Leaf Area Index Values Research Articles

WOFOST-N: An improved WOFOST model with nitrogen module for simulation of Korla Fragrant pear tree growth and nitrogen dynamics

Few studies have focused on simulating fruit tree growth under fertilization constraints and evaluating nitrogen utilization efficiency. This study aimed to improve the WOFOST (World Food Studies) model by embedding the nitrogen dynamics module into the original WOFOST model (WOFOST-N) to simulate the Korla Fragrant pear tree growth, nitrogen transport, nitrogen use efficiency and nitrogen stress. In addition, we used the BBCH scale to provide a more detailed description of the phenological development process of pear trees to improve phenology simulation performance. Field trials were conducted in 2021 and 2022 in two pear orchards in the Alar region of China. The results showed that the simulated total dry weight (leaves and fruits, TAGPl,f) and leaf area index (LAI) growth dynamics agreed well with the measured values in each treatment in 2021 and 2022. The R2 values of the simulated TAGPl,f was between 0.87 and 0.97, and the R2 values of the simulated LAI were between 0.80 and 0.95. The improved model also showed relatively high simulation accuracy (8.2 % ≤ NRMSE ≤ 20.5 % for TAGPl,f, 7.1 % ≤ NRMSE ≤ 20.1 % for LAI). The improved model had good global simulation performance for soil nitrogen content in six treatments with a good consistency (R2 = 0.87 in 2021 and 0.89 in 2022) and low error (NRMSE = 12.4 % in 2021 and 11.8 in 2022). The simulated yields and nitrogen use efficiencies (NUE) in the two orchards also showed good performance, with R2 = 0.94 and 0.93, NRMSE = 5.2 % and 7.5 % for yields, R2 = 0.93 and 0.96, NRMSE = 10.7 % and 9.6 % for NUE, respectively. The proposed method provides a promising method to simulate fruit trees' growth and nitrogen dynamic effects.

Simulation Model Construction of Plant Height and Leaf Area Index Based on the Overground Weight of Greenhouse Tomato: Device Development and Application

Plant height and leaf area index (LAI) are crucial growth indicators that reflect the growth status of tomatoes in greenhouses, enabling accurate determinations to effectively estimate crop transpiration and formulate irrigation strategies for reducing agricultural water waste. There is a need for the increased application of related models to simulate tomato growth indices in the traditional greenhouse production in China. This study proposes a nondestructive, real-time monitoring and simulation device for measuring tomato plant height and leaf area index. The weight of aboveground tomatoes was obtained by suspending tomato plants on dynamometers, while the total weight of stem and leaf organs was determined using a distribution coefficient simulation model. The R2 value between the measurements from the electronic scale and those from the aboveground fresh weight device for tomatoes was 0.937, with an RMSE value of 0.05 kg. The monitoring device did not affect the average tomato growth during operation. The device will not affect the growth of tomatoes during monitoring. A multiple linear regression was used to compare the measured and simulated values of the plant height and leaf area index of various types of greenhouse tomatoes cultivated in different greenhouse types. The average R2 value for simulating plant height was 0.817 with an RMSE of 10.81 cm. The average R2 value for the leaf area index was 0.854, with an RMSE of 0.55 m2·m−2. The simulated values for plant height and leaf area index closely matched the measured values, indicating that the model has high accuracy and applicability in traditional Chinese greenhouses (solar greenhouses and insulated plastic greenhouses). However, further optimization is required for commercially produced, continuous plastic greenhouses equipped with greenhouse environmental control equipment.

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.

Avaliação visual do índice de área foliar em campos de café (Coffea arabica L.)

