Variation In Leaf Area Index Research Articles

Estimating Effective Leaf Area Index of Winter Wheat Using Simulated Observation on Unmanned Aerial Vehicle-Based Point Cloud Data

Within-field variation of leaf area index (LAI) plays an essential role in field crop monitoring and yield forecasting. Although unmanned aerial vehicle (UAV)-based optical remote sensing method can overcome the spatial and temporal resolution limitations associated with satellite imagery for fine-scale within-field LAI estimation of field crops, image correction and calibration of UAV data are very challenging. In this study, a physical-based method was proposed to automatically calculate crop effective LAI (LAIe) using UAV-based 3-D point cloud data. Regular high spatial resolution RGB images were used to generate point cloud data for the study area. The proposed method, simulated observation of point cloud (SOPC), was designed to obtain the 3-D spatial distribution of vegetation and bare ground points and calculate the gap fraction and LAIe from a UAV-based 3-D point cloud dataset at vertical, 57.5°, and multiview angle of a winter wheat field in London, Ontario, Canada. Results revealed that the derived LAIe using the SOPC multiview angle method correlates well with the LAIe derived from ground digital hemispherical photography, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.76. The root mean square error and mean absolute error for the entire experiment period from May 11 to May 27 were 0.19 and 0.14, respectively. The newly proposed method performs well for LAIe estimation during the main leaf development stages (BBCH 20-39) of the growth cycle. This method has the potential to become an alternative approach for crop LAIe estimation without the need for ground-based reference measurements, hence save time and money.

Global biosphere–climate interaction: a causal appraisal of observations and models over multiple temporal scales

Abstract. Improving the skill of Earth system models (ESMs) in representing climate–vegetation interactions is crucial to enhance our predictions of future climate and ecosystem functioning. Therefore, ESMs need to correctly simulate the impact of climate on vegetation, but likewise feedbacks of vegetation on climate must be adequately represented. However, model predictions at large spatial scales remain subjected to large uncertainties, mostly due to the lack of observational patterns to benchmark them. Here, the bidirectional nature of climate–vegetation interactions is explored across multiple temporal scales by adopting a spectral Granger causality framework that allows identification of potentially co-dependent variables. Results based on global and multi-decadal records of remotely sensed leaf area index (LAI) and observed atmospheric data show that the climate control on vegetation variability increases with longer temporal scales, being higher at inter-annual than multi-month scales. Globally, precipitation is the most dominant driver of vegetation at monthly scales, particularly in (semi-)arid regions. The seasonal LAI variability in energy-driven latitudes is mainly controlled by radiation, while air temperature controls vegetation growth and decay in high northern latitudes at inter-annual scales. These observational results are used as a benchmark to evaluate four ESM simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Findings indicate a tendency of ESMs to over-represent the climate control on LAI dynamics and a particular overestimation of the dominance of precipitation in arid and semi-arid regions at inter-annual scales. Analogously, CMIP5 models overestimate the control of air temperature on seasonal vegetation variability, especially in forested regions. Overall, climate impacts on LAI are found to be stronger than the feedbacks of LAI on climate in both observations and models; in other words, local climate variability leaves a larger imprint on temporal LAI dynamics than vice versa. Note however that while vegetation reacts directly to its local climate conditions, the spatially collocated character of the analysis does not allow for the identification of remote feedbacks, which might result in an underestimation of the biophysical effects of vegetation on climate. Nonetheless, the widespread effect of LAI variability on radiation, as observed over the northern latitudes due to albedo changes, is overestimated by the CMIP5 models. Overall, our experiments emphasise the potential of benchmarking the representation of particular interactions in online ESMs using causal statistics in combination with observational data, as opposed to the more conventional evaluation of the magnitude and dynamics of individual variables.

Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India

Forests play a vital role in biological cycles and environmental regulation. To understand the key processes of forest canopies (e.g., photosynthesis, respiration and transpiration), reliable and accurate information on spatial variability of Leaf Area Index (LAI), and its seasonal dynamics is essential. In the present study, we assessed the performance of biophysical parameter (LAI) retrieval methods viz. Look-Up Table (LUT)-inversion, MLRA-GPR (Machine Learning Regression Algorithm- Gaussian Processes Regression) and empirical models, for estimating the LAI of tropical deciduous plantation using ARTMO (Automated Radiative Transfer Models Operator) tool and Sentinel-2 satellite images. The study was conducted in Central Tarai Forest Division, Haldwani, located in the Uttarakhand state, India. A total of 49 ESUs (Elementary Sampling Unit) of 30 m × 30 m size were established based on variability in composition and age of plantation stands. In-situ LAI was recorded using plant canopy imager during the leaf growing, peak and senescence seasons. The PROSAIL model was calibrated with site-specific biophysical and biochemical parameters before used to the predicted LAI. The plantation LAI was also predicted by an empirical approach using optimally chosen Sentinel-2 vegetation indices. In addition, Sentinel-2 and MODIS LAI products were evaluated with respect to LAI measurements. MLRA-GPR offered best results for predicting LAI of leaf growing (R2 = 0.9, RMSE = 0.14), peak (R2 = 0.87, RMSE = 0.21) and senescence (R2 = 0.86, RMSE = 0.31) seasons while LUT inverted model outperformed VI’s based parametric regression model. Vegetation indices (VIs) derived from 740 nm, 783 nm and 2190 nm band combinations of Sentinel-2 offered the best prediction of LAI.

Leaf Area Index evaluation in vineyards using 3D point clouds from UAV imagery

The Leaf Area Index (LAI) is an ecophysiology key parameter characterising the canopy-atmosphere interface where most of the energy fluxes are exchanged. However, producing maps for managing the spatial and temporal variability of LAI in large croplands with traditional techniques is typically laborious and expensive. The objective of this paper is to evaluate the reliability of LAI estimation by processing dense 3D point clouds as a cost-effective alternative to traditional LAI assessments. This would allow for high resolution, extensive and fast mapping of the index, even in hilly and not easily accessible regions. In this setting, the 3D point clouds were generated from UAV-based multispectral imagery and processed by using an innovative methodology presented here. The LAI was estimated by a multivariate linear regression model using crop canopy descriptors derived from the 3D point cloud, which account for canopy thickness, height and leaf density distribution along the wall. For the validation of the estimated LAI, an experiment was conducted in a vineyard in Piedmont: the leaf area of 704 vines was manually measured by the inclined point quadrant approach and six UAV flights were contextually performed to acquire the aerial images. The vineyard LAI estimated by the proposed methodology showed to be correlated with the ones obtained by the traditional manual method. Indeed, the obtained R2 value of 0.82 can be considered fully adequate, compatible to the accuracy of the reference LAI manual measurement.

The importance of physiological, structural and trait responses to drought stress in driving spatial and temporal variation in GPP across Amazon forests

Abstract. The capacity of Amazon forests to sequester carbon is threatened by climate-change-induced shifts in precipitation patterns. However, the relative importance of plant physiology, ecosystem structure and trait composition responses in determining variation in gross primary productivity (GPP) remain largely unquantified and vary among models. We evaluate the relative importance of key climate constraints to GPP, comparing direct plant physiological responses to water availability and indirect structural and trait responses (via changes to leaf area index (LAI), roots and photosynthetic capacity). To separate these factors we combined the soil–plant–atmosphere model with forcing and observational data from seven intensively studied forest plots along an Amazon drought stress gradient. We also used machine learning to evaluate the relative importance of individual climate factors across sites. Our model experiments showed that variation in LAI was the principal driver of differences in GPP across the gradient, accounting for 33 % of observed variation. Differences in photosynthetic capacity (Vcmax and Jmax) accounted for 21 % of variance, and climate (which included physiological responses) accounted for 16 %. Sensitivity to differences in climate was highest where a shallow rooting depth was coupled with a high LAI. On sub-annual timescales, the relative importance of LAI in driving GPP increased with drought stress (R2=0.72), coincident with the decreased importance of solar radiation (R2=0.90). Given the role of LAI in driving GPP across Amazon forests, improved mapping of canopy dynamics is critical, opportunities for which are offered by new satellite-based remote sensing missions such as GEDI, Sentinel and FLEX.

