Canopy Reflectance Research Articles

Evaluation of the Monitoring Capability of 20 Vegetation Indices and 5 Mainstream Satellite Band Settings for Drought in Spring Wheat Using a Simulation Method

This study simulated the canopy reflectance of spring wheat at five distinct growth stages (jointing, booting, heading, flowering, and pustulation) and under four drought scenarios (no drought, mild drought, moderate drought, and severe drought) using the PROSAIL radiative transfer model, and it identified the wavelength range most sensitive to drought. Additionally, the efficacy of 5 mainstream satellites (Sentinel-2, Landsat 8, Worldview-2, MODIS, and GF-2) and 20 commonly utilized remote sensing vegetation indicators (NDVI, SAVI, EVI, ARVI, GVMI, LSWI, VSDI, NDGI, SWIRR, NDWI, PRI, NDII, MSI, WI, SRWI, DSWI, NDREI1, NDREI2, ZMI, and MTCI) in drought monitoring was evaluated. The results indicated that the spectral response characteristics of spring wheat canopy reflectance vary significantly across the growth stages. Notably, the wavelength ranges of 1405–1505 nm and 2140–2190 nm were identified as optimal for drought monitoring throughout the growth period. Considering only the spectral bands, MODIS band 7 was determined to be the most suitable satellite band for monitoring drought in spring wheat at different growth stages. Among the 20 indices examined, WI, MSI, and SRWI, followed by LSWI and GVMI calculated using MODIS bands 2 and 6 as well as bands 8 and 11 of Sentinel-2, demonstrated superior capabilities in differentiating drought scenarios. These conclusions have important implications because they provide valuable guidance for selecting remote sensing drought monitoring data and vegetation indices, and they present insights for future research on the design of new remote sensing indices for assisting drought monitoring and the configuration of remote sensing satellite sensors.

Large-Scale Controls on the Leaf Economic Spectrum of the Overstory Tree Species Metrosideros polymorpha

The role of intraspecific trait variation in functional ecology has gained traction in recent years as many papers have observed its importance in driving community diversity and ecology. Yet much of the work in this field relies on field-based trait surveys. Here, we used continuous canopy trait information derived from remote sensing data of a highly polymorphic tree species, Metrosideros polymorpha, to quantify environmental controls on intraspecific trait variation. M. polymorpha, an endemic, keystone tree species in Hawai’i, varies morphologically, chemically, and genetically across broad elevation and soil substrate age gradients, making it an ideal model organism to explore large-scale environmental drivers of intraspecific trait variation. M. polymorpha canopy reflectance (visible to shortwave infrared; 380–2510 nm) and light detection and ranging (LiDAR) data collected by the Global Airborne Observatory were modeled to canopy trait estimates of leaf mass per area, chlorophyll a and b, carotenoids, total carbon, nitrogen, phosphorus, phenols, cellulose, and top of canopy height using previously developed leaf chemometric equations. We explored how these derived traits varied across environmental gradients by extracting elevation, slope, aspect, precipitation, and soil substrate age data at canopy locations. We then obtained the feature importance values of the environmental factors in predicting each leaf trait by training random forest models to predict leaf traits individually. Of these environmental factors, elevation was the most important predictor for all canopy traits. Elevation not only affected canopy traits directly but also indirectly by influencing the relationships between soil substrate age and canopy traits as well as between nitrogen and other traits, as indicated by the change in slope between the variables at different elevation ranges. In conclusion, intraspecific variation in M. polymorpha traits derived from remote sensing adheres to known leaf economic spectrum (LES) patterns as well as interspecific LES traits previously mapped using imaging spectroscopy.

