Leaf Area Index Values Research Articles

Estimating biomass of woody plants that grow in the different As-contaminated techno-soils in the ore-bearing provinces of Eastern Germany

Establishing the role of woody species as an instrument for heavy metal bioaccumulation is a relevant issue today in the context of the development of the phytoremediation system. The article presents the results of studies on the influence of different Arsenic (As) concentrations in soil on the development of aboveground biomass in Betula pendula Roth. and Populus tremula L. stands under conditions of reclamation plantings. The studies were conducted in 30 locations of birch and poplar tree plantations within the ore-producing regions of Saxony (Eastern Germany) in soil with different levels of As contamination. The highest As content was noted in the technosoil of the Davidschacht site, where the metalloid content was 229.3 times greater compared with a value in a conditionally uncontaminated area (Großschirma). The values of leaf area index and aboveground biomass obtained in field measurements were presented. The aboveground biomass values in the investigated plantations ranged from 189.9 ±10.16 to 201.8 ± 19.09 t/ha, and leaf area index values ranged from 1.74 ± 0.29 to 2.05 ± 0.16 m2/m2. Sentinel-2A multispectral images were processed for the construction of a map of the aboveground biomass distribution within the region under study. The values of the spectral indices for leaf area index were obtained with subsequent construction of the regression dependence of the aboveground biomass in the plantings on this indicator. The RMSE value for the developed model of the dependence of aboveground biomass on the leaf area index was 17.84 t/ha, which could be considered as satisfactory and can serve as a basis for practical application of the model developed. The inverse trend in relation to locations with different levels of soil contamination with As was determined for the aboveground biomass indicator. Within the region under study, the highest value of aboveground biomass in the stands was found for the area with the lowest As level. The results showed that the correlation coefficient between the highest of the optimal spectral indices, the leaf area index, and the aboveground biomass in B. pendula and P. tremula plantings was statistically significant and approached the value of 0.7. The results presented can become a theoretical basis for monitoring the accumulation of aboveground biomass of tree stands in areas with different levels of soil contamination with As. In perspective, the presented model of biomass estimation based on spectral technologies can serve as an application basis for rapid assessment of the growth and development parameters of forest stands in As-contaminated areas.

Impacts of Climate Warming and Humidification on Vegetation Activity over the Tibetan Plateau

Vegetation is the most vulnerable component of terrestrial ecosystems to climate change. In recent decades, there has been a significant warming and humidification trend in the Tibetan Plateau. It is crucial to study and analyze the impact of these changes on the ecosystem and their future trends for protecting the Tibetan Plateau’s ecosystem. This study collected and analyzed climate (temperature, precipitation) data and vegetation index data (the normalized difference vegetation index (NDVI) and the leaf area index (LAI)), as well as data from significance tests combined with Mann–Kendall tests and Sen’s slope estimation. The effects of temperature and precipitation factors on vegetation indices were revealed, leading to a multiple regression model predicting NDVI and LAI value changes under climate change from 2021 to 2100. The results indicate a general increase in temperature and precipitation levels across the Tibetan Plateau between 2000 and 2020. The climate experienced a clear pattern of warming and moist conditions, with the southeast region experiencing warmer and wetter conditions, and the northwest region experiencing drier and colder conditions. The trends of the LAI and NDVI values of the Tibetan Plateau indicated a general increase, with a gradual decline from the southeast to the northwest. Precipitation and temperature were differentially correlated with the NDVI and LAI values across various regions of the plateau. Between 2021 and 2100, the Tibetan Plateau is expected to experience year-on-year increases in both precipitation and temperature levels. However, the increase in precipitation was found to be less significant than that of the climate and, comparatively, smoother. There is a certain correlation between the NDVI and LAI values, and the changes in temperature and precipitation. The variations of both are more influenced by temperature than precipitation, with an overall increasing trend observed over the years, which is also quite evident. This study could serve as a scientific foundation and a point of reference for monitoring vegetation changes over a long period of time on the plateau, as well as for the planning and execution of ecological development in the Tibetan Plateau.

