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The literature points out the need for leaf area (LA) calibration models that are suitable for specific varieties (variety-specific). These models should be capable of coping with different crop conditions, growth stages, and agronomic practices. The objective of the current study was to develop a model for estimating the LA of ​​maize (Zea mays L.), considering the entire growth cycle, based on non-destructive allometric measurements. The proposed model was derived from a multiple regression analysis of LA data obtained from digital image processing, including the number of leaves per plant (NL) and the product of major leaf length per major leaf width of the greater leaf (MLL × MLW). A high percent of data variability in the LA of maize plants was explained by the model, both in the calibration and validation phases (R2 = 0.90; n = 30). Overall, the selected model presented good performance in the estimation of LA of maize, variety PAN 53, cultivated under the conditions of the present study area. Additionally, the model enabled the estimation of LA at different stages of the crop cycle. The results indicated a positive potential for using the developed model to support several maize cultural practices. Key words: Allometry, non-destructive measurement, modelling, Zea mays.

The dynamic effects of kaolin clay particle film application on the temperature and spectral reflectance of leaves of two autochthonous cultivars (Touriga Nacional (TN, n=32) and Touriga Franca (TF, n=24)) were studied in the Douro wine region. The study was implemented in 2017, in conditions prone to multiple environmental stresses that include excessive light and temperature as well as water shortage. Light reflectance from kaolin-sprayed leaves was higher than the control (leaves without kaolin) on all dates. Kaolin’s protective effect over leaves’ temperatures was low on the 20 days after application and ceased about 60 days after its application. Differences between leaves with and without kaolin were explained by the normalized maximum leaf temperature (T_max_f_N), reflectance at 400 nm, 532 nm, and 737 nm, as assessed through TN data. The wavelengths of 532 nm and 737 nm are associated with plant physiological processes, which support the selection of these variables for assessing kaolin’s effects on leaves. The application of principal component analysis to the TF data, based on these four variables (T_max_f_N and reflectances: 400, 532, 737 nm) selected for TN, explained 83.56% of data variability (considering two principal components), obtaining a clear differentiation between leaves with and without kaolin. The T_max_f_N and the reflectance at 532 nm were the variables with a greater contribution for explaining data variability. The results improve the understanding of the vines’ response to kaolin throughout the grapevine cycle and support decisions about the re-application timing.

Predawn leaf water potential (Ψpd) is widely used to assess plant water status. Also, pigments concentration work as proxy of canopy’s water status. Spectral data methods have been applied to monitor and assess crop’s biophysical variables. This work developed two models to estimate Ψpd using a hand-held spectroradiometer (400−1010 nm) to obtain canopy and foliar reflectance in four dates of 2018 and a pressure chamber to measure Ψpd. Two modelling approaches, combining spectral data and several machine learning algorithms (MLA), were used to estimate Ψpd in a commercial vineyard in the Douro Wine Region. The first approach estimated Ψpd through vine’s canopy reflectance; several vegetation indices (VIs) were computed and selected, namely the SPVIopt1_950;596;521; SPVIopt2_896;880;901; PRI_CI2opt_539;560,573;716 and NPCIopt_983;972, as well as a time-dynamic variable based on Ψpd (Ψpd_0). The second modelling approach is based on pigments’ concentrations; several VIs were optimized for non-correlated pigments of vine’s leaves, assessed by its hyperspectral reflectance. The following variables for Ψpd estimation were selected through stepwise forward method: Ψpd_0; NRIgreen_LUT520;532; NRIgreen_LWC540;551. The B-MARS algorithm performed the best results for both modelling approaches, presenting a RRMSE in both validation modelling approaches between 13–14%.