Introduction of Variable Correlation for the Improved Retrieval of Crop Traits Using Canopy Reflectance Model Inversion

Look-up table (LUT)-based canopy reflectance models are considered robust methods to estimate vegetation attributes from remotely sensed data. However, the LUT inversion approach is sensitive to measurements and model uncertainties, which raise the ill-posed inverse problem. Therefore, regularization options are needed to mitigate this problem and reduce the uncertainties of estimates. In this study, we introduce a new method to regularize the LUT inversion approach to improve the accuracy of biophysical parameters (leaf area index (LAI) and fractional vegetation cover (fCover)). This was achieved by incorporating known variable correlations that existed at the test site into the LUT approach to correlate the model variables of the Soil–Leaf–Canopy (SLC) model using the Cholesky decomposition algorithm. The retrievals of 27 potato plots obtained from the regularized LUT (LUTreg) were compared with the standard LUT (LUTstd), which did not consider variable correlations. Different solutions from both types of LUTs (LUTreg and LUTstd) were utilized to improve the quality of the model outputs. Results indicate that the present method improved the accuracy of LAI estimation, with the coefficient of determination R2 = 0.74 and normalized root-mean-square error NRMSE = 24.45% in LUTreg, compared with R2 = 0.71 and NRMSE = 25.57% in LUTstd. In addition, the variability of LAI decreased in LUTreg (5.10) compared with that in LUTstd (12.10). Hence, our results give new insight into the impact of adding the correlation between variables to the LUT inversion approach to improve the accuracy of estimations. In this study, only two correlated variables (LAI and fCover) were examined; in subsequent studies, the full correlation matrix based on the Cholesky algorithm should be explored.

Evaluation of the Simple Algorithm for Yield Estimate Model in Winter Wheat Simulation under Different Irrigation Scenarios

Winter wheat (Triticum aestivum L.) is one of the major grain crops grown in Guanzhong Plain, China. Rapid and accurate crop growth and yield estimation are important for agricultural monitoring and policy decision‐making. Crop model simulation is an effective way to provide objective growth and yield forecast. In this study, we investigated the applicability of the Simple Algorithm for Yield Estimate (SAFY) model for estimating winter wheat dry shoot biomass (MS,d) and grain yield, using two growing seasons field data from different irrigation scenarios. Results showed that the leaf area index (LAI) could be reasonably well simulated, with a minimum root mean square estimate (RMSE) of 0.11. The water stress intensity of each irrigation scenario was well accounted for through stress factor of effective radiation use efficiency. Good accuracy were achieved for MS,d simulation in both calibration (RMSE = 0.054–0.183 kg m−2) and validation (RMSE = 0.146 kg m−2) datasets, but showed a general overestimation during later growth stages. The grain yield was well estimated with a relative error of 1.9% to 16.7% in 2013 to 2014 and 0.1% to 16.7% in 2014 to 2015. The comparison between estimated and measured yield of all irrigation scenarios were robust in both seasons, with the minimum RMSE and MRE of 35.0 g m−2 and 4.7%, respectively. This work demonstrates the potential of a simple crop model for estimating biomass and yield only by calibrate the LAI without further in situ data, and prefigures assimilation application with remote sensing data in future.Core Ideas The SAFY model was evaluated in different irrigation scenarios. The variation of LAI was highly sensitive to leaf partition parameters. SAFY effectively simulated wheat biomass and yield under various irrigation scenarios. A general overestimation on biomass was noted during later growth stages.

Spatiotemporal variation of vegetation dynamics and correlations with climatic factors in the Tibetan Plateau, China

The Tibetan Plateau has been recognized as one of the most sensitive areas responding to climate change, and has becomes a hotspot for coupled studies on terrestrial ecosystem variation and climate change. Leaf area index (LAI) is a key indicator that reflects vegetation dynamics and has been widely used to analyze the responses of vegetation to climate change. In this study, the spatiotemporal variation of LAI in the growing season and its correlations with climatic factors were analyzed. The results showed that the spatial pattern of LAI decreased from southeast to northwest. In terms of the temporal trend of LAI, 85% of the total study area experienced an increased trend. Additionally, 74% of the whole plateau will experience an improved vegetation growth in the future. Furthermore, temperature, precipitation and solar radiation all showed positive correlations with LAI for most of the study area. Our results effectively revealed the variation of LAI and its correlations with climatic factors. However, grassland in the plateau have been shown to have a greater and more rapid response to climatic fluctuations. Therefore, more managements should be made by local governments to improve the fragile environment, especially for areas with a decreasing LAI trend.