The impact of two bacterial species and five aqueous plant extracts on yield and yield components of rice (Oryza sativa L.) and surof (Echinochloa crus-galli L.)

Chemical exudates from some plants to the environment have been reported as causative agents of adverse effect on growth of neighboring plants and thus affecting normal growth in their natural environment. The mode of action of Chemical exudates on plants is involves a large number of biochemical reactions. This research aimed to investigate the effect of foliar application of different levels of two bacterial species and five aqueous plant extracts on the yield and yield components of Oryza sativa L. and Echinochloa crus-galli L. This research was conducted as a factorial based on randomized complete block design (RCBD) with three factors with three replications during the 2017–2018 crop year. The main factor included aqueous extracts of Sorghum (Sorghum bicolor L. Moench), Broccoli (Brassica oleracea var. Italica), Nettle (Urtica dioica), Eucalyptus (Eucalyptus globulus Labill), Elderberry (Sambucus nigra L.), and Streptomyces sp-albos containing thaxtomin, Xanthomonas campestris, and control (without aqueous plant extracts and surfactant). The sub-factor includes foliar application treatment with different levels of zero (control), 5, 10, and 15% of extracts, and the third experimental factor included Oryza sativa L. and E. crus-galli L. The results indicated the plant height, leaf area index (LAI), grain yield, biological yield, chlorophyll a, chlorophyll b, total chlorophyll, and grain protein content decreased, and the grain starch content increased with increasing the extract concentration; the highest effect was obtained at 15% of extracts. The lowest value of leaf area index (3.13) were found in higher concentration of X. campestris followed by Elderberry extract (5%, 10% and 15%) and Nettle (10%) extracts. It was also found that using extract reduced yield characteristics and components of Oryza sativa L., E. crus-galli L., and Elderberry extract showed the highest negative effect. According to the results, Oryza sativa L. had higher plant height, LAI, grain yield, biological yield, harvest index, chlorophyll, grain protein content, and grain starch content than E. crus-galli L. In all extractions, control treatment had the highest number of panicles plant−1 in both plants (rice and E. crus-galli L., 260.1 and 117.1 mg g−1 FW). The results suggested that various extracts, especially Nettle, and 15% of Elderberry can control E. crus-galli L. at the germination stage under field conditions.

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

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

Grain Yield, Biomass Accumulation, and Leaf Photosynthetic Characteristics of Rice under Combined Salinity-Drought Stress

Simultaneous stresses of salinity and drought often coincide during rice-growing seasons in saline lands, primarily due to insufficient water resources and inadequate irrigation facilities. Consequently, combined salinity-drought stress poses a major threat to rice production. In this study, two salinity levels (NS, non-salinity; HS, high salinity) along with three drought treatments (CC, control condition; DJ, drought stress imposed at jointing; DH, drought stress imposed at heading) were performed to investigate their combined influences on leaf photosynthetic characteristics, biomass accumulation, and rice yield formation. Salinity, drought, and their combination led to a shortened growth period from heading to maturity, resulting in a reduced overall growth duration. Grain yield was reduced under both salinity and drought stress, with a more substantial reduction under the combined salinity-drought stress. The combined stress imposed at heading caused greater yield losses in rice compared with the stress imposed at jointing. Additionally, the combined salinity-drought stress induced greater decreases in shoot biomass accumulation from heading to maturity, as well as in shoot biomass and nonstructural carbohydrate (NSC) content in the stem at heading and maturity. However, it increased the harvest index and NSC remobilization reserve. Salinity and drought reduced the leaf area index and SPAD value of flag leaves and weakened the leaf photosynthetic characteristics as indicated by lower photosynthetic rates, transpiration rates, and stomatal conductance. These reductions were more pronounced under the combined stress. Salinity, drought, and especially their combination, decreased the activities of ascorbate peroxidase, catalase, and superoxide dismutase, while increasing the contents of malondialdehyde, hydrogen peroxide, and superoxide radical. Our results indicated a more significant yield loss in rice when subjected to combined salinity-drought stress. The individual and combined stresses of salinity and drought diminished antioxidant enzyme activities, inhibited leaf photosynthetic functions, accelerated leaf senescence, and subsequently lowered assimilate accumulation and grain yield.

