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Pyrenean glaciers are the largest in southern Europe. Their survival is threatened by climate change, highlighting the significance of their study. This research presents an assessment of changes in the glacierized area and thickness of Pyrenean glaciers from 2011 to 2020, using high‐resolution optical satellite, airborne lidar and UAV images. The total glacierized area has shrunk by 23.2% and thickness has decreased on average by 6.3 m. These two variables show no correlation for individual glaciers. Although climatic conditions do not vary much among glaciers, their evolution was heterogeneous during the study period. The smaller glaciers (<10 ha) show a higher variability in their area decrease and thickness loss whereas the four largest glaciers (>10 ha) have a more homogeneous response. This can be attributed to the generally larger influence of local topography on the response of the smaller Pyrenean glaciers. There is no sign of slowdown in glacier shrinkage respect to previous decades. Plain Language Summary: Pyrenean glaciers are the largest in southern Europe. Their survival is threatened by climate change, highlighting the significance and relevance of their study. This study presents an assessment of changes in the glacierized area and thickness of Pyrenean glaciers during 2011–2020 based on UAV, optical satellite imagery and lidar observations. In this period, their total area shrank by 23.2% and thickness decreased by 6.3 m on average. Although climatic conditions do not vary much among glaciers, their evolution was heterogeneous during the observed period. The smaller Pyrenean glaciers (area <10 ha) are highly controlled by local topography, whereas the largest glaciers are predominantly influenced by regional climate forcing. There is no sign of slowdown in shrinkage of Pyrenean glaciers respect to previous decades. This indicates the continuous decline of Pyrenean glaciers toward an ice‐free mountain range in the coming decades. Key Points: The glacierized area shrank by 23.2% and thickness decreased on average by 6.3 m during the 2011–2020 periodThere is no sign of slowdown in glacier shrinkage respect to previous decadesThe smaller Pyrenean glaciers are influenced by local topography as they show highly contrasted evolution under the same climatic conditions [ABSTRACT FROM AUTHOR]

Due to emerging high spectral resolution, remote sensing techniques and ongoing developments to retrieve the spectrally resolved vegetation fluorescence spectrum from several scales, the light reactions of photosynthesis are receiving a boost of attention for the monitoring of the Earth's carbon balance. Sensor-retrieved vegetation fluorescence (from leaf, tower, airborne or satellite scale) originating from the excited antenna chlorophyll a molecule has become a new quantitative biophysical vegetation parameter retrievable from space using global imaging techniques. However, to retrieve the actual quantum efficiencies, and hence a true photosynthetic status of the observed vegetation, all signal distortions must be accounted for, and a high-precision true vegetation reflectance must be resolved. ESA's upcoming Fluorescence Explorer aims to deliver such novel products thanks to technological and instrumental advances, and by sophisticated approaches that will enable a deeper understanding of the mechanics of energy transfer underlying the photosynthetic process in plant canopies and ecosystems. [ABSTRACT FROM AUTHOR]

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This work combines very detailed measurements from terrestrial laser scanner (TLS), ground-based interferometry radar (GB-SAR) and ground-penetrating radar (GPR) to diagnose current conditions and to analyse the recent evolution of the Monte Perdido Glacier in the Spanish Pyrenees from 2011 to 2017. Thus, this is currently one of the best monitored small glacier (<0.5 km2) worldwide. The evolution of the glacier surface was surveyed with a TLS evidencing an important decline of 6.1 ± 0.3 m on average, with ice losses mainly concentrated over 3 years (2012, 2015 and 2017). Ice loss is unevenly distributed throughout the study period, with 10–15 m thinning in some areas while unchanged areas in others. GB-SAR revealed that areas with higher ice losses are those that are currently with no or very low ice motion. In contrast, sectors located beneath the areas with less ice loss are those that still exhibit noticeable ice movement (average 2–4.5 cm d─1 in summer, and annual movement of 9.98 ma─1 from ablation stakes data). GPR informed that ice thickness was generally <30 m, though locally 30–50 m. Glacier thinning is still accelerating and will lead to extinction of the glacier over the next 50 years. [ABSTRACT FROM AUTHOR]

Variations in photosynthesis still cause substantial uncertainties in predicting photosynthetic CO2 uptake rates and monitoring plant stress. Changes in actual photosynthesis that are not related to greenness of vegetation are difficult to measure by reflectance based optical remote sensing techniques. Several activities are underway to evaluate the sun-induced fluorescence signal on the ground and on a coarse spatial scale using space-borne imaging spectrometers. Intermediate-scale observations using airborne-based imaging spectroscopy, which are critical to bridge the existing gap between small-scale field studies and global observations, are still insufficient. Here we present the first validated maps of sun-induced fluorescence in that critical, intermediate spatial resolution, employing the novel airborne imaging spectrometer HyPlant. HyPlant has an unprecedented spectral resolution, which allows for the first time quantifying sun-induced fluorescence fluxes in physical units according to the Fraunhofer Line Depth Principle that exploits solar and atmospheric absorption bands. Maps of sun-induced fluorescence show a large spatial variability between different vegetation types, which complement classical remote sensing approaches. Different crop types largely differ in emitting fluorescence that additionally changes within the seasonal cycle and thus may be related to the seasonal activation and deactivation of the photosynthetic machinery. We argue that sun-induced fluorescence emission is related to two processes: (i) the total absorbed radiation by photosynthetically active chlorophyll; and (ii) the functional status of actual photosynthesis and vegetation stress. [ABSTRACT FROM AUTHOR]

Chlorophyll fluorescence (ChF) emission is a direct indicator of the photosynthetic activity of vegetation, which is a key parameter of the carbon cycle. This paper analyses chlorophyll fluorescence evolution at leaf level during a complete diurnal cycle in simulated and natural conditions, for two species under different stress conditions. Absolute spectral radiance of the ChF emission is obtained allowing a quantitative derivation of the fluorescence yield of the ChF, which correlates well with established fluorescence instruments. The studied cases show that the ChF emission is mainly driven by the photosynthetic active radiation during the whole cycle, but the fluorescence yield is severely reduced during the central hours of the day when the plant is under stress due to light and heat. Results show that the Fraunhofer Line Discriminator method is an accurate way of retrieving quantitative values of ChF from remote sensing sensors at 760 nm and suggest that the mid-morning period is the best time of the day to maximize signal levels while identifying vegetation stress state. [ABSTRACT FROM AUTHOR]

