Green Area Index Research Articles

Mapping winter wheat crop traits dynamic change and growth performance for variable rate application using Sentinel-1 and Sentinel-2

Site specific crop management for variable rate application is extensively recognized as a method for distributing agricultural input unevenly across a field, tailored to the diverse requirement of different areas. From the previous study, this approach proven to reduce agricultural input expenses by 10 % without impacting yield and ensure environmental sustainability. This study presents a new approach to delineate management zones for precision agriculture using crop biophysical property variability assessment within winter wheat fields. A multivariate random forest framework was developed to estimate winter wheat’s biophysical properties within fields from surface reflectance and backscatters of Sentinel-1 and Sentinel-2. Combining Sentinel-1 and Sentinel-2 data resulted in more precise estimation of the green area index (R²=0.98), aboveground dry biomass (R²=0.90), plant height (R²=0.94), and leaf nitrogen content (R²=0.78). Sentinel-2 alone was particularly effective in estimating shoot density (R²=0.94). These estimates were then used to create management zones for precision agriculture, classified based on agronomic performance benchmarks. The fuzzy c-mean clustering algorithm helped generate homogeneous management zones, considering the biophysical variations within fields.The ultimate goal is to integrate these biophysical property maps and management zones into crop management workflows. This integration will assist farmers in recognizing field variability and understanding its causes. Moreover, the spatial distribution of these zones supports variable rate application, guiding farmers towards more efficient, profitable, and sustainable crop management practices.

High-Throughput Phenotyping: Application in Maize Breeding

In breeding programs, the demand for high-throughput phenotyping is substantial as it serves as a crucial tool for enhancing technological sophistication and efficiency. This advanced approach to phenotyping enables the rapid and precise measurement of complex traits. Therefore, the objective of this study was to estimate the correlation between vegetation indices (VIs) and grain yield and to identify the optimal timing for accurately estimating yield. Furthermore, this study aims to employ photographic quantification to measure the characteristics of corn ears and establish their correlation with corn grain yield. Ten corn hybrids were evaluated in a Complete Randomized Block (CRB) design with three replications across three locations. Vegetation and green leaf area indices were estimated throughout the growing cycle using an unmanned aerial vehicle (UAV) and were subsequently correlated with grain yield. The experiments consistently exhibited high levels of experimental quality across different locations, characterized by both high accuracy and low coefficients of variation. The experimental quality was consistently significant across all sites, with accuracy ranging from 79.07% to 95.94%. UAV flights conducted at the beginning of the crop cycle revealed a positive correlation between grain yield and the evaluated vegetation indices. However, a positive correlation with yield was observed at the V5 vegetative growth stage in Lavras and Ijaci, as well as at the V8 stage in Nazareno. In terms of corn ear phenotyping, the regression coefficients for ear width, length, and total number of grains (TNG) were 0.92, 0.88, and 0.62, respectively, demonstrating a strong association with manual measurements. The use of imaging for ear phenotyping is promising as a method for measuring corn components. It also enables the identification of the optimal timing to accurately estimate corn grain yield, leading to advancements in the agricultural imaging sector by streamlining the process of estimating corn production.

Comparing and combining data-driven and model-driven approaches to monitor wheat green area index with high spatio-temporal resolution satellites