Improving Photosynthesis and Grain Yield in Wheat through Ridge–Furrow Ratio Optimization

The ridge–furrow planting pattern is an effective strategy to improve grain yield, and changes in the ridge and furrow microenvironments affect wheat yield. However, the mechanism by which wheat yields are increased at different ridge–furrow ratios is unclear. In this study, four planting modes, namely the traditional planting mode (M1) and ridge–furrow ratios of 50:50 cm (M2), 75:50 cm (M3), and 100:50 cm (M4), were established for wheat under field conditions from 2021 to 2023; the effects of different treatments on light energy utilization, dry matter accumulation and transport, and grain yield were studied. The findings demonstrated that the M3 treatment exhibited the highest enhancements in parameters such as leaf area index (LAI), canopy photosynthetically effective radiation interception rate, relative chlorophyll content (SPAD) index, and net photosynthetic rate. Moreover, the M3 treatment displayed superior grain filling compared to other treatments. The post-anthesis assimilate accumulation in the M3 treatment was 11.93%, 4.69%, and 13.13% higher than that of M1, M2, and M4 treatments, respectively, and the grain yield in M3 increased by 7.70–9.56%, 3.13–4.91%, and 8.69–10.90% compared with those in M1, M2, and M4 in the two growing seasons, respectively. In summary, under the conditions of this study, the M3 treatment led to higher LAI and SPAD values in flag leaves post-anthesis compared to the other treatments. Moreover, M3 optimized canopy structure, led to the highest canopy interception rate, and increased photosynthetic rates per individual plant. Consequently, there was a significant increase in post-anthesis dry matter accumulation, resulting in the highest grain yield achieved among the treatments.

Unlocking the secrets of wheat production: Evaluating phosphorus levels and seeding rates in alluvial soil conditions

A research study was conducted to determine the effect of different P levels and seeding rates on the growth, phenology, and yield of wheat. The experiment used a randomized complete block design with split plot arrangement and four replications. The main plots consisted of six seeding rates, ranging from 50 to 175 kg/ha, while the sub-plots received six different P levels, ranging from 50 to 200 kg/ha. In consecutive years, the study revealed significant effects of phosphorus levels, seeding rates, and their interaction on various growth characteristics and yield parameters of wheat. These included days to 50% heading and 50% maturity, leaf area index (LAI), leaf area duration (LAD), crop growth rate (CGR), net assimilation rate (NAR), and grain yield (GY). Phosphorus (P) level of 140 kg/ha resulted in the highest CGR, NAR, and GY in both years of the study. At a seeding density of 125 kg/ha, similar findings were observed, with the maximum CGR, NAR, and GY recorded. The highest interaction values for CGR, NAR, and GY were achieved with the combination of 140 kg P/ha and a seeding rate of 125 kg/ha. By utilizing a seed rate of 175 kg/ha and a P level of 200 kg/ha, the study revealed the highest values for LAI and LAD. Additionally, the maximum protein content was observed in treatments where seeds and P were applied at 50 and 200 kg/ha, respectively. In summary, the study provided conclusive evidence that the application of suitable seeding rates and P levels can yield significant improvements in the growth, phenology, and yield of wheat. The findings could be beneficial for farmers and researchers in improving wheat production.