This paper evaluates state-of-the-art parametric and nonparametric approaches for the estimation of leaf chlorophyll content (Chl), leaf area index, and fractional vegetation cover from space. The parametric approach involves comparison of established and generic narrowband vegetation indices (VIs) and the Normalized Area Over reflectance Curve method, which calculates the continuum spectral region sensitive to Chl. However, as not all available bands take part in these spectral algorithms, it remains unclear whether optimal estimations are achieved. Alternatively, the nonparametric approach is based on Gaussian process (GP) techniques and allows inclusion of all bands. GP builds a nonlinear regression as a linear combination of spectra mapped to a high-dimensional space. Moreover, GP provides an indication of the most contributing bands for each parameter, a weight for the most relevant spectra contained in the training data set, and a confidence estimate of the retrieval. GP has previously demonstrated to be competitive in accuracy with support vector regression and neural networks. Results from hyperspectral Compact High Resolution Imaging Spectrometer data over the Spanish Barrax test site show that GP outperformed the VIs in assessing the vegetation properties when using at least four out of the 62 bands. GP identified most contributing bands in the red and red edge and, to a lower extent, in the blue and NIR parts of the spectrum. Since the proposed GP method is able to build robust relationships between the parameter of interest and only a few bands, it is a promising approach for multispectral data as well. [ABSTRACT FROM AUTHOR]

An atmospheric correction algorithm for the retrieval of land surface reflectance from imagery acquired by the Medium Resolution Imaging Spectrometer (MERIS) on-board the European Space Agency (ESA) ENVIronmental SATellite (ENVISAT) platform has been implemented. The algorithm is designed to estimate the main atmospheric parameters needed in the correction, aerosol and water vapour contents, from the image itself, leading to an optimal characterization of the atmospheric state at the time of image acquisition. Once the atmospheric state has been defined, a second step deals with the retrieval of surface reflectance, accounting for the contribution of surface elevation and roughness as well as the atmospheric adjacency effect. The first part of this paper is devoted to the description of the method, outlining the main steps in the atmospheric characterization and in the subsequent surface reflectance retrieval. The validation task is detailed in the second part. Aerosol Optical Thickness (AOT) and water vapour content from different sites of the AErosol RObotic NETwork (AERONET) have been compared with the method's retrievals. Root Mean Square Errors (RMSEs) equal to 0.085, 0.065 and 0.048 are found for AOT at 440, 550 and 870 nm, respectively. Comparison with in situ measurements shows a satisfactory performance except for episodes of Saharan dust intrusions. For water vapour, a high correlation between MERIS and AERONET results is found, although the RMSE equals 0.316 g cm-2 due to a systematic overestimation when compared with AERONET data. It decreases to 0.098 when the retrievals are compared with the ESA water vapour level 2 product. [ABSTRACT FROM AUTHOR]

The Albufera of Valencia, a Mediterranean coastal lagoon, has experienced a shift to hypertrophic conditions over the past 40 years due to agricultural and urban-industrial pollution. From August 2023 to early 2024, the water of the lagoon turned reddish-brown. This change has been observed in the past, but never with this intensity or duration, which typically occurs during periods of drought. In this study, carotenoid concentrations were analyzed in relation to precipitation and temperature using field and remote sensing data from February 2016 to December 2023. In November 2023, samples showed unusually high concentrations of carotenoids. The study confirmed the effectiveness of a new algorithm for estimating carotenoids using Sentinel-2 imagery to complement chlorophyll-a data. Results showed that temperature and precipitation significantly influenced carotenoid/chlorophyll-a ratio, highlighting a climatic control of phytoplankton community structure. These results highlight the importance of long-term monitoring and conservation efforts to address climate change and human impacts. This research is a first step in using optical properties of lakes as an indicator of phytoplankton dynamics under environmental stress and warns of the potential for increased occurrence or persistence of such phenomena with future climate trends. [ABSTRACT FROM AUTHOR]

Vegetation plays a vital role in connecting ecosystems and climate features. The biodiversity of vegetation is one of the most important features for evaluating ecosystems and it is becoming increasingly important with the threat of global warming. To clarify the effects of climate change on forest biodiversity in Northeast China, time-series NDVI data, meteorological data and land cover data from 2010 to 2021 were acquired, and the forest biodiversity of Northeast China was evaluated. The effect of climate change on forest biodiversity was analyzed, and the results indicated that the forest biodiversity features increased from west to east in Northeast China. There was also an increasing trend from 2010 to 2021, but the rate at which forest biodiversity was changing varied with different forest types of Northeast China, as different climatic factors had a different impact on forest biodiversity in different forest types. Average annual temperature, annual accumulated precipitation, CO2 fertilization and solar radiation were the main factors affecting forest biodiversity changing trends. This research indicated the potential impact of climate change on forest ecosystems, as it emphasized with evidence that climate change has a catalytic effect on forest biodiversity in Northeast China. [ABSTRACT FROM AUTHOR]

Aboveground carbon stocks (AGCs) in forests play an important role in understanding carbon cycle processes. The global forestry sector has been working to find fast and accurate methods to estimate forest AGCs and implement dynamic monitoring. The aim of this study was to explore the effects of backpack LiDAR and UAV multispectral imagery on AGC estimation for two tree species (Larix gmelinii and Betula platyphylla) and to emphasize the accuracy of the models used. We estimated the AGC of Larix gmelinii and B. platyphylla forests using multivariate stepwise linear regression and random forest regression models using backpack LiDAR data and multi-source remote sensing data, respectively, and compared them with measured data. This study revealed that (1) the diameter at breast height (DBH) extracted from backpack LiDAR and vegetation indices (RVI and GNDVI) extracted from UAV multispectral imagery proved to be extremely effective in modeling for estimating AGCs, significantly improving the accuracy of the model. (2) Random forest regression models estimated AGCs with higher precision (Xing'an larch R2 = 0.95, RMSE = 3.99; white birch R2 = 0.96, RMSE = 3.45) than multiple linear regression models (Xing'an larch R2 = 0.92, RMSE = 6.15; white birch R2 = 0.96, RMSE = 3.57). (3) After combining backpack LiDAR and UAV multispectral data, the estimation accuracy of AGCs for both tree species (Xing'an larch R2 = 0.95, white birch R2 = 0.96) improved by 2% compared to using backpack LiDAR alone (Xing'an larch R2 = 0.93, white birch R2 = 0.94). [ABSTRACT FROM AUTHOR]

