Changes In Leaf Area Index Research Articles

Crop growth, carbon sequestration and soil erosion in an organic vineyard of the Villány Wine District, Southwest Hungary

A more resilient adaptation to changing climate calls for crop diversification in vineyards, too. As a contribution to the H2020 collaborative project of the European Union, called Diverfarming, and part of the agroecological experiments during 2018 and 2019, grapevine biomass growth was monitored in connection with carbon storage types in soil and in the deposits removed by soil erosion. Phenometry was carried out interpreting segmented images to follow changes in biomass. It was found that crop growth could be best described by the Richards growth function. The distinction between grapevine and intercrop growth, however, requires further refinement in image analysis. In the laboratory TOC and Ntotal were measured for both the soil and the plant organs as well as for the eroded sediments. Greenhouse gas emissions and photosynthesis were monitored. Looking at the change of Leaf Area Index (LAI) over the growing period, image analysis pointed out the role of cut shoots from pruning in the C and N cycles. Maximum leaf area (at ripening) for guyot cultivation technique was extimated at 7,840 m2 ha-1. Soil loss by erosion was established by sediment traps at the end of vinestock rows. The grain size distribution analysis led to the remarkable result that as erosion proceeded, the ratio of the sand fraction increased but remained within the range for the textural class of loam. Organic matter contents grew to 38 g kg-1. The rate of soil erosion is higher in ploughed than in grassed interrows by orders of magnitude.

Challenging a Global Land Surface Model in a Local Socio-Environmental System

Land surface models (LSMs) predict how terrestrial fluxes of carbon, water, and energy change with abiotic drivers to inform the other components of Earth system models. Here, we focus on a single human-dominated watershed in southwestern Michigan, USA. We compare multiple processes in a commonly used LSM, the Community Land Model (CLM), to observational data at the single grid cell scale. For model inputs, we show correlations (Pearson’s R) ranging from 0.46 to 0.81 for annual temperature and precipitation, but a substantial mismatch between land cover distributions and their changes over time, with CLM correctly representing total agricultural area, but assuming large areas of natural grasslands where forests grow in reality. For CLM processes (outputs), seasonal changes in leaf area index (LAI; phenology) do not track satellite estimates well, and peak LAI in CLM is nearly double the satellite record (5.1 versus 2.8). Estimates of greenness and productivity, however, are more similar between CLM and observations. Summer soil moisture tracks in timing but not magnitude. Land surface reflectance (albedo) shows significant positive correlations in the winter, but not in the summer. Looking forward, key areas for model improvement include land cover distribution estimates, phenology algorithms, summertime radiative transfer modelling, and plant stress responses.

Evaluation of vegetation and evapotranspiration changes in Iran using satellite data and ground measurements

Evapotranspiration (ET) is a critical component of the water cycle on a global scale. Changes in leaf area index (LAI) influence ET changes. Satellite products are among the effective methods of estimating ET and LAI at local, regional, and global scales. Therefore, the purpose of our study was to evaluate three global remote sensing products of ET and two products of LAI and investigate their simultaneous changes in Iran over the 2000 to 2015 period. Selected ET products include Global Land Evaporation Amsterdam Model (GLEAM), Global Land Data Assimilation System (GLDAS2.0), and Moderate Resolution Imaging Spectroradiometer ET (MODIS ET) products. LAI products include MODIS LAI and Global Land Surface Satellite (GLASS) LAI products. Validation using the 23 selected evaporation stations showed that among the three ET products MODIS has the best performance. Three ET products consistently show an increasing trend in annual ET over a large part of Iran. The mean annual trend values obtained from GLEAM3.0a, GLDAS, and MODIS ET products are 2.022, 1.359, and 0.429 mm/year, respectively. Among LAI products, GLASS had the highest correlation with the selected ET product (0.78). On average, the trend of intra-annual changes of LAI across Iran did not show a significant increasing or decreasing trend. Moreover, in areas where no significant change was observed in LAI, ET changes were unpredictable and depended on other factors. Overall, the results showed a relatively strong correlation between the selected ET and vegetation satellite products in the study area.

Global variation in diurnal asymmetry in temperature, cloud cover, specific humidity and precipitation and its association with leaf area index.

