The decline in biodiversity in Mediterranean-type ecosystems (MTEs) and other shrublands underscores the importance of understanding the trends in species loss through consistent vegetation mapping over broad spatial and temporal ranges, which is increasingly accomplished with optical remote sensing (imaging spectroscopy). Airborne missions planned by the National Aeronautics and Space Administration (NASA) and other groups (e.g., US National Ecological Observatory Network, NEON) are essential for improving high-quality maps of vegetation and plant species. These surveys require robust and efficient ground calibration/validation data; however, barriers to ground-data collection exist, such as steep terrain, which is a common feature of Mediterranean-type ecosystems. We developed and tested a method for rapidly collecting ground-truth data for shrubland plant communities across steep topographic gradients in southern California. Our method utilizes semi-aerial photos taken with a high-resolution digital camera mounted on a telescoping pole to capture groundcover, and a point-intercept image-classification program (Photogrid) that allows efficient sub-sampling of field images to derive vegetation percent-cover estimates while reducing human bias. Here, we assessed the quality of data collection using the image-based method compared to a traditional point-intercept ground survey and performed time trials to compare the efficiency of various survey efforts. The results showed no significant difference in estimates of percent cover and Simpson’s diversity derived from the point-intercept and those derived using the image-based method; however, there was lower correspondence in estimates of species richness and evenness. The image-based method was overall more efficient than the point-intercept surveys, reducing the total survey time by 13 to 46 min per plot depending on sampling effort. The difference in survey time between the two methods became increasingly greater when the vegetation height was above 1 m. Due to the high correspondence between estimates of species percent cover derived from the image-based compared to the point-intercept method, we recommend this type of survey for the verification of remote-sensing datasets featuring percent cover of individual species or closely related plant groups, for use in classifying UAS imagery, and especially for use in MTEs that have steep, rugged terrain and other situations such as tall, dense-growing shrubs where traditional field methods are dangerous or burdensome.

Downscaling Chlorophyll-a (Chl-a) concentration derived from satellite image is crucial for refined applications such as water quality monitoring. However, the precision of downscaling is usually constrained by various environmental and geophysical factors. In this paper, we develop a downscaling method for Chl-a concentration to improve precision, especially for inland lakes with different nutrient status and surrounding environment. The method downscales the Sentinel-3 Chl-a concentration from 300 m to 30 m, based on an integration of the multivariate analysis (MVA) and the gradient boosting decision tree (GBDT) model. Firstly, we analyzed 21 Chl-a concentration related indices derived from Landsat-8 TIRS, Sentinel-1 SAR, and Sentinel-2 MSI images, to identify optimal factors for Chl-a concentration variability. Secondly, a GBDT regression model integrated the optimal factors and Sentinel-3 Chl-a concentrations at coarse resolution, is constructed to convey the nonlinear relations between them. Finally, fine-resolution Chl-a concentrations were produced by employing the GBDT regression model to auxiliary factors at fine scale for 12 large inland lakes across China. The results indicated that the proposed MVA-GBDT method effectively inferred the spatial variability of Chl-a concentration with a mean RMSE of 4.505 mg/m ³ , an improvement of 5% ∼ 39% over other downscaling methods. Furthermore, for lakes with large water quality heterogeneity, the method led to a cross validation RMSE and a difference in accuracy of 5.371 mg/m ³ and 0.866 mg/m ³ , respectively. In addition, this study examined the significance of the auxiliary factors and found that the NDCI (normalized difference chlorophyll index) and WST (water surface temperature) were the two most important factors for MVA-GBDT to detect coarse spatial resolution of Chl-a concentration, particularly for NDCI in lakes with high nutrient contrasts. These findings contribute to the generation of fine-scale Chl-a concentrations in lakes and support related applications.

