Medium Resolution Satellite Data Research Articles

Optimization of Reservoir Water Quality Parameters Retrieval and Treatment Using Remote Sensing and Artificial Neural Networks

Inland water bodies are critical ecosystems that serve several functions including the provision of freshwater, regulation of climate and hydrological flows, and pollution control. Therefore, effective monitoring and management of these water resources is critical for sustainable water supply systems. This study evaluated the possible use of satellite data to estimate water quality parameters (WQPs) in an inland water body. The study also used artificial neural network (ANN) models in addition to the satellite data to determine the optimum coagulant dose for water treatment. The use of earth observations and machine learning methods has not been done extensively in developing countries, specifically, in water quality monitoring and management. The study utilized empirical multivariate regression modelling (EMRM) of the spectral reflectances from satellite data for the retrieval of Chla-a, Turbidity, and total suspended solids (TSS) concentrations in an inland water body. Using MLP-ANN modelling, the extracted spectral reflectance values from the selected sampling points in the reservoir were used as model inputs for the prediction of treated WQPs. A second MLP-ANN model was developed to predict the optimum coagulant dose required for raw water treatment. The R2 values achieved with AN model 1 were 0.81, 0.76, and 0.81 respectively for TSS, turbidity, and Chl-a, and 0.99 for the optimum coagulant dose. The study concluded that spectral reflectance from medium resolution satellite data products can be used to estimate WQPs from inland water bodies. Further, the ANN models demonstrate that extracted water quality data from satellite images can be used to inform ANN models for water quality predictions, and for the optimization of water treatment plant operations.

Agroforestry area mapping using medium resolution satellite data and object-based image analysis

Agroforestry is gaining increased attention within global policy processes and has been promoted as a strategy for working towards better food security, climate change adaptation and mitigation and livelihood resilience. With this gaining importance of agroforestry, site-specific studies are required to delineate different agroforestry systems and estimate the exact area under agroforestry. In this study remote sensing and GIS techniques were used for the estimation of area under agroforestry using medium-resolution satellite (sentinel 2A and 2B) data. Agroforestry area classification and estimation were done in e-Cognition developer software (10.0 version) through multiresolution segmentation and object-based image analysis (OBIA). The agroforestry area estimated in the erstwhile Warangal district was 3753 ha with an overall accuracy of 86% and a kappa coefficient of 0.84. The major agroforestry systems observed in the erstwhile Warangal district were eucalyptus, subabul, malabar neem, teak, sandalwood and red sanders.

Identification of Pine Wilt Disease-Infested Stands Based on Single- and Multi-Temporal Medium-Resolution Satellite Data

Pine wilt disease (PWD) is known for its high lethality and rapid transmission, earning it the name “cancer of the pine tree”. The prompt removal of infested pine trees is an effective measure for preventing and controlling pine wilt disease. Accurate and efficient monitoring technologies are crucial for the scientific prevention and control of this plant disease. Currently, numerous remote sensing monitoring studies have been conducted on pine wilt disease. However, there is limited research on the temporal identification of PWD-infested forest stands over large areas. To build classification models, this study utilized three machine learning algorithms: artificial neural network (ANN), random forest (RF), and support vector machine (SVM). We aimed to investigate the effectiveness of single-temporal and multi-temporal Landsat and Sentinel-2 satellite images PWD-infested forest stands detection. The results indicated that, at a spatial resolution of 30 m, Landsat-9 and Sentinel-2 remote sensing images effectively identified PWD-infested forest stands, with classification accuracies of 77.87% and 78.91%, respectively. Higher spatial resolutions in Sentinel-2 remote sensing images were associated with improved identification capabilities. Furthermore, multi-temporal Landsat satellite data (with a classification accuracy of 85.95%) significantly enhanced the performance of the monitoring model compared to single-temporal Landsat satellite data (with a classification accuracy of 77.87%). The RGI difference was found to be the optimal vegetation index. In conclusion, by combining multi-temporal and single-time-phase Landsat remote sensing data, a monitoring model for PWD-infested forest stands was constructed. It achieved a classification accuracy of 88.26%. In this study, a higher accuracy in identifying pine wilt disease and a lower economic cost were achieved by Landsat and Sentinel images, offering valuable insights for the management of pine wilt disease.