UAV Hyperspectral Data Combined with Machine Learning for Winter Wheat Canopy SPAD Values Estimation

Chlorophyll is an important indicator for monitoring crop growth and is vital for agricultural management. Therefore, rapid and accurate estimation of chlorophyll content is important for decision support in precision agriculture to accurately monitor the SPAD (Soil and Plant Analyzer Development) values of winter wheat. This study used winter wheat to obtain canopy reflectance based on UAV hyperspectral data and to calculate different vegetation indices and red-edge parameters. The best-performing vegetation indices and red-edge parameters were selected by Pearson correlation analysis and multiple stepwise regression (MSR). SPAD values were estimated using a combination of vegetation indices, vegetation indices and red-edge parameters as model factors, two types of machine learning (ML), a support vector machine (SVM), and a backward propagation neural network (BPNN), and partial least squares regression (PLSR) for four growth stages of winter wheat, and validated using independent samples. The results show that for the same data source, the best vegetation indices or red-edge parameters for estimating SPAD values differed at different growth stages and that combining vegetation indices with red-edge parameters gave better estimates than using only vegetation indices as an input factor for estimating SPAD values. There is no significant difference between PLSR, SVM, and BPNN methods in estimating SPAD values, with better stability of the estimated models using machine learning methods. Different growth stages have a large impact on winter wheat SPAD values estimates, with the accuracy of the four growth stage models increasing in the following order: booting < heading < filling < flowering. This study shows that using a combination of vegetation indices and red-edge parameters can improve SPAD values estimates compared to using vegetation indices alone. In the future, the choice of appropriate factors and methods will need to be considered when constructing models to estimate crop SPAD values.

Physically based illumination correction for sub-centimeter spatial resolution hyperspectral data

Vegetation biophysical- and chemical traits, defined on the basis of leaf area, can be retrieved from their spectral reflectance. Ultra high resolution hyperspectral images, such as ones collected from drones, allows measuring the spectra of individual leaves. The reflectance signal of such data is calibrated with respect to the top-of-canopy (TOC) irradiance, as the local illumination conditions on leaf surfaces are largely unknown and can vary significantly from the TOC conditions. We developed an inversion algorithm that uses the PROSPECT leaf radiative transfer model and the theory of spectral invariants to retrieve the actual leaf reflectance from TOC-calibrated hyperspectral images. Compared with more traditional canopy reflectance models, this approach accounts for the spatial variation in leaf-level irradiance visible in sub-centimeter-resolution images and is computationally more efficient. We used simulated and measured leaf and canopy reflectance data to validate the approach and found the retrieved leaf reflectances to match closely the actual reflectances (relative RMSD was 12% for simulated data on the average and below 10% for measured data). The proposed method provides an efficient approach for illumination correction, enabling reliable, physically based applications for monitoring vegetation biochemical and biophysical properties from ultra-high-resolution spectral imagery.

Retracted: Nitrogen Inversion Model in a Wetland Environment Based on the Canopy Reflectance of Emergent Plants

Retracted: Nitrogen Inversion Model in a Wetland Environment Based on the Canopy Reflectance of Emergent Plants

Improving the ability of PRI in light use efficiency estimation by distinguishing sunlit and shaded leaves in rice canopy

ABSTRACT Light use efficiency (LUE) plays an important role in gross primary production (GPP), so accurate estimation of LUE is crucial. The estimation of LUE using the photochemical reflectance index (PRI) is considerably promising, but on the other hand there is always uncertainty in obtaining the PRI due to the changing viewing angle and sun position in remote sensing observations. The objective of this study was to distinguish between sunlit and shaded leaves to improve the performance of PRI estimating LUE, and we used rice canopy reflectance data collected by the automatic multi-angle remote sensing platform and flux data for each half hour from 9:00 to 15:00 in Shouxian, Anhui Province, China. We modified the two-leaf model based on the characteristics of the rice canopy, was thought of by distinguishing between sunlit and shaded leaves PRI by two-leaf model on the rice canopy. The results showed that the -LUE correlation improved relative to PRI-LUE on clear days and was essentially negligible on cloudy days. We found that the photosynthetically active radiation (PAR), temperature, and humidity had some influence on , the -LUE correlation was strongest at higher temperature and vapour pressure deficit (VPD) and was linear with the change of temperature. We conclude that the modified two-leaf model improves the integrity of the PRI remote sensing observation and increases the accuracy of its LUE estimation by adequately separating sunlit and shaded leaves in the rice canopy.