The impacts of the changing climate on the biological world vary across latitudes, habitats and spatial scales. By contrast, the time of day at which these changes are occurring has received relatively little attention. As biologically significant organismal activities often occur at particular times of day, any asymmetry in the rate of change between the daytime and night-time will skew the climatic pressures placed on them, and this could have profound impacts on the natural world. Here we determine global spatial variation in the difference in the mean annual rate at which near-surface daytime maximum and night-time minimum temperatures and mean daytime and mean night-time cloud cover, specific humidity and precipitation have changed over land. For the years 1983-2017, we derived hourly climate data and assigned each hour as occurring during daylight or darkness. In regions that showed warming asymmetry of >0.5°C (equivalent to mean surface temperature warming during the 20th century) we investigated corresponding changes in cloud cover, specific humidity and precipitation. We then examined the proportional change in leaf area index (LAI) as one potential biological response to diel warming asymmetry. We demonstrate that where night-time temperatures increased by >0.5°C more than daytime temperatures, cloud cover, specific humidity and precipitation increased. Conversely, where daytime temperatures increased by >0.5°C more than night-time temperatures, cloud cover, specific humidity and precipitation decreased. Driven primarily by increased cloud cover resulting in a dampening of daytime temperatures, over twice the area of land has experienced night-time warming by >0.25°C more than daytime warming, and has become wetter, with important consequences for plant phenology and species interactions. Conversely, greater daytime relative to night-time warming is associated with hotter, drier conditions, increasing species vulnerability to heat stress and water budgets. This was demonstrated by a divergent response of LAI to warming asymmetry.

Future greening of the Earth may not be as large as previously predicted

Changes in global vegetation growth and its drivers during recent decades have been well studied with satellite data, ecosystem models and field experiments. However, a systematic understanding of how global vegetation will respond to projected changes in climate and atmospheric composition is still lacking. Here, we analyze changes in projected global leaf area index (LAI) from 16 Coupled Model Intercomparison Project phase 5 (CMIP5) and 17 phase 6 (CMIP6) Earth System Models (ESMs) during the 21st century under future scenarios RCP2.6, RCP4.5 and RCP8.5 and future scenarios SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5, respectively. In addition to the widely-used multi-model ensemble mean (MME) method, we employed the Reliability Ensemble Averaging (REA) strategy to integrate the modeled results. The REA integration weights were determined by combining the performance of the ESMs in simulating present-day LAI changes, which was based on a comparison with three long-term remote sensing LAI data sets, with their convergence of future predictions. The results suggest that global LAI will increase under all seven future scenarios with both integration methods. The magnitude of LAI growth is expected to increase with the forcing levels of the scenarios. The ESMs integrated with the REA weights predicted significantly smaller magnitudes and lower uncertainties in global LAI growth by the end of the 21st century than those integrated with the MME method. Both REA and MME results suggest that the growth in atmospheric CO2 concentration is the main positive driver of the projected increment of global LAI, which is partly offset by the negative effects of global warming. Our improved comprehensive prediction based on CMIP5 and CMIP6 ESMs using the REA integration strategy provides a more robust estimation of global vegetation change during the 21st century, and is expected to help better understand the state of the planet in the coming decades.

Canopy structure and understory light characteristics of a natural Quercus aliena var. acuteserrata forest in China northwest: Influence of different forest management methods

Close-to-nature forest management (CNFM), structure-based forest management (SBFM), and secondary forest comprehensive silviculture (SFCS) are three forest management methods that have been promoted in China. Unfortunately, changes to the canopy structure and understory light characteristics after forest management remain unclear. We compared the canopy structure and light characteristics of a natural Quercus aliena var. acuteserrata forest under the four management methods of CNFM, SBFM, SFCS and unmanaged control (CK) after four years of using the WinScanopy instrument. The results suggest that (1) the leaf area index (LAI) values in the CNFM and SBFM were higher than that in the CK, the mean leaf angle (MLA) value in the SBFM was significantly lower than that in the CK. (2) The values of total under radiant energy (TU) and directed radiant energy (Dir) in the SFCS were significantly higher than that in the CK, while the values of diffused radiant energy (Dif) in the CNFM and SBFM were significantly lower than that in the CK. The extinction coefficient (EC) value in three forest management measure was significantly lower than that in the CK. (3) The canopy structure was sensitive to changes in tree crowding, and the light characteristics were sensitive to changes in forest layer numbers, LAI and CO, which explained 81.19% and 94.88% of the total variations in the canopy structure and light characteristics, respectively. Thus, CNFM and SBFM altered the canopy structure, which may be attributed to the LAI that was influenced by tree crowding, and the three forest treatments altered the understory light environment, which may be influenced mainly by the canopy structure. CNFM and SBFM increased the leaf area index of the stand and improved the understory light environment in a natural Quercus aliena var. acuteserrata forest.