Multi-Temporal and Multiscale Satellite Remote Sensing Imagery Analysis for Detecting Pasture Area Changes after Grazing Cessation Due to the Fukushima Daiichi Nuclear Disaster

Despite advancements in remote sensing applications for grassland management, studies following the 2011 Fukushima Daiichi nuclear disaster have often been constrained by limited satellite imagery with insufficient focus on pasture changes. Utilizing different resolutions of optical satellite data is essential for monitoring spatiotemporal changes in grasslands. High resolutions provide detailed spatial information, whereas medium-resolution satellites offer an increased frequency and wider availability over time. This study had two objectives. First, we investigated the temporal changes in a mountainous pasture in Japan from 2007 to 2022 using high-resolution data from QuickBird, WorldView-2, and SPOT-6/7, along with readily available medium-resolution data from Sentinel-2 and Landsat-5/7/8. Second, we assessed the efficacy of different satellite image resolutions in capturing these changes. Grazing ceased in the target area after the 2011 Fukushima Daiichi nuclear accident owing to radiation. We categorized the images as grasses, broadleaf trees, and conifers. The results showed a 36% decline using high-resolution satellite image analysis and 35% using Landsat image analysis in the unused pasture area since grazing suspension in 2011, transitioning primarily to broadleaf trees, and relative stabilization by 2018. Tree encroachment was prominent at the eastern site, which has a lower elevation and steeper slope facing north, east, and south. WorldView-2 consistently outperformed Landsat-8 in accuracy. Landsat-8’s classification variation impedes its ability to capture subtle distinctions, particularly in zones with overlapping or neighboring land covers. However, Landsat effectively detected area reductions, similar to high-resolution satellites. Combining high- and medium-resolution satellite data leverages their respective strengths, compensates for their individual limitations, and provides a holistic perspective for analysis and decision-making.

Evaluating the Impact of Seismic Activity on the Slope Stability of the Western Coast of Lefkada Island Using Remote Sensing Techniques, Geographical Information Systems, and Field Data

The current research aims to examine the long-term evolution of the western cliffs of Lefkada Island following the occurrence of the last two strong earthquakes, on 14 August 2003 and 17 November 2015, respectively. Medium resolution satellite data (Landsat) and very high-resolution data (Ikonos, Pleiades, and airphotos) were processed in Google Earth Engine and Erdas imagine software, respectively. The study area covers a 20 km-long region of the western cliffs of Lefkada Island, extending from Egremni beach to the South to Komilio beach to the North. Relief, vegetation, and inclination changes were detected in the ArcGis environment. The results were associated with in situ data provided through the installation of a sediment trap. The analysis of the results proved that seismicity is the main factor that formed the western coastline of Lefkada Island, affecting the integrity of the cliffs. Specifically, large earthquakes cause immediate vegetation and topographic (inclination changes, mass movements) modifications in the western cliffs of the island. Meanwhile, small earthquakes (magnitudes < 4.1) contribute to the cliff’s evolution during the inter-seismic era. The intensity of these aforementioned changes was closely related to the seismic activity that occurred in the vicinity of the study area. In addition, it was found that precipitation and wind do not exert a similar influence on the cliff’s evolution.

A novel spectral index for mapping blue colour-coated steel roofs (BCCSRs) in urban areas using Sentinel-2 data

ABSTRACT Blue colour-coated steel roofs (BCCSRs) offer a lightweight and economical option to concrete and other cladding in buildings, but they are also controversial for altering the surface energy budget and water cycle. Obtaining spatial information about BCCSRs is crucial for exploring the environmental impacts of man-made landscapes. However, existing methods are not always effective due to the variety of BCCSR types and background conditions. To overcome these limitations, we proposed a new index (called BCCSI) based on Sentinel-2 multispectral images to map the commonly used BCCSRs. Five typical study areas were chosen worldwide to develop and validate the BCCSI. Based on spectral analysis, we constructed the BCCSI using the blue, red, green, and shortwave infrared 2 (SWIR2) bands to highlight the BCCSR while suppressing the background condition. Compared with five existing indices, the BCCSI was effective in the visual evaluation, separability analysis and BCCSR mapping. Moreover, the BCCSI achieved similar accuracy to the supervised classifier while avoiding the time-consuming and laborious effort of sample collection. Furthermore, the BCCSI showed its applicability in medium-resolution satellite data, such as Landsat-8 imagery. Thus, the proposed BCCSI provides a viable scheme for global BCCSR mapping and analysis.