Wetlands play a vital role in regulating the global carbon cycle, providing biodiversity, and reducing flood risks. These functions maintain ecological balance and ensure human well-being. Timely, accurate monitoring of wetlands is essential, not only for conservation efforts, but also for achieving Sustainable Development Goals (SDGs). In this study, we combined Sentinel-1/2 images, terrain data, and field observation data collected in 2020 to better understand wetland distribution. A total of 22 feature variables were extracted from multi-source data, including spectral bands, spectral indices (especially red edge indices), terrain features, and radar features. To avoid high correlations between variables and reduce data redundancy, we selected a subset of features based on recursive feature elimination (RFE) and Pearson correlation analysis methods. We adopted the random forest (RF) method to construct six wetland delineation schemes and incorporated multiple types of characteristic variables. These variables were based on remote sensing image pixels and objects. Combining red-edge features, terrain data, and radar data significantly improved the accuracy of land cover information extracted in low-mountain and hilly areas. Moreover, the accuracy of object-oriented schemes surpassed that of pixel-level methods when applied to wetland classification. Among the three pixel-based schemes, the addition of terrain and radar data increased the overall classification accuracy by 7.26%. In the object-based schemes, the inclusion of radar and terrain data improved classification accuracy by 4.34%. The object-based classification method achieved the best results for swamps, water bodies, and built-up land, with relative accuracies of 96.00%, 90.91%, and 96.67%, respectively. Even higher accuracies were observed in the pixel-based schemes for marshes, forests, and bare land, with relative accuracies of 98.67%, 97.53%, and 80.00%, respectively. This study's methodology can provide valuable reference information for wetland data extraction research and can be applied to a wide range of future research studies. [ABSTRACT FROM AUTHOR]

A lack of high‐density rootzone soil moisture (θRZ) observations limits the estimation of continental‐scale, space‐time contiguous θRZ dynamics. We derive a proxy of daily θRZ dynamics — active rootzone degree of saturation (SRZ) — by recursive low‐pass (LP) filtering of surface soil moisture (θS) within a terrestrial water‐energy coupling (WEC) framework. We estimate the LP filter parameters and WEC thresholds for the piecewise‐linear coupling between SRZ and evaporative fraction (EF) at remote sensing and field scale over the Contiguous U.S. We use θS from the Soil Moisture Active‐Passive (SMAP) satellite and 218 in‐situ stations, with EF from the Moderate Resolution Imaging Spectroradiometer. The estimated SRZ compares well against SMAP Level‐4 estimates and in‐situ θRZ, at the corresponding scale. The instantaneous hydrologic state (SRZ) vis‐à‐vis the WEC thresholds is proposed as a rootzone soil moisture stress index (SMSRZ) for near‐real‐time operational agricultural drought monitoring and agrees well with established drought metrics. Plain Language Summary: Rootzone soil moisture plays a vital role in agricultural, hydrological, and ecosystem processes. The available spaceborne satellites for monitoring soil moisture can only capture variability in a shallow soil layer at the surface, typically limited to the top 5 cm. Hence, spatiotemporally continuous estimation of rootzone soil moisture dynamics typically relies on soil moisture estimates from land‐surface models, which are subject to errors in the surface meteorological forcing data, process formulations, and model parameters. Some studies suggest that the rootzone soil moisture dynamics can be estimated by filtering the high‐frequency variability in the surface soil moisture. However, such "filters" require observed rootzone data (often unavailable at high spatial density) for calibration. This study uses the relationship between surface soil moisture and evaporative fraction derived using spaceborne observations from the Soil Moisture Active Passive mission and the Moderate Resolution Imaging Spectroradiometer to estimate rootzone soil moisture dynamics for the Contiguous U.S. at 9 km grid resolution. We further demonstrate that this approach can be extended into a near‐real‐time agricultural drought monitor to assess drought impacts on vegetation using surface soil moisture observations. Key Points: Terrestrial water‐energy coupling is used to parameterize low‐pass filter to estimate rootzone dynamics from surface soil moistureRootzone degree of saturation and water‐energy coupling thresholds are estimated using evaporative fraction and surface soil moistureSMAP‐based rootzone degree of saturation can used for operational, near‐real‐time agricultural drought monitoring over Contiguous U.S [ABSTRACT FROM AUTHOR]

The remote sensing of sun-induced chlorophyll fluorescence (SIF) is an emerging technique with immense potential for terrestrial vegetation sciences. However, the interpretation of fluorescence data is often hindered by the complexity of observed land surfaces. Therefore, advanced remote sensing models, particularly physically based simulations, are critical to accurately interpret SIF data. In this work, we propose a three-dimensional (3D) radiative transfer model that employs the Monte Carlo ray-tracing technique to simulate the excitation and transport of SIF within plant canopies. This physically based approach can quantify the various radiative processes contributing to the observed SIF signal with high fidelity. The model's performance is rigorously evaluated by comparing the simulated SIF spectra and angular distributions to field measurements, as well as conducting systematic comparisons with an established radiative transfer model. The results demonstrate the proposed model's ability to reliably reproduce the key spectral and angular characteristics of SIF, with the coefficient of determination ( R 2 ) exceeding 0.98 and root mean square error (RMSE) being less than 0.08 mW m−2 sr−1 nm−1 for both the red and far-red fluorescence peaks. Furthermore, the model's versatile representation of canopy structures, enabled by the decoupling of radiation and geometry, is applied to study the impact of 3D structure on SIF patterns. This capability makes the proposed model a highly attractive tool for investigating SIF distributions in realistic, heterogeneous canopy environments. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of advancements in the detection; evaluation; and management of invasive plant species (IPS) using diverse remote sensing (RS) techniques and machine learning (ML) methods. Analyzing the high-resolution datasets received from drones, satellites, and aerial photography enables the perfect cartography technique and analysis of the spread and various impacts of ecology on IPS. The majority of current research on hyperspectral imaging with unmanned aerial vehicle (UAV) enhanced by ML has significantly improved the accuracy and efficiency of identifying mapping IPS, and it also serves as a powerful instrument for ecological management. The integrative association is essential to manage the alien species better, as researchers from multiple other fields participate in modeling innovative methods and structures. Incorporating advanced technologies like light detection and ranging (LiDAR) and hyperspectral imaging shows potential for improving spatial and spectral analysis approaches and utilizing ML approaches such as a support vector machine (SVM), random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and deep convolutional neural network (DCNN) analysis for detecting complex IPS. The significant results indicate that ML methods, most importantly SVM and RF, are victorious in recognizing the alien species via analyzing RS data. This report emphasizes the importance of continuous research efforts to improve predictive models, fill gaps in our understanding of the connections between climate, urbanization and invasion dynamics, and expands conservation initiatives via utilizing RS techniques. This study also highlights the potential for RS data to refine management plans, enabling the implementation of more efficient strategies for controlling IPS and preserving ecosystems. [ABSTRACT FROM AUTHOR]