ABSTRACT The application of leaf area index (LAI) in coffee crop management depends on the availability of methodologies for proper estimation. The objective of this study was to develop a methodology for the visual assessment of LAI in coffee fields and to establish a protocol for training, evaluation, and feedback for evaluators. Four rounds of LAI measurements were conducted using visual estimates, two instruments (LAI 2200-C and AccuPAR LP-80), and defoliation of coffee hedgerows in Poás, Costa Rica. In each round, five workers visually estimated the LAI values on two occasions separated by 15 days, and feedback reinforcement was provided to each worker at the end of each round. Visual assessments showed high repeatability and reproducibility and the estimates were adjusted to the linear regression model in most cases. Evaluators improved their capacity to visually assess the LAI throughout the rounds, as the value of R2increased consistently for most workers, with values as high as 0.87. Instrumentation evaluation of LAI produced R2values of 0.5-0.6, with significant underestimation bias. The performance of the different methods is discussed in the context of widely spaced hedgerows. The proposed visual methodology constitutes a statistically sound, rapid, simple, and reliable method for determining the LAI of coffee fields to aid in decision-making for crop management.

Spatio-temporal analysis of LAI using multisource remote sensing data for source region of Yellow River Basin

Introduction: The Leaf area index (LAI) of source region of yellow river basin is an important indicator for environmental sustainability. Most studies focus on the trend of LAI in Yellow River Source Region (YRSR) in accordance with both climate change and human actives. However, quantifying the effect of human activities on LAI is difficult but urgently needed. Specifically, Particle Matter 2.5 (PM2.5) can be an indirect indicator of human activities.Methods: In this study, we explored the potential dependence of LAI on temperature, precipitation, and PM2.5 in different land cover types in YRSR with linear regression and correlation analysis.Results: Over the period of 2001–2020, the climate in the region has been warming and becoming more humid, leading to overall improvements in vegetation. The mean LAI values varied between seasons, with summer having the highest and winter having the lowest LAI. The analysis of the LAI trends revealed that the mean LAI has been steadily increasing, particularly in the eastern region. The correlation analysis showed a significant positive correlation between annual average LAI and both annual precipitation and temperature, indicating that temperature has a greater impact on vegetation growth. The analysis of land cover types showed that most types exhibited a unimodal trend in LAI throughout the year, except for construction land which had two distinct peaks. Human-induced land cover change had a small impact on the overall increase in LAI. Furthermore, the interannual variation of PM2.5 showed a downward trend, with a strong correlation with the trend of LAI. Additionally, multiple linear regression analysis and residual trend analysis showed that climate factors had the strongest impact on LAI.Conclusion: The study highlights the spatiotemporal variations of LAI in the YRSR and its correlation with climatic and human factors. The findings suggest that climate change plays a crucial role in the vegetation growth and LAI in the region.

Eastern Gamagrass Model Simulation Parameters for Diverse Ecotypes: Leaf Area Index, Light Extinction Coefficient, and Radiation Use Efficiency

Eastern gamagrass (Tripsacum dactyloides) is highly palatable, ideal for grazing and hay production in the United States. It is deep rooted and resilient, tolerant of flooding and drought. Objectives of this study were to develop plant parameters for different ecotypes of this grass. Data collected in field plots of diverse ecotypes included biomass, leaf area index (LAI), light extinction coefficient (k), and radiation use efficiency (RUE). Average LAI was 1.06 and average k was −1.05. The power response of k to LAI offers a new approach to simulating light interception at very low LAI values and throughout the range of LAI values of these ecotypes and similar grass species. The RUE values, which ranged from 1.16 to 4.31 g/MJ, highlight the immense diversity of eastern gamagrass. The high RUE values for the most productive ecotypes emphasizes the importance of this grass species for hay and grazing. While not as large as switchgrass (Panicum virgatum) values, it is still a prominent forage species that even is comparable to maize (Zea mays L.) in productivity when expressed as radiation use efficiency. These results are an important step in developing relationships and parameters to simulate the different ecotypes of this grass with process-based models.