Evapotranspiration estimation using Surface Energy Balance Model and medium resolution satellite data: An operational approach for continuous monitoring

Monitoring spatial and temporal trends of water use is of utmost importance to ensure water and food security in river basins that are challenged by water scarcity and climate change induced abnormal weather patterns. To quantify water consumption by the agriculture sector, continuous monitoring is required over different spatial scales ranging from field (< 1 ha) to basin. The demand driven requirement of covering large areas yet providing spatially distributed information makes the use of in-situ measurement devices unfeasible. Earth observation satellites and remote sensing techniques offer an effective alternative in estimating the consumptive use of water (Actual EvapoTranspiration (ETa) fluxes) by using periodic observations from the visible and infrared spectral region. Optical satellite data, however, is often hindered by noises due to cloud cover, cloud shadow, aerosols and other satellite related issues such as Scan Line Corrector (SLC) failure in Landsat 7 breaking the continuity of temporal observations. These gaps have to be statistically filled in order to compute aggregated seasonal and annual estimates of ETa. In this paper, we introduce an approach to develop a gap-filled multi-year monthly ETa maps at medium spatial resolution of 30 m. The method includes two major steps: (i) estimation of ETa using the python based implementation of surface energy balance model called PySEBAL and (ii) temporal interpolation using Locally Weighted Regression (LWR) model followed by spline based spatial interpolation to fill the gaps over time and space. The approach is applied to a large endorheic Lake Urmia Basin (LUB) basin with a surface area of ~ 52,970 km2 in Iran for the years 2013–2015 using Landsat 7 and 8 satellite data. The results show that the implemented gap filling approach could reconstruct the monthly ETa dynamics over different agriculture land use types, while retaining the high spatial variability. A comparison with a similar dataset from FAO WaPOR reported a very high correlation with R2 of 0.93. The study demonstrates the applicability of this approach to a larger basin which is extendible and reproducible to other geographical areas.

Using PlanetScope images to investigate the evolution of small glaciers in the Alps

Small glaciers (having an area smaller than 1 km2) dominate in terms of total number in the Alps and it is important to study their evolution. In this study we assessed the potential of daily, high resolution (3 m pixel size) PlanetScope images for investigating small Alpine glaciers. We compared the evolution of five small glaciers, covering an area lower or slightly higher than 1 km2, located in different areas of the Alps and having different characteristics and meteorological conditions. The investigated glaciers are: Bors (0.645 km2, Piedmont, Italy), Seewjinen (1.339 km2, Valais, Switzerland), Pizzo Ferrè (0.442 km2, Lombardy, Italy), Gran Zebrù (0.579 km2, Lombardy, Italy) and Solda Orientale (0.829 km2, South Tirol, Italy). We manually mapped the glacier outline and the highest position of the transient snowline (TSL) through PlanetScope images from the end of the ablation season for each year from 2017 to 2021 and each glacier. The snow cover area ratio (SCAR) was retrieved using the TSLs.We found that the glaciers lost between 4 and 14% of their area between 2017 of 2021, with a mean area loss of −1.5% per year. The strongest shrinkage (14%) is recorded at the smallest glacier (Pizzo Ferrè). The highest TSL showed a variability from year to year. For two glaciers, a supervised classification of the surface facies (snow, ice and supraglacial debris) was performed on all the available cloud-free images from the 2017–2021 ablation seasons.PlanetScope images, with their high spatial and temporal resolution proved to be a valuable tool for studying small glaciers. They permitted to map small glaciers with small uncertainties (between 2.2 and 5.1% of the glacier area), much lower than with medium resolution satellite data (e.g. Landsat 15/30 m, Sentinel 10 m) and to increase the number of images available for analysis. The daily repeat cycle is well suited for increasing the probability to find images from the end of the ablation season with minimum snow cover, in order to identify the real end-of-summer snowline.

