Search Results (579)

Soybeans are key in generating foreign currency for the world economy. Geotechnologies, through vegetation indices (VIs) generated by orbital images or remotely piloted aircraft, are essential tools for assessing the impact of climate on productivity and the ecoclimatic suitability of crops. This study aimed to correlate the growth indices from the CLIMEX model, previously validated, with VIs derived from orbital remote sensing and ecological niche modeling for soybean cultivation in six irrigated pivots located in the northwest of Minas Gerais, Brazil. The maximum normalized difference vegetation index (NDVImax) and the maximum soil-adjusted vegetation index (SAVImax) were extracted from Landsat-8 OLI/TIRS sensor images for the 2016 to 2019 harvests during the R1 to R3 phenological stages. The maximum NDVI values varied across the study regions and crops, ranging from 0.27 to 0.95. Similarly, SAVI values exhibited variability, with the maximum SAVI ranging from 0.13 to 0.85. The growth index (GI_w), derived from the CLIMEX model, ranged from 0.88 to 1. The statistical analysis confirmed a significant correlation (p < 0.05) between NDVImax and GI_w only for the 2018/19 harvest, with a Pearson correlation coefficient of r = 0.86, classified as very strong. Across all harvests, NDVI consistently outperformed SAVI in correlation strength with GI_w. Using geotechnologies through remote sensing shows promise for correlating spectral indices and climate suitability models. However, when using a valid model, all crops did not correlate. Still, our study has the potential to be improved by investigating new hypotheses, such as using drone images with better resolution (spatial, spectral, temporal, and radiometric) and adjusting the response of soybean vegetation indices and the phenological stage. Our results correlating the CLIMEX model of growth indices with vegetation indices have the potential for monitoring soybean cultivation and analyzing the performance of varieties but require a more in-depth view to adapt the methodology. Full article

Accurately predicting the vegetation index (VI) of the Yangtze River Basin and analyzing its spatiotemporal trends are essential for assessing vegetation dynamics and providing recommendations for environmental resource management in the region. This study selected the key climate factors most strongly correlated with three vegetation indexes (VI): the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and kernel Normalized Difference Vegetation Index (kNDVI). Historical VI and climate data (2001–2020) were used to train, validate, and test a CNN-BiLSTM-AM deep learning model, which integrates the strengths of Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention Mechanism (AM). The performance of this model was compared with CNN-BiLSTM, LSTM, and BiLSTM-AM models to validate its superiority in predicting the VI. Finally, climate simulation data under three Shared Socioeconomic Pathway (SSP) scenarios (SSP1-1.9, SSP2-4.5, and SSP5-8.5) were used as inputs to the CNN-BiLSTM-AM model to predict the VI for the next 20 years (2021–2040), aiming to analyze spatiotemporal trends. The results showed the following: (1) Temperature, precipitation, and evapotranspiration had the highest correlation with VI data and were used as inputs to the time series VI model. (2) The CNN-BiLSTM-AM model combined with the EVI achieved the best performance (R² = 0.981, RMSE = 0.022, MAE = 0.019). (3) Under all three scenarios, the EVI over the next 20 years showed an upward trend compared to the previous 20 years, with the most significant growth observed under SSP5-8.5. Vegetation in the source region and the western part of the upper reaches increased slowly, while significant increases were observed in the eastern part of the upper reaches, middle reaches, lower reaches, and estuary. The analysis of the predicted EVI time series indicates that the vegetation growth conditions in the Yangtze River Basin will continue to improve over the next 20 years. Full article

Multispectral sensors are integral to vegetation analysis, particularly in the calculation of various vegetation indices (VIs). The use of integrated multispectral sensors has become prevalent in research, although their effectiveness is influenced by several factors. This highlights the need for ongoing research into enhancement techniques to improve the accuracy and reliability of vegetation status estimation. This study investigated the impact of field of view (FOV) variability on normalized differential vegetation index (NDVI) accuracy using a multispectral sensor. Data were collected from a wheat field at four growth stages (GS) (GS 1, GS 2, GS 3, and GS 4, at 10, 34, 70, and 84 days after sowing (DAS), respectively) and the sensors were mounted around 100 cm above the crop canopy. An active sensor was used to provide reference data for assessing multispectral measurement. A program was developed using the Python (ver. 3.10) programming language to process the global navigation satellite system (GNSS) coordinates and segment the images to align with the FOV of the active sensor and extracting the reflectance data for NDVI calculation. The results showed that proper FOV alignment significantly improved regression metrics (R² and RMSE) at all growth stages, with R² improvements ranging from 3% to 33%, and RMSE reductions from 0.03 to 0.06, respectively. The high vegetative growth stage was less affected due to the FOV misalignment. These techniques are promising toward improving NDVI accuracy, especially during early and mid-growth stages of the crop. Full article