SHADING OF TREE CANOPIES ON INTERTOWN ROADS THROUGHOUT FOREST LANDSCAPES: ABDİPAŞA-OVACUMA GREENWAY OF TURKEY

Greenways are the systems that link both the urban and rural landscapes. Thus, they establish cultural and natural corridors between these landscapes. Being one of the major rural landscapes, forests supply ecosystem services by their recreation potential as well as wood and oxygen production. Hence, forest ecosystems that include roads constitute rural greenways. The canopies of the trees and shrubs supply shading on these road surfaces. These greenways play a significant recreational and safety role within the overall forest landscapes. They provide shading, recreation, and safety for both the drivers and passengers. Thereby, tree and shrub canopy parameters such as the closure and coverage are the determining factors on the safety, shading and recreation potential. Amongst those tree and shrub canopy parameters, the Leaf Area Index (LAI), Light Transmission (LT), Gap Fraction (BO) and Canopy Openness (CO) are the prominent ones. Therefore, the greenway route along the road between Ulus Subprovince (Bartın) and Safranbolu Subprovince (Karabük) at the northwestern Turkey, was chosen as the study and research field in this study. The oriental planes (Platanus orientalis L.) are the immediate roadside trees with their canopies shading on the surface that road. Along 14 km road route, 71 different points were defined for the hemispherical photographing during the summer of 2021. Based on the analyses of these hemispherical photographs using the version 3.2 of the Hemisfer program, the LAI values ranged between 1.89 m2 m-2 and 2.79 m2 m-2 being 2.34 m2 m-2 on average. However, the average LT and CO percentages were 17.9% and 18.2%, whereas the average GF percentage was 7.7%. According to the overall results of this study, the canopy parameters indicated relatively high canopy closure and coverage values even though with alternating values and percentages for the different sections of the greenway route.

A method to estimate leaf area index from VIIRS surface reflectance using deep transfer learning

The leaf area index (LAI) retrieval methods based on traditional neural networks require a large number of training samples constructed from remote sensing data or simulation data using radiative transfer models. Furthermore, the training samples for the neural networks are sensor-specific. Therefore, the existing training samples for a sensor cannot be directly applied to estimate LAI values from remote sensing data acquired by another sensor. In addition, a large number of currently available LAI ground measurements (considered as “true values”) are not used to construct training datasets of the neural networks to further improve the accuracy of the retrieved LAI values. In this study, a method based on deep transfer learning is proposed to retrieve LAI values from the Visual Infrared Imaging Radiometer Suite (VIIRS) surface reflectance data based on the existing training dataset established from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and the fused LAI values of the MODIS and Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) LAI products. A transferable model is constructed by using a deep belief network (DBN) composed of a restricted Boltzmann machine (RBM) and a back propagation (BP) network. The DBN is pretrained by the existing training dataset, and the hyperparameters of the DBN are determined by using a Bayesian optimization algorithm. Then, the RBM parameters of the pretrained DBN are frozen, and the BP parameters are fine-tuned by a small sample dataset composed of the LAI ground measurements and the corresponding VIIRS surface reflectance data. Finally, the VIIRS surface reflectance data are inputted into the fine-tuned DBN to estimate LAI values. The retrieved LAI values were cross-compared with the MODIS and VIIRS LAI products and were directly evaluated with LAI ground measurements at the Ground-Based Observations for Validation (GBOV) and Implementing Multi-Scale Agricultural Indicators Exploiting Sentinels (IMAGINES) sites. The results demonstrate that the retrieved LAI values at these sites with different vegetation types show reasonable seasonality and the spatial distributions of the retrieved LAI values are similar to those of the MODIS and VIIRS LAI products. The direct validation results show that the LAI values retrieved by the DBN with fine-tuned BP parameters (RMSE over the USA: 0.48, RMSE over Europe: 0.71) are obviously superior to the LAI values retrieved by the DBN without fine-tuned BP parameters (RMSE over the USA: 0.70, RMSE over Europe: 0.98). This study demonstrates that deep transfer learning can effectively retrieve LAI values from the VIIRS surface reflectance data with limited LAI ground measurement samples and the existing training dataset.

Assessment of DSSAT and AquaCrop models to simulate soybean and maize yield under water stress conditions

