Variation In Leaf Area Index Research Articles

Assimilation of NEON Observations Into a Process‐Based Carbon Cycle Model Reveals Divergent Mechanisms of Carbon Dynamics in Temperate Deciduous Forests

AbstractTerrestrial ecosystems can potentially alleviate or exacerbate climate change by regulating atmospheric CO2 concentration. Divergent predictions of the terrestrial C sink by the Earth System Models (ESMs) indicate no unified mechanism regarding abiotic and biotic response to climate change. The amount and diversity of observations of the terrestrial C cycle create an opportunity to improve the predictive capacity of C‐cycle models. Modeling study with observations could provide valuable insights into the controls on interannual variability of the terrestrial C sink. In this study, we used data from three deciduous forest sites in the National Ecological Observatory Network (NEON) to do site‐specific parameterizations in the Terrestrial ECOsystem model (TECO), and explore controls of the net C uptake. Calibrated TECO explained 58%–83% of variation in leaf area index and 35%–40% in net ecosystem C exchange. Root mean square (percentage) errors were 131–436 gC/m2 (0.9%–3.3%) for wood C pool, 3–73 gC/m2 (0.5%–11.5%) for fine root C, and 170–763 gC/m2 (1.9%–8.0%) for soil C pool. Calibrated parameters revealed site‐specific processes in phenology and turnover of leaves, fine roots, slow‐decomposing soil C, and temperature sensitivity of organic matter decomposition among the three deciduous forest sites. These findings imply the presence of bias in models using parameters at plant functional type (PFT) level, which is the case for many ESMs relying on land surface components (i.e., PFTs), rather than site‐specific parameters. Lastly, the magnitude of the terrestrial C sink in deciduous forests increased with temperature and this increase was caused by the temperature‐driven stimulation of the gross primary production.

UAV Oblique Imagery with an Adaptive Micro-Terrain Model for Estimation of Leaf Area Index and Height of Maize Canopy from 3D Point Clouds

Leaf area index (LAI) and height are two critical measures of maize crops that are used in ecophysiological and morphological studies for growth evaluation, health assessment, and yield prediction. However, mapping spatial and temporal variability of LAI in fields using handheld tools and traditional techniques is a tedious and costly pointwise operation that provides information only within limited areas. The objective of this study was to evaluate the reliability of mapping LAI and height of maize canopy from 3D point clouds generated from UAV oblique imagery with the adaptive micro-terrain model. The experiment was carried out in a field planted with three cultivars having different canopy shapes and four replicates covering a total area of 48 × 36 m. RGB images in nadir and oblique view were acquired from the maize field at six different time slots during the growing season. Images were processed by Agisoft Metashape to generate 3D point clouds using the structure from motion method and were later processed by MATLAB to obtain clean canopy structure, including height and density. The LAI was estimated by a multivariate linear regression model using crop canopy descriptors derived from the 3D point cloud, which account for height and leaf density distribution along the canopy height. A simulation analysis based on the Sine function effectively demonstrated the micro-terrain model from point clouds. For the ground truth data, a randomized block design with 24 sample areas was used to manually measure LAI, height, N-pen data, and yield during the growing season. It was found that canopy height data from the 3D point clouds has a relatively strong correlation (R2 = 0.89, 0.86, 0.78) with the manual measurement for three cultivars with CH90. The proposed methodology allows a cost-effective high-resolution mapping of in-field LAI index extraction through UAV 3D data to be used as an alternative to the conventional LAI assessments even in inaccessible regions.

