Research on Bathymetric Inversion Capability of Different Multispectral Remote Sensing Images in Seaports.

In recent years, remote sensing has become an indispensable supplementary method for determining water depth in the seaports. At present, many scholars use multi-spectral satellite data to invert the water depth of the seaports, but how to select the appropriate satellite data in the seaports area is worth exploring. In this article, the differences in the retrieving ability between domestic and foreign multispectral images are compared, through building the random forest model and the band ratio model, which use different multispectral images to conduct retrieving water depth in Nanshan Port in conjunction with the WBMS multi-beam sounding system. The band ratio model and random forest model are chosen for water depth exploration, remote sensing images use GF-6, GF-2, Sentinel-2B, and Landsat 8 OLI data, which are all popular and easily accessible. The final experiment results from the constant adjustment of the model parameter show that the domestic series of GF-6 images performed the best in this experiment. The Root Mean Square Error (RMSE) and Mean Relative Error (MRE) of the random forest model are only 1.202 and 0.187, respectively. Simultaneously, it is discovered that the 'Red Edge' band of GF-6 is also very helpful in improving the accuracy of water depth inversion, which is rarely mentioned in previous studies. To some extent, the preceding studies demonstrate that it is possible to investigate water depth using common multispectral remote sensing images. In the case of some bathymetry inversion models or in some waters, the aforementioned study demonstrates that it is possible to examine the water depth using domestic remote sensing images that are superior to foreign multispectral images in terms of bathymetry inversion ability.

Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots.

The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for selecting the best varieties, improving yield predictions, and supporting ecosystem monitoring and conservation efforts. In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. A UAV acquires multispectral reflectance channels across a rice field of subplots containing different genotypes. Based on the ground-truth data, yields are characterized for the 59 plots and correlated with the Vegetation Indices (VIs) calculated from the photogrammetric mapping. The VIs are weighted by the segmentation of the plants from the soil and used as a feature matrix to estimate, via machine learning models, the biomass and nitrogen of the selected rice genotypes. The genotype IR 93346 presented the highest yield with a biomass gain of 10,252.78 kg/ha and an average daily biomass gain above 49.92 g/day. The VIs with the highest correlations with the ground-truth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. The machine learning model that performed best in estimating the variables of the 59 plots was the Gaussian Process Regression (GPR) model with a correlation factor of 0.98 for wet biomass, 0.99 for dry biomass, and 1 for nitrogen. The results presented demonstrate that it is possible to characterize the yields of rice plots containing different genotypes through ground-truth data and VIs.

Utilizing Spectral, Structural and Textural Features for Estimating Oat Above-Ground Biomass Using UAV-Based Multispectral Data and Machine Learning.

Accurate and timely monitoring of biomass in breeding nurseries is essential for evaluating plant performance and selecting superior genotypes. Traditional methods for phenotyping above-ground biomass in field conditions requires significant time, cost, and labor. Unmanned Aerial Vehicles (UAVs) offer a rapid and non-destructive approach for phenotyping multiple field plots at a low cost. While Vegetation Indices (VIs) extracted from remote sensing imagery have been widely employed for biomass estimation, they mainly capture spectral information and disregard the 3D canopy structure and spatial pixel relationships. Addressing these limitations, this study, conducted in 2020 and 2021, aimed to explore the potential of integrating UAV multispectral imagery-derived canopy spectral, structural, and textural features with machine learning algorithms for accurate oat biomass estimation. Six oat genotypes planted at two seeding rates were evaluated in two South Dakota locations at multiple growth stages. Plot-level canopy spectral, structural, and textural features were extracted from the multispectral imagery and used as input variables for three machine learning models: Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR). The results showed that (1) in addition to canopy spectral features, canopy structural and textural features are also important indicators for oat biomass estimation; (2) combining spectral, structural, and textural features significantly improved biomass estimation accuracy over using a single feature type; (3) machine learning algorithms showed good predictive ability with slightly better estimation accuracy shown by RFR (R2 = 0.926 and relative root mean square error (RMSE%) = 15.97%). This study demonstrated the benefits of UAV imagery-based multi-feature fusion using machine learning for above-ground biomass estimation in oat breeding nurseries, holding promise for enhancing the efficiency of oat breeding through UAV-based phenotyping and crop management practices.

Assessing spatiotemporal changes in landcover using geospatial and remote sensing techniques in the Southern fringes of Chennai.