Synergetic inversion of leaf area index and leaf chlorophyll content using multi-spectral remote sensing data

ABSTRACT Individual inversions of Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) have problems due to the mutual interference between these two vegetation parameters on remote sensing signals. We therefore explore synergetic inversion of these two parameters to improve their inversion accuracy. We selected subtropical forest plantations, where canopy reflectance data were collected using a DJI Phantom 4 Multispectral Unmanned Aerial Vehicle (UAV) every month during 2021–2022. Monthly in-situ observations of LAI and Clumping Index (CI) were also made in 23 broadleaf tree plots of dimension 12 m × 12 m. Vegetation Indices (VI) were calculated with the mean reflectance of all pixels at 0.06 m resolution within each sampling plot, and only those VIs with highest sensitivities to LAI or LCC were selected and correlated to LAI and LCC. An empirical model in the form of VI = f(LAI, LCC) was constructed for synergetic inversion of LAI and LCC. For the purpose of comparison, two models VI = f(LAI) and VI = f(LCC) were also constructed and used for the inversions of LAI and LCC, separately. The synergetic inversion model yields R 2 = 0.60 and RMSE = 2.80 cm2/cm2 for LAI and R 2 = 0.45 and RMSE = 32.71 μg/cm2 for LCC, whereas the separate inversion models result in R 2 = 0.59 and RMSE = 2.82 cm2/cm2 for LAI and R 2 = 0.35 and RMSE = 35.86 μg/cm2 for LCC. Moreover, we found that the inclusion of VIs containing a red edge band in the synergetic inversion can effectively improve the inversion accuracy. The proposed synergetic inversion method based on multiple VIs would be an effective way to separate the mutual interference between LAI and LCC and improve the accuracy of LCC inversion from remote sensing data.

Citrus Canopy SPAD Prediction under Bordeaux Solution Coverage Based on Texture- and Spectral-Information Fusion

Rapid and nondestructive prediction of chlorophyll content and response to the growth of various crops using remote sensing technology is a prominent topic in agricultural remote sensing research. Bordeaux mixture has been extensively employed for managing citrus diseases, such as black star and ulcer disease. However, the presence of pesticide residues in Bordeaux mixture can significantly modify the spectral response of the citrus canopy, thereby exerting a substantial influence on the accurate prediction of agronomic indices in fruit trees. In this study, we used unmanned aerial vehicle (UAV) multispectral imaging technology to obtain remote sensing imagery of Bordeaux-covered citrus canopies during the months of July, September, and November. We integrated spectral and texture information to construct a high-dimensional feature dataset and performed data downscaling and feature optimization. Furthermore, we established four machine learning models, namely, partial least squares regression (PLS), ridge regression (RR), ridge, random forest (RF), and support vector regression (SVR). Our objectives were to identify the most effective prediction model for estimating the SPAD (soil plant analysis development) value of Bordeaux-covered citrus canopies, assess the variation in prediction accuracy between fused features and individual features, and investigate the impact of Bordeaux solution on the spectral reflectance of the citrus canopy. The results showed that (1) the impact of Bordeaux mixture on citrus canopy reflectance bands ranked from the highest to the lowest as follows: near-infrared band at 840 nm, red-edge band at 730 nm, blue band at 450 nm, green band at 560 nm, and red band at 650 nm. (2) Fused feature models had better prediction ability than single-feature modeling, with an average R2 value of 0.641 for the four model test sets, improving by 0.117 and 0.039, respectively, compared with single-TF (texture feature) and -VI (vegetation index) modeling, and the test-set root-mean-square error (RMSE) was 2.594 on average, which was 0.533 and 0.264 lower than single-TF and -VI modeling, respectively. (3) Multiperiod data fusion effectively enhanced the correlation between features and SPAD values and consequently improved model prediction accuracy. Compared with accuracy based on individual months, R improved by 0.013 and 0.011, while RMSE decreased by 0.112 and 0.305. (4) The SVR model demonstrated the best performance in predicting citrus canopy SPAD under Bordeaux solution coverage, with R2 values of 0.629 and 0.658, and RMSE values of 2.722 and 2.752 for the training and test sets, respectively.