Comparative analysis of multi-source data for machine learning-based LAI estimation in Argania spinosa

In this study, we conducted a comprehensive assessment of Leaf Area Index (LAI) estimation using three distinct sources of satellite data: Sentinel-2 imagery, drone imagery (UAVs), and Mohammed VI satellite data. The main objective was to identify the most reliable and precise dataset for predicting LAI, with a focus on evaluating the performance of Random Forest models. For Sentinel-2 imagery, our Random Forest model achieves a robust R-squared (R2) value of 0.89, signifying a strong alignment between predicted and measured LAI values. The associated root-mean-square error (RMSE) is 0.4, indicating high predictive accuracy. In the context of UAVs, our Random Forest model excels, exhibiting an impressive R2 value of 0.93, highlighting a substantial correlation between predicted and measured LAI. The RMSE for drone imagery stands at 0.37, showcasing exceptional predictive accuracy. Finally, the Random Forest model trained on Mohammed VI satellite data yields an R2 value of 0.92, underlining its strong fit with measured LAI values. The RMSE for Mohammed VI imagery is 0.39, further underscoring the model's exceptional predictive accuracy. This comparative analysis underscores the importance of selecting the most suitable satellite data source for LAI estimation in Argania spinosa. UAV imagery emerges as the most accurate choice, closely followed by Mohammed VI Satellite and Sentinel-2 imagery. These findings offer valuable insights for effective monitoring of Argania spinosa and advancing sustainable land management practices in rural ecosystems.

Monitoring of mangrove forests vegetation based on optical versus microwave data: A case study western coast of Saudi Arabia

Abstract Normalized difference vegetation index (NDVI) is one of the parameters of vegetation that can be studied by remote sensing of land surface with Sentinel-2 (S-2) satellite image. The NDVI is a nondimensional index that depicts the difference in plant cover reflectivity between visible and near-infrared light and can be used to measure the density of green on a piece of land. On the other hand, the dual-pol radar vegetation index (DpRVI) is one of the indices studied using multispectral synthetic aperture radar (SAR) images. Researchers have identified that SAR images are highly sensitive to identify the buildup of biomass from leaf vegetative growth to the flowering stage. Vegetation biophysical characteristics such as the leaf area index (LAI), vegetation water content, and biomass are frequently used as essential system parameters in remote sensing data assimilation for agricultural production models. In the current study, we have used LAI as a system parameter. The findings of the study revealed that the optical data (NDVI) showed a high correlation (up to 0.712) with LAI and a low root-mean-square error (0.0296) compared to microwave data with 0.4523 root-mean-square error. The NDVI, LAI, and DpRVI mean values all decreased between 2019 and 2020. While the DpRVI continued to decline between 2020 and 2021, the NDVI and LAI saw an increase over the same period, which was likely caused by an increase in the study area’s average annual rainfall and the cautious stance of the Red Global (RSG) project on sustainability.

Combining machine learning and remote sensing-integrated crop modeling for rice and soybean crop simulation.

Machine learning (ML) techniques offer a promising avenue for improving the integration of remote sensing data into mathematical crop models, thereby enhancing crop growth prediction accuracy. A critical variable for this integration is the leaf area index (LAI), which can be accurately assessed using proximal or remote sensing data based on plant canopies. This study aimed to (1) develop a machine learning-based method for estimating the LAI in rice and soybean crops using proximal sensing data and (2) evaluate the performance of a Remote Sensing-Integrated Crop Model (RSCM) when integrated with the ML algorithms. To achieve these objectives, we analyzed rice and soybean datasets to identify the most effective ML algorithms for modeling the relationship between LAI and vegetation indices derived from canopy reflectance measurements. Our analyses employed a variety of ML regression models, including ridge, lasso, support vector machine, random forest, and extra trees. Among these, the extra trees regression model demonstrated the best performance, achieving test scores of 0.86 and 0.89 for rice and soybean crops, respectively. This model closely replicated observed LAI values under different nitrogen treatments, achieving Nash-Sutcliffe efficiencies of 0.93 for rice and 0.97 for soybean. Our findings show that incorporating ML techniques into RSCM effectively captures seasonal LAI variations across diverse field management practices, offering significant potential for improving crop growth and productivity monitoring.