Peri-urban areas are transitional zones on the outer boundaries of cities. These regions have immense growth potential, and it is necessary to observe the land use landcover changes to understand the dynamics of these transformations. The area selected for this study is towards the Southern fringe of Chennai, Tamil Nadu, India, and is analyzed using multi-spectral satellite imagery from Landsat 5 and 8. The primary objective of the study is to assess the change in landcover classes, namely water, land, and vegetation, over a 30-year study period between 1991 and 2021. The peri-urban regions majorly are arable land. Hence, NDVI is considered a suitable index to monitor the landcover changes. The spatiotemporal analysis indicates an increase of 19.43% in land /barren areas towards the Northern parts near the study area and along the transit and industrial corridors. No significant changes are observed in the areas of vegetation that could be attributed to efforts taken to conserve reserve forests and increase green zones in newer developments. A steep depletion of 46.86% of water bodies observed in the region also corresponds to water scarcity problems. Accuracy was assessed using ground-truthing methods, computing the confusion matrix and Kappa coefficient. NDVI is used efficiently in the landcover classification but does not indicate the difference between built-up areas and barren land. Change detection map prepared using ARCGIS indicates the areas that have been converted to other landcover over a period of 30 years. The study reveals an urgent need to bring in policy decisions to conserve waterbodies and green spaces in the initial stages of urban planning for sustainable developments in the fringe areas.

Automatic Detection of Olive Tree Canopies for Groves with Thick Plant Cover on the Ground.

Marking the tree canopies is an unavoidable step in any study working with high-resolution aerial images taken by a UAV in any fruit tree crop, such as olive trees, as the extraction of pixel features from these canopies is the first step to build the models whose predictions are compared with the ground truth obtained by measurements made with other types of sensors. Marking these canopies manually is an arduous and tedious process that is replaced by automatic methods that rarely work well for groves with a thick plant cover on the ground. This paper develops a standard method for the detection of olive tree canopies from high-resolution aerial images taken by a multispectral camera, regardless of the plant cover density between canopies. The method is based on the relative spatial information between canopies.The planting pattern used by the grower is computed and extrapolated using Delaunay triangulation in order to fuse this knowledge with that previously obtained from spectral information. It is shown that the minimisation of a certain function provides an optimal fit of the parameters that define the marking of the trees, yielding promising results of 77.5% recall and 70.9% precision.

Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops.

Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery. The first method is called GFKuts, an approach that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo-based K-means, and a guided image filtering for the extraction of canopy vegetation indices associated with biomass yield. The second method is based on a Graph-Based Data Fusion (GBF) approach that does not depend on calculating vegetation-index image reflectances. Both methods are experimentally tested and compared through rice growth stages: vegetative, reproductive, and ripening. Biomass estimation correlations are calculated and compared against an assembled ground-truth biomass measurements taken by destructive sampling. The proposed GBF-Sm-Bs approach outperformed competing methods by obtaining biomass estimation correlation of 0.995 with R2=0.991 and RMSE=45.358 g. This result increases the precision in the biomass estimation by around 62.43% compared to previous works.

Mapping of Agricultural Subsurface Drainage Systems Using Unmanned Aerial Vehicle Imagery and Ground Penetrating Radar.

Agricultural subsurface drainage systems are commonly installed on farmland to remove the excess water from poorly drained soils. Conventional methods for drainage mapping such as tile probes and trenching equipment are laborious, cause pipe damage, and are often inefficient to apply at large spatial scales. Knowledge of locations of an existing drainage network is crucial to understand the increased leaching and offsite release of drainage discharge and to retrofit the new drain lines within the existing drainage system. Recent technological developments in non-destructive techniques might provide a potential alternative solution. The objective of this study was to determine the suitability of unmanned aerial vehicle (UAV) imagery collected using three different cameras (visible-color, multispectral, and thermal infrared) and ground penetrating radar (GPR) for subsurface drainage mapping. Both the techniques are complementary in terms of their usage, applicability, and the properties they measure and were applied at four different sites in the Midwest USA. At Site-1, both the UAV imagery and GPR were equally successful across the entire field, while at Site-2, the UAV imagery was successful in one section of the field, and GPR proved to be useful in the other section where the UAV imagery failed to capture the drainage pipes' location. At Site-3, less to no success was observed in finding the drain lines using UAV imagery captured on bare ground conditions, whereas good success was achieved using GPR. Conversely, at Site-4, the UAV imagery was successful and GPR failed to capture the drainage pipes' location. Although UAV imagery seems to be an attractive solution for mapping agricultural subsurface drainage systems as it is cost-effective and can cover large field areas, the results suggest the usefulness of GPR to complement the former as both a mapping and validation technique. Hence, this case study compares and contrasts the suitability of both the methods, provides guidance on the optimal survey timing, and recommends their combined usage given both the technologies are available to deploy for drainage mapping purposes.