The timely and precise estimation of crop aboveground biomass (AGB) is crucial for evaluating crop development and forecasting yields. The objective is to examine the differences, advantages, and limitations between time series parameters and single-time-phase indicators derived from various vegetation indices in AGB estimation. Moreover, we aim to quantitatively investigate and elucidate the impact of the topographic and geographic conditions of the study region on the estimation of highland barley AGB. Results indicate that AGB simulations utilizing time series parameters from vegetation index time series (VI-TS) curves yield satisfactory results for all three VIs, with the exception of the Normalized Difference Vegetation Index (NDVI), which encounters saturation issues. The performance metrics are as follows: the Enhanced Vegetation Index (EVI) (R² = 0.73, RMSE = 20.24 g/m²), the Soil-Adjusted Vegetation Index (SAVI) (R² = 0.67, RMSE = 20.97 g/m²), and the Normalized Difference Mountain Vegetation Index (NDMVI) (R² = 0.54, RMSE = 24.92 g/m²). The inclusion of our quantitative terrain factor improves the simulation accuracies of NDVI, SAVI, and NDMVI. Overall, the terrain factor has a beneficial impact on the highland barley AGB simulation outcomes. This study establishes a foundational framework for the timely and precise estimation of highland barley biomass, crucial for monitoring agricultural production in plateau mountainous regions. Full article

Evapotranspiration (ET) plays a crucial role in the surface water cycle and energy balance, and accurate ET estimation is essential for study in various domains, including agricultural irrigation, drought monitoring, and water resource management. Remote sensing (RS) technology presents an efficient approach for estimating ET at regional scales; however, existing RS retrieval algorithms for ET are intricate and necessitate a multitude of parameters. The land surface temperature–vegetation index (LST-VI) space method and statistical regression by machine learning (ML) offer the benefits of simplicity and straightforward implementation. This study endeavors to identify the optimal long-term sequence LST-VI space method and ML for ET estimation under conditions of limited observed variables, (LST, VI, and near-surface air temperature). A comparative analysis of their performance is undertaken using ground-based flux observations and MOD16 ET data. The findings can be summarized as follows: (1) Long-term remote sensing data can furnish a more comprehensive background field for the LST-VI space, achieving superior fitting accuracy for wet and dry edges, thereby enabling precise ET estimation with the following metrics: correlation coefficient (r) = 0.68, root mean square error (RMSE) = 0.76 mm/d, mean absolute error (MAE) = 0.49 mm/d, and mean bias error (MBE) = −0.14 mm. (2) ML generally produces more accurate ET estimates, with the Random Forest Regressor (RFR) demonstrating the highest accuracy: r = 0.79, RMSE = 0.61 mm/d, MAE = 0.42 mm/d, and MBE = −0.02 mm. (3) Both ET estimates derived from the LST-VI space and ML exhibit spatial distribution characteristics comparable to those of MOD16 ET data, further attesting to the efficacy of these two algorithms. Nevertheless, when compared to MOD16 data, both approaches exhibit varying degrees of underestimation. The results of this study can contribute to water resource management and offer a fresh perspective on remote sensing estimation methods for ET. Full article

Rapid and accurate leaf area index (LAI) determination is important for monitoring daylily growth, yield estimation, and field management. Because of low estimation accuracy of empirical models based on single-source data, we proposed a machine-learning algorithm combining optical and microwave remote-sensing data as well as the random forest regression (RFR) importance score to select features. A high-precision LAI estimation model for daylilies was constructed by optimizing feature combinations. The RFR importance score screened the top five important features, including vegetation indices land surface water index (LSWI), generalized difference vegetation index (GDVI), normalized difference yellowness index (NDYI), and backscatter coefficients VV and VH. Vegetation index features characterized canopy moisture and the color of daylilies, and the backscatter coefficient reflected dielectric properties and geometric structure. The selected features were sensitive to daylily LAI. The RFR algorithm had good anti-noise performance and strong fitting ability; thus, its accuracy was better than the partial least squares regression and artificial neural network models. Synergistic optical and microwave data more comprehensively reflected the physical and chemical properties of daylilies, making the RFR-VI-BC05 model after feature selection better than the others ( r = 0.711, RMSE = 0.498, and NRMSE = 9.10%). This study expanded methods for estimating daylily LAI by combining optical and radar data, providing technical support for daylily management. Full article