Resilience of a subtropical rainforest to annual typhoon disturbance: Lessons from 25-year data of leaf area index

Quantifying ecosystem resilience is critical for predicting how shifts in disturbance regime affect ecosystem structure and function as a result of recent climate change. Studies of disturbance-ecosystem interactions often focus on one or a few disturbance events but the interactions may vary considerably among disturbance events. We used a 25-year LAI (leaf area index) record of the Fushan Experimental Forest (FEF) of northeastern Taiwan to explore forest LAI resilience to annual typhoon disturbance. The four typhoons of 1994 caused the most dramatic change of LAI in both the magnitude and the rate, as well as the longest time for a full recovery. However, typhoons varied in their effects, and not all intense typhoons caused major impact suggesting that result derived from one or a few disturbance events is unlikely to capture the full spectrum of disturbance effect. Using LAI of 4.26 measured following a non-typical year of no typhoon occurrence as a reference, it took 11─16 years before the predicted LAI to return to the reference level, which represents the resilience (time to return) of the forest LAI under minimal typhoon effects. Using the event-based analyses, the average degree of return was 0.86, with values as low as 0.45, suggesting that the LAI of the FEF is not always able to fully recover within the inter-typhoon time period (<1 year). Once returned to the reference state, the LAI of the FEF stayed at the reference state for several months to several years. With annual typhoon disturbance and a return time longer than one year and may be up to more than one decade, the LAI of the FEF is constantly undergoing the disturbance-recovery process and rarely reaches an equilibrium state. Therefore, a system experiencing frequent disturbance such as FEF might be better characterized by non-equilibrium temporal dynamics of its key functions.

Impacts of climate and vegetation leaf area index changes on global terrestrial water storage from 2002 to 2016

Terrestrial water storage (TWS) has a major impact on the water, energy, and biogeochemical fluxes of the land surface. The spatiotemporal features of TWS variations, as well as the roles of climate change and human activities in TWS variations, have been of key concerns and need to be further investigated. Here, using the data products from the Gravity Recovery and Climate Experiment and the Global Land Data Assimilation System, together with some auxiliary data on climate, evapotranspiration, and vegetation leaf area index (LAI), we investigated the spatiotemporal variations of global TWS and how TWS was partitioned into its different components. We further quantified the sensitivity of TWS to changes in climate and vegetation LAI, as well as the impacts of climate and vegetation LAI changes on TWS during 2002–2016. The results showed that global TWS declined at a rate of 0.04 mm month−1 during 2002–2016 with a spatially explicit pattern and a distinct seasonal pattern, although the trend was negligible before October 2009 and only became obvious after that. With the seasonal variations of precipitation, temperature, and LAI together, the seasonal variations of TWS were able to be explained by ≥50% in the tropical and subtropical regions. With the joint changes in LAI, temperature, and precipitation, TWS increased by ≤ ~10 mm month−1 in the subarctic and inland temperate regions, and some tropical and subtropical regions; by contrast declined by ≤ ~10 mm month−1 in some regions such as the Central Africa. Our study shed light on the spatiotemporal characteristic of the TWS variations and quantified the impacts of climate and vegetation LAI changes on TWS. The findings can provide not only important evidence for the effectiveness of past ecosystem management intervention programs in water retention service but also guidance for planning ecosystem management intervention programs in the near future.