Tree morphology dependent transpiration reduction function of Schefflera arboricola for landfill cover restoration

Abstract Changes in hydrological processes and water resources required to sustain vegetation for ecological restoration of landfill covers and post mining sites in arid environments pose challenges in the context of extended droughts. Knowledge of actual threshold and wilting suction values based on tree morphological feature or plant age is essential for understanding the variation of root water uptake with drought stress and numerically predict the pore water pressure profile in root zone. The objective of this study is to quantify the transpiration reduction function (TRF; in terms of stomatal conductance (SC) and xylem sap flow (SF)) of Schefflera arboricola, considering the effects of tree morphology. Continuous drought condition was applied on the plant quantified with leaf area index (LAI) values at 0.5, 2 and 3.5, wherein each LAI represent tree age. The soil matric suction (ψ) and volumetric water content were measured by embedded sensors in the root zone. Based on the TRF obtained from SC values, a unique threshold suction (ψ NTR t) ranging from 30 to 50 kPa was identified. Beyond this ψ NTR t, measured leaf abscisic acid concentration increased up to 35 ng/mL, indicating the start of water stress avoidance mechanism. It is evident that ψ NTR t is independent of tree morphological parameter- leaf area to root length ratio (LA/RL). On the contrary, a threshold suction (ψ SAP t), depending on LA/RL ratio, can be determined, indicating the start of xylem cavitation. This ψ SAP t values ranging from 80 to 500 kPa depending on the LA/RL value, imply that the plant could significantly resist xylem embolization at higher LA/RL. In contrast, the plant with low LA/RL values have less tolerance of drought stress and hence low survivability. The results from this research study can be vital for devising and predicting plant available water in water scarce arid environments by a flux-based approach which is dependent on the tree age.

Estimation of oil palm’s leaf area index (LAI) using Unmanned Aerial Vehicle (UAV) images

Leaf Area Index (LAI) is a parameter commonly used to indicate oil palm growth and production. The destructive method is the standard method to estimate LAI. However, this method requires much effort and cost and can only be done at a specific period. Unmanned Aerial Vehicles (UAVs), which have been widely used for mapping and calculating trees in oil palm plantations, have the potential to estimate LAI values quickly, efficiently, and without disturbing the oil palm tree. This research was conducted to develop the model for predicting LAI based on UAV images. Data was collected from six plots of 12-year-old oil palm trees at Adolina Estate, Serdang Bedagai, North Sumatra, Indonesia. The research was conducted on six varieties released by IOPRI, consisting of DxP Yangambi, DxP Langkat, DxP Simalungun, DyP Dumpy, DxP LaMe, and DxP PPKS 540. The estimated canopy cover from the UAV images was employed as an independent factor (x) compared to the calculated LAI from the destructive method, which was used as a dependent factor (y). The results showed that the LAI estimation using UAV imagery followed a linear model with R-squared values ranging from 0.3874-0.9556. In conclusion, despite requiring further research, UAV images could be used as rapid tools to estimate oil palm LAI.

Divergent climatic effects on forest greenness modulated by temperature and precipitation gradients in the Yellow River Basin, China

How climatic effects on forest greenness vary between different temperature and precipitation gradients in climate–sensitive and ecologically fragile areas, such as the Yellow River Basin (YRB) in China, is not well understood. Here we used meteorological data and remotely sensed leaf area index (LAI) data to investigate climatic effects on forest LAI changes along temperature–precipitation gradients in the YRB from 2001 to 2020. We found that changing precipitation thresholds divided forests into higher and lower LAI areas. The precipitation thresholds increased from 400 mm mean annual precipitation (MAP) in areas with a mean annual temperature (MAT) below 4 °C to 500 mm MAP in areas with a MAT above 4 °C. Forest LAI increased significantly from 2001 to 2020. The MAT and MAP thresholds changed when dividing forests into different areas with different LAI values and climatic effects. Forests greening was mainly driven by temperature in areas with LAI values between 3 and 6 m2 m−2 but was mainly driven by precipitation in areas with LAI values less than 3 m2 m−2. The threshold of MAP isopleth increased from 500 mm to 700 mm and then decreased to 500 mm, while the MAT increased from less than 0 °C to 5 °C and then increased to 12 °C. These divergent climatic effects were caused by the combined effects of water resource limitations and increasing evapotranspiration. Identifying MAT and MAP thresholds is helpful for decision–maker and managers to optimize forest restoration more accurately in future ecological protection projects facing the challenge of limited water resources in the YRB.

Two stages segmentation for Leaf Area Index estimation using digital cover photography.