Monitoring crops with high spatio-temporal resolution satellites provides valuable observations to ensure food security in the global change context. This study focuses on estimating the Green Area Index (GAI) to monitor wheat crops with a spatial resolution of 3 m and daily satellite observations from the SuperDove constellation. With an easier access to large training datasets of ground GAI measurements, and the improvement of the realism of radiative transfer model simulations, the choice of the optimal approach (data-driven or model-driven) constitutes a key question when retrieving GAI from satellite observations.This study compares a data-driven and a model-driven approach to estimate GAI from the SuperDove satellites. Both approaches are based on Gaussian Process Regression (GPR) machine learning techniques. The data-driven approach uses over 300 ground GAI measurements collected from 12 sites in China and France, each with 20 to 51 contrasting plots. The model-driven approach uses 10,000 simulations of top of canopy reflectance and the corresponding GAI values generated by the LESS radiative transfer model applied to 3D scenes built with the ADEL-Wheat (Architectural model of Development based on L-systems) model.Results confirm that the SuperDove reflectance are reliable and consistent with Sentinel-2 values. When estimating GAI using GPR with SuperDove top of canopy reflectance, the model-driven approach (R2 = 0.83, RMSE = 0.80, Accuracy = 0.01 and Precision = 0.80) generally outperforms the data-driven approach (R2 = 0.80, RMSE = 0.88, Accuracy = −0.13 and Precision = 0.87), except for small GAI values. In-silico experiments show that the uncertainties in the ground-measured GAI and the size and diversity of the training datasets limit the data-driven approach. In contrast, the model-driven approach is mostly constrained by the realism of the reflectance simulations, particularly for low GAI values.Two ensemble solutions based on the weighted average of the two previous approaches are then proposed: the global ensemble solution (R2 = 0.86, RMSE = 0.75, A = −0.06 and P = 0.74) where the weight is assumed independent from the GAI values, and the adaptive ensemble solution (R2 = 0.85, RMSE = 0.76, A = −0.08 and P = 0.76) where the weight depends on the GAI values. Both solutions perform similarly, improving both data-driven and model-driven approaches. Finally, applying both solutions to monitor wheat plots along the growth cycle allows clear differentiation of nitrogen modalities and cultivar effects. However, a minimum plot size of 12 m × 12 m (4 × 4 pixels) is recommended to minimize the co-registration errors and increase estimate precision.

Comparative Performance of Aerial RGB vs. Ground Hyperspectral Indices for Evaluating Water and Nitrogen Status in Sweet Maize

This study analyzed the capability of aerial RGB (red-green-blue) and hyperspectral-derived vegetation indices to assess the response of sweet maize (Zea mays var. saccharata L.) to different water and nitrogen inputs. A field experiment was carried out during 2020 by using both remote RGB images and ground hyperspectral sensor data. Physiological parameters (i.e., leaf area index, relative water content, leaf chlorophyll content index, and gas exchange parameters) were measured. Correlation and multivariate data analysis (principal component analysis and stepwise linear regression) were performed to assess the strength of the relationships between eco-physiological measured variables and both RGB indices and hyperspectral data. The results revealed that the red-edge indices including CIred-edge, NDRE and DD were the best predictors of the maize physiological traits. In addition, stepwise linear regression highlighted the importance of both WI and WI:NDVI for prediction of relative water content and crop temperature. Among the RGB indices, the green-area index showed a significant contribution in the prediction of leaf area index, stomatal conductance, leaf transpiration and relative water content. Moreover, the coefficients of correlation between studied crop variables and GGA, NDLuv and NDLab were higher than with the hyperspectral indices measured at the ground level. The findings confirmed the capacity of selected RGB and hyperspectral indices to evaluate the water and nitrogen status of sweet maize and provided opportunity to expand experimentation on other crops, diverse pedo-climatic conditions and management practices. Hence, the aerially collected RGB vegetation indices might represent a cost-effective solution for crop status assessment.

UAV-based canopy monitoring: calibration of a multispectral sensor for green area index and nitrogen uptake across several crops