Detection of Multidecadal Changes in Vegetation Dynamics and Association with Intra-Annual Climate Variability in the Columbia River Basin

Remotely-sensed Leaf Area Index (LAI) is a useful metric for assessing changes in vegetation cover and greeness over time and space. Satellite-derived LAI measurements can be used to assess these intra- and inter-annual vegetation dynamics and how they correlate with changing regional and local climate conditions. The detection of such changes at local and regional levels is challenged by the underlying continuity and extensive missing values of high-resolution spatio-temporal vegetation data. Here, the feasibility of functional data analysis methods was evaluated to improve the exploration of such data. In this paper, an investigation of multidecadal variation in LAI is conducted in the Columbia River Watershed, as detected by NOAA Advanced Very High-Resolution Radiometer (AVHRR) satellite imaging. The inter- and intra-annual correlation of LAI with temperature and precipitation were then investigated using data from the European Centre for Medium-Range Weather Forecasts global atmospheric re-analysis (ERA-Interim) in the period 1996–2017. A functional cluster analysis model was implemented to identify regions in the Columbia River Watershed that exhibit similar long-term greening trends. Across this region, a multidecadal trend toward earlier and higher annual LAI peaks was detected, and strong correlations were found between earlier and higher LAI peaks and warmer temperatures in late winter and early spring. Although strongly correlated to LAI, maximum temperature and precipitation do not demonstrate a similar strong multidecadal trend over the studied time period. The modeling approach is proficient for analyzing tens or hundreds of thousands of sampled sites without parallel processing or high-performance computing (HPC).

Leaf Area Index Estimation of Pergola-Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods

The leaf area index (LAI), a valuable variable for assessing vine vigor, reflects nutrient concentrations in vineyards and assists in precise management, including fertilization, improving yield, quality, and vineyard uniformity. Although some vegetation indices (VIs) have been successfully used to assess LAI variations, they are unsuitable for vineyards of different types and structures. By calibrating the light extinction coefficient of a digital photography algorithm for proximal LAI measurements, this study aimed to develop VI-LAI models for pergola-trained vineyards based on high-resolution RGB and multispectral images captured by an unmanned aerial vehicle (UAV). The models were developed by comparing five machine learning (ML) methods, and a robust ensemble model was proposed using the five models as base learners. The results showed that the ensemble model outperformed the base models. The highest R2 and lowest RMSE values that were obtained using the best combination of VIs with multispectral data were 0.899 and 0.434, respectively; those obtained using the RGB data were 0.825 and 0.547, respectively. By improving the results by feature selection, ML methods performed better with multispectral data than with RGB images, and better with higher spatial resolution data than with lower resolution data. LAI variations can be monitored efficiently and accurately for large areas of pergola-trained vineyards using this framework.

Assessment of cotton and sorghum stand establishment using UAV‐based multispectral and DSLR‐based RGB imagery

AbstractPlant density and canopy cover are key agronomic traits for cotton (Gossypium hirsutum L.) and sorghum [Sorghum bicolor (L.) Moench] phenotypic evaluation. The objective of this study was to evaluate utility of broadband red–green–blue (RGB) and narrowband green, red, red‐edge, and near‐infrared spectral data taken by an unmanned aerial vehicle (UAV), and RGB taken by a digital single‐lens reflex camera for assessing the cotton and sorghum stands. Support Vector Machine was used to analyze UAV images, whereas ImageJ was used for RGB images. Fifteen vegetation indices (VIs) were evaluated for their accuracy, predictability, and residual yield. All VIs had Cohen's k > .65, F score > .63, and User and Producer accuracy of more than 71 and 69%, respectively. Soil‐adjusted vegetation indices (SAVIs) among narrowband VIs and excess green minus excess red (ExG–ExR) among broadband VIs provided more agreeable estimates of cotton and sorghum density than the remaining VIs with R2 and index of agreement (IoA) up to .79 and .92, respectively. The estimated canopy cover explained up to 83 and 82% variability in leaf area index (LAI) of cotton and sorghum, respectively. The ImageJ produced R2 from .79 to .90 and .83 to .86 and IoA .89 to .97 and ∼.91 between estimated and observed cotton and sorghum density, respectively. ImageJ explained up to 82 and 79% variability in cotton and sorghum LAI, respectively. Although ImageJ can give close estimates of crop density and cover, UAV‐based narrowband VIs still can provide an agreeable, reliable, and time‐efficient estimate of these attributes.

