Vegetation Monitoring Research Articles

Machine learning-based crop type detection and vegetation mon-itoring using Sentinel-1 SAR and Landsat 8

This study explores the use of machine learning for crop type detection and vegetation monitoring in arid and semi-arid regions, specifically in Biskra and Khenchela, Algeria. Traditional field-based agricultural monitoring is labor-intensive and limited in spatial coverage, prompting the need for more efficient and scalable methods. The research integrates Synthetic Aperture Radar (SAR) data from Sentinel-1 and optical imagery from Landsat 8 to enhance the classification of different crop types and track vegetation dynamics. Key metrics such as radar backscatter and Normalized Difference Vegetation Index (NDVI) are employed to distinguish between irrigated and rainfed crops. The Random Forest algorithm is utilized for classification, with training data derived from field surveys, high-resolution satellite imagery, and existing land cover maps. The Google Earth Engine (GEE) platform facilitates large-scale data processing, providing real-time agricultural analysis. Over the 12-month study period (January to December 2020), the integrated approach demonstrated high accuracy, achieving over 85% precision in classifying key crops such as date palms, cereals, and vegetables. Results indicate that SAR and NDVI data provide complementary insights into vegetation health, phenology, and agricultural practices in different climatic zones. This methodology offers a promising solution for improving agricultural monitoring and resource management in arid and semi-arid regions, enhancing food security and optimizing water usage. The study’s findings also underscore the value of satellite-based remote sensing for large-scale, real-time agricultural analysis.

The Effect of Vegetation Ecological Restoration by Integrating Multispectral Remote Sensing and Laser Point Cloud Monitoring Technology

This research aims to evaluate and monitor the effectiveness of vegetation ecological restoration by integrating Multispectral Remote Sensing (MRS) and laser point cloud (LPC) monitoring technologies. Traditional vegetation restoration monitoring methods often face challenges of inaccurate data and insufficient coverage, and the use of MRS or LPC techniques alone has its limitations. Therefore, to more accurately monitor the vegetation restoration status, this study proposes a new monitoring method that combines the advantages of the large-scale coverage of MRS technology and the high-precision three-dimensional structural data analysis capability of LPC technology. This new method was applied in the Daqing oilfield area of China, aiming to provide effective ecological restoration assessment methods through the precise monitoring and analysis of regional vegetation growth and coverage. The results showed that there was a negative correlation between the vegetation humidity index and vegetation growth in the Daqing oilfield in 2023. The estimated monitoring effect of the research method could reach over 90%, and the coverage area of hydrangea restoration in the monitoring year increased by 7509 km2. The research technology was closer to the actual coverage situation. The simulation image showed that the vegetation coverage in the area has significantly improved after returning farmland to forests. Therefore, the technical methods used can effectively monitor the ecological restoration of vegetation, which has great research significance for both vegetation restoration and monitoring.

The Impact of Seasonal Climate on Dryland Vegetation NPP: The Mediating Role of Phenology

Dryland ecosystems are highly sensitive to climate change, making vegetation monitoring crucial for understanding ecological dynamics in these regions. In recent years, climate change, combined with large-scale ecological restoration efforts, has led significant greening in China’s arid areas. However, the mechanisms through which seasonal climate variations regulate vegetation growth are not yet fully understood. This study hypothesizes that seasonal climate change affects net primary productivity (NPP) of vegetation by influencing phenology. We focused on China’s Windbreak and Sand-Fixation Ecological Function Conservation Areas (WSEFCAs) as representative regions of dryland vegetation. The Carnegie–Ames–Stanford Approach (CASA) model was used to estimate vegetation NPP from 2000 to 2020. To extract phenological information, NDVI data were processed using Savitzky–Golay (S–G) filtering and threshold methods to determine the start of season (SOS) and end of season (EOS). The structural equation model (SEM) was constructed to quantitatively assess the contributions of climate change (temperature and precipitation) and phenology to variations in vegetation NPP, identifying the pathways of influence. The results indicate that the average annual NPP in WSEFCAs increased from 55.55 gC/(m2·a) to 75.01 gC/(m2·a), exhibiting uneven spatial distribution. The pathways through which seasonal climate affects vegetation NPP are more complex and uneven. Summer precipitation directly promoted NPP growth (direct effect = 0.243, p < 0.001) while also indirectly enhancing NPP by significantly advancing SOS (0.433, p < 0.001) and delaying EOS (−0.271, p < 0.001), with an indirect effect of 0.133. This finding highlights the critical role of phenology in vegetation growth, particularly in regions with substantial seasonal climate fluctuations. Although the overall ecological environment of WSEFCAs has improved, significant regional disparities remain, especially in northwestern China. This study introduces causal mediation analysis to systematically explore the mechanisms through which seasonal climate change impacts vegetation NPP in WSEFCAs, providing new insights into the broader implications of climate change and offering scientific support for ecological restoration and management strategies in arid regions.