Fresh sweet and waxy maize (Zea mays) are valuable specialty crops in southern China. Hyperspectral remote sensing offers a powerful tool for detecting maize foliar nutrients non-destructively. This study aims to investigate the capability of leaf spectroscopy (SVC HR-1024i spectrometer, wavelength range: 400–2500 nm) to retrieve maize foliar nutrients. Specifically, we (1) explored the effects of nitrogen application rates (0, 150, 225, 300, and 450 kg·N·ha⁻¹), maize cultivars (GLT-27 and TGN-932), and growth stages (third leaf (vegetation V3), stem elongation stage (vegetation V6), silking stage (reproductive R2), and milk stage (reproductive R3)) on foliar nutrients (nitrogen, phosphorus, and carbon) and leaf spectra; (2) evaluated the transferability of the regression and physical models in retrieving foliar nutrients across maize cultivars. We found that the PLSR (partial least squares regression), SVR (support vector machine regression), and RFR (random forest regression) regression model accuracies were fair within a specific cultivar, with the highest R² of 0.60 and the lowest NRMSE (normalized RMSE = RMSE/(Max − Min)) of 17% for nitrogen, R² of 0.19 and NRMSE of 21% for phosphorous, and R² of 0.45 and NRMSE of 19% for carbon. However, when these cultivar-specific models were used to predict foliar nitrogen across cultivars, lower R² and higher NRMSE values were observed. For the physical model, which does not rely on the dataset, the R² and NRMSE for foliar chlorophyll-a and -b (C_ab), carotenoid (C_xc), and equivalent water thickness (EWT) were 0.76 and 15%, 0.67 and 34%, and 0.47 and 21%, respectively. However, the prediction accuracy for foliar nitrogen, expressed as foliar protein in PROSPECT-PRO, was lower, with an R² of 0.22 and NRMSE of 27%, which was comparable to that of the regression models. The primary reasons for this limited transferability were attributed to (1) the insufficient number of samples and (2) the lack of strong absorption features for foliar nutrients within the 400–2500 nm wavelength range and the confounding effects of other foliar biochemicals with strong absorption features. Future efforts are needed to investigate the physical mechanisms underlying hyperspectral remote sensing of foliar nutrients and incorporate transfer learning techniques into foliar nutrient models.

Potatoes, as the world’s fourth-largest staple crop, are vital for global food security. Efficient methods for assessing yield and quality are essential for policy-making and optimizing production. Traditional yield assessment techniques remain destructive, labor-intensive, and unsuitable for large-scale monitoring. While remote sensing has offered a promising alternative, current approaches largely depend on empirical correlations rather than physiological mechanisms. This limitation arises because potato tubers grow underground, rendering their traits invisible to aboveground sensors. This study investigated the mechanisms underlying hyperspectral remote sensing for assessing belowground yield traits in potatoes. Field experiments with four cultivars and five nitrogen treatments were conducted to collect foliar biochemistries (chlorophyll, nitrogen, and water and dry matter content), yield traits (tuber yield, fresh/dry weight, starch, protein, and water content), and leaf spectra. Two approaches were developed for predicting belowground yield traits: (1) a direct method linking leaf spectra to yield via statistical models and (2) an indirect method using structural equation modeling (SEM) to link foliar biochemistry to yield. The SEM analysis revealed that foliar nitrogen exhibited negative effects on tuber fresh weight (path coefficient b = −0.57), yield (−0.37), and starch content (−0.30). Similarly, leaf water content negatively influenced tuber water content (0.52), protein (−0.27), and dry weight (−0.42). Conversely, chlorophyll content showed positive associations with both tuber protein (0.59) and dry weight (0.56). Direct models (PLSR, SVR, and RFR) achieved higher accuracy for yield (R² = 0.58–0.84) than indirect approaches (R² = 0.16–0.45), though the latter provided physiological insights. The reduced accuracy in indirect methods primarily stemmed from error propagation within the SEM framework. Future research should scale these leaf-level mechanisms to canopy observations and integrate crop growth models to improve robustness across environments. This work advances precision agriculture by clarifying spectral–yield linkages in potato systems, offering a framework for hyperspectral-based yield prediction.