Potato is a major food crop in China. Its development and nutritional state can be inferred by the content of chlorophyll in its canopy. However, the existing study on applying feature extraction and optimization algorithms to determine the canopy SPAD (Soil–Plant Analytical Development) values of potatoes at various fertility stages is inadequate and not very reliable. Using the Pearson feature selection algorithm and the Competitive Adaptive Reweighted Sampling (CARS) method, the Vegetation Index (VI) with the highest correlation was selected as a training feature depended on multispectral orthophoto images from unmanned aerial vehicle (UAV) and measured SPAD values. At various potato fertility stages, Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) inversion models were constructed. The models’ parameters were then optimized using the Grey Wolf Optimizer (GWO) and Sparrow Search Algorithm (SSA). The findings demonstrated a higher correlation between the feature selected VI and SPAD values; additionally, the optimization algorithm enhanced the models’ prediction accuracy; finally, the addition of the fertility stage feature considerably increased the accuracy of the full fertility stage in comparison to the single fertility stage. The models with the highest inversion accuracy were the CARS-SSA-RF, CARS-SSA-XGBoost, and Pearson-SSA-XGBoost models. For the single-fertility and full-fertility phases, respectively, the optimal coefficients of determination (R²s) were 0.60, 0.66, and 0.87, the root-mean-square errors (RMSEs) were 2.63, 3.23, and 2.39, and the mean absolute errors (MAEs) were 2.00, 2.75, and 1.99. Full article

Spaceborne instruments have an irreplaceable role in detecting fundamental vegetation features that link physical properties to ecological theory, but their success depends on our understanding of the complex dynamics that control plant spectral properties—a scale-dependent challenge. We explored differences between the warmer and cooler areas of tree canopies with a ground-based experimental layout consisting of a spectrometer and a thermal camera mounted on a portable crane that enabled synergies between thermal and spectral reflectance measurements at the fine scale. Thermal images were used to characterise the thermal status of different parts of a dense circular cluster of containerised trees, and their spectral reflectance was measured. The sensitivity of the method was found to be unaffected by complex interactions. A statistically significant difference in both reflectance in the visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) bands and absorption features related to the chlorophyll, carotenoid, and water absorption bands was found between the warmer and cooler parts of the canopy. These differences were reflected in the Photochemical Reflectance Index with values decreasing as surface temperature increases and were related to higher carotenoid content and lower Leaf Area Index (LAI) values of the warmer canopy areas. With the increasingly improving resolution of data from airborne and spaceborne visible, near-infrared, and shortwave infrared (VSWIR) imaging spectrometers and thermal infrared (TIR) instruments, the results of this study indicate the potential of synergies between thermal and spectral measurements for the purpose of more accurately assessing the complex biochemical and biophysical characteristics of vegetation canopies. Full article

Unmanned aerial vehicles (UAVs) equipped with multi-sensor remote sensing technologies provide an efficient approach for mapping spatial and temporal variations in vegetation traits, enabling advancements in precision monitoring and modeling. This study’s objective was to analyze UAV multiple sensors’ performance in monitoring winter wheat chlorophyll content (SPAD), plant nitrogen accumulation (PNA), and N nutrition index (NNI). A two-year field experiment with five N fertilizer treatments was carried out. The color indices (CIs, from RGB sensors), vegetation indices (VIs, from multispectral sensors), and temperature indices (TIs, from thermal sensors) were derived from the collected images. XGBoost (extreme gradient boosting) was applied to develop the models, using 2021 data for training and 2022 data for testing. The excess green minus excess red index, red green ratio index, and hue (from CIs), and green normalized difference vegetation index, normalized difference red-edge index, and normalized difference vegetation index (from VIs), showed high correlations with three N indicators. At the pre-heading stage, the best performing CIs correlated better than the VIs; this was reversed in the post-heading stage. CIs outperformed VIs in SPAD (CIs: R²(coefficient of determination) = 0.66, VIs: R² = 0.61), PNA (CIs: R² = 0.68, VIs: R² = 0.64), and NNI (CIs: R² = 0.64, VIs: R² = 0.60) in the pre-heading stage, whereas VI-based models achieved slightly higher accuracies in post-heading and all stages compared to CIs. Models built with CIs + VIs significantly improved the models’ performance compared to single-sensor models. Adding TIs to CIs and CIs + VIs further improved the models’ performance slightly, especially at the post-heading stage, resulting in the best model performance with three sensors. These findings highlight the effectiveness of UAV systems in estimating wheat N and establish a framework for integrating RGB, multispectral, and thermal sensors to enhance model accuracy in precision vegetation monitoring. Full article