Aim of study: To evaluate the performance of DSSAT and AquaCrop models in the estimation of soybean and grain maize yield under water stress conditions in a semi-arid region. Area of study: Kermanshah, Iran. Material and methods: AquaCrop and DSSAT were assessed to simulate soybean and maize. Both models were calibrated using field data. Field experiments were performed in a randomized complete block design with eight and four irrigation treatments for soybeans and maize, respectively with three replications. Measures of Normalized Root Mean Square Error (nRMSE) and Nash-Sutcliffe Model Efficiency were used to evaluate the accuracy of the models. For this purpose, simulated values of leaf area index / green crop canopy, grain yield, biomass, and soil moisture were compared with measured data. Main results: Results indicated that the CROPGRO-Soybean in DSSAT software simulated more accurate crop growth of soybean than AquaCrop. The average nRMSE of the DSSAT model for estimating soil moisture, leaf area index, grain yield, and biomass were 6%, 14%, 16% and 20%, respectively. For maize, AquaCrop simulated crop growth more reliably than CERES-maize. The average nRMSE of 3%, 10%, 13% and 27% of the Aquacrop model in simulating the parameters of soil moisture, green crop canopy, biomass, and grain yield. Research highlights: Considering the better performance of AquaCrop for maize and DSSAT for soybean in the study area, it is not possible to propose a specific model to simulate the growth of all crops in a region.

Spatiotemporal pattern of vegetation water use efficiency between 2003 and 2017 and its coupling relationship with artificial carbon sequestration in the karst region of Southwestern China

After extensive ecological restoration in the karst region of Southwestern China, a potential zone for achieving “carbon neutrality” has emerged, despite facing water scarcity. We analyzed the dynamics of water use efficiency (WUE) and its correlations with soil moisture (SM) and leaf area index (LAI) from 2003 to 2017 using PML-V2 data and multiple datasets for SM and LAI. Advantageous areas for artificial carbon sequestration (CS) were also identified. Key findings are as follows: (1) Temporally, WUE exhibited fluctuating growth at an annual rate of 0.01 gCmm−1H2O (P < 0.05). The advantage areas for CS accounted for 15.96%, with over 31.74% of regions needing management intervention. (2) Monthly WUE was highest in the peak forest plain (PFP) landform (2.88 gCmm−1H2O), while PFP experiencing an annual decrease of −0.0021 gCmm−1H2O. (3) Monthly WUE was highest in forests (2.88 gCmm−1H2O), followed by shrubs (2.49 gCmm−1H2O), farmland (2.32 gCmm−1H2O) and grassland (1.93 gCmm−1H2O), while farmland showing the highest rate of increase (0.02). (4) WUE and SM exhibited both positive (14.26%∼26.02%) and negative (14.19%∼30.98%) correlations. In positive areas, WUE decreased as the drought stress index (DSI) increased for all vegetation types. Clear DSI threshold observed for farmland (0.29 to 0.42) and grassland (0.19 to 0.30). However, the peak values for forests and shrubs were less pronounced. There was a transitional point in the negative impact of DSI on WUE, which increased at a faster rate for all vegetation types when DSI exceeded 0.53. (5) WUE and LAI exhibited both positive (27.09%∼30.25%) and negative (23.37%∼34.57%) correlations. In positive areas, WUE increased as LAI increased for all vegetation types. However, forests exhibited threshold LAI values of 1.85 based on MODIS data, 2.71 based on GLASS data, and 2.59 based on GEOV2 data, shrubs had values of 1.69, 2.66, and 2.23, respectively. While grassland displayed LAI values of 0.79, 0.70, and 0.72, respectively. In negative areas, WUE decreased as LAI increased for all vegetation types. The minimum WUE values were observed when the LAI of farmland was 3.14 based on MODIS data, 4.05 based on GLASS data, and for grassland, it was 1.04, 1.97, and 1.76 based on the GEOV2 data, respectively. This study helps us to identify areas with potential for enhancing CS. It also assists in making informed decisions regarding the implementation of ecological restoration initiatives by considering SM as the limiting factor for adjustment measures and utilizing LAI as the reference standard.