Small Scale Rainfall Partitioning in a European Beech Forest Ecosystem Reveals Heterogeneity of Leaf Area Index and Its Connectivity to Hydro-and Atmosphere

(1) Background: Leaf area index (LAI) is an essential structural property of plant canopies and is functionally related to fluxes of energy, water, carbon, and light in ecosystems; coupling the biosphere to the geo-, hydro-, and atmosphere. There is an increasing need for more accurate and traceable measurements among several spatial scales of investigation and modelling. We hypothesize that the spatial variability of LAI at the scale of crown sections of a single European beech (Fagus sylvatica L.) tree in a highly structured, mixed European beech-Norway spruce stand can be determined by simultaneous records of precipitation; (2) Methods: Spatially explicit measurements of throughfall were conducted repeatedly below beech and in forest gaps for rain events in leafed and in leafless periods. Subsequent analysis with a new regression approach resulted in estimating leaf and twig water storage capacities (SCleaf/twig) at point level independent of within-crown lateral flow mechanisms. Inverse modelling was used to estimate spatial litterfall (n = 99) distribution and litter production (mass, area, numbers) for single trees, as a function of diameter at breast height; (3) Results: As revealed by a linear mixed-effects model, SCleaf at the center of a beech canopies amounts to 4.9 mm in average and significantly decreases in the direction of the crown edges to an average value of 1.1 mm. Based on diameter-sensitive prediction of litter production, specific leaf area wetting capacity amounts to 0.260 l·m−2. A linear within-canopy dynamic of LAI was found with a mean of 17.6 m2·m−2 in the center and 4.0 m2·m−2 at the edges; and (4) Conclusions: The application of the method provided plausible results and can be extended to further throughfall datasets and tree species. Unravelling the causes and magnitude of spatial- and temporal heterogeneity of forest ecosystem properties contribute to overall progress in geosciences by improving the understanding how the biosphere relates to the hydro- and atmosphere.

Seasonality of leaf area index and photosynthetic capacity for better estimation of carbon and water fluxes in evergreen conifer forests

Leaf area index (LAI), defined as one half the total leaf area per unit ground area, and Vcmax, representing the maximal carboxylation rate of leaves, are two most significant parameters used in most Terrestrial Biosphere Models (TBMs). The ability of TBMs to simulate gross primary productivity (GPP) and evapotranspiration (ET) for evergreen needle-leave forests (ENF) can be significantly hampered by uncertainties in LAI and Vcmax. Remotely sensed (RS) LAI for ENF is generally underestimated in winter, early spring and late autumn. Although constant Vcmax throughout the growing season is often used in TBMs for GPP and ET modeling, it could vary significantly under leaf aging and stressed conditions. There were recent studies that apply seasonal leaf chlorophyll constraints on GPP modeling for croplands and deciduous forests, but little attention is given to the influence of the seasonality of either LAI or Vcmax on GPP or ET estimations for the ENF biome. In this study, we pay special attention to this biome, with the purpose of investigating if the representations of seasonal LAI and Vmax variations are essential in TBMs. To serve this purpose, the University of Toronto LAI product Version 2 was corrected for its seasonal variation using leaf lifespan and in-situ measurements at eight ENF sites in Canada. Seasonal Vcmax variation was derived from the MERIS Terrestrial Chlorophyll Index (MTCI) through downscaling it to the leaf level using a scheme with a general vertical nitrogen distribution within the canopy. Leaf chlorophyll content (LCC) is thus derived from MTCI and converted to Vcmax using empirical equations. Four model cases with and without considerations of the seasonal LAI and Vcmax variations were tested and compared. Validation against eddy covariance measurements indicates that the case with both LAI and Vcmax variations produced the highest R2, lowest root mean square error (RMSE) and lowest mean absolute error (MAE) for both GPP and ET simulations, and thus outperforms all other cases without considering the variations or with consideration of one of the variations only. In this best case, the simulated daily GPP yields R2 of 0.91, RMSE of 0.91 g C m−2 and MAE of 0.65 g C m−2, while the simulated daily ET yields R2 of 0.8, RMSE of 0.52 mm and MAE of 0.34 mm. Most improvements were found in spring and autumn. Not only the correlations between the seasonal trajectories of model simulation and observation were improved, but also the annual total GPP and ET were more accurately estimated. The smallest mean absolute relative bias to eddy covariance measurements is 9% for GPP and 15% for ET, both were found in the best case. Moreover, improvements in GPP were more pronounced than in ET. Our results highlight the significance of considering both seasonal structural and physiological characteristics of leaves in TBMs. Considering the important role that evergreen coniferous forests play in global terrestrial ecosystems, global simulations of GPP and ET in space and time can benefit from the proper representation of seasonal variations in canopy structure and leaf physiology as represented by LAI and Vcmax, respectively.