Evaluation of Soil Management Effect on Crop Productivity and Vegetation Indices Accuracy in Mediterranean Cereal-Based Cropping Systems.

Mostly, precision agriculture applications include the acquisition and elaboration of images, and it is fundamental to understand how farmers' practices, such as soil management, affect those images and relate to the vegetation index. We investigated how long-term conservation agriculture practices, in comparison with conventional practices, can affect the yield components and the accuracy of five vegetation indexes. The experimental site is a part of a long-term experiment established in 1994 and is still ongoing that consists of a rainfed 2-year rotation with durum wheat and maize, where two unfertilized soil managements were repeated in the same plots every year. This study shows the superiority of no tillage over conventional tillage for both nutritional and productive aspects on durum wheat. The soil management affects the vegetation indexes' accuracy, which is related to the nitrogen nutrition status. No-tillage management, which is characterized by a higher content of soil organic matter and nitrogen availability into the soil, allows obtaining a higher accuracy than the conventional tillage. So, the users of multispectral cameras for precision agriculture applications must take into account the soil management, organic matter, and nitrogen content.

Metrology for Agriculture and Forestry 2019.

This Special Issue is focused on recent advances in integrated monitoring and modelling technologies for agriculture and forestry. The selected contributions cover a wide range of topics, including wireless field sensing systems, satellite and UAV remote sensing, ICT and IoT applications for smart farming.

A comparative study of remote sensing classification methods for monitoring and assessing desert vegetation using a UAV-based multispectral sensor.

Restoration programs require long-term monitoring and assessment of vegetation growth and productivity. Remote sensing technology is considered to be one of the most powerful technologies for assessing vegetation. However, several limitations have been observed with regard to the use of satellite imagery, especially in drylands, due to the special structure of desert plants. Therefore, this study was conducted in Kuwait's Al Abdali protected area, which is dominated by a Rhanterium epapposum community. This work aimed to determine whether Unmanned Aerial Vehicle (UAV) multispectral imagery could eliminate the challenges associated with satellite imagery by examining the vegetation indices and classification methods for very high multispectral resolution imagery using UAVs. The results showed that the transformed difference vegetation index (TDVI) performed better with arid shrubs and grasses than did the normalized difference vegetation index (NDVI). It was found that the NDVI underestimated the vegetation coverage, especially in locations with high vegetation coverage. It was also found that Support Vector Machine (SVM) and Maximum Likelihood (ML) classifiers demonstrated a higher accuracy, with a significant overall accuracy of 93% and a kappa coefficient of 0.89. Therefore, we concluded that SVM and ML are the best classifiers for assessing desert vegetation and the use of UAVs with multispectral sensors can eliminate some of the major limitations associated with satellite imagery, particularly when dealing with tiny plants such as native desert vegetation. We also believe that these methods are suitable for the purpose of assessing vegetation coverage to support revegetation and restoration programs.

Using Unmanned Aerial Vehicles in Postfire Vegetation Survey Campaigns through Large and Heterogeneous Areas: Opportunities and Challenges.

This study evaluated the opportunities and challenges of using drones to obtain multispectral orthomosaics at ultra-high resolution that could be useful for monitoring large and heterogeneous burned areas. We conducted a survey using an octocopter equipped with a Parrot SEQUOIA multispectral camera in a 3000 ha framework located within the perimeter of a megafire in Spain. We assessed the quality of both the camera raw imagery and the multispectral orthomosaic obtained, as well as the required processing capability. Additionally, we compared the spatial information provided by the drone orthomosaic at ultra-high spatial resolution with another image provided by the WorldView-2 satellite at high spatial resolution. The drone raw imagery presented some anomalies, such as horizontal banding noise and non-homogeneous radiometry. Camera locations showed a lack of synchrony of the single frequency GPS receiver. The georeferencing process based on ground control points achieved an error lower than 30 cm in X-Y and lower than 55 cm in Z. The drone orthomosaic provided more information in terms of spatial variability in heterogeneous burned areas in comparison with the WorldView-2 satellite imagery. The drone orthomosaic could constitute a viable alternative for the evaluation of post-fire vegetation regeneration in large and heterogeneous burned areas.