Improving crop mapping in Brazil’s Cerrado from a data cubes-derived Sentinel-2 temporal analysis

The Sentinel-2/MultiSpectral Instrument (S2/MSI) expands the frequency of satellite observations, which is relevant to elaborate detailed and timely land use and land cover (LULC) classifications. However, storing, managing, and processing big data is costly and challenging, inducing a dimensionality reduction by modeling images as composite products. Contrastingly, it obliterates the temporal resolution improvement. As LULC changes are subtle over time, little is said about how much detail we lost by degrading temporal resolution. Data cube architectures enable storing, accessing, and modeling big data, mitigating losses. Brazil Data Cube (BDC) produces multidimensional data cube collections from different medium-resolution satellite data for Brazil, including S2/MSI. Here, we evaluated three BDC S2/MSI data cubes (two 16-day composites and one unblended, with the MSI original temporal resolution) to map a dynamic-and-representative region in the far-Western Bahia agricultural belt frontier, Cerrado biome, at crop type level. We incorporate spectral indices, ground samples, and crop calendars into a Random Forest-based temporal analysis. Overall accuracies (0.91 and 0.92 for composites, and 0.96 reached for the unblended) highlight the S2/MSI temporal resolution for improving mapping tasks. Given the impact of the cropland frontier expansion over Cerrado in Brazil’s commodity production, detecting subtle landscape variations can improve agri-environmental policies.

Benchmark for Building Segmentation on Up-Scaled Sentinel-2 Imagery

Currently, we can solve a wide range of tasks using computer vision algorithms, which reduce manual labor and enable rapid analysis of the environment. The remote sensing domain provides vast amounts of satellite data, but it also poses challenges associated with processing this data. Baseline solutions with intermediate results are available for various tasks, such as forest species classification, infrastructure recognition, and emergency situation analysis using satellite data. Despite these advances, two major issues with high-performing artificial intelligence algorithms remain in the current decade. The first issue relates to the availability of data. To train a robust algorithm, a reasonable amount of well-annotated training data is required. The second issue is the availability of satellite data, which is another concern. Even though there are a number of data providers, high-resolution and up-to-date imagery is extremely expensive. This paper aims to address these challenges by proposing an effective pipeline for building segmentation that utilizes freely available Sentinel-2 data with 10 m spatial resolution. The approach we use combines a super-resolution (SR) component with a semantic segmentation component. As a result, we simultaneously consider and analyze SR and building segmentation tasks to improve the quality of the infrastructure analysis through medium-resolution satellite data. Additionally, we collected and made available a unique dataset for the Russian Federation covering area of 1091.2 square kilometers. The dataset provides Sentinel-2 imagery adjusted to the spatial resolution of 2.5 m and is accompanied by semantic segmentation masks. The building footprints were created using OpenStreetMap data that was manually checked and verified. Several experiments were conducted for the SR task, using advanced image SR methods such as the diffusion-based SR3 model, RCAN, SRGAN, and MCGR. The MCGR network produced the best result, with a PSNR of 27.54 and SSIM of 0.79. The obtained SR images were then used to tackle the building segmentation task with different neural network models, including DeepLabV3 with different encoders, SWIN, and Twins transformers. The SWIN transformer achieved the best results, with an F1-score of 79.60.