Relationship Between Leaf Area Index and Atmospheric Cooling of Tree Species

Presence of tree canopy is known to strongly influence ambient temperature and other micro-climatic conditions underneath. Therefore, planting trees with close or dense crown can be used as effective measure to provide thermal cover to species of flora and fauna adapted to shady and cooler environment. The cooling produced by a tree is exclusively the combined results of physical and physiological functions of its canopy. Tree canopy is one of the most important, physiologically active components that establish interaction between the terrestrial environment and the atmosphere which regulates various bio-physiological processes. Leaf Area Index (LAI) is one of the most reliable indicators of growth and vigour of a tree. We compared LAI and corresponding understory temperature of a few isolated trees of Ficus benjamina, Mangifera indica, Anthocephalus chinensis, Lagerstroemia floribunda and Peltophorum africanum belonging to evergreen, semi-deciduous and deciduous nature to establish the relationship between the two parameters. A great variation in LAIs of deciduous and semi-deciduous species was observed on account of leaf shedding and fast development of crown after emergence of new leaves whereas the variation was lesser in evergreen species. A strong positive correlation was found between LAI and cooling produced by A. chinensis, L. floribunda and P. africanum whereas no linear relation was established in case of M. indica and F. benjamina.

Light interception, chemical, and productive characteristics of elephant grass subjected to different cutting intervals

This study aimed to evaluate the interception of light (%IL), leaf area index (LAI), and the chemical and productive characteristics of elephant grass (Pennisetum purpureum Schum) under different defoliation frequencies (30, 45, 60, 75, and 90 days after cutting). The experimental design was completely randomized with five replications and five treatments (cutting frequencies). The chemical composition was analyzed in a factorial arrangement 2 ×5 (two fractions: leaf and stem, and five defoliation frequencies). The pattern of variation in IL and LAI in function of the frequency of cutting was ascending order. The maximum values of %IL and LAI occurred at 75 and 90 days after cutting with 98.46%; 98.72% (%IL) and 7.08; 8.10 (LAI), respectively. There was no effect of cutting frequency on the total yield of forage, leaf production, generation of stem and dead material, tillers alive, dead tillers and height. Only the leaf/stem ratio was not influenced (P &gt; 0.05) among the factors studied. The frequency of cutting influenced (P &lt; 0.05) the chemical composition, where decreased cutting frequency significantly increased the levels of dry matter, neutral detergent fiber, acid detergent fiber, cellulose, and ash in two fractions evaluated (leaf and stem); the crude protein content showed a decline from the moment the frequency of cutting was increased. The LAI, and productive and chemical characteristics of elephant grass were influenced by increased frequency of cutting. Limiting the cutting of the grass to 60 days implies an optimal point of production and quality.

Estimating forest stand structure attributes from terrestrial laser scans.