Natural and Anthropogenic Variation of Stand Structure and Aboveground Biomass in Niger Delta Mangrove Forests

Mangrove forests are important coastal wetlands because of the ecosystem services they provide especially their carbon potential. Mangrove forests productivity in the Niger Delta are poorly quantified and at risk of loss from oil pollution, deforestation, and invasive species. Here, we report the most extensive stem girth survey yet of mangrove plots for stand and canopy structure in the Niger Delta, across tidal and disturbance gradients. We established twenty-five geo-referenced 0.25-ha plots across two estuarine basins. We estimated aboveground biomass (AGB) from established allometric equations based on stem surveys. Leaf area index (LAI) was recorded using hemispherical photos. We estimated a mean AGB of 83.7 Mg ha–1 with an order of magnitude range, from 11 to 241 Mg ha–1. We found significantly higher plot biomass in close proximity to a protected site and tidal channels, and the lowest in the sites where urbanization and wood exploitation was actively taking place. The mean LAI was 1.45 and ranged fivefold from 0.46 to 2.41 and there was a significant positive correlation between AGB and LAI (R2 = 0.31). We divided the plots into two disturbance regimes and three nipa palm (Nypa fruticans) invasion levels. Lower stem diameter (5–15 cm) accounted for 70% of the total biomass in disturbed plots, while undisturbed regimes had a more even (∼25%) contribution of different diameter at breast height (DBH) size classes to AGB. Nipa palm invasion also showed a significant link to larger variations in LAI and the proportion of basal area removed from plots. We conclude that mangrove forest degradation and exploitation is removing larger stems (>15 cm DBH), preferentially from these mangroves forests and creates an avenue for nipa palm colonization. This research identifies opportunities to manage the utilization of mangrove resources and reduce any negative impact. Our data can be used with remote sensing to estimate biomass in the Niger Delta and the inclusion of soil, leaf properties and demographic rates can analyze mangrove-nipa competition in the region.

Hyperspectral Characteristics and Scale Effects of Leaf and Canopy of Summer Maize under Continuous Water Stresses

The scale effect problem is one of the most challenging issues in remote sensing studies. However, the research on the methodology and theory of the scale effect is scarcely applied in practice. To this end, in this study, 3 years of field experimental data of continuous water stresses on summer maize were used for this purpose. Furthermore, the Prospect and Sail models were employed to investigate the scale effects of reflectance characteristics and vegetation indexes. The results indicated that the spectral characteristics of canopy and leaf of summer maize were similar under continuous water stresses at various stages. The reflectance at the canopy level was distinct from that at the leaf level, considering the soil background differences. From leaf to canopy scales, with the increase in the leaf area index (LAI), the spectral reflectance of all treatments in the visible band decreased, but increased in the near-infrared band, and the reflectance was saturated when LAI increased to 5. The reflectance difference caused by LAI variation was enlarged as the drought stress intensified in the short-wave infrared band. The spectral reflectance in the near-infrared band was susceptible to leaf inclination angle (LIA) variation and changed significantly, especially in the closed canopy. With the increase in LAI, the difference vegetation index (DVI) and normalized difference vegetation index (NDVI) values under each treatment showed a gradually increasing trend. With the increase in LIA, the DVI value decreased gradually, and the DVI value under the saturated canopy was significantly higher than that under the unclosed canopy. However, the NDVI values of all treatments did not change with LIA, mostly under the closed canopy. Overall, the results demonstrated that LAI had a more significant influence on canopy reflectance than LIA. In addition, NDVI was not able to capture the LAI and LIA information when the canopy was closed, but DVI performed better.

Crop Water Stress Index as a Proxy of Phenotyping Maize Performance under Combined Water and Salt Stress

The crop water stress index (CWSI), based on canopy temperature (Tc), has been widely used in evaluating plant water status and planning irrigation scheduling, but whether CWSI can diagnose the stress status of crops and predict the physiological traits and growth under combined water and salt stress remains to be further studied. Here, a model of CWSI was established based on the continuous measurements of Tc for two maize genotypes (ZD958 and XY335) under two water and salt conditions, combined with growth stage-specific non-water-stressed baselines (NWSB). The relationships between physiology, growth, and yield of maize with CWSI were analyzed. There were significant differences in NWSB between the two maize genotypes at the same and different growth stages; thus, growth stage-specific NWSBs were used. The difference in NWSB was due to the difference and change in effective leaf width. CWSI was closely related to leaf water potential, stomatal conductance, and net photosynthetic rate under different water and salt stress, and also explained the variations in leaf area index, biomass, water use, and yield. Collectively, CWSI can be used as a proxy indicator of high-throughput phenotyping maize performance under combined water and salt stress, which will be valuable for predicting yield and improving water use efficiency.