Remote Sensing and GIS in Natural Resource Management: Comparing Tools and Emphasizing the Importance of In-Situ Data

Remote sensing (RS) and Geographic Information Systems (GISs) provide significant opportunities for monitoring and managing natural resources across various temporal, spectral, and spatial resolutions. There is a critical need for natural resource managers to understand the expanding capabilities of image sources, analysis techniques, and in situ validation methods. This article reviews key image analysis tools in natural resource management, highlighting their unique strengths across diverse applications such as agriculture, forestry, water resources, soil management, and natural hazard monitoring. Google Earth Engine (GEE), a cloud-based platform introduced in 2010, stands out for its vast geospatial data catalog and scalability, making it ideal for global-scale analysis and algorithm development. ENVI, known for advanced multi- and hyperspectral image processing, excels in vegetation monitoring, environmental analysis, and feature extraction. ERDAS IMAGINE specializes in radar data analysis and LiDAR processing, offering robust classification and terrain analysis capabilities. Global Mapper is recognized for its versatility, supporting over 300 data formats and excelling in 3D visualization and point cloud processing, especially in UAV applications. eCognition leverages object-based image analysis (OBIA) to enhance classification accuracy by grouping pixels into meaningful objects, making it effective in environmental monitoring and urban planning. Lastly, QGIS integrates these remote sensing tools with powerful spatial analysis functions, supporting decision-making in sustainable resource management. Together, these tools when paired with in situ data provide comprehensive solutions for managing and analyzing natural resources across scales.

A Study on the Classification of Shrubs and Grasses on the Tibetan Plateau Based on Unmanned Aerial Vehicle Multispectral Imagery

The ecosystem of the Qinghai–Tibet Plateau is highly fragile due to its unique geographical conditions, with vegetation playing a crucial role in maintaining ecological balance. Thus, accurately monitoring the distribution of vegetation in the plateau region is of paramount importance. This study employs UAV multispectral imagery in combination with four machine-learning models—Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Random Forest (RF)—to investigate the impact of different features and their combinations on the fine classification of shrubs and grasses on the Qinghai–Tibet Plateau, including Salix psammophila, Populus simonii Carrière, Kobresia tibetica, and Kobresia pygmaea. The results indicate that near-infrared spectral information can improve classification accuracy, with improvements of 5.21%, 1.65%, 6.64%, and 5.03% for Salix psammophila, Populus simonii Carrière, Kobresia tibetica, and Kobresia pygmaea, respectively. Feature selection effectively reduces redundant information and enhances model classification accuracy, with all four machine-learning models achieving the best performance on the optimized feature set. Furthermore, the RF model performs best on the optimized feature set, achieving an overall accuracy (OA) of 95.32% and a kappa coefficient of 0.94. This study provides important scientific support for the fine classification and ecological monitoring of plateau vegetation.

Functional ecology of plant communities as a guide for vegetation management

Plant functional traits determine how individual plants cope with varying environmental conditions, and extensive literature supports their role in influencing ecosystem properties. The study of plant functional traits could help the prediction of species responses to disturbances and environmental change, thus providing invaluable knowledge to refine conservation strategies of species and vegetation. However, applications of functional ecology for vegetation management are still underrepresented.The articles in this Special Issue of FLORA offer cutting-edge insights on the variation of plant traits and ecological strategies in response to environmental drivers, such as anthropogenic disturbance, climate and land use change. The results demonstrate how trait-based approaches can be used for the prediction of climate and land use change impacts on vulnerable sets of species or on biogeochemical cycles, reinforcing the concept that functional plant ecology can help identify the most effective procedures for vegetation management, such as the choice of mowing intensity in grasslands or planning vegetation recovery after wildfires.Our knowledge of plant trait coordination at the community level is still undefined, especially concerning the integration of above- and belowground traits. This represents a missed opportunity for vegetation management, especially considering that a complete understanding of the dynamics of the entire ecosystem is crucial for complete understanding of ecosystem properties. Since plant functional traits can represent a straightforward tool for monitoring changes in ecosystem structure and functions, their use could support vegetation monitoring as required by different programs of nature conservation. It would be useful to embrace a perspective that integrates plant traits with systematic conservation planning, ensuring effective and sustainable conservation efforts.