The characteristics of drought as one of the environmental events may affect climate changes in the future. The purpose of this study is to evaluate the effect of meteorological drought on vegetation dynamics from 2001 to 2021 in the Golestan province, northern Iran, using remote sensing satellite images. First, using MATLAB software, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated in time scales of 3, 6, 9, and 12 months for the Golestan province with a statistical period of 20 years and zoned in ArcGIS software with IDW (Inverse Distance Weighting) Interpolation Method. In the next step, we obtained the maximum value of the Enhanced Vegetation Index (EVI) per month from the MODI3Q1 product of the MODIS spectrometer. Also, using Tersest software, the correlation and line slope of EVI index changes were calculated based on SPEI index changes. The results of correlation analysis showed that in 54.19% of the area of Golestan province, 6-month SPEI changes have the maximum correlation with the EVI index compared to SPEI in other periods. As a result, the highest impact of drought on vegetation in most parts of the study area is related to the 6-month SPEI in May. The results of vegetation sensitivity to meteorological drought showed that in the south of Golestan province, which is covered with dense forest conditions and includes the eastern slopes of Alborz spread in the form of a strip from the west to the east of the province, the vegetation has relatively low sensitivity to the phenomenon of drought due to suitable climatic conditions and the relative humidity of the air. The research results can be used in long-term planning for sustainable natural resources management. [ABSTRACT FROM AUTHOR]

Elevated temperatures in urban centers have become a common problem in cities around the world. However, the climate problems in suburban areas are equally severe; there is an urgent need to find zero-carbon ways to mitigate this problem. Recent studies have revealed the thermal performance of vegetation, buildings, and water surfaces. They functioned differently regarding the climate at different periods of the day. Accordingly, this study synthesizes remote sensing technology and meteorology station observation data to deeply explore the differences in the role of each climate-influencing factor in the suburban areas of Chengdu. The land surface temperature (LST) and air temperature (Ta) were used as thermal environmental indicators, while the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), and altitude were used as environmental factors. The results showed that the relevant influences of the environmental factors on the climate in the sample areas were significantly affected by the time of the day. The NDVI (R2 = 0.5884), NDBI (R2 = 0.3012), and altitude (R2 = 0.5638) all showed strong correlations with Ta during the night (20:00–7:00), which gradually weakened after sunrise, yet the NDWI showed a poorer cooling effect during the night, which gradually strengthened after sunrise, reaching a maximum at 15:00 (R2 = 0.5012). One reason for this phenomenon was the daily weather changes. These findings facilitate the advancement of the understanding of the climate in suburban areas and provide clear directions for further thermal services targeted towards people in different urban areas. [ABSTRACT FROM AUTHOR]

This research studies the characteristics of snow-covered area (SCA) from two vastly different sensors: optical (Moderate-Resolution Imaging Spectroradiometer, or MODIS, equipped on board the Terra satellite) and radar (Synthetic Aperture Radar (SAR) on-board Sentinel-1 satellites). The focus are the five mountain ranges of the Iberian Peninsula (Cantabrian System, Central System, Iberian Range, Pyrenees, and Sierra Nevada). The MODIS product was selected to identify SCA dynamics in these ranges using the Probability of Snow Cover Presence Index (PSCPI). In addition, we evaluate the potential advantage of the use of SAR remote sensing to complete optical SCA under cloudy conditions. For this purpose, we utilize the Copernicus High-Resolution Snow and Ice SAR Wet Snow (HRS&I SWS) product. The Pyrenees and the Sierra Nevada showed longer-lasting SCA duration and a higher PSCPI throughout the average year. Moreover, we demonstrate that the latitude gradient has a significant influence on the snowline elevation in the Iberian mountains (R2 ≥ 0.84). In the Iberian mountains, a general negative SCA trend is observed due to the recent climate change impacts, with a particularly pronounced decline in the winter months (December and January). Finally, in the Pyrenees, we found that wet snow detection has high potential for the spatial gap-filling of MODIS SCA in spring, contributing above 27% to the total SCA. Notably, the additional SCA provided in winter is also significant. Based on the results obtained in the Pyrenees, we can conclude that implementing techniques that combine SAR and optical satellite sensors for SCA detection may provide valuable additional SCA data for the other Iberian mountains, in which the radar product is not available. [ABSTRACT FROM AUTHOR]

This study investigates the escalating impact of wildfire smoke on the remote sensing of phytoplankton biomass in freshwater systems. Wildfire smoke disrupts the accuracy of Chlorophyll-a (Chl-a) retrieval models, with Chl-a often used as a proxy for quantifying phytoplankton biomass. Given the increasing frequency and intensity of wildfires, there is a need for the development and refinement of remote sensing methodologies to effectively monitor phytoplankton dynamics under wildfire-impacted conditions. Here we developed a novel approach using Landsat's coastal/aerosol band (B1) to screen for and categorize levels of wildfire smoke interference. By excluding high-interference data (B1 reflectance > 0.07) from the calibration set, Chl-a retrieval model performance using different Landsat band formulas improved significantly, with R2 increasing from 0.55 to as high as 0.80. Our findings demonstrate that Rayleigh-corrected reflectance, combined with B1 screening, provides a robust method for monitoring phytoplankton biomass even under moderate smoke interference, outperforming full atmospheric correction methods. This approach enhances the reliability of remote sensing in the face of increasing wildfire events, offering a valuable tool for the effective management of aquatic environments. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, is one of the few biological water quality indices detectable using satellite observations. However, models for estimating Chl-a from satellite signals are currently unavailable for many lakes. The application of Chl-a prediction algorithms may be affected by the variance in optical complexity within lakes. Using Lake Winnipeg in Canada as a case study, we demonstrated that separating models by the lake's basins [north basin (NB) and south basin (SB)] can improve Chl-a predictions. By calibrating more than 40 commonly used Chl-a estimation models using Landsat data for Lake Winnipeg, we achieved higher correlations between in situ and predicted Chl-a when building models with separate Landsat-to-in situ matchups from NB and SB (R2 = 0.85 and 0.76, respectively; p < 0.05), compared to using matchups from the entire lake (R2 = 0.38, p < 0.05). In the deeper, more transparent waters of the NB, a green-to-blue band ratio provided better Chl-a predictions, while in the shallower, highly turbid SB, a red-to-green band ratio was more effective. Our approach can be used for rapid Chl-a modeling in large lakes using cloud-based platforms like Google Earth Engine with any available satellite or time series length. [ABSTRACT FROM AUTHOR]