The Implementation of the Mangrove Quality Index: A Way to Overcome Overestimation and Classification Concerns in Detecting Mangrove Forest Cover

The increasing applications of Geographic Information Systems (GIS) and Remote Sensing (RS) for mapping, predicting, and monitoring are practical for sustainable mangrove ecosystem management. This study evaluated various geospatial techniques for detecting healthy mangroves on the eastern coast of Sri Lanka, including single spectral indices, supervised/unsupervised classification, and developed methods using Landsat data. The use of medium-resolution satellite data and the uniqueness of the mangrove ecosystem are generally involved in discriminating healthy mangroves from non-mangrove areas. This study focused on detecting degraded narrow patches of mangroves on the Eastern coast of Sri Lanka using Landsat 8 remote sensing data and five vegetation indices. The accuracy of the results was assessed using randomly generated points. The study used ArcGIS Desktop software for processing, analyzing, and integrating spatial data to meet the research objectives. The mangroves were detected using Landsat 8 OLI satellite images from 2018 and 2021. The results showed high overestimation/underestimation and misclassification of mangroves, thus applying Mangrove Quality Index (MQI). Findings of MQI provide insights into overall mangrove health and identify three degradation classes of mangroves on the Eastern coast of Sri Lanka. The application of MQI in well-developed and degraded mangrove ecosystems merits further investigations, which provide reliable information for conservation priorities.

UAV-based reference data for the prediction of fractional cover of standing deadwood from Sentinel time series

Increasing tree mortality due to climate change has been observed globally. Remote sensing is a suitable means for detecting tree mortality and has been proven effective for the assessment of abrupt and large-scale stand-replacing disturbances, such as those caused by windthrow, clear-cut harvesting, or wildfire. Non-stand replacing tree mortality events (e.g., due to drought) are more difficult to detect with satellite data – especially across regions and forest types. A common limitation for this is the availability of spatially explicit reference data. To address this issue, we propose an automated generation of reference data using uncrewed aerial vehicles (UAV) and deep learning-based pattern recognition. In this study, we used convolutional neural networks (CNN) to semantically segment crowns of standing dead trees from 176 UAV-based very high-resolution (<4 cm) RGB-orthomosaics that we acquired over six regions in Germany and Finland between 2017 and 2021. The local-level CNN-predictions were then extrapolated to landscape-level using Sentinel-1 (i.e., backscatter and interferometric coherence), Sentinel-2 time series, and long short term memory networks (LSTM) to predict the cover fraction of standing deadwood per Sentinel-pixel. The CNN-based segmentation of standing deadwood from UAV imagery was accurate (F1-score = 0.85) and consistent across the different study sites and years. Best results for the LSTM-based extrapolation of fractional cover of standing deadwood using Sentinel-1 and -2 time series were achieved using all available Sentinel-1 and --2 bands, kernel normalized difference vegetation index (kNDVI), and normalized difference water index (NDWI) (Pearson’s r = 0.66, total least squares regression slope = 1.58). The landscape-level predictions showed high spatial detail and were transferable across regions and years. Our results highlight the effectiveness of deep learning-based algorithms for an automated and rapid generation of reference data for large areas using UAV imagery. Potential for improving the presented upscaling approach was found particularly in ensuring the spatial and temporal consistency of the two data sources (e.g., co-registration of very high-resolution UAV data and medium resolution satellite data). The increasing availability of publicly available UAV imagery on sharing platforms combined with automated and transferable deep learning-based mapping algorithms will further increase the potential of such multi-scale approaches.

Mapping and quantifying pelagic Sargassum in the Atlantic Ocean using multi-band medium-resolution satellite data and deep learning