Mapping Leaf Mass Per Area and Equivalent Water Thickness from PRISMA and EnMAP

With the continued advancement of spaceborne hyperspectral sensors, hyperspectral remote sensing is evolving as an increasingly pivotal tool for high-precision global monitoring applications. Novel image spectroscopy data, e.g., the PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), can rapidly and non-invasively capture subtle spectral information of terrestrial vegetation, facilitating the precise retrieval of the required vegetation parameters. As critical vegetation traits, Leaf Mass per Area (LMA) and Equivalent Water Thickness (EWT) hold significant importance for comprehending ecosystem functionality and the physiological status of plants. To address the demand for high-precision vegetation parameter datasets, a hybrid modeling approach was proposed in this study, integrating the radiative transfer model PROSAIL and neural network models to retrieve LMA and EWT from PRISMA and EnMAP images. To achieve this objective, canopy reflectance was simulated via PROSAIL, and the optimal band combinations for LMA and EWT were selected as inputs to train neural networks. The evaluation of the hybrid inversion models over field measurements showed that the RMSE values for the LMA and EWT were 4.11 mg·cm−2 and 9.08 mg·cm−2, respectively. The hybrid models were applied to PRISMA and EnMAP images, resulting in LMA and EWT maps displaying adequate spatial consistency, along with cross-validation results showing high accuracy (RMSELMA = 5.78 mg·cm−2, RMSEEWT = 6.84 mg·cm−2). The results demonstrated the hybrid inversion model’s universality and applicability, enabling the retrieval of vegetation parameters from image spectroscopy data and offering a valuable contribution to hyperspectral remote sensing for vegetation monitoring, though the availability of field measurement data remained a significant challenge.

Sentinel-2A Image Reflectance Simulation Method for Estimating the Chlorophyll Content of Larch Needles with Pest Damage

With the development of remote sensing technology, the estimation of the chlorophyll content (CHLC) of vegetation via satellite data has become an important means of monitoring vegetation health, and high-precision estimation has been the focus of research in this field. In this study, we used larch affected by Yarl’s larch looper (Erannis jacobsoni Djak) in the boundary region of Mongolia as the research object, simulated the multispectral reflectance, downscaled Sentinel-2A satellite data, performed mixed-pixel decomposition, analyzed the potential of Sentinel-2A satellite data for estimating the chlorophyll content by calculating the spectral indices (SIs) and spectral derivatives (SDFs) of images, and then extracted sensitive spectral features as the model training set. Spectral features sensitive to the chlorophyll content were extracted to establish the training set, and, finally, the chlorophyll content estimation model for larch was constructed on the basis of the partial least squares algorithm (PLSR). The results revealed that SI and SDF based on simulated remote sensing data were highly sensitive to the chlorophyll content under the influence of pests, with the SAVI and EVI2 spectral indices as well as the D_B2 and D_B5 spectral derivatives being the most sensitive to the chlorophyll content. The estimation models based on simulated data performed significantly better than models without simulated data in terms of accuracy, especially those based on SDF-PLSR. The simulated spectral reflectance well reflected the spectral characteristics of the larch canopy and was sensitive to damaged larch, especially in the green light, red edge, and near-infrared bands. The proposed approach improves the accuracy of chlorophyll content estimation via Sentinel-2A data and enhances the ability to monitor changes in the chlorophyll content under complex forest conditions through simulations, providing new technical means and a theoretical basis for forestry pest monitoring and vegetation health management.