In California and other agricultural regions that are facing challenges with water scarcity, accurate estimates of crop evapotranspiration (ETc) can support agricultural entities in ongoing efforts to improve on-farm water use efficiency. Remote sensing approaches for calculating ETc can be used to support wide area mapping of crop coefficients and ETc with the goal of increasing access to spatially and temporally distributed information for these variables, and advancing the use of evapotranspiration (ET) data in irrigation scheduling and management. We briefly review past work on the derivation of crop coefficients and ETc data from satellite-derived vegetation indices (VI) and evaluate the accuracy of a VI-based approach for calculation of ETc using a well instrumented, drip irrigated sugar beet (Beta vulgaris) field in the California Central Valley as a demonstration case. Sugar beets are grown around the world for sugar production, and are also being evaluated in California as a potential biofuel crop as well as for their ability to scavenge nitrogen from the soil, with important potential benefits for reduction of nitrate leaching from agricultural fields during the winter months. In this study, we evaluated the accuracy of ETc data from the Satellite Irrigation Management Support (SIMS) framework for sugar beets using ET data from a weighing lysimeter and a flux station instrumented with micrometeorological instrumentation. We used the Allen and Pereira (A&P) approach, which was developed to estimate single and basal crop coefficients from crop fractional cover (fc) and height, and combined with satellite-derived fc data and grass reference ET (ETo) data as implemented within SIMS to estimate daily ETc from SIMS (ETc-SIMS) for the sugar beet crop. The accuracy of the daily ETc-SIMS data was evaluated against daily actual ET data from the weighing lysimeter (ETa-lys) and actual ET calculated using an energy balance approach from micrometeorological instrumentation (ETa-eb). Over the course of the 181-day production cycle, ETc-SIMS totaled 737.1 mm, which was within 7.7% of total ETa-lys and 3.7% of ETa-eb. On a daily timestep, SIMS mean bias error was −0.31 mm/day relative to ETa-lys, and 0.15 mm/day relative to ETa-eb. The results from this study highlight the potential utility of applying satellite-based fc data coupled with the A&P approach to estimate ETc for drip-irrigated crops.

Ongoing declines in biodiversity caused by global environmental changes call for adaptive conservation management, including the assessment of habitat suitability spatiotemporal dynamics potentially affecting species persistence. Remote sensing (RS) provides a wide-range of satellite-based environmental variables that can be fed into species distribution models (SDMs) to investigate species-environment relations and forecast responses to change. We address the spatiotemporal dynamics of species’ habitat suitability at the landscape level by combining multi-temporal RS data with SDMs for analysing inter-annual habitat suitability dynamics. We implemented this framework with a vulnerable plant species (Veronica micrantha), by combining SDMs with a time-series of RS-based metrics of vegetation functioning related to primary productivity, seasonality, phenology and actual evapotranspiration. Besides RS variables, predictors related to landscape structure, soils and wildfires were ranked and combined through multi-model inference (MMI). To assess recent dynamics, a habitat suitability time-series was generated through model hindcasting. MMI highlighted the strong predictive ability of RS variables related to primary productivity and water availability for explaining the test-species distribution, along with soil, wildfire regime and landscape composition. The habitat suitability time-series revealed the effects of short-term land cover changes and inter-annual variability in climatic conditions. Multi-temporal SDMs further improved predictions, benefiting from RS time-series. Overall, results emphasize the integration of landscape attributes related to function, composition and spatial configuration for improving the explanation of ecological patterns. Moreover, coupling SDMs with RS functional metrics may provide early-warnings of future environmental changes potentially impacting habitat suitability. Applications discussed include the improvement of biodiversity monitoring and conservation strategies.

In the past few decades, research has developed a multitude of strategies, methods and technologies to reduce consumptive water use on farms for adaptation to the increasing incidence of water scarcity, agricultural droughts and multi-sectoral competition for water. The adoption of these water-saving practices implies accurate quantification of crop water requirements with the FAO56 crop coefficient approach, under diverse water availability and management practices. This paper critically reviews notions and means for maintaining high levels of water consumed through transpiration, land and water productivity, and for minimizing non-beneficial water consumption at farm level. Literature published on sound and quantified experimentation was used to evaluate water-saving practices related to irrigation methods, irrigation management and scheduling, crop management, remote sensing, plant conditioners, mulching, soil management and micro-climate regulation. Summary tables were developed on the benefits of these practices, their effects on non-beneficial water consumption, crop yields and crop water productivity, and the directions for adjustment of FAO56 crop coefficients when they are adopted. The main message is that on-farm application of these practices can result in water savings to a limited extent (usually