Remote sensing has proven to be a vital tool for monitoring and forecasting the quality and yield of crops. The utilization of innovative technologies such as Solar-Induced Fluorescence (SIF) and satellite measurements of column-averaged CO2 (xCO2) can enhance these estimations. SIF is a signal emitted by crops during photosynthesis, thus indicating photosynthetic activities. The concentration of atmospheric CO2 is a critical factor in determining the efficiency of photosynthesis. The aim of this study was to investigate the correlation between satellite-derived Solar-Induced Chlorophyll Fluorescence (SIF), column-averaged CO2 (xCO2), and Normalized Difference Vegetation Index (NDVI) and their association with sugarcane yield and sugar content in the field. This study was carried out in south-central Brazil. We used four localities to represent the region: Pradópolis, Araraquara, Iracemápolis, and Quirinópolis. Data were collected from orbital systems during the period spanning from 2015 to 2016. Concurrently, monthly data regarding tons of sugarcane per hectare (TCH) and total recoverable sugars (TRS) were gathered from 24 harvest locations within the studied plots. It was observed that TRS decreased when SIF values ranged between 0.4 W m−2 sr−1 μm−1 and 0.8 W m−2 sr−1 μm−1, particularly in conjunction with NDVI values below 0.5. TRS values peaked at 15 kg t−1 with low NDVI and xCO2 values, alongside SIF values lower than 0.4 W m−2 sr−1 μm−1 and greater than 1 W m−2 sr−1 μm−1. These findings underscore the potential of integrating SIF, xCO2, and NDVI measurements in the monitoring and forecasting of yield and sugar content in sugarcane crops. [ABSTRACT FROM AUTHOR]

Ensuring global food security amid mounting challenges, such as population growth, disease infestations, resource limitations, and climate change, is a pressing concern. Anticipated increases in food demand add further complexity to this critical issue. Plant pathogens, responsible for substantial crop losses (up to 41%) in major crops like wheat, rice, maize, soybean, and potato, exacerbate the situation. Timely disease detection is crucial, yet current practices often identify diseases at advanced stages, leading to severe infestations. To address this, remote sensing and Hyperspectral imaging (HSI) have emerged as robust and nondestructive techniques, exhibiting promising results in early disease identification. Integrating machine learning algorithms with image data sets enables precise spatial–temporal disease identification, facilitating timely detection, predictive modeling, and effective disease management without compromising fitness or climate adaptability. By harnessing these cutting-edge technologies and data-driven decision-making, growers can optimize input costs while achieving enhanced yields, making significant strides toward global food security in the face of climate change risks. This review will discuss some of the foundational concepts of remote sensing, several platforms used for remote sensing data collection, successful application of the approach, and its future perspective. [ABSTRACT FROM AUTHOR]

This article reviews the spectroscopic principles related to three established satellite-based systems for global greenhouse gas monitoring, namely Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), Greenhouse Gases Observing Satellite (GOSAT) and Orbiting Carbon Observatory (OCO) series. It reveals that the worldwide endeavor of atmospheric carbon dioxide (CO2) and other greenhouse gas measurements have not only alerted mankind about the climate hazards but also have enriched basic physical science, especially spectroscopy that need more elucidation. This review points out that the technology of remote sensing of trace gases has achieved several radically new dimensions in science research, such as the shift from in situ chemical analysis to distant spectroscopic measurements and development of space-borne hyperspectral observation systems. It has implemented fundamentally new spectroscopic techniques, namely double-pendulum interferometer and imaging spectroscopy, and executed novel statistical procedures for assessing atmospheric radiative absorption. New avenues are opened, such as implementing differential optical absorption spectroscopy from space and assessing solar-induced chlorophyll fluorescence at global level. In brief, this review highlights several new spectroscopic applications of the known physical principles that were conceptualized in course of developing the remote sensing systems for atmospheric CO2 and other greenhouse gases. [ABSTRACT FROM AUTHOR]

Grassland ecosystems play a critical role in global carbon cycling and environmental health. Understanding the intricate link between grassland vegetation traits and underlying soil properties is crucial for effective ecosystem monitoring and management. This review paper examines advancements in utilizing Radiative Transfer Models (RTMs) and hyperspectral remote sensing to bridge this knowledge gap. We explore the potential of vegetation spectra as an integrated measure of soil characteristics, acknowledging the value of other remote sensing sources. Our focus is on studies leveraging hyperspectral data from proximal and airborne sensors, while discussing the impact of spatial scale on trait retrieval accuracy. Finally, we explore how advancements in global satellite remote sensing contribute to vegetation trait detection. This review concludes by identifying current challenges, outlining future research directions, and highlighting opportunities for improved understanding of the vegetation-soil property interaction. [ABSTRACT FROM AUTHOR]

Real-time and accurate assessment of the photosynthetic rate is of great importance for monitoring the contribution of leaves to the global carbon cycle. The electron transport rate is a critical parameter for accurate simulation of the net photosynthetic rate, which is highly sensitive to both light conditions and the biochemical state of the leaf. Although various approaches, including hyperspectral remote sensing techniques, have been proposed so far, the actual electron transport rate is rarely quantified in real time other than being derived from the maximum electron transport (Jmax) at a reference temperature in most gas exchange models, leading to the decoupling of gas exchange characteristics from environmental drivers. This study explores the potential of using incident light intensity, hyperspectral reflectance data, and their combination for real-time quantification of the actual electron transport rate (Ja) in mango leaves. The results show that the variations in Ja could be accurately estimated using a combination of incident light intensity and leaf reflectance at 715 nm, with a ratio of performance to deviation (RPD) value of 2.12 (very good predictive performance). Furthermore, the Ja of sunlit leaves can be predicted with an RPD value of about 2.60 using light intensity and a single-band reflectance value within 760–1320 nm, while the actual electron transport rate of shaded leaves can only be predicted with a lower RPD value of 1.73 (fair performance) using light intensity and reflectance at 685 nm. These results offer valuable insights into developing non-destructive, rapid methods for real-time estimation of actual electron transport rates using hyperspectral remote sensing data and incident light conditions. [ABSTRACT FROM AUTHOR]