Increased control of vegetation on global terrestrial energy fluxes

Changes in vegetation structure are expected to influence the redistribution of heat and moisture; however, how variations in the leaf area index (LAI) affect this global energy partitioning is not yet quantified. Here, we estimate that a unit change in LAI leads to 3.66 ± 0.45 and −3.26 ± 0.41 W m−2 in latent (LE) and sensible (H) fluxes, respectively, over the 1982–2016 period. Analysis of an ensemble of data-driven products shows that these sensitivities increase by about 20% over the observational period, prominently in regions with a limited water supply, probably because of an increased transpiration/evaporation ratio. Global greening has caused a decrease in the Bowen ratio (B = H/LE) of −0.010 ± 0.002 per decade, which is attributable to the increased evaporative surface. Such a direct LAI effect on energy fluxes is largely modulated by plant functional types (PFTs) and background climate conditions. Land surface models (LSMs) misrepresent this vegetation control, possibly due to underestimation of the biophysical responses to changes in the water availability and poor representation of LAI dynamics. Changes in the leaf area index alter the distribution of heat and moisture. The change in energy partitioning related to leaf area, increasing latent and decreasing sensible fluxes over the observational period 1982–2016, is moderated by plant functional type and background climate.

Investigating the leaf area index changes in response to climate change (case study: Kasilian catchment, Iran)

Vegetation cover plays an important role in the hydrologic cycle of Kasilian catchment in Iran. This study aimed to estimate leaf area index (LAI), as an important vegetation factor in hydrologic loses, in response to climate change in the future period (2020–2039) over Kasilian catchment located in the north of Iran. For this purpose, LAI was simulated by gridded BIOME-BGC in 319 pixels within the case study domain over the study period (2004–2013) for three dominant land covers of the Kasilian catchment including deciduous broadleaf forest (DBF), shrubs, and C3 grasses, and BIOME-BGC accuracy has been assessed using MODIS-derived LAI. Then, monthly projections of climate variables obtained from the average of 9 AOGCMs-AR5 in the future period (2020–2039) and annual projection of CO2 level from 2004 to 2039 under RCP2.6 and RCP8.5 scenarios were used to assess the impact of climate change on LAI. Results show that LAI will increase in response to the overall predicted rise in temperature, precipitation, and CO2 level under both scenarios in all pixels. This increase under the RCP8.5 scenario is predicted to be more than RCP2.6 scenario so that the mean LAI in Kasilian catchment will increase by 3.1% and 2.2% under RCP8.5 and RCP2.6 scenarios, respectively. In addition, our analysis showed that DBF land cover will be more sensitive to climate change in this catchment.

Litter dynamics, leaf area index and forest floor respiration as indicators for understanding the role of Nepalese alder in white oak forests in central Himalaya, India

Abstract The present study is an attempt to understand the role of Nepalese alder (Alnus nepalensis D. Don) in litter production, leaf litter decomposition, leaf area index (LAI) and rate of forest floor respiration as indicators of carbon flux in white oak (Quercus leucotrichophora A. Camus) forests. Five forest stands of oak mixed alder (OMA) and five oak without alder (OWA) were selected along a basal area gradient under similar ecological conditions in Indian central Himalaya. We estimated monthly variations in litterfall, leaf litter decomposition and leaf area index (LAI) in Q. leucotrichophora and A. nepalensis in OMA and OWA stands. Soil and litter CO2 effluxes were estimated seasonally using the soda-lime absorption method. The seasonal pattern of litterfall showed a bimodal trend in OMA and unimodal trend in OWA forest stands. Forest floor biomass (leaf, twig, reproductive parts, herbaceous and miscellaneous litter) was highest in summer (707–1080 g/m2) followed by winter (414–678 g/m2) and rainy (281–541 g/m2) seasons in OMA stands. In OWA stands, forest floor biomass was highest in summer (463–646 g/m2) followed by rainy (321–411 g/m2) and winter (149–279 g/m2) seasons. Total litter production, leaf litter decomposition rates, change in LAI and soil respiration were significantly higher (p

Mitigating chilling stress of pepper plant (Capsicum annuum L.) using Dimethyl Ether combustion gas in controlled greenhouse