Influence of Leaf Area Index on the Heat Index of a Tropic Urban Park

In the wake of climate change, cities need to adapt to global warming. In this context, the use of afforestation to improve the microclimate may assist in raising the quality of life for population. This objective requires research that analyzes how the variations in parameters related to canopy dynamics, such as the leaf area index (LAI) and photosynthetically active radiation (PAR) can influence thermal comfort indices. To contribute to this research, this study measured the air temperature, relative air humidity, PAR, and LAI on a monthly basis from July, 2017, to June, 2018, in an urban park in a tropical region of Brazil. Kriging maps were created for the heat index (HI), and multiple polynomial regression models were adjusted to estimate the HI using PAR and LAI data. After defining the models, positive and negative variations of LAI were tested to observe if any changes in HI occurred. The simulated results showed greater sensitivity to negative variations in LAI, in which a 50% reduction in LAI decreased the HI by 28%, particularly during the dry period.

Estimation of leaf area index for winter wheat at early stages based on convolutional neural networks

Leaf area index (LAI) is a key growth trait to characterize the winter wheat growth at early stages. However, there needs more study on the low cost and fine-scale estimation of LAI of winter wheat. In this study, a LAI estimation method of winter wheat at the early stages was proposed based on low-cost RGB images and deep learning. The time-series canopy images of winter wheat at early stages were collected for two consecutive growth seasons (growth season 2018 and 2019), based on which the proposed model, as well as the compared models, were built. In the following step, the performances of these models were compared and analyzed. The influences of the input image with different pixel resolutions and the network depth on the model performance were discussed. Moreover, transfer learning was used to test the generalization ability of the proposed model. The results showed that the proposed estimation model could reflect the time-series variation of LAI of winter wheat at early stages. The proposed model with the input image of 128 × 128 pixel resolution achieved the best performance (R2 = 0.82, NRMSE = 24.89%), outperforming the compared models. The generalization test showed that the proposed model had a good generalization ability, achieving accurate LAI estimations for growing season 2019. However, deepen the network by adding extra SAME convolutional layers could not improve the model performance. In conclusion, based on the convolutional neural network (CNN) and low-cost RGB images, the proposed model is fast and accurate in estimating the LAI of winter wheat at early stages. This method can meet the need for LAI estimation of winter wheat at early stages and provide support for growth monitoring and agronomic management of winter wheat at early stages.

Temporal variability in evapotranspiration and energy partitioning over a seasonally flooded scrub forest of the Brazilian Pantanal

Brazilian Pantanal is the largest seasonal wetland in the world and it plays an important role in regulating the local and global water cycle and energy balance. The Pantanal landscape consists of a mosaic of temporary and permanent aquatic habitats, floodable and non-floodable pastures, and forests; however, patterns of energy exchange are poorly understood, especially for seasonally flooded forests. Thus, the main objective of this research was to assess the variability of evapotranspiration (ET) and to evaluate biophysical controls on ET for a seasonally flooded scrub-forest. We used eddy covariance to measure latent (LE) and sensible (H) heat fluxes and linked both micrometeorological and remote sensing data to assess the connection between water dynamics and phenology. Our data suggest that 72% of the net radiation (Rn) was consumed by LE + H, even during the dry season, indicating that trees can access deep water when surface water is limiting. The decoupling factor (0.63 ± 0.08) suggests that the canopy is decoupled from the atmosphere and ET is essentially sensitive to Rn. The patterns of ET and vapor pressure deficit suggest that ET was more dependent on the Rn received and less dependent on stomatal control, but still exhibited a strong relationship with biotic factors such as enhanced vegetation index (EVI), which is sensitive to seasonal variations in leaf area index and canopy cover. Stomatal limitations of this seasonally flooded forest appear to be minimal, even in the dry season, presumably due to the relatively high-water table, the ability of trees to acquire subsurface water, and the predominance of secondary species with high rates of leaf gas exchange. These data indicate that seasonally flooded tropical forests of the Pantanal are consistent sources of water vapor to the atmosphere even during seasonal drought.