Leaf Area Index (LAI) is the ratio of ground surface area covered by leaves. LAI plays a significant role in the structural characteristics of forest ecosystems. Therefore, an accurate estimation process is needed. One method for estimating LAI is using Digital Cover Photography. However, most applications for processing LAI using digital photos do not consider the brown color of plant parts. Previous research, which includes brown color as part of the calculation, potentially produced biased results by the increased pixel count from the original photo. This study aims to enhance the accuracy of LAI estimation. The proposed methods consider the brown color while minimizing errors. Image processing is carried out in two stages to separate leaves and non-leaf pixels by using the RGB color model for the first stage and applying the CIELAB color model in the second stage. Proposed methods and existing applications are evaluated against the actual LAI value obtained using Terrestrial Laser Scanning (TLS) as the ground truth. The results demonstrate that the proposed methods effectively identify non-leaf parts and exhibit the lowest error rates compared to other methods. In conclusion, this study provides alternative techniques to enhance the accuracy of LAI estimation in forest ecosystems.

Plant Biostimulants as an Effective Tool for Increasing Physiological Activity and Productivity of Different Sugar Beet Varieties

Drought and high temperatures are among most dangerous attributes of climate change, which negatively affects the quantity and quality of sugar beet production. One of the most effective tools for eliminating unwanted effects is the application of biostimulants during the growing season. In this study, a 4 × 3 factorial scheme was adopted: Two biostimulant treatments, namely (i) pure extract from brown seaweed Ascophylum nodosum (B1) and (ii) concentrate from the seaweed Ascophylum nodosum and humus substances (B2), were compared to a control treatment (B0) in an experiment with four sugar beet varieties (Fischer, Fabius, Nicolaus, Lucius). The two-year research proved the significant influence of biostimulants on all monitored physiological and production parameters of sugar beet, with the exception of potassium content. Biostimulants positively influenced the results of root yield, polarized and white sugar yield, and the values of LAI (leaf area index), NDVI (normalized difference vegetation index), and PRI (photochemical reflectance index), while the positive effect on sugar content was only in the case of B1 treatment. The production potential fluctuated significantly depending on the observed interaction, but it can be concluded that the most limiting factor of production is the course of weather conditions. However, after treatment with biostimulants, an increased root yield (B2) and sugar content (B1) were found. Moreover, in this experiment, a strong positive relationship between root yield and physiological parameters (NDVI and PRI) and LAI was proven, while the relationship of sugar content to these parameters was weak. Monitoring of the physiological response to biostimulant application shows a high potential from the sustainability perspective in the context of sugar beet production. In addition, the impact on the height and quality of production was evident.

Near-infrared digital hemispherical photography enables correction of plant area index for woody material during leaf-on conditions

Indirect optical measurement techniques enable efficient and non-destructive estimation of plant area index (PAI). However, because they cannot distinguish between foliage and other canopy elements, corrections are needed to determine leaf area index (LAI), which is typically the property of interest. In this study, we investigate near-infrared digital hemispherical photography (DHP) as a means of estimating and correcting for woody material. Using data collected at a deciduous broadleaf forest site, we show that near-infrared DHP could successfully estimate effective wood area index (WAIe) and wood area index (WAI) during leaf-on conditions, providing similar mean values (WAIe = 0.88, WAI = 1.53) to those determined from visible DHP during leaf-off conditions (WAIe = 0.87, WAI = 1.38). This information was used to correct estimates of effective PAI (PAIe) and PAI, enabling effective LAI (LAIe) and LAI to be derived with low RMSD (0.33 for LAIe and 0.76 for LAI), NRMSD (12% for LAIe and 19% for LAI), and bias (−0.01 for LAIe and −0.16 for LAI). Not correcting for woody material led to overestimation of LAIe by 31% on average and 46% in the worst observed case, and the degree of overestimation was further enlarged for LAI (42% on average and 61% in the worst observed case). In agreement with previous studies, the effects of clumping and woody area were found to be partly compensatory. On average, PAIe provided a reasonable approximation of LAI without correction, though overestimation of 52% and underestimation of 20% occurred at the lowest and highest LAI values, respectively. Compared to WAIe and WAI measurement using leaf-off visible DHP, near-infrared DHP offers two crucial advantages: i) data collection can be conducted at the same time as leaf-on PAIe and PAI measurements, and ii) it is likely that the approach could provide an indirect WAIe and WAI measurement option for evergreen species.