Monitoring and assessment of seasonal land cover changes using remote sensing: a 30-year (1987-2016) case study of Hamoun Wetland, Iran.

The availability of Landsat data allows improving the monitoring and assessment of large-scale areas with land cover changes in rapid developing regions. Thus, we pretend to show a combined methodology to assess land cover changes (LCCs) in the Hamoun Wetland region (Iran) over a period of 30-year (1987-2016) and to quantify seasonal and decadal landscape and land use variabilities. Using the pixel-based change detection (PBCD) and the post-classification comparison (PCC), four land cover classes were compared among spring, summer, and fall seasons. Our findings showed for the water class a higher correlation between spring and summer (R2 = 0.94) than fall and spring (R2 = 0.58) seasons. Before 2000, ~ 50% of the total area was covered by bare soil and 40% by water. However, after 2000, more than 70% of wetland was transformed into bare soils. The results of the long-term monitoring period showed that fall season was the most representative time to show the inter-annual variability of LCCs monitoring and the least affected by seasonal-scale climatic variations. In the Hamoun Wetland region, land cover was highly controlled by changes in surface water, which in turn responded to both climatic and anthropogenic impacts. We were able to divide the water budget monitoring into three different ecological regimes: (1) a period of high water level, which sustained healthy extensive plant life, and approximately 40% of the total surface water was retained until the end of the hydrological year; (2) a period of drought during high evaporation rates was observed, and a mean wetland surface of about 85% was characterized by bare land; and (3) a recovery period in which water levels were overall rising, but they are not maintained from year to year. After a spring flood, in 2006 and 2013, grassland reached the highest extensions, covering till more than 20% of the region, and the dynamics of the ecosystem were affected by the differences in moisture. The Hamoun wetland region served as an important example and demonstration of the feedbacks between land cover and land uses, particularly as pertaining to water resources available to a rapidly expanding population.

Multispectral airborne imagery in the field reveals genetic determinisms of morphological and transpiration traits of an apple tree hybrid population in response to water deficit.

Genetic studies of response to water deficit in adult trees are limited by low throughput of the usual phenotyping methods in the field. Here, we aimed at overcoming this bottleneck, applying a new methodology using airborne multispectral imagery and in planta measurements to compare a high number of individuals.An apple tree population, grafted on the same rootstock, was submitted to contrasting summer water regimes over two years. Aerial images acquired in visible, near- and thermal-infrared at three dates each year allowed calculation of vegetation and water stress indices. Tree vigour and fruit production were also assessed. Linear mixed models were built accounting for date and year effects on several variables and including the differential response of genotypes between control and drought conditions.Broad-sense heritability of most variables was high and 18 quantitative trait loci (QTLs) independent of the dates were detected on nine linkage groups of the consensus apple genetic map. For vegetation and stress indices, QTLs were related to the means, the intra-crown heterogeneity, and differences induced by water regimes. Most QTLs explained 15-20% of variance.Airborne multispectral imaging proved relevant to acquire simultaneous information on a whole tree population and to decipher genetic determinisms involved in response to water deficit.

Channel morphological change monitoring using high-resolution LiDAR-derived DEM and multi-temporal imageries.

Natural processes and human activities both cause morphological changes in channels. Remote sensing products are often used to assess planform changes, but they tend to overlook vertical changes. However, considering both planform and vertical changes is crucial for a comprehensive evaluation of morphological changes. Using spatiotemporal aerial imagery and topographic data, remote sensing plays a vital role in evaluating channel morphological changes and flood-carrying capacity. This study aimed to investigate the morphological changes of a creek in an urban catchment using very high-resolution remote sensing products. In this study, we developed a new framework for investigating overall channel morphology change by employing very high-resolution aerial imagery and a LiDAR-derived digital elevation model (DEM). By digitizing channel boundaries using ArcGIS Pro 3.0, and analyzing various morphological parameters, erosion, and deposition patterns, we examined the impact of urban expansion and infrastructure development on channel adjustments. Channel adjustments have been performed in the case study catchment (Dry Creek, South Australia, Australia) due to urban expansion and development of infrastructure in the downstream reaches. Our findings revealed a significant southwest shift in the planform of the channel, with a maximum shift of 478 m and an average shift of 217 m since 1998. This alteration resulted in an increase in the sinuosity index reaching 1.2. Over the period from 2018 to 2022, the channel experienced a net deposition depth of 3.4 cm to 3.6 cm in downstream reaches. The annual deposition volume in the downstream reaches was 1963 m3, necessitating regular desilting to prevent channel capacity loss and flooding in the surrounding environment. This study also highlights the incremental growth of riparian vegetation within the channel, which affects surface roughness, channel slope, and carrying capacity. These findings provide a valuable baseline for future investigations into stream channel morphology changes and emphasize the importance of implementing appropriate measures such as desilting and vegetation management to mitigate deposition levels, reduce flood risks, and enhance the overall health and functionality of Dry Creek. The framework used in this study can be applied to other case studies employing reliable and high-resolution remote sensing data products.