Integrating remote sensing modeling for drought assessment and vegetation monitoring in El Tarf, Algeria: a 40-year analysis

The escalating frequency and intensity of drought events, exacerbated by anthropogenic climate change, present critical challenges to hydrological resources, ecosystem resilience, and agricultural productivity. In North Africa, and specifically Algeria, comprehending the spatiotemporal patterns of drought is essential for sustainable water resource management and ecological conservation. This investigation focuses on the El Tarf region, located in northeastern Algeria, recognized for its ecological diversity and agricultural relevance. Employing a synergistic methodology, this study integrates the Standardized Precipitation Evapotranspiration Index (SPEI) for long-term drought severity evaluation with the Normalized Difference Vegetation Index (NDVI) to monitor vegetation phenology and health over time. Utilizing the computational power of Google Earth Engine, we process extensive temporal datasets of SPEI spanning from 1980 to 2023, complemented by high-resolution satellite imagery for NDVI computation. This comprehensive approach facilitates an in-depth analysis of drought dynamics, their severity, and impacts on vegetative cover in El Tarf over the past four decades. Our findings indicate significant long-term drought trends, with notable dry spells occurring between 1994 and 2003. Severe drought events were particularly pronounced in December 2022 (SPI = -2.50) and May 2002 (SPI = -2.2), correlating with substantial precipitation deficits. Although areas experiencing extreme drought have diminished, there has been an increase in moderate drought occurrences, suggesting an uptick in drought frequency, which poses risks to hydrological resources and ecosystem health. These observed patterns align with regional climate change projections, highlighting an urgent necessity for adaptive management strategies in agriculture and water resources.

NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area

Cold ecosystems are experiencing a warming rate that is twice as fast as the global average and are particularly vulnerable to the consequences of climate change. In mountain ecosystems, it is particularly important to monitor vegetation to understand ecosystem dynamics, biodiversity conservation, and the resilience of these fragile ecosystems to global change. Hence, we used satellite data acquired by Sentinel-2 to perform a comparative assessment of the Normalized Difference Vegetation Index (NDVI) and the Plant Phenology Index (PPI) in mountainous regions (canton of Valais-Switzerland in the European Alps) for monitoring vegetation dynamics of four types: deciduous trees, coniferous trees, grasslands, and shrublands. Results indicate that the NDVI is particularly noisy in the seasonal cycle at the beginning/end of the snow season and for coniferous trees, which is consistent with its known snow sensitivity issue and difficulties in retrieving signal variation in dense and evergreen vegetation. The PPI seems to deal with these problems but tends to overestimate peak values, which could be attributed to its logarithmic formula and derived high sensitivity to variations in near-infrared (NIR) and red reflectance during the peak growing season. Concerning seasonal parameters retrieval, we find close concordance in the results for the start of season (SOS) and end of season (EOS) between indices, except for coniferous trees. Peak of season (POS) results exhibit important differences between the indices. Our findings suggest that PPI is a robust remote sensed index for vegetation monitoring in seasonal snow-covered and complex mountain environments.

Effects of Soil Nutrient Restoration Aging and Vegetation Recovery in Open Dumps of Cold and Arid Regions in Xinjiang, China

Open-pit coal mining inevitably damages the soil and vegetation in mining areas. Currently, the restoration of cold and arid open-pit mines in Xinjiang, China, is still in the initial exploratory stage, especially the changes in soil nutrients in spoil dumps over time. Dynamic remote sensing monitoring of vegetation in mining areas and their correlation are relatively rare. Using the Heishan Open Pit in Xinjiang, China, as a case, soil samples were collected during different discharge periods to analyze the changes in soil nutrients and uncover the restoration mechanisms. Based on four Landsat images from 2018 to 2023, the remote sensing ecological index (RSEI) and fractional vegetation cover (FVC) were obtained to evaluate the effect of mine restoration. Additionally, the correlation between vegetation changes and soil nutrients was analyzed. The results indicated that (i) the contents of nitrogen (N), phosphorus (P), potassium (K), and organic matter (OM) in the soil increased with the duration of the restoration period. (ii) When the restoration time of the dump exceeds 5 years, N, P, K, and OM content is higher than that of the original surface-covered vegetation area. (iii) Notably, under the same restoration aging, the soil in the artificial mine restoration demonstration base had significantly higher contents of these nutrients compared to the soil naturally restored in the dump. (iv) Over the past five years, the RSEI and FVC in the Heishan Open Pit showed an overall upward trend. The slope remediation and mine restoration project significantly increased the RSEI and FVC values in the mining area. (v) Air humidity and surface temperature were identified as key natural factors affecting the RSEI and FVC in cold and arid open pit. The correlation coefficients between soil nutrient content and vegetation coverage were higher than 0.78, indicating a close and complementary relationship between the two. The above results can clarify the time–effect relationship between natural recovery and artificial restoration of spoil dumps in cold and arid mining areas in Xinjiang, further promoting the research and practice of mine restoration technology in cold and arid open pits.