Geographic Object-based Image Analysis (GEOBIA) is increasingly used to process high-spatial resolution imagery, with applications ranging from single species detection to habitat and land cover mapping. Image segmentation plays a key role in GEOBIA workflows, allowing to partition images into homogenous and mutually exclusive regions. Nonetheless, segmentation techniques require a robust parameterization to achieve the best results. Frequently, inappropriate parameterization leads to sub-optimal results and difficulties in comparing distinct methods. Here, we present an approach based on Genetic Algorithms (GA) to optimize image segmentation parameters by using the performance scores from object-based classification, thus allowing to assess the adequacy of a segmented image in relation to the classification problem. This approach was implemented in a new R package called SegOptim, in which several segmentation algorithms are interfaced, mostly from open-source software (GRASS GIS, Orfeo Toolbox, RSGISLib, SAGA GIS, TerraLib), but also from proprietary software (ESRI ArcGIS). SegOptim also provides access to several machine-learning classification algorithms currently available in R, including Gradient Boosted Modelling, Support Vector Machines, and Random Forest. We tested our approach using very-high to high spatial resolution images collected from an Unmanned Aerial Vehicle (0.03 – 0.10 m), WorldView-2 (2 m), RapidEye (5 m) and Sentinel-2 (10 – 20 m) in six different test sites located in northern Portugal with varying environmental conditions and for different purposes, including invasive species detection and land cover mapping. The results highlight the added value of our novel comparison of image segmentation and classification algorithms. Overall classification performances (assessed through cross-validation with the Kappa index) ranged from 0.85 to 1.00. Pilot-tests show that our GA-based approach is capable of providing sound results for optimizing the parameters of different segmentation algorithms, with benefits for classification accuracy and for comparison across techniques. We also verified that no particular combination of an image segmentation and a classification algorithm is suited for all the tasks/objectives. Consequently, it is crucial to compare and optimize available methods to understand which one is more suited for a certain objective. Our approach allows a closer integration between the segmentation and classification stages, which is of high importance for GEOBIA workflows. The results from our tests confirm that this integration has benefits for comparing and optimizing both processes. We discuss some limitations of the SegOptim approach (and potential solutions) as well as a future roadmap to expand its current functionalities.

Original Paper This study aimed to estimate the daily crop evapotranspiration (ETc) of soilless cut ‘Red Naomi’ roses, cultivated in a commercial glass greenhouse, using climatic and crop predictors. A multiple stepwise regression technique was applied for estimating ETc using the daily relative humidity, stem leaf area and number of leaves of the bended stems. The model explained 90% of the daily ETc variability (R2 = 0.90, n = 33, P < 0.0001) measured by weighing lysimeters. The mean relative difference between the observed and the estimated daily ETc was 9.1%. The methodology revealed a high accuracy and precision in the estimation of daily ETc info:eu-repo/semantics/publishedVersion

The advances achieved during the last 30 years demonstrate the aptitude of the remote sensing-based vegetation indices (VI) for the assessment of crop evapotranspiration (ETc) and irrigation requirements in a simple, robust and operative manner. The foundation of these methodologies is the well-established relationship between the VIs and the basal crop coefficient (Kcb), resulting from the ability of VIs to measure the radiation absorbed by the vegetation, as the main driver of the evapotranspiration process. In addition, VIs have been related with single crop coefficient (Kc), assuming constant rates of soil evaporation. The direct relationship between VIs and ET is conceptually incorrect due to the effect of the atmospheric demand on this relationship. The rising number of Earth Observation Satellites potentiates a data increase to feed the VI-based methodologies for estimating and mapping either the Kc or Kcb, with improved temporal coverage and spatial resolution. The development of operative platforms, including satellite constellations like Sentinels and drones, usable for the assessment of Kcb through VIs, opens new possibilities and challenges. This work analyzes some of the questions that remain inconclusive at scientific and operational level, including: (i) the diversity of the Kcb-VI relationships defined for different crops, (ii) the integration of Kcb-VI relationships in more complex models such as soil water balance, and (iii) the operational application of Kcb-VI relationships using virtual constellations of space and aerial platforms that allow combining data from two or more sensors.

Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.