Leaf Area Index Variations in Ecoregions of Ardabil Province, Iran

The leaf area index (LAI) is an important vegetation biophysical index that provides broad information on the dynamic behavior of an ecosystem’s productivity and related climate, topography, and edaphic impacts. The spatiotemporal changes of LAI were assessed throughout Ardabil Province—a host of relevant plant communities within the critical ecoregion of a semi-arid climate. In a comparative study, novel data from Google Earth Engine (GEE) was tested against traditional ENVI measures to provide LAI estimations. Moreover, it is of important practical significance for institutional networks to quantitatively and accurately estimate LAI, at large areas in a short time, and using appropriate baseline vegetation indices. Therefore, LAI was characterized for ecoregions of Ardabil Province using remote sensing indices extracted from Landsat 8 Operational Land Imager (OLI), including the Enhanced Vegetation Index calculated in GEE (EVIG) and ENVI5.3 software (EVIE), as well as the Normalized Difference Vegetation Index estimated in ENVI5.3 software (NDVIE). Moreover, a new field measurement method, i.e., the LaiPen LP 100 portable device (LP 100), was used to evaluate the accuracy of the derived indices. Accordingly, the LAI was measured in June and July 2020, in 822 ground points distributed in 16 different ecoregions-sub ecoregions having various plant functional types (PFTs) of the shrub, bush, and tree. The analyses revealed heterogeneous spatial and temporal variability in vegetation indices and LAIs within and between ecoregions. The mean (standard deviation) value of EVIG, EVIE, and NDVIE at a province scale yielded 1.1 (0.41), 2.20 (0.78), and 3.00 (1.01), respectively in June, and 0.67 (0.37), 0.80 (0.63), and 1.88 (1.23), respectively, in July. The highest mean values of EVIG-LAI, EVIE-LAI, and NDVIE-LAI in June are found in Meshginshahr (1.40), Meshginshahr (2.80), and Hir (4.33) ecoregions and in July are found in Andabil ecoregion respectively with values of 1.23, 1.5, and 3.64. The lowest mean values of EVIG-LAI, EVIE-LAI, and NDVIE-LAI in June were observed for Kowsar (0.67), Meshginshahr (1.8), and Neur (2.70) ecoregions, and in July, the Bilesavar ecoregion, respectively, with values of 0.31, 0.31, and 0.81. High correlation and determination coefficients (r > 0.83 and R2 > 0.68) between LP 100 and remote sensing derived LAI were observed in all three PFTs (except for NDVIE-LAI in June with r = 0.56 and R2 = 0.31). On average, all three examined LAI measures tended to underestimate compared to LP 100-LAI (r > 0.42). The findings of the present study could be promising for effective monitoring and proper management of vegetation and land use in the Ardabil Province and other similar areas.

Cross-ECV consistency at global scale: LAI and FAPAR changes

A framework is proposed for assessing the physical consistency between two terrestrial Essential Climate Variables (ECVs) products retrieved from Earth Observation at global scale. The methodology assessed the level of agreement between the temporal variations of Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The simultaneous changes were classified according to their sign, magnitude and level of confidence, whereby the respective products uncertainties were taken into consideration. A set of proposed agreement metrics were used to identify temporal and spatial biases of non-coherency, non-significance, sensitivity and the overall level of agreement of the temporal changes between two ECVs. We applied the methodology using the Joint Research Center (JRC) Two-stream Inversion Package (TIP) products at 1 km, those provided by the Copernicus Global Land Service (CGLS) based on the SPOT/VGT and Proba-V at 1 km, and the MODIS MCD15A3 at 500 m. In addition, the same analysis was applied with aggregated products at a larger scale over Southern Africa. We found that the CGLS LAI and FAPAR products lacked consistency in their spatial and temporal changes and were severely affected by trends. The MCD15A3 products were characterized by the highest number of non-coherent changes between the two ECVs but temporal inconsistencies were mainly located over the eastern hemisphere. The JRC-TIP products were highly consistent. The results showed the advantages of physically-based retrieval algorithms, in both JRC-TIP and MODIS products, and indicated also that, except for MODIS over forests, aggregated products using an uncertainty-based weighted average led to higher agreement between the ECVs changes.