Pelagic Sargassum in the Atlantic Ocean is an important habitat for marine animals, yet frequent and massive beaching events around the Caribbean Sea have caused many problems. Currently, both retrospective assessment and near real-time monitoring over synoptic scales rely on medium-resolution multi-band satellite imagery and algorithms based on the alternative floating algae index (AFAI) or its alternatives. Despite the success in assessing long-term bloom patterns and providing synoptic information in near real-time, two weaknesses still exist: 1) due to spatial resolution limitations and optical complexity from the ocean, there are often false positive detection in nearshore waters (up to 30 km from shoreline). These waters are currently masked to avoid providing false information, thus leading to data gaps in nearshore waters; 2) due to perturbations by cloud-adjacent straylight and cloud shadows, both false positive and false negative detections are often encountered. The current solution is to dilate the cloudmask to make the adjacent pixels invalid, thus leading to data loss. In this work, a computer deep-learning model based on the Res-UNet architecture, namely U-net, was developed to overcome these difficulties. The model was trained and validated using a total of 8518 medium-resolution “ground truth” images carefully prepared from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites. After accounting for subpixel Sargassum coverage, the over accuracy of the DL model, as measured by its F1 score, is ∼92.5% (without masking nearshore waters or cloud-adjacent pixels), which is higher than that of the current AFAI model (86.2% after masking nearshore waters and cloud-adjacent pixels). Application of the DL model to the 2021 MODIS data (a major Sargassum year) showed substantially improved valid data coverage (monthly increases of 20–34%) in both nearshore waters and offshore waters, and increased Sargassum biomass during the summer months (by 10–20%) from the entire Sargassum belt extending from the west Africa to the Gulf of Mexico. In the minor Sargassum year of 2016, the DL model led to an average increase of about 7% in Sargassum biomass. The DL model also appears to be applicable to the Visible Infrared Imaging Radiometer Suite (VIIRS) from the Suomi National Polar-orbiting Partnership (SNPP) satellite and the Ocean Land Colour Instrument (OLCI) onboard the Sentinel 3A and 3B satellites. Merging multi-sensor observations (including MODIS) resulted in substantially improved valid data coverage in daily to weekly composites, thus improving near real-time monitoring.

Sentinel-2 high-resolution data for river discharge monitoring

River monitoring is an open issue due to many intrinsic problems of the ground monitoring network. Over the last few decades, the role of satellite sensors in river discharge estimation is significantly increased thanks to the strong growth in technologies and applications. Focusing on daily river discharge measurements, a non-linear regression model has been used to link the near-infrared (NIR) reflectance ratio between a dry and a wet pixel around the section of a river to the ground measurements of river discharge. The use of medium-resolution satellite data, such as those from MODIS sensors, enables to monitor high and low flows in medium-sized catchments (<100,000 km2), thanks to satellite frequent revisit time and wide spatial coverage. However, such sensors are not suitable to provide information for medium-narrow rivers (< 250 m wide), nor to study river features and patterns that are averaged within a single pixel. Here, we investigated the use of Sentinel-2 NIR reflectances to support the hypothesis that a higher spatial resolution, i.e. 10 m, is able to better identify the wet pixels, more related to the river dynamics, with obvious advantages for river discharge estimation compared to the medium resolution sensors (e.g., MODIS at 250 m). Moreover, it also allows both a finer distinction between vegetation, soil and water and the characterization of water turbidity in the river area. A new formulation enriched by the sediment component is proposed together with a first step toward an uncalibrated procedure to select the wet pixels. Google Earth Engine (GEE) platform has been employed for the data analysis, allowing to avoid the download of big amounts of data, fostering the reproducibility of the analysis in different locations. The accuracy of the river discharges derived from Sentinel-2 reflectances is evaluated against the in-situ observations from selected gauging stations along two Italian rivers, Po and Tiber. The results confirm the good performances obtained with high-resolution images over the Po River, with average Nash-Sutcliffe efficiency ranging between 0.39 and 0.56 for the different configurations adopted. Relatively worse results were obtained over the Tiber River where the Nash-Sutcliffe efficiency ranged between 0.2 and 0.61, due to an issue on the registration of Sentinel-2 images.

Change detection of Jamuna River and its impact on the local settlements

ABSTRACT Spatio-temporal change detection analysis of Jamuna River using low and medium-resolution satellite data between the years of 1972 and 2013 are dynamic to assess river spatio-temporal changes and their impacts on the local settlements over the specific time. This study employs remote sensing and geographic information system (GIS) proficiencies to identify the river bank shifting, erosion, and depositional features along with the population displacement. The satellite image of LANDSAT multispectral (MSS), thematic mapper (TM), and operational land imager (OLI) and thermal infrared sensor (TIRS) were used in this study. Supervised and unsupervised classification techniques have been used to identify land cover types and detect the changes using ArcGIS Pro 2.3.2. Supervised classifications performed well by validating ≥90% overall accuracy. The result shows total erosion was 3356 ha from 1972 to 2013, while the deposition was 5342 ha. Erosion and deposition cause the bank line shifting, river widening, and settlement displacement. Hundreds of displaced people suffered from loss of land, livelihood, increased poverty, food insecurity, lack of sanitation, and drinking water facilities. Thus, this study will be earnest in assisting the environmental management and associated planning including necessary measures.