High-resolution multispectral and RGB dataset from UAV surveys of ten cocoa agroforestry typologies in Côte d'Ivoire.

This paper introduces a dataset of aerial imagery captured during the 2022 cocoa growing season in the central-western region of Côte d'Ivoire. The images were acquired using a multispectral camera mounted on a DJI Phantom 4 unmanned aerial vehicle (UAV). The agricultural land surveyed encompasses 10 different types of cocoa-based agroforestry systems, each ranging from 2.6 ha to 8.3 ha, totaling 7638 images and covering 30 ha. The UAV mission was conducted at an altitude of 80 m, with a side overlap of 70 % and a front overlap of 80 %. This configuration achieved ground sampling distances (GSD) ranging from 4.2 to 4.6 cm providing high-resolution detailed imagery of those lands. These high-resolution RGB and multispectral images can be used to characterize the structural complexity of the systems as well as the abundance, and the health of the trees in these cocoa-based systems. It can be a valuable resource for researchers in the fields of ecology, agriculture, and environmental monitoring. The dataset supports a wide range of applications, from precision agriculture to sustainable cocoa land use management, making it a pivotal tool for enhancing agricultural practices and ecosystem management in Ivorian regions facing environmental and economic challenges.

Corrigendum: Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery.

[This corrects the article DOI: 10.3389/fpls.2023.1284235.].

Tree-level almond yield estimation from high resolution aerial imagery with convolutional neural network.

Introduction: Estimating and understanding the yield variability within an individual field is critical for precision agriculture resource management of high value tree crops. Recent advancements in sensor technologies and machine learning make it possible to monitor orchards at very high spatial resolution and estimate yield at individual tree level. Methods: This study evaluates the potential of utilizing deep learning methods to predict tree-level almond yield with multi-spectral imagery. We focused on an almond orchard with the 'Independence' cultivar in California, where individual tree harvesting and yield monitoring was conducted for ~2,000 trees and summer aerial imagery at 30cm was acquired for four spectral bands in 2021. We developed a Convolutional Neural Network (CNN) model with a spatial attention module to take the multi-spectral reflectance imagery directly for almond fresh weight estimation at the tree level. Results: The deep learning model was shown to predict the tree level yield very well, with a R2 of 0.96 (±0.002) and Normalized Root Mean Square Error (NRMSE) of 6.6% (±0.2%), based on 5-fold cross validation. The CNN estimation captured well the patterns of yield variation between orchard rows, along the transects, and from tree to tree, when compared to the harvest data. The reflectance at the red edge band was found to play the most important role in the CNN yield estimation. Discussion: This study demonstrates the significant improvement of deep learning over traditional linear regression and machine learning methods for accurate and robust tree level yield estimation, highlighting the potential for data-driven site-specific resource management to ensure agriculture sustainability.

High-resolution characterization of intertidal areas and lowest astronomical tidal surface by use of Sentinel-2 multispectral imagery and hydrodynamic modeling: Case-study in Cadiz Bay (Spain).

In intertidal areas, the zonation is determined by factors such as sea-level and topography. For this reason, a mixed methodology for the characterization (topography, extension, and zonation) of maximum intertidal areas is presented, based upon multispectral satellite imagery, hydrodynamic modeling, and in situ measurements. The methodology is applied and validated in the inner Cadiz Bay (SW Spain) as a case study. Satellite-derived waterlines were vertically referenced by tide-gauge measurements of sea-level; the resulting partial topography of intertidal areas was integrated into available hydrographic/topographic data to elaborate a high-resolution (10 m) model grid from which hydrodynamic simulations were conducted. Model results for lowest and highest tidal level situations were used to characterize the maximum intertidal areas within the inner Cadiz Bay, as well as the lowest astronomical tidal surface (LAT). The obtained LAT, referenced with respect the chosen geoid and/or ellipsoid, was identified with the vertical reference surface for Hydrography (VRSH) in this environment, complementing and improving the official VRSH presently being developed for Spanish waters. Obtained results show errors of the order of 1 cm for sea-level amplitudes and <1 min for the main tidal lags when comparing with tide-gauge data. Further applications of this exportable, relatively fast, low-cost, and accurate methodology are outlined.