Remnant explosive devices are a deadly nuisance to both military personnel and civilians. Traditional mine detection and clearing is dangerous, time-consuming, and expensive. And routine production and testing of explosives can create groundwater contamination issues. Remote detection methods could be rapidly deployed in vegetated areas containing explosives as they are known to cause stress in vegetation that is detectable with hyperspectral sensors. Hyperspectral imagery was employed in a mesocosm study comparing stress from a natural source (drought) to that of plants exposed to two different concentrations of Royal Demolition Explosive (RDX; 250 mg kg⁻¹, 500 mg kg⁻¹). Classification was accomplished with the machine learning algorithms Support Vector Machine (SVM), Random Forest (RF), and Least Discriminant Analysis (LDA). Leaf-level plant data assisted in validating plant stress induced by the presence of explosives and was detectable. Vegetation indices (VIs) have historically been used for dimension reduction due to computational limitations; however, we measured improvements in model precision, recall, and accuracy when using the complete range of available wavelengths. In fact, almost all models applied to spectral data outperformed their index counterparts. While challenges exist in scaling research efforts from the greenhouse to the field (i.e., weather, solar lighting conditions, altitude when imaging from a UAV, runoff containment, etc.), this experiment is promising for subsequent research efforts at greater scale and complexity aimed at detecting emerging contaminants. Full article

Innovative methods for estimating commercial-scale switchgrass yields and feedstock quality are essential to optimize harvest logistics and biorefinery efficiency for sustainable aviation fuel production. This study utilized vegetation indices (VIs) derived from multispectral images to predict biomass yield and lignocellulose concentrations of advanced bioenergy-type switchgrass cultivars (“Liberty” and “Independence”) under two N rates (28 and 56 kg N ha⁻¹). Field-scale plots were arranged in a randomized complete block design (RCBD) and replicated three times at Urbana, IL. Multispectral images captured during the 2021–2023 growing seasons were used to extract VIs. The results show that linear and exponential models outperformed partial least square and random forest models, with mid-August imagery providing the best predictions for biomass, cellulose, and hemicellulose. The green normalized difference vegetation index (GNDVI) was the best univariate predictor for biomass yield (R² = 0.86), while a multivariate combination of the GNDVI and normalized difference red-edge index (NDRE) enhanced prediction accuracy (R² = 0.88). Cellulose was best predicted using the NDRE (R² = 0.53), whereas hemicellulose prediction was most effective with a multivariate model combining the GNDVI, NDRE, NDVI, and green ratio vegetation index (GRVI) (R² = 0.44). These findings demonstrate the potential of UAV-based VIs for the in-season estimation of biomass yield and cellulose concentration. Full article

Accurate diagnosis of plant phosphorus nutritional status is critical for optimizing agricultural practices and enhancing resource efficiency. Existing methods are limited to qualitatively assessing plant phosphorus nutritional status and cannot quantitatively estimate the plant’s phosphorus requirements. Moreover, these methods are time-consuming, making them impractical for large-scale application. In this study, we developed an advanced phosphorus absorption model (Pabs) that integrates the phosphorus nutrition index (PNI) and phosphorus use efficiency (PUE). The PUE, a critical metric for assessing phosphate fertilizer use efficiency, was quantified by comparing yields under fertilized and unfertilized conditions. Utilizing the Agricultural Production Systems Simulator (APSIM) model, we simulated maize (Zea mays L.) phosphorus concentration (P) and aboveground biomass (Bio) under varying phosphorus application rates. The model exhibited robust performance, achieving an R² above 0.95 and an RMSE below 0.22. Based on the APSIM model simulations, a phosphorus dilution curve (Pc = 3.17 Bio^−0.29, R² = 0.98) was established, reflecting the dilution trends of phosphorus across growth stages. Furthermore, the use of vegetation indices (VIS) to evaluate phosphorus nutritional status also showed promising results, with inversion accuracies exceeding 0.70. To validate the model, field sampling was conducted in maize-growing regions of Changchun. Results demonstrated a correct diagnosis rate of 75%, underscoring the model’s capacity to accurately estimate phosphorus requirements on a regional scale. These findings highlight the Pabs model as a reliable tool for precision phosphorus management, offering significant potential to optimize fertilization strategies and support sustainable agricultural systems. Full article