Harmful algae blooms (HABs) have been reported with greater frequency in lakes across New York State (NYS) in recent years. In situ sampling is used to assess water quality, but such observations are time intensive and therefore practically limited in their spatial extent. Previous research has used remote sensing imagery to estimate phytoplankton pigments (typically chlorophyll-a or phycocyanin) as HAB indicators. The primary goal of this study was to validate a remote sensing-based method to estimate cyanobacteria concentrations at high temporal (5 days) and spatial (10–20 m) resolution, to allow identification of lakes across NYS at a significant risk of algal blooms, thereby facilitating targeted field investigations. We used Google Earth Engine (GEE) as a cloud computing platform to develop an efficient methodology to process Sentinel-2 image collections at a large spatial and temporal scale. Our research used linear regression to model the correlation between in situ observations of chlorophyll-a (Chl-a) and phycocyanin and indices derived from Sentinel-2 data to evaluate the potential of remote sensing-derived inputs for estimating cyanobacteria concentrations. We tested the performance of empirical models based on seven remote-sensing-derived indices, two in situ measurements, two cloud mitigation approaches, and three temporal sampling windows across NYS lakes for 2019 and 2020. Our best base model (R2 of 0.63), using concurrent sampling data and the ESA cloud masking—i.e., the QA60 bitmask—approach, related the maximum peak height (MPH) index to phycocyanin concentrations. Expanding the temporal match using a one-day time window increased the available training dataset size and improved the fit of the linear regression model (R2 of 0.71), highlighting the positive impact of increasing the training dataset on model fit. Applying the Cloud Score+ method for filtering cloud and cloud shadows further improved the fit of the phycocyanin estimation model, with an R2 of 0.84, but did not result in substantial improvements in the model's application. The fit of the Chl-a models was generally poorer, but these models still had good accuracy in detecting moderate and high Chl-a values. Future work will focus on exploring alternative algorithms that can incorporate diverse data sources and lake characteristics, contributing to a deeper understanding of the relationship between remote sensing data and water quality parameters. This research provides a valuable tool for cyanobacteria parameter estimation with confidence quantification to identify lakes at risk of algal blooms. [ABSTRACT FROM AUTHOR]

This comprehensive review explores the ecological significance of the Argane stands (Argania spinosa) in southwestern Morocco and the pivotal role of remote sensing technology in monitoring forest ecosystems. Argane stands, known for their resilience in semi-arid and arid conditions, serve as a keystone species, preventing soil erosion, maintaining ecological balance, and providing habitat and sustenance to diverse wildlife species. Additionally, they produce an extremely valuable Argane oil, offering economic opportunities and cultural significance to local communities. Remote sensing tools, including satellite imagery, LiDAR, drones, radar, and GPS precision, have revolutionized our capacity to remotely gather data on forest health, cover, and responses to environmental changes. These technologies provide precise insights into canopy structure, density, and individual tree health, enabling assessments of Argane stand populations and detection of abiotic stresses, biodiversity, and conservation evaluations. Furthermore, remote sensing plays a crucial role in monitoring vegetation health, productivity, and drought stress, contributing to sustainable land management practices. This review underscores the transformative impact of remote sensing in safeguarding forest ecosystems, particularly the Argane forest stands, and highlights its potential for continued advancements in ecological research and conservation efforts. [ABSTRACT FROM AUTHOR]

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We propose a novel mono-temporal framework with a physical basis and ecological consistency to retrieve fire severity at very high spatial resolution. First, we sampled the Composite Burn Index (CBI) in 108 field plots that were subsequently surveyed through unmanned aerial vehicle (UAV) flights. Then, we mimicked the field methodology for CBI assessment in the remote sensing framework. CBI strata were identified through individual tree segmentation and geographic object-based image analysis (GEOBIA). In each stratum, wildfire ecological effects were estimated through the following methods: (i) the vertical structural complexity of vegetation legacies was computed from 3D-point clouds, as a proxy for biomass consumption; and (ii) the vegetation biophysical variables were retrieved from multispectral data by the inversion of the PROSAIL radiative transfer model, with a direct physical link with the vegetation legacies remaining after canopy scorch and torch. The CBI scores predicted from UAV ecologically related metrics at the strata level featured high fit with respect to the field-measured CBI scores (R2 > 0.81 and RMSE < 0.26). Conversely, the conventional retrieval of fire effects using a battery of UAV structural and spectral predictors (point height distribution metrics and spectral indices) computed at the plot level provided a much worse performance (R2 = 0.677 and RMSE = 0.349). [ABSTRACT FROM AUTHOR]

Forest defoliating pests are significant global forest disturbance agents, posing substantial threats to forest ecosystems. However, previous studies have lacked systematic analyses of the continuous spatiotemporal distribution characteristics over a complete 3–5 year disaster cycle based on remote sensing data. This study focuses on the Dendrolimus superans outbreak in the Changbai Mountain region of northeastern China. Utilizing leaf area index (LAI) data derived from Sentinel-2A satellite images, we analyze the extent and dynamic changes of forest defoliation. We comprehensively examine the spatiotemporal patterns of forest defoliating pest disasters and their development trends across different forest types. Using the geographical detector method, we quantify the main influencing factors and their interactions, revealing the differential impacts of various factors during different growth stages of the pests. The results show that in the early stage of the Dendrolimus superans outbreak, the affected area is extensive but with mild severity, with newly affected areas being 23 times larger than during non-outbreak periods. In the pre-hibernation stage, the affected areas are smaller but more severe, with a cumulative area reaching up to 8213 hectares. The spatial diffusion characteristics of the outbreak follow a sequential pattern across forest types: Larix olgensis, Pinus sylvestris var. mongolica, Picea koraiensis, and Pinus koraiensis. The most significant influencing factor during the pest development phase was the relative humidity of the year preceding the outbreak, with a q-value of 0.27. During the mitigation phase, summer precipitation was the most influential factor, with a q-value of 0.12. The combined effect of humidity and the low temperatures of 2020 had the most significant impact on both the development and mitigation stages of the outbreak. This study's methodology achieves a high-precision quantitative inversion of long-term disaster spatial characteristics, providing new perspectives and tools for real-time monitoring and differentiated control of forest pest infestations. [ABSTRACT FROM AUTHOR]

Heavy metal contamination in soils and vegetation poses a significant problem due to its toxicity and persistence. Toxic effects on vegetation include not only impaired growth, reduced yields, and even plant death but also biodiversity loss and ecosystem degradation. Addressing this issue requires comprehensive monitoring and remediation efforts to mitigate the environmental, human health, and ecological impacts. This review examines the state-of-the-art methodologies and advancements in remote sensing applications for detecting and monitoring heavy metal contamination in soil and its subsequent effects on vegetation. By synthesizing the current research findings and technological developments, this review offers insights into the efficacy and potential of remote sensing for monitoring heavy metal contamination in terrestrial ecosystems. However, current studies focus on regression and AI methods to link spectral reflectances and indices to heavy metal concentrations, which poses limited transferability to other areas, times, spectral discretizations, and heavy metal elements. We conclude that one important way forward is the more thorough understanding and simulation of the related physico-chemical processes in soils and plants and their effects on the spectral signatures. This would offer a profound basis for remote sensing applications for individual circumstances and would allow disentangling heavy metal effects from other stressors such as droughts or soil salinity. [ABSTRACT FROM AUTHOR]