Monitoring trophic status using in situ data and Sentinel-2 MSI algorithm: lesson from Lake Malombe, Malawi.

With excessive nutrient enrichment exacerbated by anthropogenic drivers, many standing water bodies are changing from oligotrophic to mesotrophic, eutrophic, and finally hypertrophic-negatively affecting ecosystem functioning, biodiversity, and human populations. Efforts have been devoted to developing novel algorithms for estimating chlorophyll-a (chl-a), cyno-blooms, and floating vegetation. However, to this date, little research has focused on freshwater lakes in the data-scarce Sub-Saharan African countries such as Malawi. We, therefore, estimated the trophic status of Lake Malombe in Malawi-a lake likely to be affected by eutrophication and algal bloom-emerging threats to freshwater ecosystem functioning globally-especially with the onset of climatic and anthropogenic drivers. We integrated in situ data with high-resolution Sentinel-2 Multispectral Imagery Analysis (MSI). We independently assessed the remote sensing technique using in situ data and tested the model at multiple stages. The scatter plot showed that most points were in the 95% confidence interval. The validation results between the measured in situ chl-a concentrations and the Sentinel-2 MSI-based chl-a retrieval had a root mean square error (RMSE) of 2.88 µg/L. The chl-a concentrations retrieved from MSI images were consistent with in situ data, indicating that the normalized difference chlorophyll index (NDCI) algorithm estimated chl-a concentrations in Lake Malombe with acceptable accuracy. Dissolved oxygen (DO), sulfate (SO42-), nitrite [Formula: see text], soluble reactive phosphorous [Formula: see text]), total dissolved solids (TDS), and chl-a, except for temperatures from the hot-dry-season, cold-dry-windy-season, and rainy-season, were significantly different (P < 0.05). The Sentinel-2 MSI imagery analysis also depicted similar results, with high chl-a concentration reported in March (rainy season) and October (hot-dry season) and the lowest from May to August (cold-dry-windy season). On the contrary, the ANOVA results for water quality parameters from all five points had P > 0.05. The correlation matrix showed coefficients of (0.798 < r < 0.930, n = 30, P < 0.005), suggesting that Lake Malombe is homogenous. Our results demonstrate that integrating remote sensing based on MSI imagery and in situ data to estimate chl-a can provide an effective tool for monitoring eutrophication in small, medium, and large standing waterbodies-crucial information required to respond to global ecological and climatic dynamics.

Estimating potassium in potato plants based on multispectral images acquired from unmanned aerial vehicles.

Plant potassium content (PKC) is a crucial indicator of crop potassium nutrient status and is vital in making informed fertilization decisions in the field. This study aims to enhance the accuracy of PKC estimation during key potato growth stages by using vegetation indices (VIs) and spatial structure features derived from UAV-based multispectral sensors. Specifically, the fraction of vegetation coverage (FVC), gray-level co-occurrence matrix texture, and multispectral VIs were extracted from multispectral images acquired at the potato tuber formation, tuber growth, and starch accumulation stages. Linear regression and stepwise multiple linear regression analyses were conducted to investigate how VIs, both individually and in combination with spatial structure features, affect potato PKC estimation. The findings lead to the following conclusions: (1) Estimating potato PKC using multispectral VIs is feasible but necessitates further enhancements in accuracy. (2) Augmenting VIs with either the FVC or texture features makes potato PKC estimation more accurate than when using single VIs. (3) Finally, integrating VIs with both the FVC and texture features improves the accuracy of potato PKC estimation, resulting in notable R 2 values of 0.63, 0.84, and 0.80 for the three fertility periods, respectively, with corresponding root mean square errors of 0.44%, 0.29%, and 0.25%. Overall, these results highlight the potential of integrating canopy spectral information and spatial-structure information obtained from multispectral sensors mounted on unmanned aerial vehicles for monitoring crop growth and assessing potassium nutrient status. These findings thus have significant implications for agricultural management.