In this study, the possibility of enhancing chilling stress tolerance of pepper plant (Capsicum annuum Linnaeus) during early growth stages was investigated using DME combustion gas in controlled greenhouse. The experiment was conducted to determine the performance of DME combustion gas when used as a fuel for DME burner for raising temperature and CO2 concentration in greenhouse and also to examine its effects on morphology parameters of hot pepper in winter season. To analyze the relationship between the application of DME and morphology parameters of pepper plant, three treatments (DME-1, DME-2 and DME-3) for three controlled greenhouses were assayed. DME-1 and DME-2 treatments consisted of average DME flow quantity in duct were 17.4 m3/min and 10.2 m3/min, respectively to greenhouse-1 and greenhouse-2 and no DME gas was supplied to greenhouse-3 which was left as control (DME-3). Morphology parameters such as plant height, leaf area and leaf area index (LAI), net assimilation rate (NAR), relative growth rate (RGR), fresh weight and dry weight were measured for eight weeks for each treatment and analyzed using completely randomized designs through analysis of variance with a significance level of P &lt; 0.05. Although DME-1, DME-2 and DME-3 received same crop management practices and controlled environmental factors, the highest changes (p &lt; 0.05) of plant height, leaf area, LAI and fresh weight were found from the DME-1 treatment, followed by DME-2. A comparison of relative growth rates among the treatments indicated more rapid relative growth rate of morphology parameters at vegetative phase of plant implying better yield. Therefore endorsed quantify of DME combustion gas for a specified crop can be applied to greenhouse to improve the plant growth and enhance yield with mitigating chilling stress in winter season.

Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: Part 2. Carbon Cycling

AbstractGrasslands are one of the most widely distributed and abundant vegetation types globally, and land surface models struggle to accurately simulate grassland carbon dioxide, energy, and water fluxes. Here we hypothesize that this is due to land surface models having difficulties in reproducing grassland phenology, in particular in response to the seasonal and interannual variability of precipitation. Using leaf area index (LAI), net primary productivity, and flux data at 55 sites spanning climate zones, the aim of this study is to evaluate a novel prognostic phenology model (Simple Biosphere Model, SiB4) while simultaneously illustrating grassland relationships across precipitation gradients. Evaluating from 2000 to 2014, SiB4 predicts daily LAI, carbon, and energy fluxes with root‐mean‐square errors &lt; 15% and individual biases &lt;10%; however, not including management likely reduces its performance. Grassland mean annual LAI increases linearly with mean annual precipitation, with both SiB4 and the Moderate Resolution Imaging Spectroradiometer (MODIS) showing a 0.13 increase in LAI per 100‐mm increase in precipitation. Both gross primary production and ecosystem respiration increase with growing season length by ∼8.5 g C m−2 per day, with SiB4 and Fluxnet estimates within 18%. Despite differences in mean annual precipitation and growing season length, all grassland sites shift to seasonal carbon sinks one month prior to peak uptake. During a U.S. drought, MODIS and SiB4 had nearly identical LAI responses, and the LAI change due to drought was less than the LAI change across the precipitation gradient, indicating that grassland drought response is not as strong as the overlying climate response.

Seedlings regenerative dynamics of dominant trees in a secondary forest in montane region of eastern Liaoning Province, China

To reveal the seedlings dynamics of dominant trees and their response to canopy changes (as indicated by changes in leaf area index), we analyzed the seedlings dynamics of dominant trees and canopy structure during forest regeneration based on three-year (2014 to 2016) censuses and hemispherical photography in a 4 hm2 forest dynamics monitoring plot. The results showed that canopy LAI first decreased and then increased during the survey period, with significant interannual difference. The seedlings of 12 dominant tree species showed notable response to the change of canopy structure, with the response varying among different tree species. The relative abundance or frequency of seedlings for neutral tree species generally increased with the decreases of canopy LAI. The relative abundance or frequency of seedlings of light-demanding tree species generally decreased with the increases of canopy LAI. In multi-response permutation procedures by analyzing the numeric composition and distribution of dominant tree seedlings, there was significant difference among the three censuses. Such interannual difference gradually decreased from 2014 to 2016. Significant change occurred in the indicator species of dominant tree seedlings, reflecting their responses to the changes of canopy structure.