Meta-analysis of leaf area index, canopy height and root depth of three bioenergy crops and their effects on land surface modeling

The expected large-scale expansion of biofuel production in climate change mitigation scenarios calls for improvements in the representation of bioenergy crops in land surface models. Leaf area index (LAI), canopy height (CH) and root depth (RD) are key parameters that regulate exchanges of heat and moisture between land and atmosphere. This study performs a meta-analysis combining unique data from 34 observational studies and 14 countries to estimate monthly variability in LAI, CH, and RD of three main bioenergy crops: miscanthus (MSC), switchgrass (SWG), and reed canary grass (RCG). Using the Community Land Model v.5.0 and the results from the meta-analysis, we also tested the effects that variability in parameterization of LAI and CH have on key components of the surface energy budget, relative to prescribed values. Results from the meta-analysis show a strong seasonality of LAI and CH, with mean (± one standard error) LAI values at the peak summer month of 6.05 ± 0.84, 5.56 ± 0.75, and 5.39 ± 1.15 m2/m2, and maximum CH of 246 ± 23, 147 ± 16, and 156 ± 10 cm, for MSC, SWG, and RCG, respectively. These values are typically larger than the default parameterizations in CLM. Information on RD was limited and average values are 172 ± 56, 165 ±46, and 193 ± 11 cm, for MSC, SWG, and RCG, respectively. The seasonal cycles of latent heat, sensible heat and surface albedo are primarily affected by the range of LAI values, and less sensitive to variability in CH. Relative to the default values, higher LAI values increase latent heat and decrease sensible heat, with the highest absolute changes in summer. They also decrease surface albedo in winter months due to a larger snow masking effect. Our results offer a basis to compare experimental work and modelling studies, improve parameterization in land surface models, and identify the importance of vegetation structure parameters to evaluate key climate processes in response to bioenergy crops.

Feedback From Vegetation to Interannual Variations of Indian Summer Monsoon Rainfall

AbstractInterannual variations of Indian summer monsoon rainfall (ISMR) are modulated by external forcings such as El Niño Southern Oscillation, Indian Ocean Dipole, and the Atlantic Niño. Vegetation over land responds to variations in ISMR, but the feedback from vegetation to ISMR variability has not been fully explored yet. To address this gap, we perform two simulations with the regional Weather Research and Forecasting model coupled to the Community Land Surface Model (WRF‐CLM) for the period of 2004–2018. We use the same boundary forcing from ERA‐interim reanalysis for the two experiments, but with two different vegetation prescriptions, (1) observed, interannually varying Leaf Area Index (LAI), obtained from satellite images/data (VAR‐LAI); and (2) climatological Leaf Area Index from the same product, to suppress interannual LAI variations (CLIM‐LAI). We find that the correlation coefficient of simulated total seasonal rainfall with the observed data is higher for VAR‐LAI simulation as compared to CLIM‐LAI. To elicit causality among eco‐hydro‐climatological variables, we develop a network based on information theory, i.e., a process network. We find that LAI plays a major role in influencing precipitation in the network through evapotranspiration. The number of links originating from LAI and evapotranspiration increases during drought years, making the eco‐hydro‐climatological network denser. Our findings indicate that the ISMR predictions and projections need to represent the time‐varying LAI to fully capture the varying feedbacks from evolving vegetation to the atmosphere especially during drought years.