Vegetation Representation Influences Projected Streamflow Changes in the Colorado River Basin

Abstract Vegetation parameters for the Variable Infiltration Capacity (VIC) hydrologic model were recently updated using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Previous work showed that these MODIS-based parameters improved VIC evapotranspiration simulations when compared to eddy covariance observations. Due to the importance of evapotranspiration within the Colorado River basin, this study provided a basin-by-basin calibration of VIC soil parameters with updated MODIS-based vegetation parameters to improve streamflow simulations. Interestingly, while both configurations had similar historic streamflow performance, end-of-century hydrologic projections, driven by 29 downscaled global climate models under the RCP8.5 emissions scenario, differed between the two configurations. The calibrated MODIS-based configuration had an ensemble mean that simulated little change in end-of-century annual streamflow volume (+0.4%) at Lees Ferry, Arizona, relative to the historical period (1960–2005). In contrast, the previous VIC configuration, which is used to inform decisions about future water resources in the Colorado River basin, projected an 11.7% decrease in annual streamflow. Both VIC configurations simulated similar amounts of evapotranspiration in the historical period. However, the MODIS-based VIC configuration did not show as much of an increase in evapotranspiration by the end of the century, primarily within the upper basin’s forested areas. Differences in evapotranspiration projections were the result of the MODIS-based vegetation parameters having lower leaf area index values and less forested area compared to previous vegetation estimates used in recent Colorado River basin hydrologic projections. These results highlight the need to accurately characterize vegetation and better constrain climate sensitivities in hydrologic models. Significance Statement Understanding systemic changes in annual Colorado River basin flows is critical for managing long-term reservoir levels. Single-digit percentage decreases have the potential to degrade the regions’ water supply, hydropower generation, and environmental concerns. Hydrology projections under climate change have largely been based on simulations from the Variable Infiltration Capacity model. Updating the model’s vegetation representation based on updated satellite information highlighted the sensitivity of the hydrologic projections to the models’ vegetation representation primarily within forested areas. This updated model did not increase in evapotranspiration by the end of the century as much as previous simulations. This increased the mean and ensemble spread of the projected streamflow changes, emphasizing the need to properly characterize the hydrologic model’s vegetation parameters and better constrain model climate sensitivity.

Different Concentrations of Chemical Topping Agents Affect Cotton Yield and Quality by Regulating Plant Architecture

Manual removal of the main stem tips of cotton (Gossypium hirsutum L.) is a traditional topping approach in China. However, chemical topping has become an inevitable trend. Therefore, it is of great significance to study the effect and appropriate concentration of agents for advancement of cotton whole process mechanization management technology. A two-year field experiment was conducted to evaluate the effects of different methods of topping on cotton yield and plant architecture in Shihezi, Xinjiang, China. Plant topping included manual topping, non-topping as the control, and chemical topping (high, medium, and low concentrations of topping agent) to determine a suitable topping method and topping agent concentration for machine-picked cotton. Chemical topping was performed using fortified mepiquat chloride (DPC+). Chemical topping and manual topping increased seed cotton yield compared with non-topping. Compared with non-topping, high, medium, and low concentrations of topping agent significantly increased the yield by 19.5–27.9%, 24.1–29.4%, and 24.3–28.4%, respectively. Topping treatment mainly regulated yield and total boll number per unit area by affecting the boll number per plant and had a certain positive effect on fiber strength but no significant effect on boll weight. Chemical topping affected both vertical and horizontal plant architecture characteristics of cotton; the plant height of low-, medium-, and high-concentration treatments increased by 7.2–11.4 cm, 4.0–5.7 cm, and 2.3–4.4 cm, respectively, compared with manual topping and decreased by 5.1–7.8 cm, 8.3–13.5 cm, and 9.4–16.9 cm, respectively, compared with non-topping. The number of main stem internodes was significantly different between high- and low-concentration treatments, which showed that the higher the concentration, the lower the number of the main stem internodes. Chemical topping controlled the increase in the length of the upper branches, the higher the concentration, the lower the increase in the length of the upper fruit branch. Compared with non-topping, the medium concentration of topping agent controlled the number of fruit branches, similar to manual topping. The role of upper internode length of cotton cannot be ignored under chemical topping. The peak leaf area index (LAI) of medium- and high-concentration treatments was delayed for 1 week in the late growth period (after topping for 28 d) compared with manual topping. The LAI values were high, and the duration of high values was prolonged. The optimal chemical topping agent was the medium concentration.