Leaf area index estimation under wheat powdery mildew stress by integrating UAV‑based spectral, textural and structural features

The infection of powdery mildew changes the internal physiological activity and external morphology of winter wheat, resulting in damage to crop health. Leaf area index (LAI) is an important agronomic parameter to assess crop growth. However, how to accurately and quickly interpret the spatial variation of LAI under powdery mildew stress has become a difficult problem. To detect the spatial variation and improve the estimation accuracy of winter wheat LAI under powdery mildew stress, unmanned aerial vehicle (UAV) multispectral remote sensing technique was used to continuously observe winter wheat infected with diseases in the field. The artificial inoculation experiment of powdery mildew was carried out in the experimental base of the Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Xinxiang, China in 2022. The occurrence of powdery mildew changed the canopy spectral reflectance and morphology of winter wheat. Parameters of vegetation indices (VIs), structure (Str) and texture (Tex) features were used to quantify the spectral reflectance and morphology of winter wheat canopy. Sensitivity analysis of different types of features and LAI and crop disease index (DI) was conducted to obtain specific features responding to LAI and DI. Partial least squares method (PLSR) was used to construct LAI estimation model under powdery mildew stress. The results of this study showed that (i) in contrast to crop height, crop coverage and canopy volume maintained consistency with the time change of LAI, which could be utilized to assess LAI variation under powdery mildew stress. (ii) In general, Tex features were more sensitive to LAI and DI than the Str and VIs. (iii) The R2 and RMSE of LAI estimated under powdery mildew stress by Tex, Str and VIs were 0.56, 0.50, 0.45 and 0.32, 0.35, and 0.36, respectively. The combination of VIs, Str, and Tex provided the highest estimation accuracy (R2 = 0.71, RMSE = 0.26). Compared with VIs, Str, Tex, VIs + Str, VIs + Tex, Str + Tex, the R2 of winter wheat LAI estimation under powdery mildew stress was increased by 58%, 42%, 27%, 22%, 15%, and 8%, respectively. This study analyzed the spatial variation of LAI of winter wheat under powdery mildew stress using remote sensing technique, which provided an important theoretical basis for crop disease control in the field.

Influence of different soil reflectance schemes on the retrieval of vegetation LAI and FVC from PROSAIL in agriculture region

The PROSAIL model is widely used to retrieve vegetation parameters from remote sensing data in agriculture regions. Soil reflectance is a key input to the PROSAIL model, but its influence on vegetation parameters retrieval accuracy of crops is rarely discussed. Therefore, this study investigated the influence of different soil reflectance schemes on leaf area index (LAI) and fractional vegetation cover (FVC) retrieval accuracy based on the PROSAIL model in an agriculture region. Firstly, three sources of soil reflectance including ICRAF-ISRIC soil spectral library (SR_SSL), general spectral vectors model (SR_GSV), and ASD spectroradiometer measurement (SR_ASD) and two reflectance extraction schemes were used to generate input soil reflectance for the PROSAIL model to simulate canopy reflectance. Then, the LAI and FVC retrieval models were developed using the random forest algorithm and validated using field survey data. Determination coefficient (R2), Root Mean Square Error (RMSE) and normalized RMSE (NRMSE) were used to evaluate the accuracy of LAI and FVC retrieval. Under the direct extraction scheme, the LAI retrieval based on SR_ASD (R2 = 0.78, RMSE = 0.613, NRMSE = 0.269) achieved better performance than SR_GSV (R2 = 0.73, RMSE = 0.671, NRMSE = 0.294) and SR_SSL (R2 = 0.71, RMSE = 0.762, NRMSE = 0.334), whereas the performances of FVC retrieval were comparable for SR_ASD (R2 = 0.91, RMSE = 0.084, NRMSE = 0.136), SR_GSV (R2 = 0.90, RMSE = 0.086, NRMSE = 0.139) and SR_SSL (R2 = 0.89, RMSE = 0.091, NRMSE = 0.147). The influence of soil reflectance on LAI retrieval is larger than that of FVC in this study. Furthermore, soil reflectance was more important for canopies characterized by low LAI and FVC. In addition, the combination of SR_SSL and multiplication coefficients scheme could be conveniently used for large areas vegetation parameters retrieval. While SR_GSV and SR_ASD with direct extraction scheme was suitable for small areas with field survey data and more homogeneous region.