The predawn leaf water potential (ѱpd) is an eco-physiological indicator widely used for assessing vines water status and thus supporting irrigation management in several wine regions worldwide. However, the ѱpd is measured in a short time period before sunrise and the collection of a large sample of points is necessary to adequately represent a vineyard, which constitute operational constraints. In the present study, an alternative method based on hyperspectral data derived from a handheld spectroradiometer and machine learning algorithms was tested and validated for assessing grapevine water status. Two test sites in Douro wine region, integrating three grapevine cultivars, were studied for the years of 2014, 2015, and 2017. Four machine learning regression algorithms were tested for predicting the ѱpd as a continuous variable, namely Random Forest (RF), Bagging Trees (BT), Gaussian Process Regression (GPR), and Variational Heteroscedastic Gaussian Process Regression (VH-GPR). Three predicting variables, including two vegetation indices (NRI554,561 and WI900,970) and a time-dynamic variable based on the ѱpd (ѱpd_0), were applied for modelling the response variable (ѱpd). Additionally, the predicted values of ѱpd were aggregated into three classes representing different levels of water deficit (low, moderate, and high) and compared with the corresponding classes of ѱpd observed values. A root mean square error (RMSE) and a mean absolute error (MAE) lower or equal than 0.15 MPa and 0.12 MPa, respectively, were obtained with an external validation data set (n = 71 observations) for the various algorithms. When the modelling results were assessed through classes of values, a high overall accuracy was obtained for all the algorithms (82–83%), with prediction accuracy by class ranging between 79% and 100%. These results show a good performance of the predictive models, which considered a large variability of climatic, environmental, and agronomic conditions, and included various grape cultivars. By predicting both continuous values of ѱpd and classes of ѱpd, the approach presented in this study allowed obtaining 2-levels of accurate information about vines water status, which can be used to feed management decisions of different types of stakeholders.

Soil moisture (SM) at three depths (15, 25, and 30 cm), derived from the optical trapezoidal model (OPTRAM), was used for multiyear, multisite monitoring of agricultural droughts over two agricultural crops (Maize and Soybean) in southern Mozambique. The OPTRAM was implemented using satellite data from Sentinel-2 and was validated against field SM assessed by gravimetric methods and by Watermark Sensors in sandy-soils with very low water holding capacity (0.13 cm3 / cm3). The OPTRAM model estimated the SM at 15 and 25 cm yielding a R2 ≥ 0.79 and RMSE ≤ 0.030 cm3 / cm3. The OPTRAM-derived SM was successfully used as input to compute and map the soil water deficit index, an indicator of agricultural drought. The results indicate that OPTRAM can provide useful information to improve water productivity in cropland under the specific conditions of Mozambique agricultural systems and for early warning systems development.

A new procedure is proposed for estimating actual basal crop coefficients from vegetation indices (Kcb VI) considering a density coefficient (Kd) and a crop coefficient for bare soil. Kd is computed using the fraction of ground cover by vegetation (fc VI), which is also estimated from vegetation indices derived from remote sensing. A combined approach for estimating actual crop coefficients from vegetation indices (Kc VI) is also proposed by integrating the Kcb VI with the soil evaporation coefficient (Ke) derived from the soil water balance model SIMDualKc. Results for maize, barley and an olive orchard have shown that the approaches for estimating both fc VI and Kcb VI compared well with results obtained using the SIMDualKc model after calibration with ground observation data. For the crops studied, the correlation coefficients relative to comparing the actual Kcb VI and Kc VI with actual Kcb and Kc obtained with SIMDualKc were larger than 0.73 and 0.71, respectively. The corresponding regression coefficients were close to 1.0. The methodology herein presented and discussed allowed for obtaining information for the whole crop season, including periods when vegetation cover is incomplete, as the initial and development stages. Results show that the proposed methods are adequate for supporting irrigation management info:eu-repo/semantics/publishedVersion

Non-destructive, accurate, user-friendly and low-cost approaches to determining crop leaf area (LA) are a key tool in many agronomic and physiological studies, as well as in current agricultural management. Although there are models that estimate cut rose LA in the literature, they are generally designed for a specific stage of the crop cycle, usually harvest. This study aimed to estimate the LA of cut “Red Naomi” rose stems in several phenological phases using morphological descriptors and allometric measurements derived from image processing. A statistical model was developed based on the “multiple stepwise regression” technique and considered the stem height, the number of stem leaves, and the stage of the flower bud. The model, based on 26 stems (232 leaves) collected at different developmental stages, explained 95% of the LA variance (R2 = 0.95, n = 26, p < 0.0001). The mean relative difference between the observed and the estimated LA was 8.2%. The methodology had a high accuracy and precision in the estimation of LA during crop development. It can save time, effort, and resources in determining cut rose stem LA, enhancing its application in research and production contexts.