AbstractThe fast and accurate provision of within-season data of green area index (GAI) and total N uptake (total N) is the basis for crop modeling and precision agriculture. However, due to rapid advancements in multispectral sensors and the high sampling effort, there is currently no existing reference work for the calibration of one UAV (unmanned aerial vehicle)-based multispectral sensor to GAI and total N for silage maize, winter barley, winter oilseed rape, and winter wheat.In this paper, a practicable calibration framework is presented. On the basis of a multi-year dataset, crop-specific models are calibrated for the UAV-based estimation of GAI throughout the entire growing season and of total N until flowering. These models demonstrate high accuracies in an independent evaluation over multiple growing seasons and trial sites (mean absolute error of 0.19–0.48 m2 m−2 for GAI and of 0.80–1.21 g m−2 for total N). The calibration of a uniform GAI model does not provide convincing results. Near infrared-based ratios are identified as the most important component for all calibrations. To account for the significant changes in the GAI/ total N ratio during the vegetative phase of winter barley and winter oilseed rape, their calibrations for total N must include a corresponding factor. The effectiveness of the calibrations is demonstrated using three years of data from an extensive field trial. High correlation of the derived total N uptake until flowering and the whole-season radiation uptake with yield data underline the applicability of UAV-based crop monitoring for agricultural applications.

Daily monitoring of Effective Green Area Index and Vegetation Chlorophyll Content from continuous acquisitions of a multi-band spectrometer over winter wheat

Green area index (GAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) are key variables that are closely related to crop growth. Concurrent and continuous monitoring of GAI, LCC and CCC is critical to keep consistency among variables and make decisions for field precision managements. Previous studies have developed several instruments and algorithms to monitor continuous GAI, while the autonomous monitoring of three variables simultaneously has been lacking. This study presents a novel algorithm to retrieve daily GAI, LCC and CCC from continuous directional observations acquired by a fixed and economic affordable multi-band spectrometer (6 bands covering red, red-edge and near infrared domains) and a photosynthetically active radiation (PAR) sensor in the field. It is composed of three main steps, corresponding to three crucial questions when retrieving variables under natural environments using multi-band spectrometer installed on a near-surface platform: diffuse fraction in each spectral band, radiometric calibration and diurnal sun variation of daily acquisitions. First, we estimated diffuse fraction in each spectral band from the relationship with PAR diffuse fraction based on simulations of the 6S atmospheric radiative transfer model. Second, we computed the relative value of each band to the reference of mean of measurements on all six bands from near-surface measurements, in place of absolute radiometric calibration to limit the influence of changing illumination conditions. In the third step, we combined PROSAIL canopy radiative transfer model and kernel-driven models to retrieved GAI, LCC and CCC from artificial neural network using above spectral diffuse fraction and diurnal multi-angle relative observations. The algorithm was evaluated over 43 IoTA (Internet of things for Agriculture) systems that were installed in 29 wheat fields in France from March to May 2019. Results showed that our method provides good estimates of GAI with root mean square error (RMSE) of 0.54, relative RMSE (RRMSE) of 26.95%, R2 of 0.86, LCC (RMSE = 12.06 μg/cm2, RRMSE = 33.34%, R2 = 0.52) and CCC (RMSE = 0.23 g/m2, RRMSE = 24.58%, R2 = 0.93). This study shows great potentials for concurrent estimates of GAI, LCC and CCC from continuous ground measurements. It will be useful over other vegetations or other near-surface platforms for simultaneous estimations of biophysical variables.

HemiPy: A Python module for automated estimation of forest biophysical variables and uncertainties from digital hemispherical photographs