The NOMAD (“Nadir and Occultation for MArs Discovery”) spectrometer suite on board the ExoMars Trace Gas Orbiter (TGO) has been designed to investigate the composition of Mars’ atmosphere, with a particular focus on trace gases, clouds and dust. The detection sensitivity for trace gases is considerably improved compared to previous Mars missions, compliant with the science objectives of the TGO mission. This will allow for a major leap in our knowledge and understanding of the Martian atmospheric composition and the related physical and chemical processes. The instrument is a combination of three spectrometers, covering a spectral range from the UV to the mid-IR, and can perform solar occultation, nadir and limb observations. In this paper, we present the science objectives of the instrument and explain the technical principles of the three spectrometers. We also discuss the expected performance of the instrument in terms of spatial and temporal coverage and detection sensitivity. [ABSTRACT FROM AUTHOR]

Wildfires are recognized as major contributors to forest loss and soil degradation on a global scale. Understanding the cumulative effects of fire regimes on forest ecosystems and soil dynamics necessitates a deeper exploration of wildfire-vegetation-soil interactions over the long term. This study delves into the wildfire-landscape dynamics within the "Baixa Limia Serra do Xurés" Natural Park, a region prone to fires in Galicia, Spain. By analyzing available statistical and remote sensing data, we identified significant shifts in fire regimes and landscape dynamics between the periods of 2000-2010 and 2010-2020. Our findings indicate a potential extension of the fire season, reflecting the impacts of climate change. Despite improvements in firefighting capabilities, the occurrence of large fires is on the rise in the Natural Park, underscoring the need for proactive management strategies in such areas. Notably, significant fire events in 2011, 2016, 2017, and 2020 extensively affected wooded areas, constituting the majority of the burned area. Shrubs and forests emerged as particularly vulnerable, with varying degrees of burn severity influencing post-fire vegetation recovery rates. While shrublands expanded their coverage between 2000 and 2010, rocky areas with sparse vegetation showed an increase over the subsequent decade (2010-2020), indicating soil degradation and potential desertification in areas affected by recurrent and severe fires, especially within zones designated for the highest levels of protection (with fire rotation periods of less than 1 year). In conclusion, this study provides valuable insights into the impacts of wildfires, changes in land cover, and post-fire soil-vegetation dynamics, which can inform management and conservation efforts in fire-prone mountainous regions. Leveraging advanced remote sensing techniques enables the monitoring of cumulative soil degradation resulting from repeated wildfires over extended periods. [ABSTRACT FROM AUTHOR]

Restored wetlands should be closely monitored to fully evaluate the effectiveness of restoration efforts. However, regular post-restoration monitoring can be time-consuming and expensive, and is often absent or inadequate. Satellite and airborne remote sensing systems have proven to be cost-effective tools in many fields, but they have not been widely used to monitor ecological restoration. This study assessed the potential of airborne hyperspectral remote sensing to monitor water mass characteristics of experimental temporary ponds in the Mediterranean region. These ponds were created during marsh restoration in Doñana National Park (south-west Spain). We used hyperspectral images acquired by the CASI-1500 hyperspectral airborne sensor to estimate and map water depth, turbidity and chlorophyll a in a subset of the 96 new ponds. The high spatial and spectral resolution of the CASI sensor allowed us to detect differences between ponds in water depth, turbidity and chlorophyll a, providing accurate mapping of these three variables, and a useful method to assess restoration success. High levels of spatial variation were recorded between different ponds, which likely generates high diversity in the animal and plant species that they contain. These results highlight the great potential of hyperspectral sensors for the long-term monitoring of wetland complexes in the Mediterranean region and elsewhere. [ABSTRACT FROM AUTHOR]

Effective yield forecasting is a key strategy for adaptation when facing food loss to climate variability. Currently, solar-induced chlorophyll fluorescence (SIF) is an emerging remote-sensing index owing to its high relevance to plant photosynthesis, and sensitivity to drought. Despite many studies have focused on drought monitoring and production assessment by SIF, little puts it into practice for in-season yield prediction. In this study, we combined multi-source satellite and meteorological data, especially coupling with subseasonal-to-seasonal (S2S) dynamic atmospheric prediction climate model (IAP-CAS FGOALS-f2), with an addition of SIF, to predict maize yields in the U.S. Corn Belt, based on the developed machine learning dynamical hybrid model (MHCF). By comparison, we found that SIF performed well in the correlation analysis with yield, with average correlations up to 0.719 in August. Then we utilized different algorithms, different models (S2S data for MHCF, climate data for the Benchmark), and different input combinations to train and predict maize yields. All four algorithms using SIF significantly improved prediction performance. S2S + VIs + SIF combination (FGOALS-f2、NDVI、EVI、SIF) can achieve the best performance, while the XGBoost algorithm reached 0.897 of R2. With the best combination, it can achieve 4 months before maize harvest (with R2 value of 0.85, and RMSE < 13 bu/acre). In 2012, the year had a severe drought, although predictive capability decreased in all the predictions, the models with SIF still maintained robust and improved the prediction (improved R2 by 5.92%, and RMSE decreased by 18.08% of XGBoost). According to the study, it can be expected, the combination of MHCF and SIF will play a greater role in subseasonal yield prediction. We also provide an operational proposition of hybrid yield forecasting method to fully integrating climate prediction and machine learning for early notice of crop production losses. [ABSTRACT FROM AUTHOR]

Water conservation efforts and studies receive special attention, versatile and constantly developing remote sensing methods especially so. The quality and quantity of algae fundamentally influence the ecosystems of water bodies. Inland lakes are less-frequently studied despite their essential ecological role compared to ocean and sea waters. One of the reasons for this is the small-scale surface extension, which poses challenges during satellite remote sensing. In this study, we investigated the correlations between remote-sensing- (via Seninel-2 satellite) and laboratory-based results in different chlorophyll-a concentration ranges. In the case of low chlorophyll-a concentrations, the measured values were between 15 µg L−1 and 35 µg L−1. In the case of medium chlorophyll-a concentrations, the measured values ranged between 35 and 80 µg L−1. During high chlorophyll-a concentrations, the results were higher than 80 µg L−1. Finally, under extreme environmental conditions (algal bloom), the values were higher than 180 µg L−1. We also studied the accuracy and correlation and the different algorithms applied through the Acolite (20231023.0) processing software. The chl_re_mishra algorithm of the Acolite software gave the highest correlation. The strong positive correlations prove the applicability of the Sentinel-2 images and the Acolite software in the indication of chlorophyll-a. Because of the high CDOM concentration of Lake Naplás, the blue–green band ratio underestimated the concentration of chlorophyll-a. In summer, higher chlorophyll-a was detected in both laboratory and satellite investigations. In the case of extremely high chlorophyll-a concentrations, it is significantly underestimated by satellite remote sensing. This study proved the applicability of remote sensing to detect chlorophyll-a content but also pointed out the current limitations, thus assigning future development and research directions. [ABSTRACT FROM AUTHOR]