Using topographic attributes to predict the density of vegetation layers in a wet eucalypt forest

ABSTRACT Mapping the structure of forest vegetation with field surveys or high-resolution light detection and ranging (LiDAR) data is costly. We tested whether landscape topography and underlying geology could predict the vegetation density of a 19 km2 area of wet eucalypt forest at the Warra Long-Term Ecological Research Supersite, Tasmania, Australia. Using spatial layers for 12 topographic attributes derived from digital terrain models (DTMs) and a geology layer, we predicted the vegetation density of three strata with a high degree of accuracy (validation root mean square error ranged from 9.0% to 13.7%). The DTMs with 30 m resolution provided greater predictive accuracy than DTMs with higher resolution. The importance of different variables depended on spatial resolution and strata. Among the predictor variables, geology generally had the highest predictive importance, followed by solar radiation. Topographic Position Index, aspect, and System for Automated Geoscientific Analyses (SAGA) Wetness Index had moderate importance. This study demonstrates that geological and topographic attributes can provide useful predictions for the density of vegetation layers in a tall wet sclerophyll primary forest. Given the good performance of the model based on 30 m DTM resolution, the predictive power of the models could be tested on a larger geographical area using lower-density LiDAR point clouds combined with medium-resolution satellite data.

Crop Mapping with Combined Use of European and Chinese Satellite Data

Agricultural landscapes are characterized by diversity and complexity, which makes crop mapping at a regional scale a top priority for different purposes such as administrative decisions and farming management. Project 32194 of the Dragon 4 Program was implemented to meet the requirements of crop mapping, with the specific objective to develop suitable approaches for precise crop mapping with combined uses of European and Chinese high- and medium-resolution satellite images. Two sub-projects were involved in the project. The first was to focus on the use of time series high-resolution satellite data, including Sentinel-2 (S2, European satellite data) and Gaofen-1 (GF-1, Chinese satellite data), due to their similar spectral bands for Earth observation, while the second was to focus on medium-resolution data sources, i.e., the European Project for On-Board Autonomy–Vegetation (PROBA-V) and Chinese Fengyun-3 Medium Resolution Spectral Imager (FY-3 MERSI) satellite data, also due to their similar spectral channels. The approach of the European Space Agency (ESA) Sent2Agri project for crop mapping was adapted in the first sub-project and applied to the Yellow River irrigated district (YERID) of Ningxia in northwest China in order to assess its ability to accurately identify crop types in China. The goal of the second sub-project was to explore the potential of both European and Chinese medium-resolution satellite data for crop assessment in a large area. Methods to handle the data and retrieve the required information for the precise crop mapping were developed in the study, including the adaptation of the ESA approach to GF-1 data and the application of algorithms for classification. A scheme for the validation of the crop mapping was developed in the study. The results of implementing the scheme to the YERID in Ningxia indicated that the overall accuracies of crop mapping with S2 and GF-1 can be high, up to 94–97%, and the mapping had an accuracy of 88% with the PROBA-V and FY3B-MERSI data. The very high accuracy suggests the possibility of precise crop mapping with the combined use of time series high- and medium-resolution satellite data when suitable approaches are chosen to handle the data for the classification of crop types.