Abstract Digital hemispherical photography (DHP) is widely used to derive forest biophysical variables including leaf, plant, and green area index (LAI, PAI and GAI), the fraction of intercepted photosynthetically active radiation (FIPAR), and the fraction of vegetation cover (FCOVER). However, the majority of software packages for processing DHP data are based on a graphical user interface, making programmatic analysis difficult. Meanwhile, few natively support analysis of RAW image formats, while none incorporate the propagation or provision of uncertainties. To address these limitations, we present HemiPy, an open‐source Python module for deriving forest biophysical variables and uncertainties from DHP images in an automated manner. We assess HemiPy using simulated hemispherical images, in addition to multiannual time‐series and litterfall data from several forested National Ecological Observatory Network (NEON) sites, as well as comparison against the CAN‐EYE software package. Multiannual time‐series of PAI, FIPAR and FCOVER demonstrate HemiPy's outputs realistically represent expected temporal patterns. Comparison against litterfall data reveals reasonable accuracies are achievable, with RMSE values close to the error of ~1 unit typically attributed to optical LAI measurement approaches. HemiPy's PAI, FIPAR and FCOVER outputs demonstrate good agreement with CAN‐EYE. Consistent with previous studies, when compared to simulated hemispherical images, better agreement is observed for PAI derived using gap fraction near the hinge angle of 57.5° only, as opposed to values derived using gap fraction over a wider range of zenith angles. HemiPy should prove a useful tool for processing DHP images, and its open‐source nature means that it can be adopted, extended and further refined by the user community.

Cleaner production of iron-coated quartz sand composites for efficient phosphorus adsorption in sanitary wastewater: A design of experiments (DoE) approach

Phosphorus (P) adsorption and recovery from sanitary wastewater is a promising route to produce more sustainable fertilizers. Multivariate Design of Experiments (DoE) techniques were used to develop a cleaner process to produce Fe-coated quartz sand composites with applications in Phosphorus (P) adsorption from sanitary effluents. Nine different process parameters were investigated and correlations between process inputs and physicochemical characteristics of adsorbents were elucidated. It was found that Goethite and Magnetite phases with different degrees of crystallinity were obtained depending on the conditions applied during the synthesis by coprecipitation. Statistical models fitted to an experimental multivariate database were used to predict the best conditions for synthesizing an efficient P adsorbent. In addition, a zero-waste process for the production of the adsorbents is reported. The best adsorbent produced, an aqueous suspension containing particles of a SiO2@FeOOH composite, exhibited a P (KH2PO4) adsorption capacity of 12.4 mg P/g (C0 = 10 mg P/L, pH 6.0) and robust performance when applied in simulated sanitary effluent, reaching 97% of P removal efficiency (C0 = 5 mg P/L). Considering the value of the green star area index (GSAI), the optimized method (suspension) was 65% green, reaching the maximum score for four of the ten principles involved, denoting the greenness of the proposed method. The optimized adsorbent has appropriate characteristics to be applied in sanitary effluent treatment processes for capturing and recovering residual P, contributing to a more sustainable management of natural resources.

Impact of Limited-Access Green Spaces on the Qualitative and Quantitative Indices of a City

Urban green spaces (UGSs) are fragments of city vegetation capable of providing numerous benefits, which can be measured through indices. However, even though public green spaces (PGSs) are accounted for in such indices as areas that can generate positive impacts, private green spaces (PrGSs) are not considered. Therefore, the objective of this study was to evaluate the impact and feasibility of the inclusion of limited-access green spaces (LAGSs—a type of PrGS—in green area index (GAI) and green space ratio (GSR) calculations, within the city of Lavras, Minas Gerais, Brazil, as a model. By employing geotechnologies, private squares and spaces were identified. On-site visits were employed to determine the functions (aesthetic, ecological, and/or leisure) of such spaces. For LAGSs, only those with an index higher than 50% permeability were included. The calculated indices for PGSs are as follows: PGS index (PGSI) = 0.53 m2/inhabitant, PGS ratio (PGSR) = 0.27%, and total green space (TGS) = 62,067 m2. Taking LAGSs into account, the indices are as follows: LAGS index (LAGSI) = 3.76 m2/inhabitant, LAGS ratio (LAGSR) = 1.92%, and TGS = 443,309.03 m2. The results of the analyses carried out in this study allow us to conclude that LAGSs contribute significantly to the increase in the GAI and GSR in Lavras and, therefore, to the city's environmental quality. Such spaces surpass, in extension, those of the public domain and should be included in UGS calculations when assessing the green infrastructure of cities.