AM–GM Algorithm for Evaluating, Analyzing, and Correcting the Spatial Scaling Bias of the Leaf Area Index

The leaf area index (LAI) is a crucial variable in climate, ecological, and land surface modeling. However, the estimation of the LAI from coarse-resolution remote sensing data can be affected by the spatial scaling bias, which arises from the nonlinearity of retrieval models and the heterogeneity of the land surface. This study provides an algorithm named Arithmetic Mean and Geometric Mean (AM–GM) to correct the spatial scaling bias. It is established based on negative logarithmic functions and avoids second-order stationarity. In this algorithm, relationships are derived between the scaling bias of LAI and the arithmetic and geometric means of directional gap probability for two commonly used remote sensing models, the Beer–Lambert law and a semi-empirical transfer function, respectively. According to the AM–GM algorithm, the expression representing the model nonlinearity is derived and utilized for the analysis of LAI scaling bias. Furthermore, the AM–GM algorithm is simplified by a linear relationship, which is constructed between two quantities related to the directional gap probability between two specific resolutions. Two scenes simulated by the LargE-Scale remote sensing data and image Simulation framework (LESS) model and three sites are used to evaluate the proposed algorithm and analyze the scaling bias of LAI. The validation results show that the AM–GM algorithm provides accurate correction of LAI scaling bias. The analyses based on the AM–GM algorithm demonstrate that the scaling bias of LAI increases with the increase in the LAI value, with stronger surface heterogeneity and coarser spatial resolution. The validation results of the simplified AM–GM algorithm demonstrate that at the Sud-Ouest site, the absolute value of the bias for the estimated LAI decreases from 0.10, 0.22, 0.29, and 0.31 to 0.04, 0.01, 0.04, and 0.05 at 200 m, 500 m, 1000 m, and 1500 m resolutions, respectively. In conclusion, the proposed algorithm is effective in the analysis and correction of the scaling bias for coarse-resolution LAI.

Detection of irrigation dates and amounts on maize plots from the integration of Sentinel-2 derived Leaf Area Index values in the Optirrig crop model

Detection of irrigation dates and amounts on maize plots from the integration of Sentinel-2 derived Leaf Area Index values in the Optirrig crop model

An Approach for Joint Estimation of Grassland Leaf Area Index and Leaf Chlorophyll Content from UAV Hyperspectral Data

Leaf area index (LAI) and leaf chlorophyll content (Cab) are two important indicators of vegetation growth. Due to the high-coupling of spectral signals of leaf area and chlorophyll content, simultaneous retrieval of LAI and Cab from remotely sensed date is always challenging. In this paper, an approach for joint estimation of grassland LAI and Cab from unmanned aerial vehicle (UAV) hyperspectral data was proposed. Firstly, based on a PROSAIL model, 15 typical hyperspectral vegetation indices (VIs) were calculated and analyzed to identify optimal VIs for LAI and Cab estimation. Secondly, four pairs of VIs were established and their discreteness was also calculated for building a two-dimension matrix. Thirdly, a two-layer VI matrix was generated to determine the relationship of VIs with LAI values and Cab values. Finally, LAI and Cab were jointly retrieved according to the cells of the two-layer matrix. The retrieval reduced the cross-influence between LAI and Cab. Compared with the VI empirical model and the single-layer VI matrix, the accuracy of LAI and Cab retrieved from UAV hyperspectral data based on the two-layer VI matrix was significantly improved (for LAI: R2 = 0.73, RMSE = 0.91 m2/m2 and u(SD) = 0.82 m2/m2; for Cab: R2 = 0.79, RMSE = 11.7 μg/cm2 and u(SD) = 10.84 μg/cm2). The proposed method has the potential for rapid retrieval of LAI and Cab from hyperspectral data. As a method similar to look-up table, the two-layer matrix can be used directly for LAI and Cab estimation without the need for prior measurements for training.