Climatic forcing for recent significant terrestrial drying and wetting

Terrestrial water storage (TWS) experienced a substantial change in the past few decades as detected by the Gravity Recovery and Climate Experiment (GRACE). However, the major causes of this change remain unclear, and none of the current state-of-the-art process-based hydrological models are able to reproduce the significantly drying/wetting trends in GRACE TWS. Here we investigate 12 terrestrial regions that show a significantly drying/wetting trend, using partial least-square regression (PLSR) to relate TWS anomalies to various climatic variables and leaf area index (LAI).Through PLSR modeling, we find changes in LAI, downward longwave radiation (DLW) and precipitation are most strongly associated with a wetting trend. Increases in precipitation appear to be responsible for the wetting trend in tropical/subtropical monsoon regions, while decreases in vegetation transpiration and atmospheric demand appear to be responsible for the wetting trend in extratropical regions. Enhanced atmospheric demand caused by increases in air temperature and the resulting enhanced DLW dominates the significant drying trend in the mid-latitude subtropical drylands, and in the cold and alpine regions. The PLSR modeling also suggests that, over global continents, the climatic forcing factors show a dominant impact on TWS over all the wetting regions, while only four out of the seven drying regions show climate-dominated drying, implying an additional impact of anthropogenic responses to the water stress on drying.

Climate Benefits of Intact Amazon Forests and the Biophysical Consequences of Disturbance

Tropical forests have an important regulating influence on local and regional climate, through modulating the exchange of moisture and energy between the land and the atmosphere. Deforestation disrupts this exchange, though the climatic consequences of progressive, patch-scale deforestation of formerly intact forested landscapes have not previously been assessed. Remote sensing datasets of land surface and atmospheric variables were used to compare the climate responses of Amazon evergreen broadleaf forests that lost their intact status between 2000 and 2013. Clear gradients in environmental change with increasing disturbance were observed. Leaf area index (LAI) showed progressively stronger reductions as forest loss increased, with evapotranspiration (ET) showing a comparative decline. These changes in LAI and ET were related to changes in temperature (T), with increased warming as deforestation increased. Severe deforestation of intact Amazon forest, defined as areas where canopy cover was reduced below 70 %, was shown to have increased daytime land surface T by 0.44 °C over the study period. Differences between intact and disturbed forests were most pronounced during the dry season, with severely deforested areas warming as much as 1.5 °C. Maintenance of canopy cover was identified as an important factor in minimising the impacts of disturbance. Overall, the results highlight the climate benefits provided by intact tropical forests, providing further evidence that protecting intact forests is of utmost importance.

Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models.

Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G × E × M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it difficult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use efficiency (LUECanopy). The simulations are used to both explore the sensitivity of the satellite-estimated genotype × management (G × M) parameters to the satellite retrieval regression coefficients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUECanopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coefficients lead to large uncertainty in the G × M parameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coefficient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUECanopy. The weekly change in LAI is shown to be retrievable with a correlation coefficient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models.

Response of surface air temperature to the change of leaf area index in the source region of the Yellow River by the WRF model

Leaf area index (LAI) is a crucial land-atmosphere exchange parameter. LAI change can cause a variation of other land surface parameter. In this research, three experiments were conducted to investigate the impact of LAI and albedo change on surface air temperature in the source region of the Yellow River (SRYR) using the Weather Research and Forecasting (WRF) model. Three experiments used the same settings, initial and boundary conditions except for the LAI and albedo data. The control simulation (CTL) used the WRF model climatological LAI data, the second simulation (LAI) used the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data, and the third simulation (LAIALB) used the MODIS LAI and MODIS albedo. The results show MODIS LAI is greater by 34.5% than WRF climatological LAI, and the MODIS albedo is lower by 24.3% than WRF climatological albedo over the whole growing season of 2006 in SRYR. All the experiments can simulate the surface air temperature (Ta) spatial distribution characteristics, but underestimate the values of 1.3 °C in CTL experiment and 0.6 °C in LAIALB experiment in SRYR. The simulated Ta by LAI experiment is lower 0.1 °C than by the CTL experiment, but the simulated Ta by the LAIALB experiment is obviously higher 0.6 °C than the CTL experiment. The LAI experiment shows a cooling effect because the higher MODIS LAI decreases the canopy resistance, which induces a positive average 2.1 Wm−2 latent heat flux (LH) and a negative average − 2.1 Wm−2 sensible heat flux (Hs). The LAIALB experiment presents a warming effect because of low MODIS albedo comparing WRF albedo, which changes the radiation components and results in an obvious negative − 14.0 Wm−2 upward short wave radiation, a positive 11.7 Wm−2 net radiation, and a positive 10.9 Wm−2 heat flux. In fact, more precipitation produces more snow and high surface albedo in the WRF model, which results in a cold temperature bias in SRYR.