Forest stands are often parameterized by vegetation indices such as the Leaf Area Index (LAI). However, other indices (i.e. stand denseness, espacement, canopy density, canopy cover, foliage cover, crown porosity, gap fraction) may better characterize forest structure. Terrestrial and airborne active sensor data has been used to describe canopy structural diversity and provide accurate estimates of forest structure indices. This study uses Terrestrial Laser Scanner (TLS) to characterize forest structure through the above-mentioned indices. The relationship between all of them was studied to assess the extent to which they relate and their capability to properly describe forest stands. A strong correlation was visible between LAI and the canopy density index (r = 0.87 to 0.91 depending on the extraction methods) despite the underevaluated values of the first. Even though more precise LAI estimates were expected from using co-registered multiple scans, the LAI variability proved to be low and correlations with the remaining indices weakened when compared to a single scan approach. An exception was canopy cover, a structural index that disregards the three-dimensionality of the canopy, with which the LAI obtained from multiple scans maintained a strong correlation. This suggests that multiple scanning leads to an unweighted oversampling of the scene, overshadowing its advantages in removing tree occlusions. Weak correlations were visible between classic forest structural indices (basal area density index, espacement index, denseness index) and the rest of the descriptors. Despite this exception, most of the forest indices showed average to strong correlations in-between each other. Therefore, we conclude that a better description of forest stands structure may be achieved through unsegmented single scan point cloud processing in both 3D and 2D space, optical data from the incorporated digital camera being a plus, but not an essential requirement.

Evaluation and Hydrological Application of TRMM and GPM Precipitation Products in a Tropical Monsoon Basin of Thailand

Watershed runoff is essential for water management. However, runoff materials are lacking in poorly gauged catchments and not always accessible. Microwave remote sensing offers emerging capabilities for hydrological simulation. In this study based on multi-satellite retrievals for Global Precipitation Measurement (IMERG), Tropical Rainfall Measuring Mission (TRMM) products, and World Meteorological Organization (WMO) interpolated precipitation data, we simulated runoff using a variable infiltration capacity (VIC) model and studied the differences among the results. Then, we analyzed the impacts of the runoff on a moderate-resolution imaging spectroradiometer vegetation leaf area index (LAI) during dry seasons. The results showed that (1) IMERG V5 and TRMM products are capable of monitoring the night-day rainfall diurnal cycle and have higher correlations than the WMO daily observation interpolations. However, the WMO shows less overestimation of total precipitation than remote-sensing precipitation; (2) in the downstream, the TRMM shows better runoff simulation accuracy in the tributaries, and the WMO shows better results in the mainstreams. Therefore, at basin outlets in mainstreams, the Nash–Sutcliffe efficiency coefficients of monthly runoff by the WMO are higher than the simulations by the TRMM; (3) for the whole basin during dry seasons, the LAI variation is correlated with the outlet runoff, which is similar to the correlation with three- to six-month accumulated precipitation. TRMM products can be used to depict both precipitation deficit and runoff deficit, which cause vegetation variations. Our research suggests the potential of microwave precipitation products for detailed watershed runoff simulations and water management.

Numerical Assessments of Leaf Area Index in Tropical Savanna Rangelands, South Africa Using Landsat 8 OLI Derived Metrics and In-Situ Measurements

Knowledge on rangeland condition, productivity patterns and possible thresholds of potential concern, as well as the escalation of risks in the face of climate change and variability over savanna grasslands is essential for wildlife/livestock management purposes. The estimation of leaf area index (LAI) in tropical savanna ecosystems is therefore fundamental for the proper planning and management of this natural capital. In this study, we assess the spatio-temporal seasonal LAI dynamics (dry and wet seasons) as a proxy for rangeland condition and productivity in the Kruger National Park (KNP), South Africa. The 30 m Landsat 8 Operational Land Imager (OLI) spectral bands, derived vegetation indices and a non-parametric approach (i.e., random forest, RF) were used to assess dry and wet season LAI condition and variability in the KNP. The results showed that RF optimization enhanced the model performance in estimating LAI. Moderately high accuracies were observed for the dry season (R2 of 0.63–0.72 and average RMSE of 0.60 m2/m2) and wet season (0.62–0.63 and 0.79 m2/m2). Derived thematic maps demonstrated that the park had high LAI estimates during the wet season when compared to the dry season. On average, LAI estimates ranged between 3 and 7 m2/m2 during the wet season, whereas for the dry season most parts of the park had LAI estimates ranging between 0.00 and 3.5 m2/m2. The findings indicate that Kruger National Park had high levels of productivity during the wet season monitoring period. Overall, this work shows the unique potential of Landsat 8-derived metrics in assessing LAI as a proxy for tropical savanna rangelands productivity. The result is relevant for wildlife management and habitat assessment and monitoring.