Achieving timely and non-destructive assessments of crop yields is a key challenge in the agricultural field, as it is important for optimizing field management measures and improving crop productivity. To accurately and quickly predict citrus yield, this study obtained multispectral images of citrus fruit maturity through an unmanned aerial vehicle (UAV) and extracted multispectral vegetation indices (VIs) and texture features (T) from the images as feature variables. Extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), gaussian process regression (GPR), and multiple stepwise regression (MSR) models were used to construct citrus fruit number and quality prediction models. The results show that, for fruit number prediction, the XGB model performed best under the combined input of VIs and T, with an R² = 0.792 and an RMSE = 462 fruits. However, for fruit quality prediction, the RF model performed best when only the VIs were used, with an R² = 0.787 and an RMSE = 20.0 kg. Although the model accuracy was acceptable, the number of input feature variables used was large. To further improve the model prediction performance, we explored a method that utilizes a hybrid coding particle swarm optimization algorithm (CPSO) coupled with XGB and SVM models. The coupled models had a significant improvement in predicting the number and quality of citrus fruits, especially the model of CPSO coupled with XGB (CPSO-XGB). The CPSO-XGB model had fewer input features and higher accuracy, with an R² > 0.85. Finally, the Shapley additive explanations (SHAP) method was used to reveal the importance of the normalized difference chlorophyll index (NDCI) and the red band mean feature (MEA_R) when constructing the prediction model. The results of this study provide an application reference and a theoretical basis for the research on UAV remote sensing in relation to citrus yield. Full article

Dryness is a critical limiting factor for achieving high agricultural productivity on China’s Loess Plateau (LP). High-precision, field-scale dryness monitoring is essential for the implementation of precision agriculture. However, obtaining dryness information with adequate spatial and temporal resolution remains a significant challenge. Unmanned aerial vehicle (UAV) systems can capture high-resolution remote sensing images on demand, but the effectiveness of UAV-based dryness indices in mapping the high-resolution spatial heterogeneity of dryness across different crop areas at the agricultural field scale on the LP has yet to be fully explored. Here, we conducted UAV–ground synchronized experiments on three typical croplands in the eastern Gansu province of the Loess Plateau (LP). Multispectral and thermal infrared sensors mounted on the UAV were used to collect high-resolution multispectral and thermal images. The temperature vegetation dryness index (TVDI) and the temperature–vegetation–soil moisture dryness index (TVMDI) were calculated based on UAV imagery. A total of 14 vegetation indices (VIs) were employed to construct various VI-based TVDIs, and the optimal VI was selected. Correlation analysis and Gradient Structure Similarity (GSSIM) were applied to evaluate the suitability and spatial differences between the TVDI and TVMDI for dryness monitoring. The results indicate that TVDIs constructed using the normalized difference vegetation index (NDVI) and the visible atmospherically resistant index (VARI) were more consistent with the characteristics of crop responses to dryness stress. Furthermore, the TVDI demonstrated higher sensitivity in dryness monitoring compared with the TVMDI, making it more suitable for assessing dryness variations in rain-fed agriculture in arid regions. Full article

The primary purpose of this study was to improve our understanding of remote sensing technologies and their potential application in vineyards to monitor yields and fruit composition, which could then be used for selective harvesting and winemaking. For yield and berry composition data collection, representative vines from the vineyard block were selected and geolocated, and the same vines were surveyed for remote sensing data collection by the multispectral and thermal sensors in the RPAS in 2015 and 2016. The spectral reflectance data were further analyzed for vegetation indices to evaluate the correlation between the variables. Moran’s global index and map analysis were used to determine spatial clustering patterns and correlations between variables. The results of this study indicated that remote sensing data in the form of vegetation indices from the RPAS were positively correlated with yield and berry weight across sites and years. There was a positive correlation between the thermal emission and berry pH, berry phenols, and anthocyanins in certain sites and years. Overall, remote sensing technology has the potential to monitor and predict grape quality and yield, but further research on the efficacy of this data is needed for selective harvesting and winemaking. Full article