Yield Loss and Integrated Disease Control of Rhizoctonia solani AG2-1 Using Seed Treatment and Sowing Rate of Oilseed Rape.

Rhizoctonia solani anastomosis group (AG) 2-1 is an ubiquitous soilborne pathogen causing severe damping-off of oilseed rape (OSR). In the absence of varietal resistance to AG2-1, there are limited methods for integrated disease management. The objectives of these field studies were to quantify yield losses due to AG2-1 and to determine the effectiveness of integrated control using sedaxane, fludioxonil, and metalaxyl-M applied as seed treatment on two OSR genotypes at a sowing rate of 40 (low) or 80 (high) seeds m-2. Crop assessments of green area index (GAI), vigor, and cabbage stem flea beetle (CSFB) Psylliodes chrysocephala damage were carried out at GS16, while pathogen DNA in soil was quantified using real-time PCR at GS32. Yield and seed weight losses of 41 and 18%, respectively, were associated with reduced establishment, GAI, vigor, and delayed development and flowering of OSR. Seed treatment reduced AG2-1 DNA in soil by 80%, resulting in a 94, 16, and 64% increase of establishment, thousand seed weight (TSW), and yield, respectively. Seed treatment also mitigated the effects of AG2-1 on delaying plant development, resulting in increased uniformity of crop flowering. OSR plants infected with AG2-1 suffered 27% more damage by the CSFB, indicating positive pathogen-pest interaction at the expense of the OSR host. Optimum control of AG2-1 infection was achieved by integrating low sowing rate and seed treatment. However, under dual pest and pathogen attack, high sowing rates should be combined with the use of seed treatment to mitigate seedling death and delayed development caused by AG2-1 and CSFB damage.

Forecasting sunflower grain yield using remote sensing data and statistical models

Forecasting crop production a few weeks before harvest is of strategical interest for the cooperatives which collect, store and market grains. The recent development of Sentinel satellites opened new avenues for yield forecasting at field and farm level, thanks to their operational spatial resolution and revisiting time. In this study, we combined remote sensing data (in-season green area index, GAI) and statistical modeling to forecast sunflower yield at field level for a range of cultivars and crop practices over different small production areas and years in southwestern France. From 2014–2016, 359 sunflower fields were monitored throughout the growing season in the ‘Haute-Garonne’ and ‘Gers’ administrative departments (SW France). From the satellite GAI estimates, two variables were calculated: GAImax (maximum GAI, between F1 stage and F1 + 10 days) and GAD (Green Area Duration). Different statistical modeling procedures were tested namely a linear regression (LR), a second degree polynomial regression (PR), a random forest regressor (RF) and a Gaussian process (GP). In each case, the models were tested using either GAD, GAImax or both variables, and each model was trained using in a first time GAD and GAImax obtained with linear interpolation, and in a second time, the same variables computed using the double sigmoid interpolation. In a perspective of yield prediction, GAD was calculated from anthesis to maturity but also from anthesis to 10/07, 20/07, 30/07 and 10/08 using remote sensing data. Sunflower grain yield at maturity was predicted with 10 models differing by their forms and the agronomic variables involved. At individual level, GY was slightly better predicted by models including GAD + GAImax or GAD, while models based only on GAImax were the less accurate. This was consistent with the major importance of post-anthesis radiation interception and senescence dynamics in the development of grain yield in sunflower. Better predictions were achieved in 2014, then in 2015 and finally 2016. However, at the grain catchment area level, PR models including GAD were the most accurate ones with absolute errors ranging from 0.53 to 4.68 q.ha−1 as a function of years. Only the predictions obtained with 2014 data and over the 3 years were sufficiently accurate to be of operational value for a cooperative manager.