Characterizing leaf area index of rooftop farm to assess thermal-energy performance by simulation

This paper presents an exploratory research about the possibilities to simulate the thermal performance of buildings with green roofs, or in this case rooftop farming. Different from the green roofs, a rooftop farming has a leaf area index (LAI) variable during all the year because the vegetable crops growing cycle repeats sometimes along the year. The contribution of this paper is to bring this discussion to the art state. The method uses computer simulation as a research strategy using the software Energy Plus. The research method was developed in 3 steps. In the first one, a literature review was performed in order to search data from 18 vegetable crops and 3 substrates. One of these data was the LAI, which in the second research step was simulated considering 5 different possibilities (LAI constant all the year; LAI observing monthly the growing cycle; Average LAI considering the growing cycle; crop rotation with average monthly LAI; and LAI defined each ten days considering the crop growing cycle). The results demonstrated that: a) the simplified LAI found in the literature considering the rooftop farming simulations are defined as constant; b) using LAI constant all the year can overestimate until 7% the energy use intensity (EUI) in buildings for the climate in the South of Brazil. c) Thus, in order to simulate the performance of buildings with rooftop farming it is important to develop a data base of the vegetable crop considering the LAI variation along the cycles, and also the number of cycles during the year.

The Value of L-Band Soil Moisture and Vegetation Optical Depth Estimates in the Prediction of Vegetation Phenology

Vegetation phenology is a key ecosystem characteristic that is sensitive to environmental conditions. Here, we examined the utility of soil moisture (SM) and vegetation optical depth (VOD) observations from NASA’s L-band Soil Moisture Active Passive (SMAP) mission for the prediction of leaf area index (LAI), a common metric of canopy phenology. We leveraged mutual information theory to determine whether SM and VOD contain information about the temporal dynamics of LAI that is not contained in traditional LAI predictors (i.e., precipitation, temperature, and radiation) and known LAI climatology. We found that adding SMAP SM and VOD to multivariate non-linear empirical models to predict daily LAI anomalies improved model fit and reduced error by 5.2% compared with models including only traditional LAI predictors and LAI climatology (average R2 = 0.22 vs. 0.15 and unbiased root mean square error [ubRMSE] = 0.130 vs. 0.137 for cross-validated models with and without SM and VOD, respectively). SMAP SM and VOD made the more improvement in model fit in grasslands (R2 = 0.24 vs. 0.16 and ubRMSE = 0.118 vs. 0.126 [5.7% reduction] for models with and without SM and VOD, respectively); model predictions were least improved in shrublands. Analysis of feature importance indicates that LAI climatology and temperature were overall the two most informative variables for LAI anomaly prediction. SM was more important in drier regions, whereas VOD was consistently the second least important factor. Variations in total LAI were mostly explained by local daily LAI climatology. On average, the R2s and ubRMSE of total LAI predictions by the traditional drivers and its climatology are 0.81 and 0.137, respectively. Adding SMAP SM and VOD to these existing predictors improved the R2s to 0.83 (0.02 improvement in R2s) and reduced the ubRMSE to 0.13 (5.2% reduction). Though these improvements were modest on average, in locations where LAI climatology is not reflective of LAI dynamics and anomalies are larger, we find SM and VOD to be considerably more useful for LAI prediction. Overall, we find that L-band SM and VOD observations can be useful for prediction of LAI, though the informational contribution varies with land cover and environmental conditions.

Effects of various driving factors on potential evapotranspiration trends over the main grain-production area of China while accounting for vegetation dynamics