Assessing the dynamics of forest structure complexity is a critical task in times of global warming, biodiversity loss and increasing disturbances in order to ensure the resilience of forests. Recent studies on forest biodiversity and forest structure emphasize the essential functions of deadwood accumulation and diversification of light conditions for the enhancement of structural complexity. The implementation of an experimental patch‐network in managed broad‐leaved forests within Germany enables the standardized analysis of various aggregated and distributed treatments characterized through diverse deadwood and light structures. To monitor the dynamics of enhanced forest structure complexity as seasonal and trend components, dense time‐series from high spatial resolution imagery of Sentinel‐1 (Synthetic‐Aperture Radar, SAR) and Sentinel‐2 (multispectral) are analyzed in time‐series decomposition models (BEAST, Bayesian Estimator of Abrupt change, Seasonal change and Trend). Based on several spatial statistics and a comprehensive catalog on spectral indices, metrics from Sentinel‐1 (n = 84) and Sentinel‐2 (n = 903) are calculated at patch‐level. Metrics best identifying the treatment implementation event are assessed by change point dates and probability scores. Heterogeneity metrics of Sentinel‐1 VH and Sentinel‐2 NMDI (Normalized Multi‐band Drought Index) capture the treatment implementation event most accurately, with clear advantages for the identification of aggregated treatments. In addition, aggregated structures of downed or no deadwood can be characterized, as well as more complex standing structures, such as snags or habitat trees. To conclude, dense time‐series of complementary high spatial resolution sensors have the potential to assess various aggregated forest structure complexities, thus supporting the continuous monitoring of forest habitats and functioning over time. [ABSTRACT FROM AUTHOR]

To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within specific subgroups, frequently emerge in agricultural data. Interpreting these complex patterns requires sophisticated analysis due to the presence of noise, high variability, and non-stationarity behavior in the collected samples. Here, we introduce Local Biplot, a methodological framework tailored for discerning meaningful data patterns in non-stationary contexts for precision agriculture. Local Biplot relies on the well-known uniform manifold approximation and projection method, such as UMAP, and local affine transformations to codify non-stationary and non-linear data patterns while maintaining interpretability. This lets us find important clusters for transformation and projection within a single global axis pair. Hence, our framework encompasses variable and observational contributions within individual clusters. At the same time, we provide a relevance analysis strategy to help explain why those clusters exist, facilitating the understanding of data dynamics while favoring interpretability. We demonstrated our method's capabilities through experiments on both synthetic and real-world datasets, covering scenarios involving grass and rice crops. Moreover, we use random forest and linear regression models to predict water status variables from our Local Biplot-based feature ranking and clusters. Our findings revealed enhanced clustering and prediction capability while emphasizing the importance of input features in precision agriculture. As a result, Local Biplot is a useful tool to visualize, analyze, and compare the intricate underlying patterns and internal structures of complex agricultural datasets. [ABSTRACT FROM AUTHOR]

Effective monitoring and management of inland waterbodies depend on reliable assessments of water quality through remote sensing technologies. Match-up analysis plays a significant role in investigating the comparability between in situ and remote sensing data of physical and biogeochemical variables. By exploring different spatial aggregations and temporal windows, we aimed to identify which configurations are most effective and which are less effective for the assessment of remotely sensed water quality data within the context of governmental monitoring programs. Therefore, in this study, remote sensing data products, including the variables of Secchi depth, chlorophyll-a, and turbidity, derived from the Copernicus satellites Sentinel-2 and Sentinel-3, were compared with in situ laboratory data from >100 waterbodies (lakes and reservoirs) in Germany, covering a period of 5 years (2016–2020). Processing was carried out using two different processing schemes, CyanoAlert from Brockmann Consult GmbH and eoapp AQUA from EOMAP GmbH & Co. KG, in order to analyze the influence of different processors on the results. To investigate appropriate spatial aggregations and time windows for validation (the match-up approach), we performed a statistical comparison of different spatial aggregations (1 pixel; 3 × 3, 5 × 5, and 15 × 15 macropixels; and averaging over the whole waterbody) and time windows (same day, ±1 day, and ±5 days). The results show that waterbody-wide values achieved similar accuracies and biases compared with the macropixel variants, despite the large differences in spatial aggregation and spatial variability. An expansion of the temporal window to up to ±5 days did not impair the agreement between the in situ and remote sensing data for most target variables and sensor–processor combinations, while resulting in a marked rise in the number of matches. [ABSTRACT FROM AUTHOR]

Maize northern leaf blight (MNLB), characterized by a bottom-up progression, is a prevalent and damaging disease affecting maize growth. Early monitoring is crucial for timely interventions, thus mitigating yield losses. Hyperspectral remote sensing technology is an effective means of early crop disease monitoring. However, traditional single-angle vertical hyperspectral remote sensing methods face challenges in monitoring early MNLB in the lower part of maize canopy due to obstruction by upper canopy leaves. Therefore, we propose a multiangle hyperspectral remote sensing method for early MNLB monitoring. From multiangle hyperspectral data (−60° to 60°), we extracted and selected vegetation indices (VIs) and plant traits (PTs) that show significant differences between healthy and diseased maize samples. Our findings indicate that besides structural PTs (LAI and FIDF), other PTs like Cab, Car, Anth, Cw, Cp, and CBC show strong disease discrimination capabilities. Using these selected features, we developed a disease monitoring model with the random forest (RF) algorithm, integrating VIs and PTs (PTVI-RF). The results showed that PTVI-RF outperformed models based solely on VIs or PTs. For instance, the overall accuracy (OA) of the PTVI-RF model at 0° was 80%, which was 4% and 6% higher than models relying solely on VIs and PTs, respectively. Additionally, we explored the impact of viewing angles on model accuracy. The results show that compared to the accuracy at the nadir angle (0°), higher accuracy is obtained at smaller off-nadir angles (±10° to ±30°), while lower accuracy is obtained at larger angles (±40° to ±60°). Specifically, the OA of the PTVI-RF model ranges from 80% to 88% and the Kappa ranges from 0.6 to 0.76 at ±10° to ±30°, with the highest accuracy at −10° (OA = 88%, Kappa = 0.76). In contrast, the OA ranges from 72% to 80% and the Kappa ranges from 0.44 to 0.6 at ±40° to ±60°. In conclusion, this research demonstrates that PTVI-RF, constructed by fusing VIs and PTs extracted from multiangle hyperspectral data, can effectively monitor early MNLB. This provides a basis for the early prevention and control of MNLB and offers a valuable reference for early monitoring crop diseases with similar bottom-up progression. [ABSTRACT FROM AUTHOR]