A planning framework to guide the creation of urban green spaces using existing forest fragments in the urban territory: A case study from Foz do Iguaçu, Brazil

The creation of urban green spaces (UGS) taking advantage of existing forest fragments in the urban territory and, at the same time, optimizing the provisioning of ecosystem services can contribute to make cities more livable. We developed a planning framework to guide this creation process and exemplified its application in Foz do Iguaçu City, Brazil. Forest fragments in the urban perimeter were identified in Google Earth Pro® software. These fragments were described based on a set of attributes positively related to ecosystem services’ provisioning, namely: patch size, patch shape (compactness), canopy cover, conservation status and presence of waterbodies. Scores were assigned to each attribute and fragments showing overall score equal to or higher than the cutoff limit, as well as presenting a set of prerequisites (public ownership, not being already an UGS, access by walkable roads and absence of land invasion for housing purposes), were classified as suitable for the creation of UGS. We identified 55 forest fragments, 18 of them were classified as suitable for UGS’ creation. The green area index (GAI) calculated from the 55 fragments identified varied considerably among the 11 urban regions of the city. Two criteria were used to prioritize the 18 forest fragments classified as suitable for the creation of UGS: first, to be located in regions without already-created UGS (five fragments in three regions) versus regions already endowed with such areas (13 fragments in three regions); and, secondly, in each of these two region types, to be located in the ones with the lowest GAI values. The proposed framework proved to be a useful and inexpensive method to characterize the coverage of forest fragments and to decide which fragments to consider first in actions aimed at creating UGS.

Spectral reflection and crop parameters: can the disentanglement of primary and secondary traits lead to more robust and extensible prediction models?

Recently the application of spectral reflection data for the prediction of crop parameters for applications in precision agriculture, such as green area index (GAI), total aboveground dry matter (DM), and total aboveground nitrogen content (N content) increases. However, the usability of vegetation indices (VI) for the prediction of crop parameters is strongly limited by the fact that most VI calibrations are only valid for specific crops and growth periods. The results of the presented study based on the differentiation of primary (main driver of the reflectance signal) and secondary (not directly related to reflectance signal) crop parameters. For GAI prediction, a universal (without crop-specific parametrization) simple ratio vegetation index (SR) provided good calibration (R2 adj. = 0.90, MAE = 0.32, rMAE = 22%) and evaluation results (MAE = 0.33, rMAE = 18%). The disentanglement of primary and secondary traits allowed the development of a functional two-step model for the estimation of the N content during vegetative growth (MAE = 19.2 g N m−1, rMAE = 44%). This model was based on fundamental, crop-specific relationships between the crop parameters GAI and N content. Additionally, an advanced functional approach was tested enabling the whole-season prediction of DM and confirming a reliable GAI estimation throughout the whole growing season (R2 = 0.89–0.93).

Ecosystem services in urban green areas: Contributions to the United Nations 2030 Agenda

Urban green areas are spaces that contribute to the fulfillment of the Sustainable Development Goals (SDGs) established by the United Nations 2030 Agenda. These are spaces that can increase the offer of Ecosystem Services (SEs – Serviços Ecossistêmicos), which are essential to promote well-being and a healthy environment to humans. The objective of this study is the assessment of SEs offered by a green space located in São Paulo City. The Praça Alfredo Di Cunto, also known as Horta das Flores, was selected for this study. It is a public green space located in the Mooca Subprefecture, which is a region with a low green area index (2 to 5 m²/inhabitant). To assess the SEs offered by Horta das Flores, the methodology proposed by Gaudereto et al. (2018) named Índice de Serviços Ecossistêmicos para Áreas Verdes (ISEAV – Index of Ecosystem Services for Green Areas) was applied. We highlight the following results of our study: support SEs, represented by the vegetation cover and permeable areas, contribute to the achievement of goals SDG 6, SDG 13 and SDG 15; regulating SEs contribute to nutrient cycling, because litterfall is not removed; and provisioning SEs contribute to SDG 2, because Horta das Flores has a vegetable garden with food plants including unconventional ones. Horta das Flores offers many SEs, thus contributing not only to the quality of life of the regulars, but also to a more sustainable city and community (SDG 11), as it is a safe and inclusive green space.