Accurate leaf area index estimation in sorghum using high-resolution UAV data and machine learning models

Accurate estimation of leaf area index (LAI) is essential for precision agriculture, yet traditional ground-based measurements are destructive, time-consuming, and limited in scale. This study aimed to address the need for rapid, non-destructive LAI monitoring over large areas by evaluating unmanned aerial vehicle (UAV) data and machine learning (ML) models. A field experiment with four irrigation treatments was conducted to obtain wide range of LAI values over two years. Multispectral and thermal UAV images were acquired throughout the growing season along with destructive LAI measurements. Five ML algorithms, including K-Nearest Neighbors (K-NN), Extra Trees Regressor (ETR), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR) were tested. Also, feature selection procedures were implemented to obtain useful information among the features used for the ML model. Results showed the K-NN model achieved the highest accuracy (R2 = 0.97, RMSE = 0.46, MAE = 0.197), followed by ETR. The analysis of feature selection revealed that the combination of Normalized Difference Vegetation Index (NDVI) and canopy height (NDVI×Hc) product had the highest importance, followed by Soil-Adjusted Vegetation Index (SAVI) and Green Normalized Difference Vegetation Index (GNDVI). Also, utilizing vegetation indices calculated from multiple spectral bands proved to be more effective than using individual bands alone. Overall, the study demonstrates that UAV data and ML techniques can estimate sorghum LAI precisely to support precision agriculture applications. Moreover, using low-cost UAVs equipped with multispectral sensors presents a cost-effective and reliable method for LAI estimation using ML models.

Benefit of incorporating GLASS remote sensing vegetation products in improving Noah-MP land surface temperature simulations on the Tibetan Plateau

Land Surface Temperature (LST) is important for diagnosing surface energy balance in land surface models (LSMs). However, LST simulation in current LSMs tends to show large cold biases, partially due to the reason that the model's prescribed vegetation parameters (e.g., Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) are misrepresented, especially in regions with complex topography and climate such as Tibetan Plateau. Recent advancements in remote sensing technologies provide a unique opportunity to improve the model's vegetation parameters at large scales. In this study, we practice two experiments to improve LST simulations in Noah-MP LSM by (1) incorporating LAI and FVC from the Global Land Surface Satellite (GLASS) remote sensing product (exp_RS); and (2) incorporating an empirical LAI and FVC parameterization scheme based on the soil temperature stress factor (exp_RL02). Results show that the effect of vegetation on simulated LST is the most significant in summer season when the model-satellite LAI and FVC differences are the largest. Compared to the default experiment that uses static LAI and FVC values from the model's look-up table (exp_CTL), the results in exp_RS and exp_RL02 show domain-wide improvement of the simulated LST. The LAI and FVC effect on LST are also well reflected in model's energy budget components (i.e., longwave emissivity, sensible and latent heat fluxes, etc). Validation of the model simulated soil temperature with in-situ observations further demonstrate the model improvements. Our study underscores the important role of vegetation in regulating surface energy transfer processes. Our study also highlights the feasibility and benefit of incorporating remote sensing data in improving land surface model simulations.

Predicting monthly near-surface soil temperature from air temperature and the leaf area index