Soil Water Content and High-Resolution Imagery for Precision Irrigation: Maize Yield

Improvement in water use efficiency of crops is a key component in addressing the increasing global water demand. The time and depth of the soil water monitoring are essential when defining the amount of water to be applied to irrigated crops. Precision irrigation (PI) is a relatively new concept in agriculture, and it provides a vast potential for enhancing water use efficiency, while maintaining or increasing grain yield. Neutron probes (NPs) have consistently been used as a robust and accurate method to estimate soil water content (SWC). Remote sensing derived vegetation indices have been successfully used to estimate variability of Leaf Area Index and biomass, which are related to root water uptake. Crop yield has not been evaluated on a basis of SWC, as explained by NPs in time and at different depths. The objectives of this study were (1) to determine the optimal time and depth of SWC and its relationship to maize grain yield (2) to determine if satellite-derived vegetation indices coupled with SWC could further improve the relationship between maize grain yield and SWC. Soil water and remote sensing data were collected throughout the crop season and analyzed. The results from the automated model selection of SWC readings, used to assess maize yield, consistently selected three dates spread around reproductive growth stages for most depths (p value &lt; 0.05). SWC readings at the 90 cm depth had the highest correlation with maize yield, followed closely by the 120 cm. When coupled with remote sensing data, models improved by adding vegetation indices representing the crop health status at V9, right before tasseling. Thus, SWC monitoring at reproductive stages combined with vegetation indices could be a tool for improving maize irrigation management.

Evaluation of single crop coefficient curves derived from Landsat satellite images for major crops in Iran

Improving the accuracy of single crop coefficient (Kc) for major crops at regional scales would significantly result in optimizing the consumption of agriculture water. In this study, a new approach is applied to generate Kc curves from satellite images based on the Priestley-Taylor equation. The equation mainly consists of shortwave radiation and thermal terms. Therefore in order to meet the equation’s requirements, the Landsat optical data was considered as the essential satellite-based input of the approach, yet thermal data was obtained from the MODIS imagery which was primarily downscaled using the TsHARP algorithm to fit the 30 m spatial resolution of Landsat; therefore, in this study the method is referred to as the MultiSensor Data Fusion for potential EvapoTranspiration calculation (MSDF-ET). The Kc trends were first evaluated for annual crops (forage maize, sugar beet, wheat, and barley) in two different regions (the Qazvin and Tehran-Karaj plains) using field observation data of Leaf Area Index (LAI). The method was then applied to the major crops in 31 selected plains with three different climatic conditions in Iran. Climatic conditions were categorized into cold (North West region), arid (Central region), and warm and humid (South West region). Eventually, crop water requirements (CWR) in different regions were compared considering long-term air temperature, wind, and relative humidity (RH) variations. Evaluation of the method in the Qazvin and Tehran-Karaj plains showed promising results. The Kc trend varied according to the LAI and temperature variability. Crop growth duration and especially Kc curves were successfully extracted using remotely sensed data in the studied areas. CWR in the zone with higher values of air temperature, notably in warm seasons, was higher in comparison with other climatic zones. Also lengths of the growing seasons were shorter. In conclusion, surface and groundwater resources management would be improved as the result of taking satellite data into account for Kc and subsequently CWR calculations.

Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat.

