and enhance runoff generation in cities. Urban green spaces are often proposed as a mitigation strategy to counteract these adverse effects, and many recent developments of urban climate models focus on the inclusion of green and blue infrastructure to inform urban planning. However, many models still lack the ability to account for different plant types and oversimplify the interactions between the built environment, vegetation, and hydrology. In this study, we present an urban ecohydrological model, Urban Tethys-Chloris (UT&amp;amp;C), that combines principles of ecosystem modelling with an urban canopy scheme accounting for the biophysical and ecophysiological characteristics of roof vegetation, ground vegetation, and urban trees. UT&amp;amp;C is a fully coupled energy and water balance model that calculates 2 m air temperature, 2 m humidity, and surface temperatures based on the infinite urban canyon approach. It further calculates the urban hydrological fluxes in the absence of snow, including transpiration as a function of plant photosynthesis. Hence, UT&amp;amp;C accounts for the effects of different plant types on the urban climate and hydrology, as well as the effects of the urban environment on plant well-being and performance. UT&amp;amp;C performs well when compared against energy flux measurements of eddy-covariance towers located in three cities in different climates (Singapore, Melbourne, and Phoenix). A sensitivity analysis, performed as a proof of concept for the city of Singapore, shows a mean decrease in 2 m air temperature of 1.1 ∘C for fully grass-covered ground, 0.2 ∘C for high values of leaf area index (LAI), and 0.3 ∘C for high values of Vc,max (an expression of photosynthetic capacity). These reductions in temperature were combined with a simultaneous increase in relative humidity by 6.5 %, 2.1 %, and 1.6 %, for fully grass-covered ground, high values of LAI, and high values of Vc,max, respectively. Furthermore, the increase of pervious vegetated ground is able to significantly reduce surface runoff.