Soil temperature at the land surface (Tsoil) is a key variable for modeling processes that occur belowground. Although Tsoil is correlated to the near-surface air temperature (Tair), these values are often not equal. Evidence confirms that Tsoil – Tair (ΔT) varies by ecosystem in both positive and negative directions. The goal of this study was to calibrate a semi-empirical model to predict Tsoil from Tair and its interaction with the leaf area index (LAI) at a monthly timescale. The latter variable is introduced to quantify the reduction in solar radiation reaching the land surface using Beer-Lambert's Law with an extinction coefficient k = 0.55. Bootstrap distributions for the three unknown parameters in the model were optimized using monthly Tair and Tsoil measurements from 169 eddy covariance towers in the FLUXNET2015 dataset accompanied by LAI observations at 0.5-km2 resolution from the MODIS satellite. The optimized model had a mean bias error (MBE) of -0.08 °C and a root mean square error (RMSE) of 2.26 °C. When Tsoil was assumed equal to Tair (ΔT = 0), these metrics worsened to -1.51 °C and 4.09 °C, respectively. Data from the US National Ecological Network (MBE/RMSE = -0.51°/2.81 °C) and published global predictions of ΔT obtained with a machine learning algorithm (MBE/RMSE = 0.73°/1.76 °C) validated the optimized model at the global scale. The data confirmed that ΔT tends to be positive when Tair 〈 0 °C, and transitions to negative values when Tair 〉 0 °C and LAI is greater than 1.89. In the proposed model, LAI values close to 1.89 indicate a 37 % reduction in solar radiation (e − 1 in Beer-Lambert's Law). Overall, this work confirms that Tsoil ≠ Tair in most ecosystems and that ΔT is predominantly explained by LAI in non-freezing conditions. Re-calibration is recommended before applying the model in barren ecosystems, or at spatiotemporal scales different from those in this research.

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

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

Optimal row spacing configuration to improve cotton yield or quality is regulated by plant density and irrigation rate

ContextConventional high-density planting of machine-picked cotton in Xinjiang is associated with poor light distribution and low fiber quality. The objective of this study was to assess how different row spacing configurations, irrigation rates, and planting densities impact cotton photosynthetic production and fiber quality. MethodsWe conducted a 3-year field experiment in Shihezi, Xinjiang, China, to evaluate the impact of different row spacing configurations and irrigation amounts on cotton photosynthesis and fiber quality. The four row spacing configurations used were: RS66+10H (high-density with 66 +10 cm spacing), RS66+10L (low-density with 66 +10 cm spacing), RS76H (high-density with 76 cm spacing), and RS76L (low-density with 76 cm spacing). Additionally, two irrigation amounts were tested: MD-T3 (mulch drip irrigation with three tapes on one film) and MD-T2 (mulch drip irrigation with two tapes on one film). We measured and compared the distribution of cotton canopy leaves, light, canopy apparent photosynthesis, reproductive organs and fiber quality among these groups. ResultsThe results showed that RS66+10H had a higher lint yield than RS76L under MD-T2, with an increase of 5.4–7.1%. This can be attributed to the higher fraction of photosynthetically active radiation, canopy apparent photosynthesis (CAP), and boll biomass in RS66+10H between the full boll and boll opening stages. The maximum lint yield (3227–3580 kg ha−1) was obtained in RS66+10H under MD-T3. Interestingly, RS76L had a similar lint yield to RS66+10H under MD-T3, mainly due to the middle and lower canopy having higher leaf area index (LAI) values ranging from 1.8 to 2.1. This resulted in increased CAP and cotton boll biomass of the middle and lower canopy compared to RS66+10H during the full boll to boll opening stage. Under MD-T3, RS76L also exhibited longer fiber length in the middle and lower canopy (30.2–32.0 mm and 31.6–33.3 mm, respectively) compared to RS66+10H, with a difference of 3.2–3.8% and 5.6–6.5%. Furthermore, RS76L demonstrated higher fiber strength in the middle and lower canopy (31.4–32.7 cN tex−1 and 33.2–33.9 cN tex−1, respectively) compared to RS66+10H, with a difference of 2.6–6.2% and 5.9–6.8%. Maintaining an LAI of around 2.0 during the transition from full boll to boll opening stage optimized canopy architecture and enhanced photosynthetic productivity. ConclusionUnder adequate irrigation, RS76L could be a suitable replacement for RS66+10H in order to improve cotton fiber quality by optimizing leaf-boll spatial distribution. This study provides guidance for selecting the appropriate row spacing configuration under different water conditions. RS66+10H is recommended for increasing yield under water-deficient conditions, while RS76L is suitable for improving fiber quality under adequate irrigation.