Rapid and effective acquisition of crop growth information is a crucial step of precision agriculture for making in-season management decisions. Active canopy sensor GreenSeeker (Trimble Navigation Limited, Sunnyvale, CA, USA) is a portable device commonly used for non-destructively obtaining crop growth information. This study intended to expand the applicability of GreenSeeker in monitoring growth status and predicting grain yield of winter wheat (Triticum aestivum L.). Four field experiments with multiple wheat cultivars and N treatments were conducted during 2013–2015 for obtaining canopy normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) synchronized with four agronomic parameters: leaf area index (LAI), leaf dry matter (LDM), leaf nitrogen concentration (LNC), and leaf nitrogen accumulation (LNA). Duration models based on NDVI and RVI were developed to monitor these parameters, which indicated that NDVI and RVI explained 80%, 68–70%, 10–12%, and 67–73% of the variability in LAI, LDM, LNC and LNA, respectively. According to the validation results, the relative root mean square error (RRMSE) were all <0.24 and the relative error (RE) were all <23%. Considering the variation among different wheat cultivars, the newly normalized vegetation indices rNDVI (NDVI vs. the NDVI for the highest N rate) and rRVI (RVI vs. the RVI for the highest N rate) were calculated to predict the relative grain yield (RY, the yield vs. the yield for the highest N rate). rNDVI and rRVI explained 77–85% of the variability in RY, the RRMSEs were both <0.13 and the REs were both <6.3%. The result demonstrates the feasibility of monitoring growth parameters and predicting grain yield of winter wheat with portable GreenSeeker sensor.

A numerical analysis of aggregation error in evapotranspiration estimates due to heterogeneity of soil moisture and leaf area index

Land Surface Models which determine evapotranspiration (ET) by neglecting the sub-grid heterogeneity of land-atmosphere parameters will cause aggregation biases in spatially-averaged ET estimates, considering the nonlinear dependences of ET on the heterogeneous land-atmosphere parameters. One frequently adopted strategy clusters the heterogeneous surface within a model grid into several tiles, assumed to be homogeneous, usually based on high-resolution land cover data. While the differences in bulk-averaged parameters between different tiles are considered, the heterogeneity within each tile is neglected. This study evaluated in detail the aggregation biases in the tile mean ET estimates due to applying bulk-averages of the Saturation degree of surface Soil Moisture (SSM) and Leaf Area Index (LAI) for each tile through numerical experiments. Four types of Probability Distribution Function (PDF) were used to simulate different scenarios on the heterogeneity (within a tile) of SSM and LAI, i.e., from water scarcity to wet, and from sparse to dense vegetation covered surfaces. Aggregation bias was calculated by comparing ET estimates based on bulk-averaged SSM and LAI with the one obtained by aggregation of the flux estimates based on the PDFs, which complies with energy conservation. In addition, a wide range of meteorological conditions was applied in our numerical experiments and the impacts were evaluated by binning results according to the reference ET (ET0). We found that potentially significant bias can be found in semi-arid areas. Neglecting the actual spatial variability of both SSM and LAI within tiles can lead to both large relative error (> 20%) and absolute error (> 1 mm/day) in the estimated ET. A negative bias is expected at low ET / ET0 and a positive bias is expected at large ET / ET0, regardless of climate conditions (i.e., ET0).

Modeling Winter Wheat Leaf Area Index and Canopy Water Content With Three Different Approaches Using Sentinel-2 Multispectral Instrument Data

Leaf area index (LAI) and canopy water content (CWC) are important variables for monitoring crop growth and drought, which can be estimated from remotely sensed data. The goal of this study was to evaluate the suitability of the Sentinel-2 multispectral instrument (S2 MSI) data for winter wheat LAI and CWC estimation with three different inversion approaches in the main farming region in North China. During the winter wheat key growth stages in 2017, 22 fields, each with five independent samples, the total number of sample plot is 110, were designed for experimental measurements. In this study, the LAI and CWC were retrieved separately using empirical models through different spectral indices, neural network (NN) algorithms, and lookup table (LUT) methods based on the PROSAIL model. The accuracies of the estimated LAI and CWC were assessed through in situ measurements. The results show that the LUT inversion approach was more suitable for LAI and CWC estimation than the spectral index-based empirical model or the NN algorithm. With the LUT approach, LAI was obtained with a root mean square error (RMSE) of $\text{0.43}\,{\text{m}}^{\text{2}} \cdot {\text{m}}^{-\text{2}}$ and a relative RMSE (RRMSE) of 11% using seven S2 MSI bands, and CWC was obtained with an RMSE of $\text{0.41}\,{\text{kg}} \cdot {\text{m}}^{-\text{2}}$ , and an RRMSE of 32% using five S2 MSI bands. In all the three methods, S2 MSI was sensitive to LAI variation and able to reach higher accuracies when red edge bands were used. However, CWC inversion was still a challenge using S2 MSI data.