Vegetation monitoring in mining area using canopy reflectance and landsat8 oli image

The aim of this study is to find out the vegetation monitoring in mining areas which is of great significance in grasping the ecological environment status of mining areas. As the first target of spectral reflection, the vegetation canopy directly affects the results of spectral reception. Leaf Angle Distribution (LAD) is a key canopy structural parameter, which directly determines the amount of solar radiation intercepted by the canopy. Taking the relationship between the effect of leaf inclination angle on vegetation canopy reflectance and NDVI as an entry point, the study monitored and analyzed the vegetation in Dexing mining area, China, from 2013 to 2018 by NDVI, based on which the Copper Stress Vegetation Index (CSVI), which is applicable to the narrow wavelength band of remote sensing imagery, was evaluated whether it could be applied to the wide wavelength band. The results show that the NDVI value of the horizontal distribution is significantly higher than that of other leaf inclination distributions, and the NDVI in the direction of the horizontal distribution is 0.82, while the NDVI in the direction of the hi-straight distribution is 0.75; the NDVI of the mining area decreases gradually with the increase of the area of the mining area, and the correlation coefficients of the two groups of CSVI based on the wide and narrow bands are not large, which is not suitable for the vegetation monitoring by using the Landsat8 OLI imagery. Wide band is less applicable for vegetation monitoring. Bangladesh J. Bot. 53(3): 835-847, 2024 (September) Special

A new deep learning-based model for reconstructing high-quality NDVI time-series data in heavily cloudy areas: fusion of Sentinel 1 and 2 data

ABSTRACT Reconstructing high-quality Normalized Difference Vegetation Index time series data is essential for ecological and agricultural applications but remains challenging in heavily cloudy areas. Fusing Sentinel SAR and optical data with deep learning could be helpful but is also challenging for stable models due to unstable SAR-NDVI relationships caused by imaging mechanism differences and environmental complexities. In this study, we developed a new Bidirectional Recurrent Imputation for Optical-SAR fusion (BRIOS) model to reconstruct high-quality Sentinel-2 NDVI time series data. BRIOS designs a two-layer recurrent architecture that integrates the autocorrelation of discrete, cloud-free NDVI observations into the model for a more stable SAR-NDVI relationship. Evaluating BRIOS against three baseline methods (GF-SG spatiotemporal fusion, Harmonic regression interpolation, and MCNN-Seq deep learning) across three full Sentinel-2 tiles in reconstructing 8-day NDVI time series, BRIOS consistently outperformed in scenarios of either random or continuously missing data, as evidenced by lower RMSE values (e.g. 0.075 for BRIOS vs. 0.108 for GF-SG vs. 0.143 for Harmonic regression vs. 0.303 for MCNN-Seq), better Edge index, and high linear correlation coefficients (R values up to 0.97). Further ablation experiments revealed that deep integration of NDVI autocorrelation features and SAR temporal change patterns has improved the stability and generalization of BRIOS. Discussions on the model's scalability across various cloud sizes and training dataset sizes affirm its practicality for broad-scale application in vegetation monitoring under challenging cloudy conditions.

A novel approach to estimate land surface temperature from landsat top-of-atmosphere reflective and emissive data using transfer-learning neural network