How similar is the zoning of different vegetation indices: Defining the optimal framework for monitoring grapevines’ growth within vigorous vineyards

The spatial patterns of grapevines’ vigor across vigorous vineyards were assessed through distinct vegetation indices (VI) for comparison purposes. This survey also aids the selection of the most sensitive VI to monitor the vineyards during a given optimal stage of the last period of the growing season. The canopy reflectance (ρ) of grapevines cv. Cabernet Franc and Cabernet Sauvignon (Vitis vinifera L.) was measured throughout their berry development and ripening stages. Georeferenced data of ρ at the red (680 nm), red edge (730 nm), and near-infrared (780 nm) wavebands were taken using a hand-held active sensor to compute the normalized difference red edge index (NDRE), and the normalized difference vegetation index (NDVI). Spatial predictions of both VI were estimated by geostatistical interpolation technique (kriging) and then categorized into homogeneous zones (HZ) through the Jenks natural breaks optimization method to delimit low and high vigor regions. Statistics of the inter-rater reliability between each VI zoning were calculated afterward. The relative dispersion of data around their mean was assumed as a criterion to assess the sensibility of each VI in detecting vigor variability under a given condition. The most appropriate stage to monitor the vineyards was determined based on the peak of vegetative growth inferred from the values of the VI we presumed as most sensitive. Differences between the HZ defined from the best combination of VI and stage in terms of yield, cane weight, and crop load were also examined. We demonstrated that NDRE was more sensitive than NDVI in detecting the vigor variability of high-density vegetation due to the relative dispersion of its datasets and the saturation effect of NDVI at the late stages of the grapevine growing season. The lowest and higher agreement between the VI zoning was usually detected at the berry touch and berry softening growth stages, respectively. Although there were distinct levels of concordance between NDRE and NDVI maps, their overall agreement was higher than 70% in most surveys. The disagreement in quantity or spatial allocation of vigor categories provided by each VI relied on the vineyard, growth stage, and growing season. Nevertheless, the early definition of HZ from NDRE monitoring carried out at veraison could be suitable to identify differences in cane weight at pruning, with a moderate effect size.

Proximal remote sensing and gross primary productivity in a temperate salt marsh

Salt marshes are highly productive ecosystems relevant for Blue Carbon assessments, but information for estimating gross primary productivity (GPP) from proximal remote sensing (PRS) is limited. Temperate salt marshes have seasonal canopy structure and metabolism changes, defining different canopy phenological phases, GPP rates, and spectral reflectance. We combined multi-annual PRS data (i.e., PhenoCam, discrete hyperspectral measurements, and automated spectral reflectance sensors) with GPP derived from eddy covariance. We tested the performance of empirical models to predict GPP from 12 common vegetation indices (VIs; e.g., NDVI, EVI, PSRI, GCC), Sun-Induced Fluorescence (SIF), and reflectance from different areas of the electromagnetic spectrum (i.e., VIS-IR, RedEdge, IR, and SIF) across the annual cycle and canopy phenological phases (i.e., Greenup, Maturity, Senescence, and Dormancy). Plant Senescence Reflectance Index (PSRI) from hyperspectral data and the Greenness Index (GCC) from PhenoCam, showed the strongest relationship with daily GPP across the annual cycle and within phenological phases (r2=0.30–0.92). Information from the visible-infrared electromagnetic region (VIS-IR) coupled with a partial least square approach (PLSR) showed the highest data-model agreement with GPP, mainly because of its relevance to respond to physiological and structural changes in the canopy, compared with indices (e.g., GCC) that particularly react to changes in the greenness of the canopy. The most relevant electromagnetic regions to model GPP were ∼550 nm and ∼710 nm. Canopy phenological phases impose challenges for modeling GPP with VIs and the PLSR approach, particularly during Maturity, Senescence, and Dormancy. As more eddy covariance sites are established in salt marshes, the application of PRS can be widely tested. Our results highlight the potential to use canopy reflectance from the visible spectrum region for modeling annual GPP in salt marshes as an example of advances within the AmeriFlux network.

Confounding effects of snow cover on remotely sensed vegetation indices of evergreen and deciduous trees: An experimental study.

Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance-based remotely sensed optical signals at northern latitude or high-altitude regions are readily confounded by snow coverage, hampering applications of satellite-based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness-based indices and ecosystem productivity of many evergreen-dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global-scale photosynthesis monitoring efforts using remotely sensed vegetation indices.

UAV time-series imagery with novel machine learning to estimate heading dates of rice accessions for breeding

Timely monitoring of rice heading dates is essential for estimating growth status and grain yield in rice breeding. This study aims to develop a machine learning method to classify growth stages and estimate the days from sowing to initial heading (IHD) and days from sowing to full heading (FHD) of rice accessions from unmanned aerial vehicle (UAV)-based imagery. A two-year field experiment was conducted on different rice accessions, including doubled-haploid (DH) lines, early maturing F1 hybrids, and cultivars at two different locations (i.e., EXP 1 and EXP 2). A neural network, namely Res2Net50, was first used to classify the rice growth stages based on UAV RGB images. The partial least squares regression (PLSR) model was then developed to predict IHD and FHD by using canopy reflectance extracted from multispectral images. The results showed that the Res2Net50 model successfully identified five growth stages with overall accuracies of 86% and 83% in EXP 1 and EXP 2, respectively. PLSR models developed with image data after the Res2Net50 classification achieved good performances on predicting IHD and FHD with the coefficient of determination (R2) of 0.60–0.78, root mean square error (RMSE) ranging from 3.9 days to 5.1 days, and relative root mean square error (rRMSE) varied between 4% to 6%. Model validation with ten cultivars further demonstrated the capability of proposed method for IHD and FHD predictions and achieved the rRMSE of 3%-4%, which was comparable with that obtained by field inspection. Furthermore, the feasibility of the proposed method in monitoring of the rice heading dynamics was validated by EXP 2 using the model transfer. The proposed approach enables quantification of heading dates of diverse rice accessions in breeding and could be used as a high-throughput screening technique for accelerating rice breeding.

Novel Curve Fitting Analysis of NDVI Data to Describe Turf Fertilizer Response

Evaluating the effectiveness of fertilizers on crops without traditional yield is difficult, as clipping collection is time-consuming and not indicative of the desired response to the fertilizer. Remote sensing techniques, like the normalized difference vegetation index (NDVI), have emerged as an effective tool to combat these issues. Canopy reflectance, measured by NDVI, is commonly used to differentiate turfgrass response to nitrogen (N) fertilization treatments; however, advancements in data processing are needed for greater differentiation between treatments and better testing of the effects of fertilizer responses. Presented here is an advancement in the processing of NDVI data by applying a compound exponential (pulse peak) function to changes in NDVI over time, relative to a control. This method’s effectiveness in differentiating between fertilizers was evaluated by applying polymer-coated urea (PCU), an enhanced efficiency fertilizer (EEF), and urea on turfgrass to show the usefulness of the novel curve fitting in highlighting differences in fertilizer response. The field study was carried out on an established Kentucky bluegrass (Poa pratensis L.) blend on a sandy loam soil rootzone maintained with a typical home lawn maintenance regime. Ratios of PCU to urea ranging from 100% urea to 100% PCU with increments of 20% of PCU were used to observe a continuum of response between the two fertilizer types. The N rate for all blends was 73 kg ha−1, with unfertilized plots used as a control to set baseline measurements for the curve fitting. Turfgrass response to N fertilization treatments was assessed through visual turfgrass quality ratings, dry matter yield, and canopy reflectance, where higher urea content resulted in the shortest time to maximum ∆NDVI (24 days), while 100% EEF reached maximum ∆NDVI at 56 days. The novel curve fitting analysis of the NDVI data allowed for differentiation between treatments, provided a measure of fertilizer effects, and differentiated between urea and enhanced efficiency fertilizers.