Estimation of Biomass and N Uptake in Different Winter Cover Crops from UAV-Based Multispectral Canopy Reflectance Data

Cover crops are known to provide beneficial effects to agricultural systems such as a reduction in nitrate leaching, erosion control, and an increase in soil organic matter. The monitoring of cover crops’ growth (e.g., green area index (GAI), nitrogen (N) uptake, or dry matter (DM)) using remote sensing techniques allows us to identify the physiological processes involved and to optimise management decisions. Based on the data of a two-year trial (2018, 2019) in Kiel, Northern Germany, the multispectral sensor Sequoia (Parrot) was calibrated to the selected parameters of the winter cover crops oilseed radish, saia oat, spring vetch, and winter rye as sole cover crops and combined in mixtures. Two simple ratios (SRred, SRred edge) and two normalised difference indices (NDred, NDred edge) were calculated and tested for their predicting power. Furthermore, the advantage of the species/mixture–individual compared to the universal models was analysed. SRred best predicted GAI, DM, and N uptake (R2: 0.60, 0.53, 0.45, respectively) in a universal model approach. The canopy parameters of saia oat and spring vetch were estimated by species–individual models, achieving a higher R2 than with the universal model. Comparing mixture–individual models to the universal model revealed low relative error differences below 3%. The findings of the current study serve as a tool for the rapid and inexpensive estimation of cover crops’ canopy parameters that determine environmental services.

Effective GAI is best estimated from reflectance observations as compared to GAI and LAI: Demonstration for wheat and maize crops based on 3D radiative transfer simulations

The definition of LAI (Leaf Area Index) is important when deriving it from reflectance observation for model application and validation. Canopy reflectance and the corresponding quantities of LAI, PAI (Plant Area Index), GAI (Green Area Index) and effective GAI (GAI eff ) are first calculated using a 3D radiative transfer model (RTM) applied to 3D wheat and maize architecture models. A range of phenological stages, leaf optical properties, soil reflectance, canopy structure and sun directions is considered. Several retrieval methods are compared, including vegetation indices (VIs) combined with a semi-empirical model, and 1D or 3D RTM combined with a machine learning inversion approach. Results show that GAI eff is best estimated from remote sensing observations. The RTM inversion using a 3D model provides more accurate GAI eff estimates compared with VIs and the 1D PROSAIL model with RMSE = 0.33 for wheat and RMSE= 0.43 for maize. GAI eff offers the advantage to be easily accessible from ground measurements at the decametric resolution. It was therefore concluded that the most efficient retrieval approach would be to use machine learning algorithms trained over paired GAI eff and the corresponding canopy reflectance derived either from realistic 3D canopy models or from experimental measurements. • Wheat and maize canopy reflectance are simulated with realistic 3D model. • Effective GAI is best estimated from remote sensing observations. • 3D model provides the best estimation of effective GAI compared to 1D model and Vis.

The impact of crop establishment systems in combination with applied nitrogen management on the establishment, growth and yield of winter oilseed rape in a mild Atlantic climate

Winter oilseed rape was established under five cultivation methods in field trials in Ireland between 2013 and 2016. A conventional plough-based cultivation system and a minimum tillage cultivation system were both analysed at 125 mm and 600 mm row spacing. A strip tillage cultivation system at 600 mm row spacing was also analysed. A fixed rate nitrogen application system was compared with two canopy management strategies (using crop green area index as an indicator of required nitrogen application) in both the (conventional) plough 125 mm and the (more novel) strip tillage 600 mm cultivation systems in a factorial arrangement. Establishment methods significantly influenced crop emergence in each year, with results varying between sites. Establishment systems did not significantly affect pod numbers in two of the three sites analysed. Each of the contrasting crop establishment systems gave similar seed yields in each year of this study. There was no significant effect of nitrogen management practices on green area index or seed yield. These results broaden the scope for successful winter oilseed establishment and indicate the potential to utilise the benefits of a reduced cultivation system without significantly impacting on seed yield at harvest.