Assessing the influence of crop model structure on the performance of data assimilation for sugarcane

Process-base crop models (PBM) are important tools to describe how the agricultural system responds to environmental conditions. Sugarcane represents a major world source of sugar and ethanol and its PBMs had different levels of complexity in terms of structure, i.e. how detailed their processes were described. Yet, literature has widely demonstrated that data assimilation techniques (DA) represent a valuable option for reducing model uncertainty, but the inconsistency between PBM and the assimilated variable can significantly affect the performance of DA. Such limitation is strictly connected to model structure, and a hypothesis that arises from literature is that the use of more complex models would reduce model uncertainty after DA. We accessed the performance of using two different PBMs, one more detailed (DSSAT/SAMUCA, DS) and the other more general (WOFOST, WO), by assimilating leaf area index (LAI) retrieved from Landsat 7 ETM + and 8/OLI, using the Ensemble Kalman Filter (EnKF). Both PBMs were calibrated and evaluated with a robust database of 13 experiments and evaluated against a sugarmill database to evaluate the EnKF performance, compared with simulations without DA (Open-loop, OP). Moreover, the processes involved in LAI simulations were analyzed to assess the EnKF performance. The DS had superior performance in the calibration and evaluation step with EF = 0.907, 0.878, 0.458 for stalk dry mass, stalk fresh yield (SFY), and LAI, while WO showed EF = 0.622, 0.610, 0.417 for the same variables, respectively. The calibration step affected the OP plot simulation, with DS having higher accuracy (RMSE = 31.678 Mg ha−1) and precision (R2 = 0.509), compared with WO (RMSE = 39.593 Mg ha−1; R2 = 0.458). However, after DA, both PBM presented error inconsistency with EnKF, despite the decrease in RMSE (-44.73% and −29.58%) and increase in R2 (22.15% and 36.50%) of DS and WO, respectively. The error inconsistency diverged from each PBM: the OP simulation of DS overestimated the Landsat LAI; after DA, simulated LAI decreased resulting in SFY underestimation (Bias = -11.469 Mg ha−1); WO showed OP simulations for LAI closer to Landsat’s LAI values, despite the positive Bias in SFY estimation, and so EnKF slightly reduced the SFY overestimation (Bias = 22.944 Mg ha−1). Thus, the better descriptions of DS in terms of structure did not inhibit the error inconsistency. We suggested that new studies are required to understand how the assimilated variables impact on the other state variables of the PBM.

Plant Growth and Soil Water Content Changes under Different Inter-Row Soil Management Methods in a Sloping Vineyard.

The main objective of this study was to investigate soil-plant-water interactions based on field measurements of plant reflectance and soil water content (SWC) in different inter-row managed sloping vineyards. The following three different soil management applications were studied: tilled (T), cover crops (CC), and permanent grass (NT) inter-rows. We measured SWCs within the row and between rows of vines. Each investigated row utilized 7 to 10 measurement points along the slope. Topsoil SWC and temperature, leaf NDVI and chlorophyll concentrations and leaf area index (LAI) were measured every two weeks over the vegetation period (May to November) using handheld instruments. We found that management method and slope position can significantly affect the soil's physical and chemical properties, such as clay or soil organic carbon contents. Cover crops in the inter-row significantly reduced average SWC. The in-row average topsoil SWCs and temperatures were lower in all study sites compared to the values measured in between rows. Significantly higher SWCs were observed for the upper points compared to the lower ones for CC and T treatments (58.0 and 60.9%, respectively), while the opposite was noted for NT. Grassed inter-row grapevines had significantly lower leaf chlorophyll content than the other inter-row managed sites (p < 0.001). The highest average leaf chlorophyll contents were observed in the T vineyard (16.89 CCI). Based on slope positions, the most distinguishable difference was observed for the CC: 27.7% higher chlorophyll values were observed at the top of the slope compared to the grapevine leaves at the bottom of the slope (p < 0.01). The leaf NDVI values were not as profoundly influenced by slope position in the vineyard as the chlorophyll values were. For overall LAI values, the T treatment had significantly lower values compared to NT and CC (p < 0.001). Moderate correlations were observed between NDVI and LAI and soil nitrogen and carbon content. In general, we found that both inter-row management and slope position can significantly influence soil parameters and affect plant growth, and consequently can accelerate plant stress under sub-optimal environmental conditions such as prolonged drought.

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.

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.