Early-Season forecasting of citrus block-yield using time series remote sensing and machine learning: A case study in Australian orchards

This study presents a comprehensive evaluation of seasonal, locational, and varietal variations in canopy reflectance responses in 315 commercial citrus blocks from three major growing regions in Australia. The dataset includes three different citrus types (Mandarin, Navel, Valencia) and 26 varieties. The aim is to utilize this combined information to better understand yield variation and develop improved forecasting models. Landsat satellite data spanning from October 2006 to February 2021 (1419 tiles) were used to derive reflectance values, and calculate four vegetation indices (NDVI, GNDVI, LSWI, and GCVI), for each citrus block. These indices were then analyzed alongside corresponding yield data, which consisted of 3660 individual yield records dating back to 2007. Two temporal resolutions were incorporated as predictors: spatio-temporal vegetation index time series (TS) aggregated every two months and annual time series of historical block-yield records. Six statistical and machine learning algorithms were calibrated using a leave-one-year-out cross-validation approach (LOYO CV) and validated for one-year forward prediction over a five-year period (2017–2021). The results highlight significant yield variations across years, alternate bearing patterns, and spatio-temporal changes in reflectance profiles influenced by seasonal conditions, varietal characteristics, and locations. The support vector machine (SVM) algorithm with a radial basis function kernel consistently outperformed other algorithms, indicating a non-linear relationship between citrus yield and predictors. The SVM model achieved an RMSE of 15.5 T ha−1, R2 of 0.88, MAE of 12.1 T ha−1, and MAPE of 29% in predicting block-yield across farms, varieties, and seasons. These prediction accuracy metrics demonstrate an improvement over current forecasting methods. Notably, the proposed approach utilizes freely available imagery, provides forecasts between two to nine months before harvest, and eliminates the need for infield counting of fruit load for image calibration. This approach provides an improved method for understanding seasonal yield variation and quantifying citrus block-yield, offering valuable insights for growers in harvest logistics, labor allocation, and resource management.

Simulating snow-covered forest bidirectional reflectance by extending hybrid geometric optical–radiative transfer model

Accurate simulations of the bidirectional reflectance distribution function (BRDF) of snow-covered forests are crucial for retrieving snow and canopy properties, which affects the analysis of net radiation, radiative forcing, and climate change. The anisotropic reflectance of snow in conifer forests is greatly influenced by the high reflectance of snow and the complex travel pathways of radiation in 3D canopy structures, which complicates BRDF simulations. Although bidirectional reflection models have been widely established for snow-free forests, few studies have attempted to simulate the BRDF in snow-covered forests. Therefore, we propose a new snow-covered forest bidirectional reflectance (SFBR) model by coupling geometric optical and radiative transfer models, which includes a snow anisotropic reflectance model (ART), needle leaf optical properties model (LIBERTY), canopy radiative transfer model (4SAIL), and geometric optical model (GOST2). The SFBR model mainly focuses on conifer forests with snow backgrounds; it can simulate anisotropic reflectance spectra from 400 nm to 2500 nm and is the only model to fully consider discontinuous canopy distribution, topographic effects, and snow anisotropy. The SFBR model was validated by simulating the reflectance over snow-covered forests, the results of which showed good consistency with simulations of 3D scene model and remote sensing observations. The proposed SFBR model has the potential to quantify radiation interactions between the snow background and forest overstory, evaluate the sensitivity of canopy reflectance to snow and forest properties, and retrieve snow-covered forest parameters from satellite-observed reflectance data.

Phenological stage and vegetation index for predicting corn yield under rainfed environments.

Uncrewed aerial systems (UASs) provide high temporal and spatial resolution information for crop health monitoring and informed management decisions to improve yields. However, traditional in-season yield prediction methodologies are often inconsistent and inaccurate due to variations in soil types and environmental factors. This study aimed to identify the best phenological stage and vegetation index (VI) for estimating corn yield under rainfed conditions. Multispectral images were collected over three years (2020-2022) during the corn growing season and over fifty VIs were analyzed. In the three-year period, thirty-one VIs exhibited significant correlations (r ≥ 0.7) with yield. Sixteen VIs were significantly correlated with the yield at least for two years, and five VIs had a significant correlation with the yield for all three years. A strong correlation with yield was achieved by combining red, red edge, and near infrared-based indices. Further, combined correlation and random forest an alyses between yield and VIs led to the identification of consistent and highest predictive power VIs for corn yield prediction. Among them, leaf chlorophyll index, Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index and modified normalized difference at 705 were the most consistent predictors of corn yield when recorded around the reproductive stage (R1). This study demonstrated the dynamic nature of canopy reflectance and the importance of considering growth stages, and environmental conditions for accurate corn yield prediction.