Spatial distribution and interannual variability of cyanobacteria blooms on the North-Western shelf of the Black Sea in 1985-2019 from satellite data

Long-term high and medium resolution satellite data are used to study spatial, seasonal and interannual variability of the cyanobacteria blooms (CB) in the North-Western Shelf (NWS) of the Black Sea. High-resolution Landsat data demonstrate that CB were regularly observed in the study area on satellite images in 1985-2020. Comparison of quasi-synchronous Landsat and MODIS images was used to study the characteristic features of CB spectra, which are: a “step” formed by MODIS 3 (469) and 4 (488) channel, probably related to the absorption by photoprotective carotenoids; increased reflectance in near-infrared; maximum at 547-555 nm. On the base of these features, we develop the automated algorithm of CB identification and reconstruct the daily dataset of CB over 2003-2019. The main advantage of the developed algorithm is its ability to filter out non-bloom areas in all seasons and meteorological conditions and during blooms of other algae in the deep part of the sea. Analysis of this dataset, complemented by Landsat data, allows to identify two primary sources of the CB in the NWS: the Dnieper-Bug estuary and the Danube mouth, and study the evolution of CB in these areas in detail. CB can penetrate on more than 150 km from the sources reaching in rare cases the Crimean coast. Such offshore propagation is mainly wind-driven and was often related to the upwelling frontal currents. The climatic seasonal peak of CB near the Danube is observed in May-June following the peak of river discharge in April-May. It precedes the peak of CB near the Dnieper-Bug estuary observed during maximum surface heating in August. At the same time, the case study of bloom evolution in 2009 shows that CB can be observed in these areas several times in a season, triggered mainly by periods of low winds. The strongest bloom of CB in 2003-2019 was observed in 2004-2006 and 2008-2010 following by the sharp decrease after 2011. This decrease is related to the observed reduction of the river discharge and, more important, the rise of the wind intensity in summers of the recent period.

Sentinel-2 Recognition of Uncovered and Plastic Covered Agricultural Soil

Medium resolution satellite data, such as Sentinel-2 of the Copernicus programme, offer great new opportunities for the agricultural sector, and provide insights on soil surface characteristics and their management. Soil monitoring requires a high-quality dataset of uncovered and plastic covered agricultural soil. We developed a methodology to identify uncovered soil pixels in agricultural parcels during seedbed preparation and considered the impacts of clouds and shadows, vegetation cover, and artificial covers, such as those of greenhouses and plastic mulch films. We preserved the spatial and temporal integrity of parcels in the process and analysed spectral anomalies and their sources. The approach is based on freely available tools, namely Google Earth Engine and R Programming packages. We tested the methodology on the northern region of Belgium, which is characterised by small, fragmented parcels. We selected a period between mid-April to end-May, when active agricultural management practices leave the soil bare in preparation for the main cropping season. The spectral angle mapper was used to identify soil covered by non-plastic greenhouses or temporary soil covers, such as plastic mulch films. The effect of underlying soil on temporary covers was considered. The retrogressive plastic greenhouse index was used for detecting plastic greenhouses. The result was a high quality dataset of potential bare uncovered agricultural soil that allows further soil surface characterisation. This offered an improved understanding of the use of artificial covers, their spatial distribution, and their corresponding crops during the considered period. Artificial covers occurred most frequently in maize parcels. The approach resulted in precision values exceeding 0.9 for the detection of temporary covers and non-plastic greenhouses and a sensitivity value exceeding 0.95 for non-plastic and plastic greenhouses.

Economic Comparison of Satellite, Plane and UAV-Acquired NDVI Images for Site-Specific Nitrogen Application: Observations from Italy

Defining the most profitable remote sensing platforms is a difficult decision-making process, as it requires agronomic and economic considerations. In this paper, the price and profitability of three levels of remote sensing platforms were evaluated to define a decision-making process. Prices of satellite, plane and UAV-acquired vegetation indices were collected in Italy during 2020 and compared to the economic benefits resulting from variable rate nitrogen application, according to a bibliographic meta-analysis performed on grains. The quality comparison of these three technologies was performed considering the error propagation in the NDVI formula. The errors of the single bands were used to assess the optical properties of the sensors. Results showed that medium-resolution satellite data with good optical properties could be profitably used for variable rate nitrogen applications starting from 2.5 hectares, in case of medium resolution with good optical properties. High-resolution satellites with lower optical quality were profitable starting from 13.2 hectares, while very high-resolution satellites with good optical properties could be profitably used starting from 76.8 hectares. Plane-acquired images, which have good optical properties, were profitable starting from 66.4 hectares. Additionally, a reference model for satellite image price is proposed.