Land Surface Temperature (LST) is a crucial parameter in studies of urban heat islands, climate change, evapotranspiration, hydrological cycles, and vegetation monitoring. However, conventional satellite-based approaches for LST retrieval often require additional data like land surface emissivity (LSE). Meanwhile, traditional machine learning (ML) techniques face challenges in acquiring representative training data and leveraging data from varied sources effectively. To address these issues, we introduce a novel transfer-learning (TL) neural network approach for LST retrieval using top-of-atmosphere (TOA) reflective and emissive data from Landsat. This method not only improves LST retrieval by integrating various data types but also demonstrates the potential of shortwave data in surrogating LSE information, thereby reducing dependence on explicit LSE data. Our TL approach utilized extensive simulations from the radiative transfer model (RTM) and measurements from the real world. The simulations are comprehensive, covering a wide range of atmospheric and surface scenarios, and the inclusion of real-world data mitigates the discrepancy between simulations and actual observations. When applied to a decade of Landsat-8 observations and ground measurements from 241 stations across diverse regions, our TL method significantly outperforms ML, single-channel (SC), and split-window (SW) algorithms in terms of root mean square error (RMSE), with improvements of 0.46 K, 0.84 K, and 0.57 K, respectively. This superiority underscores the advantage of integrating simulated and observed data, as well as the benefit of utilizing both reflective and emissive data without relying on uncertain LSE inputs. Our findings present a promising new TL framework for estimating LST directly from TOA data, offering a robust approach that we have made publicly available through Google Earth Engine (GEE) for broader use. The LST data retrieved by our proposed method can provide valuable insights for environmental research.

A fluorescence-electrochemical dual-mode aptasensor for ultrasensitive detection of lead based on exonuclease-catalyzed recycling and hybridization chain reaction amplification

To improve the accuracy and reliability of the detection system, this study combined the exonuclease-assisted autocatalytic target cycling fluorescence strategy with an electrochemical platform to establish a dual-mode fluorescence-electrochemical aptasensor for the ultra-sensitive quantification of lead ion (Pb2+). In the fluorescence detection strategy, ssDNA could hybridize with the aptamer to form double-stranded DNA. With the addition of Pb2+, the aptamer dissociated from the double-stranded DNA to capture Pb2+ and trigger the enzymatic digestion process, leading to the target recirculation to achieve the first signal amplification. The free ssDNA could then hybridize with S1, and the released S2 was able to trigger a hybridization chain reaction (HCR) in the electrochemical system, achieving the second signal amplification. The quantification of Pb2+ was realized by measuring both the fluorescence and the differential pulse voltammetry signals with detection limits of 0.028ng/mL and 0.788pg/mL, respectively. This fluorescence-electrochemical dual-mode aptasensor overcame the limitations of complex matrices and environmental interference, and provided a valuable reference for the development of reliable multi-signal sensors in water, tea and vegetable safety monitoring.

Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation (NVDI) with MODIS NDVI

Abstract. Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia, which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing global Advanced Very High Resolution Radiometer (AVHRR) normalised-difference vegetation index (NDVI) products for their suitability for long-term vegetation monitoring in Australia. Comparisons with the MODIS NDVI highlight significant deficiencies, particularly over densely vegetated regions. Moreover, all the assessed products failed to adequately reproduce the interannual variability in the pre-MODIS era as indicated by Landsat NDVI anomalies. To address these limitations, we propose a new approach to calibrating and harmonising NOAA's Climate Data Record of AVHRR NDVI to the MODIS MCD43A4 NDVI for Australia using a gradient-boosting decision tree ensemble method. Two versions of the datasets are developed, one incorporating climate data in the predictors (“AusENDVI-clim”: Australian Empirical NDVI-climate) and another that is independent of climate data (“AusENDVI-noclim”). These datasets, spanning 1982–2013 at a spatial resolution of 0.05° and with a monthly time step, exhibit strong correlations (r2=0.89–0.94) and low mean errors compared with MODIS MCD43A4 NDVI (mean absolute error (MAE) = 0.014–0.028, RMSE = 0.021–0.046), accurately reproducing seasonal cycles over densely vegetated regions. Furthermore, they closely replicate the interannual variability in vegetation condition in the pre-MODIS era. A reliable method for gap-filling the AusENDVI record is also developed that leverages climate, atmospheric CO2 concentration, and woody-cover fraction predictors. The resulting synthetic NDVI dataset shows excellent agreement with the MODIS MCD43A4 NDVI and the recalibrated AVHRR NDVI time series (r2=0.82–0.95, MAE = 0.016–0.029, RMSE = 0.039–0.041). Finally, we provide a complete 41-year dataset where the gap-filled AusENDVI-clim from January 1982 to February 2000 is joined with the MODIS MCD43A4 NDVI from March 2000 to December 2022. Analysing 40-year per-pixel trends in Australia's annual maximum NDVI revealed increasing values, and shifts in the timing, of the annual peak NDVI across most of the continent, underscoring the dataset's potential to address crucial questions regarding the changing vegetation phenology and its drivers. The AusENDVI dataset can be used for studying Australia's changing vegetation dynamics and downstream impacts on the terrestrial carbon and water cycles, and it provides a reliable foundation for further research into the drivers of vegetation change. AusENDVI is open access and available at https://doi.org/10.5281/zenodo.10802703 (Burton et al., 2024).