Spectral variables as criteria for selection of soybean genotypes at different vegetative stages

Understanding the association between variables obtained by multispectral imaging, such as plant canopy reflectance at different wavelengths and vegetation indices (VIs), and agronomic traits of interest for soybean is crucial for breeding programs. This approach, associated with the evaluation of the best stage for spectral data collection, can identify easier-to-measure variables to be used in the indirect selection of genotypes for more expensive-to-measure traits or those that are expressed later, resulting in a faster, labor-saving, and large-scale selection process. The hypothesis of this study is the possibility of identifying promising variables for the selection of soybean genotypes through the use of vis and that different times of collection of information regarding reflectance interfere in the selection responses. Thus, our objectives were: i) to estimate heritability (h2) for agronomic traits and VIs; ii) to estimate the gains with direct selection (DS) on the VIs and indirect selection (IS) for the agronomic traits selected by the higher heritability; iii) to estimate correlations between spectral variables, VIs, and agronomic traits; iv) to identify the most promising populations for the evaluated variables; and vi) to identify the most adequate phenological stage for spectral data collection. We installed a field experiment in a randomized block design with four repetitions, 28 F3 soybean populations, and four control treatments. Agronomic traits evaluated were: days to maturity (DM), plant height (PH), first pod insertion height (PIH), number of branches (NB), and grain yield (GY). Spectral collections were performed at the V8 (45 days after emergence), R1 (60 days after emergence) and R5 (80 days after emergence) stages. Correlations between agronomic traits, reflectance at 790 nm (near-infrared), 735 nm (RedEdge), 660 nm (red), 550 nm (green), and 450 nm (blue) wavelengths, and VIs were estimated. The heritability were estimated for agronomic traits and VIs. Our findings reveal that it is possible to select soybean genotypes using spectral variables based on heritability and phenotypic aspects. Enhanced vegetation index (EVI) and soil-adjusted vegetation index (SAVI) have high heritability values (>75%) at V8. It is possible to obtain satisfactory selection gains for agronomic traits, especially GY, based on selection on EVI and SAVI at V8 stage. By this approach, the F3 segregating populations that provided the highest selection gains were P7, P8, P9, P14, P27, and P28. Spectral data collection at V8 stage provided higher h2 for VIs and agronomic traits, while R5 is the most suitable stage for estimating associations between VIs and agronomic traits.

Evaluation of the effect of leaf spatial aggregation on chlorophyll content retrieval in open-canopy apple orchards

The real-world crowns of broadleaf tree species feature green leaves surrounding branches, resulting in leaf spatial aggregation effect in the crown. However, the impact of such leaf spatial aggregation on chlorophyll content retrieval has not yet been determined. This study investigated the effect of leaf spatial aggregation on chlorophyll content retrieval in two distinct apple orchards with open canopies. The “PROSPECT + LESS” model was used for canopy reflectance simulation, and 25 hyperspectral vegetation indices (VIs) were analyzed to identify universal VIs for various leaf aggregations. Sensitivity analysis was conducted to evaluate the impact of leaf aggregation on the relationships between VIs and chlorophyll content. An artificial neural network regression algorithm was used to retrieve chlorophyll content by reversing the radiative transfer model (RTMs). The results show that leaf aggregation significantly affects the relationships between VIs and chlorophyll content as a result of the variability in the ratio of photosynthetic vegetation pixels to background pixels captured by the sensor at the top of the canopy. TCARI/OSAVI was found to be resistant to confounding factors (e.g., leaf area index and dry matter content) and maintained stable relationships with chlorophyll content. Leaf spatial aggregation had a significant impact on chlorophyll content retrieval, especially when leaves were highly aggregated. In such cases, the spectral variation driven by the photosynthetic vegetation was masked by the background, leading to a large divergence between simulated and observed spectra. Low to moderate levels of leaf aggregation, on the other hand, provided accurate chlorophyll content retrieval in both apple orchards (R2 = 0.49 to 0.67). In conclusion, when using 3D RTMs to retrieve chlorophyll content, it is recommended to configure low to moderate levels of leaf aggregation to ensure high accuracy and efficiency.