Potential evapotranspiration (PET) is an important consideration in the study of agricultural water management and climate change. Although a number of studies have already explored the effects of climate factors on changes in PET, the quantitative effects of various driving factors needs to be further studied, as most studies have not accounted for vegetation dynamics by characterizing changes in leaf area and stomatal resistance. Here, we used the Shuttleworth–Wallace (S-W) model based on climate, vegetation, and energy factors to quantitatively analyze the effects of various driving factors at multiple spatial scales over the main grain-producing area of China (MGPAC) from 1982 to 2016. The S-W model had a satisfactory performance because S-W based Standardized Precipitation Evapotranspiration Index (SPEI) had a higher determination coefficient (0.33 VS. 0.26) than Penman-Monteith based SPEI for soil moisture standardized anomaly over MGPAC. MGPAC had a mean annual PET of 926.20 mm and showed a marked increase of 3.844 mm yr−1 during the study period. Evergreen broadleaf forest and grassland had the highest and lowest annual average PET values (1535.65 mm and 633.67 mm), respectively. Although climate factors could directly explain 79.42% of the PET increase over the MGPAC, vegetation dynamics were also identified to be a non-negligible factor explaining the increase in PET (directly explaining 18.91% of the PET trend). Specifically, vegetation dynamics contributed to explaining 67.30% and 48.91% of the PET trend in the Huang-Huai-Hai and Loess Plateau regions. These findings are closely related to changes in the temperature and leaf area index (LAI), which notably increased by 0.04 °C yr−1 (P < 0.01) and 0.0038 m2 m−2 yr−1 (P < 0.01), respectively. Regarding single drivers, variation in temperature and LAI significantly increased the PET by 3.582 mm yr−1 (P < 0.01) and 1.127 mm yr−1 (P < 0.01) across the MGPAC, respectively. This study can provide a new insight to improve agricultural production and management in China.

Water Stress is a Key Factor Influencing the Parameter Sensitivity of the WOFOST Model in Different Agro-Meteorological Conditions

Sensitivity analysis is helpful for improving the efficiency and accuracy of the calibration of crop growth models. However, parameter sensitivity is still not well understood when combined with different meteorological and production conditions, especially adverse conditions such as water stress. This study simulated the production of winter wheat in four ecological areas in Henan Province, China. The Extend Fourier Amplitude Sensitivity Test algorithm (EFAST) was used for analyzing the sensitivity of 43 crop parameters of the WOrld FOod STudies (WOFOST) model to yield, aboveground biomass, and leaf area index (LAI) with or without water-limited conditions. The results demonstrated that yield and biomass were the objective outputs, and the main limiting factors for the model results were assimilation and dry matter conversion efficiency. Under water-limited conditions, the parameter sensitivity of related extinction coefficient, early wheat leaf area, and root growth increased with increased water stress. With the process variable LAI as the target output, the parameter sensitivity varied at different growth stages, whereas the parameter sensitivity was almost the same under different agro-meteorological conditions. Under water-limited conditions, the parameter sensitivity of wheat early extinction coefficient, maximum root depth, and death rate of the leaves also increased with increased water stress. Therefore, water stress is a key factor affecting parameter sensitivity under different agro-meteorological conditions.

Warming and straw application increased soil respiration during the different growing seasons by changing crop biomass and leaf area index in a winter wheat-soybean rotation cropland

Warming and straw application are two important factors that may influence soil respiration. Investigations of the different effects of warming and straw application on soil respiration during different crop growing seasons are crucial to understand the soil carbon (C) cycles in croplands under future climate scenarios. A two-year field experiment was performed in a winter wheat-soybean cropland in subtropical east China to investigate the different effects of 1.4 °C warming and straw application on soil respiration. There were two main plots, i.e., warming and unwarmed treatments, with three straw application levels: no straw applied, low straw application (6 t ha−1) and high straw application (12 t ha−1) in each treatment. Seasonal variations in soil respiration, soil temperature, soil moisture and leaf area index (LAI) were measured. The results indicated that both warming and straw application significantly increased soil respiration during each growing season. Compared with the unwarmed treatment, warming increased soil respiration by 24.7%, 12.1%, and 12.6% in the plots with no straw applied, low straw application and high straw application, respectively, during the 2017–2019 rotation period. The mean soil respiration during the two-year rotation was significantly (P < 0.01) correlated with root biomass, stem and shoot biomass, grain yield, and LAI. The soil temperature, moisture and LAI-based model accounted for 57.9%-69.1% of the seasonal variation in soil respiration in all treatments. The positive effects of warming and straw application on soil respiration were related more to the variations in crop biomass and LAI than those in grain yield.