Pesticides in widely consumed vegetables in Bangladesh and its health risk

ABSTRACT This study analysed pesticide residues in cauliflower (Brassica oleracea botrytis) and bitter gourd (Momordica charantia), two widely consumed vegetables in Bangladesh, and assessed the associated human health risks. Vegetables were analysed using a modified QuEChERS extraction, followed by gas chromatography with flame thermionic and electron capture detectors. Results showed that in 36.7% of the samples pesticide residues were detected, 90.9% of which exceeding the MRLs of the European Commission, so highlighting serious food safety concerns. Multiple residues were detected in some samples, with chlorpyrifos being the most common present, followed by diazinon, dimethoate and quinalphos. Human health risks, calculated by estimated daily intake and hazard index, indicated potential non-carcinogenic risks from the present pesticides. This study may assist policymakers and authorities in developing strategies for safe vegetable production and quality monitoring to ensure food safety.

UAV Quantitative Remote Sensing of Riparian Zone Vegetation for River and Lake Health Assessment: A Review

River and lake health assessment (RLHA) is an important approach to alleviating the conflict between protecting river and lake ecosystems and fostering socioeconomic development, aiming for comprehensive protection, governance, and management. Vegetation, a key component of the riparian zone, supports and maintains river and lake health (RLH) by providing a range of ecological functions. While research on riparian zone vegetation is ongoing, these studies have not yet been synthesized from the perspective of integrating RLHA with the ecological functions of riparian zone vegetation. In this paper, based on the bibliometric method, the relevant literature studies on the topics of RLHA and unmanned aerial vehicle (UAV) remote sensing of vegetation were screened and counted, and the keywords were highlighted, respectively. Based on the connotation of RLH, this paper categorizes the indicators of RLHA into five aspects: water space: the critical area from the river and lake water body to the land in the riparian zone; water resources: the amount of water in the river and lake; water environment: the quality of water in the river and lake; water ecology:aquatic organisms in the river and lake; and water services:the function of ecosystem services in the river and lake. Based on these five aspects, this paper analyzes the key role of riparian zone vegetation in RLHA. In this paper, the key roles of riparian zone vegetation in RLHA are summarized as follows: stabilizing riverbanks, purifying water quality, regulating water temperature, providing food, replenishing groundwater, providing biological habitats, and beautifying human habitats. This paper analyzes the application of riparian zone vegetation ecological functions in RLH, summarizing the correlation between RLHA indicators and these ecological functions. Moreover, this paper analyzes the advantages of UAV remote sensing technology in the quantitative monitoring of riparian zone vegetation. This analysis is based on the high spatial and temporal resolution characteristics of UAV remote sensing technology and focuses on monitoring the ecological functions of riparian zone vegetation. On this basis, this paper summarizes the content and indicators of UAV quantitative remote sensing monitoring of riparian zone vegetation for RLHA. It covers several aspects: delineation of riparian zone extent, identification of vegetation types and distribution, the influence of vegetation on changes in the river floodplain, vegetation cover, plant diversity, and the impact of vegetation distribution on biological habitat. This paper summarizes the monitoring objects involved in monitoring riparian zones, riparian zone vegetation, river floodplains, and biological habitats, and summarizes the monitoring indicators for each category. Finally, this paper analyzes the challenges of UAV quantitative remote sensing for riparian zone vegetation at the current stage, including the limitations of UAV platforms and sensors, and the complexity of UAV remote sensing data information. This paper envisages the future application prospects of UAV quantitative remote sensing for riparian zone vegetation, including the development of hardware and software such as UAV platforms, sensors, and data technologies, as well as the development of integrated air-to-ground monitoring systems and the construction of UAV quantitative remote sensing platforms tailored to actual management applications.