Machine Learning with UAS LiDAR for Winter Wheat Biomass Estimations

Abstract. Biomass is an important indicator in the ecological and management process that can now be estimated at higher temporal and spatial resolutions because of unmanned aircraft systems (UAS). LiDAR sensor technology has advanced enabling more compact sizes that can be integrated with UAS platforms. Its signals are capable of penetrating through vegetation canopies enabling the capture of more information along the plant structure. Separate studies have used LiDAR for crop height, rate of canopy penetrations as related to leaf area index (LAI), and signal intensity as an indicator of plant chlorophyll status or green area index (GAI). These LiDAR products are combined within a machine learning method such as an artificial neural network (ANN) to assess the potential in making accurate biomass estimations for winter wheat.

Crop specific inversion of PROSAIL to retrieve green area index (GAI) from several decametric satellites using a Bayesian framework

The objective of this study is to evaluate the performances of a semi-empirical approach based on the Bayesian theory to retrieve Green Area Index (GAI) from multiple decametric satellites. It is designed to overcome some limitations in existing Radiative Transfer Model (RTM) inversion methods, including the high dimensionality of the inverse problem, the convergence problem due to possible equifinality, and the dependence of some RTM variables on the crop-specific architecture. The PROSAIL model is first inverted in a calibration step using the Hamiltonian Monte Carlo (HMC) algorithm over a global dataset of ground GAI measurements (for maize, wheat, and rice) and the corresponding reflectance observations from Landsat-8, Sentinel-2, and Quickbird to derive crop-specific distributions of PROSAIL input variables. These distributions were then used as prior information to predict GAI over an independent set of reflectance observations.Results show that the full Bayesian approach provides close estimates of GAI to ground truth, with respective Root Mean Square Error (RMSE) of 1.01, 1.33, and 0.97 for maize, wheat, and rice (R2=0.67, 0.76 and 0.63, respectively). The performances are better than those approaches generally reported using radiative transfer models that are non-crop-specific, like the SNAP algorithm for Sentinel-2, but are slightly behind the purely empirical models based on machine learning. However, the proposed approach provides an explicit insight of the joint distribution of PROSAIL variables that are valid for any satellite platform. This constitutes a major advantage against purely empirical models, as it enables to fully exploit large observational datasets from multiple sensors and generalize to other platforms.

Green Area Index and Soil Moisture Retrieval in Maize Fields Using Multi-Polarized C- and L-Band SAR Data and the Water Cloud Model

The green area index (GAI) and the soil moisture under the canopy are two key variables for agricultural monitoring. The current most accurate GAI estimation methods exploit optical data and are rendered ineffective in the case of frequent cloud cover. Synthetic aperture radar (SAR) measurements could allow the remote estimation of both variables at the parcel level, on a large scale and regardless of clouds. In this study, several methods were implemented and tested for the simultaneous estimation of both variables using the water cloud model (WCM) and dual-polarized radar backscatter measurements. The methods were tested on the BELSAR-Campaign data set consisting of in-situ measurements of bio-geophysical variables of vegetation and soil in maize fields combined with multi-polarized C- and L-band SAR data from Sentinel-1 and BELSAR. Accurate GAI estimates were obtained using a random forest regressor for the inversion of a pair of WCMs calibrated using cross and vertical co-polarized SAR data in L- and C-band, with correlation coefficients of 0.79 and 0.65 and RMSEs of 0.77 m2 m−2 and 0.98 m2 m−2, respectively, between estimates and in-situ measurements. The WCM, however, proved inadequate for soil moisture monitoring in the conditions of the campaign. These promising results indicate that GAI retrieval in maize crops using only dual-polarized radar data could successfully substitute for estimates derived from optical data.