River turbidity is a key indicator of water quality that influences both human activities and riverine ecosystem functioning. However, strong seasonal variability, high sensitivity to disturbances, and pronounced spatial heterogeneity make turbidity patterns difficult to characterize and generalize across river systems. In ecological modeling, incorporating environmental variables can improve model accuracy by providing process-relevant context that is not fully captured by spectral signals alone. However, this strategy has not been systematically evaluated in water-quality remote sensing, especially for cross-river turbidity retrieval. Here, we applied this strategy to build cross-river turbidity models and compared it with a spectral-only scenario. A total of 43 monitoring sites across the conterminous United States were analyzed. Four models, random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and artificial neural network (ANN), were implemented under two scenarios using spectral features alone and in combination with environmental variables. Incorporating environmental variables substantially improved overall model performance, with median R² increasing from 0.60–0.69 to 0.67–0.78 and median Kling–Gupta efficiency (KGE) increasing from 0.59–0.67 to 0.71–0.77. Among them, RF and XGBoost maintained or improved cross-site generalization under Scenario 2, with median KGE values of 0.65 and 0.67 in leave-one-site-out tests, respectively, compared with 0.56 for SVM and 0.58 for ANN. Performance gains tended to be larger at sites with stronger discharge seasonality. Site-level and range-specific analyses further suggested that the improvement was mainly associated with low-flow and low-turbidity conditions and tended to occur in smaller catchments at lower elevations. These results demonstrate that integrating spectral and environmental information improves both the accuracy and generalizability of turbidity retrieval and helps clarify when and why environmental context benefits water-quality remote sensing across diverse river systems.

Grassland foliar functional traits are closely linked to ecosystem functioning, biodiversity, and plant responses to environmental change. Hyperspectral remote sensing provides an efficient and non-destructive approach for mapping foliar traits, yet direct comparisons between UAV-based and airborne imaging spectroscopy remain limited. In this study, we evaluated the performance of UAV-based Nano and airborne Hyspex hyperspectral imagery for predicting ten foliar functional traits across experimental grassland plots at the Cedar Creek Ecosystem Science Reserve, USA. We further assessed the contributions of visible-to-near-infrared (VNIR) and shortwave infrared (SWIR) spectral regions, as well as the effects of spectral preprocessing approaches for minimizing confounding effects from canopy structure, illumination/viewing geometry, and soil background. Random Forest regression models were developed using plot-level average spectra derived from Nano and Hyspex imagery. Both UAV- and airborne-based imaging spectroscopy achieved moderate to high prediction accuracies for most foliar traits. High accuracies were obtained for non-structural carbohydrates (NSC), carotenoids, β-carotene, hemicellulose, and cellulose (R² = 0.66–0.82; NRMSE = 6–10%), while moderate accuracies were achieved for nitrogen, chlorophyll, and xanthophylls (R² = 0.51–0.74; NRMSE = 8–12%). In contrast, carbon and lignin consistently exhibited lower predictive performance (R² = 0.32–0.59; NRMSE = 9–15%). Despite covering only the VNIR spectral range, the UAV-based Nano imagery achieved accuracies comparable to those obtained using the airborne full-spectrum Hyspex imagery, indicating that high spatial resolution can partially compensate for limited spectral coverage by reducing soil background effects. The VNIR spectral region alone provided trait estimation accuracies comparable to those obtained using the full visible-to-shortwave infrared (VSWIR) spectrum, whereas SWIR wavelengths contributed only marginal improvements for a subset of structural traits. Among preprocessing approaches, vector normalization generally improved prediction performance by reducing the confounding effects of canopy structure and illumination/viewing geometry, whereas NIR_v-adjusted spectra provided limited benefits. Our findings demonstrate that UAV-based VNIR imaging spectroscopy can provide accurate and cost-effective estimation of grassland foliar functional traits. The results also highlight important trade-offs between spectral and spatial resolution in hyperspectral remote sensing and provide practical guidance for selecting imaging spectroscopy platforms and preprocessing approaches for grassland ecosystem monitoring.