Soil moisture content estimation of drip-irrigated citrus orchard based on UAV images and machine learning algorithm in Southwest China

Soil moisture content (SMC), as a pivotal component in the energy and matter exchange processes within the soil-plant-atmosphere continuum, plays a crucial role in surface water dynamics, energy fluxes, and carbon cycling within ecosystems. The development of remote sensing technology has offered new perspectives for monitoring soil moisture at regional scales. Unmanned aerial vehicles (UAV) equip with multispectral have distinct advantages for vegetation monitoring, including rapidity and cost-effectiveness, which has superior applicability and practicality. Therefore, in a 5a "Daya" late-maturing citrus orchard, the vegetation index (VI) and texture feature (TF) information of citrus canopy based on UAV multi-spectral images were extracted, and soil and plant analyzer development (SPAD) of citrus was collected. These different data sources were integrated into the framework of the random forest algorithm (RF) and genetic algorithm-optimized random forest (GA-RF) to evaluate the accuracy of surface SMC (SSMC) estimation in citrus orchard. The Biswas model was utilized to simulate the root zone SMC (RSMC). The spatiotemporal variations of SMC in citrus orchard were analyzed, and the potential of low-cost sensor-equipped drones in rapidly acquiring spatial and temporal distribution information of SMC at a large regional scale was explored. The results indicated that the GA-RF models outperformed the RF models in estimating citrus orchard SMC (with R2 ranging from 0.502 to 0.949 and RMSE ranging from 0.552 % to 3.166 % for GA-RF, compared to R2 ranging from 0.430 to 0.936 and RMSE ranging from 0.587 % to 3.449 % for the RF). The GA-RF models using VI+SPAD as inputs exhibited the best performance for SMC at depths of 5 cm, 10 cm, 20 cm and 40 cm (SMC5, SMC10, SMC20 and SMC40) across citrus growth stages (R2 ranging from 0.793 to 0.949 at 5 cm, R2 ranging from 0.702 to 0.938 at 10 cm, R2 ranging from 0.714 to 0.927 at 20 cm). In bud bust to flowering, young fruit and fruit maturation stages (stage Ⅰ, ⅠⅠ and ⅠⅤ), all models demonstrated good accuracy in estimating SMC at depth of 10 cm (R2 ranging from 0.567 to 0.908 in stage Ⅰ, with R2 ranging from 0.681 to 0.916 in stage ⅠⅠ and R2 ranging from 0.579 to 0.938 in stage ⅠⅤ). In fruit expansion stage (stage III), the models performed best in predicting SMC5 (R2 ranging from 0.698 to 0.861). The Biswas model was constructed to simulate SMC40 by utilizing the inverted SMC10 and SMC20, thereby generating spatiotemporal distribution maps of SMC at different depths in citrus orchard. The SSMC was susceptible to environmental factors, exhibiting significant spatiotemporal heterogeneity. In summary, this study illustrated that the integration of multiple data sources into GA-RF enhanced the estimation performance of SMC at different growth stages of late-maturing citrus orchard in the Southwest China. Additionally, it enabled the rapid and efficient monitoring of spatiotemporal variations in SMC, providing an effective method and practical foundation for precision irrigation and improved water use efficiency.

Improved Methods for Retrieval of Chlorophyll Fluorescence from Satellite Observation in the Far-Red Band Using Singular Value Decomposition Algorithm

Solar-induced chlorophyll fluorescence (SIF) is highly correlated with photosynthesis and can be used for estimating gross primary productivity (GPP) and monitoring vegetation stress. The far-red band of the solar Fraunhofer lines (FLs) is close to the strongest SIF emission peak and is unaffected by chlorophyll absorption, making it suitable for SIF intensity retrieval. In this study, we propose a retrieval window for far-red SIF, significantly enhancing the sensitivity of data-driven methods to SIF signals near 757 nm. This window introduces a weak O2 absorption band based on the FLs window, allowing for better separation of SIF signals from satellite spectra by altering the shape of specific singular vectors. Additionally, a frequency shift correction algorithm based on standard non-shifted reference spectra is proposed to discuss and eliminate the influence of the Doppler effect. SIF intensity retrieval was achieved using data from the GOSAT satellite, and the retrieved SIF was validated using GPP, enhanced vegetation index (EVI) from the MODIS platform, and published GOSAT SIF products. The validation results indicate that the SIF products provided in this study exhibit higher fitting goodness with GPP and EVI at high spatiotemporal resolutions, with improvements ranging from 55% to 129%. At low spatiotemporal resolutions, the SIF product provided in this study shows higher consistency with EVI and GPP spatially.