A New Integrated Vegetation Index for the Estimation of Winter Wheat Leaf Chlorophyll Content

Leaf chlorophyll content (LCC) provides valuable information about the nutrition and photosynthesis statuses of crops. Vegetation index-based methods have been widely used in crop management studies for the non-destructive estimation of LCC using remote sensing technology. However, many published vegetation indices are sensitive to crop canopy structure, especially the leaf area index (LAI), when crop canopy spectra are used. Herein, to address this issue, we propose four new spectral indices (The red-edge-chlorophyll absorption index (RECAI), the red-edge-chlorophyll absorption index/optimized soil-adjusted vegetation index (RECAI/OSAVI), the red-edge-chlorophyll absorption index/ the triangular vegetation index (RECAI/TVI), and the red-edge-chlorophyll absorption index/the modified triangular vegetation index(RECAI/MTVI2)) and evaluate their performance for LCC retrieval by comparing their results with those of eight published spectral indices that are commonly used to estimate LCC. A total of 456 winter wheat canopy spectral data corresponding to physiological parameters in a wide range of species, growth stages, stress treatments, and growing seasons were collected. Five regression models (linear, power, exponential, polynomial, and logarithmic) were built to estimate LCC in this study. The results indicated that the newly proposed integrated RECAI/TVI exhibited the highest LCC predictive accuracy among all indices, where R2 values increased by more than 13.09% and RMSE values reduced by more than 6.22%. While this index exhibited the best association with LCC (0.708** ≤ r ≤ 0.819**) among all indices, RECAI/TVI exhibited no significant relationship with LAI (0.029 ≤ r ≤ 0.167), making it largely insensitive to LAI changes. In terms of the effects of different field management measures, the LCC predictive accuracy by RECAI/TVI can be influenced by erective winter wheat varieties, low N fertilizer application density, no water application, and early sowing dates. In general, the newly developed integrated RECAI/TVI was sensitive to winter wheat LCC with a reduction in the influence of LAI. This index has strong potential for monitoring winter wheat nitrogen status and precision nitrogen management. However, further studies are required to test this index with more diverse datasets and different crops.

An optimized non-linear vegetation index for estimating leaf area index in winter wheat

Using hyperspectral remote sensing technology to monitor leaf area index (LAI) in a timely, fast and non-destructive manner is essential for accurate quantitative crop management. The relationships between existing vegetation indices (VIs) and LAI usually tend to saturate under dense canopies in crop production. The purpose of this study was to propose a new VI in which the estimating saturation is greatly weakened, and prediction accuracy is improved under conditions of high LAI in winter wheat (Triticum aestivum L.). The quantitative relationship between ground-based canopy spectral reflectance and LAI in wheat was investigated. The results showed that the optimized band combination, namely, the form of non-linear vegetation index (NLI) was more sensitive to changes in LAI. When λ(x1) = 798 nm and λ(y2) = 728 nm, the band combination NLI (798,728) had the highest R2 of 0.757. Among the common VIs, the modified triangular vegetation index 2 (MTVI2), the ratio spectral index [RSI (760,730)] and the 2-band enhanced vegetation index (EVI2) gave superior performance (R2 > 0.710) in terms of LAI estimation, but were worse than NLI (798,728). Inspired by the modified non-linear vegetation index (MNLI), NLI (798,728) was further optimized to become a novel optimized non-linear vegetation index (ONLI), which can be calculated by the formula {{left( { 1 { + 0} . 0 5} right) , times , left( { 0. 6, times ,R_{ 7 9 8}^{2} , - ,R_{ 7 2 8} } right)} mathord{left/ {vphantom {{left( { 1 { + 0} . 0 5} right) , times , left( { 0. 6, times ,R_{ 7 9 8}^{2} , - ,R_{ 7 2 8} } right)} { left( { 0. 6, times ,R_{ 7 9 8}^{2} , + ,R_{ 7 2 8} { + 0} . 0 5} right)}}} right. kern-0pt} { left( { 0. 6, times ,R_{ 7 9 8}^{2} , + ,R_{ 7 2 8} { + 0} . 0 5} right)}}. The unified ONLI model gave an R2 of 0.779 and root mean square error (RMSE) of 1.013 across all datasets. These results indicate that the novel ONLI has strong adaptability to various cultivation conditions and can provide a good estimate of LAI in winter wheat.