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Seagrass is a flowering plant (angiosperm) that inhabits shallow waters and plays a vital ecological role as a breeding ground, habitat, and food source for marine organisms. However, increasing human activities, including waste disposal, fishing, and coastal tourism, have contributed to the degradation of seagrass ecosystems. This study aims to map the spatial distribution and temporal changes of seagrass areas using Object-Based Image Analysis (OBIA) in the waters of Lancang Island, Seribu Islands, Indonesia. Image processing involved segmentation and multi-level classification (levels 1, 2, and 3) with optimal segmentation scales of 50, 10, and 1, respectively. The classification results identified three categories: non-seagrass, sparse seagrass, and dense seagrass. In 2016, seagrass covered 68.16 ha but declined to 42.37 ha by 2023, while non-seagrass areas expanded from 123.11 ha to 146.59 ha. Land-cover transitions revealed a conversion of 37.84 ha from seagrass to non-seagrass and 12.04 ha from non-seagrass to seagrass. Overall, seagrass experienced a net loss of 37.88 ha during the study period. The classification achieved an overall accuracy of 70%.

Accurate crop distribution maps provide important information for crop censuses, yield monitoring and agricultural insurance assessments. Most existing studies apply low spatial resolution satellite images for crop distribution mapping, even in areas with a fragmented landscape. Unmanned aerial vehicle (UAV) imagery provides an alternative imagery source for crop mapping, yet its spectral resolution is usually lower than satellite images. In order to produce more accurate maps without losing any spatial heterogeneity (e.g., the physical boundary of land parcel), this study fuses Sentinel-2A and UAV images to map crop distribution at a finer spatial scale (i.e., land parcel scale) in an experimental site with various cropping patterns in Heilongjiang Province, Northeast China. Using a random forest algorithm, the original, as well as the fused images, are classified into 10 categories: rice, corn, soybean, buckwheat, other vegetations, greenhouses, bare land, water, roads and houses. In addition, we test the effect of UAV image choice by fusing Sentinel-2A with different UAV images at multiples spatial resolutions: 0.03 m, 0.10 m, 0.50 m, 1.00 m and 3.00 m. Overall, the fused images achieved higher classification accuracies, ranging between 10.58% and 16.39%, than the original images. However, the fused image based on the finest UAV image (i.e., 0.03 m) does not result in the highest accuracy. Instead, the 0.10 m spatial resolution UAV image produced the most accurate map. When the spatial resolution is less than 0.10 m, accuracy decreases gradually as spatial resolution decreases. The results of this paper not only indicate the possibility of combining satellite images and UAV images for land parcel level crop mapping for fragmented landscapes, but it also implies a potential scheme to exploit optimal choice of spatial resolution in fusing UAV images and Sentinel-2A, with little to no adverse side-effects.

Chlorophyll is the most important component of crop photosynthesis, and the reviving stage is an important period during the rapid growth of winter wheat. Therefore, rapid and precise monitoring of chlorophyll content in winter wheat during the reviving stage is of great significance. The satellite-UAV-ground integrated inversion method is an innovative solution. In this study, the core region of the Yellow River Delta (YRD) is used as a study area. Ground measurements data, UAV multispectral and Sentinel-2A multispectral imagery are used as data sources. First, representative plots in the Hekou District were selected as the core test area, and 140 ground sampling points were selected. Based on the measured SPAD values and UAV multispectral images, UAV-based SPAD inversion models were constructed, and the most accurate model was selected. Second, by comparing satellite and UAV imagery, a reflectance correction for satellite imagery was performed. Finally, based on the UAV-based inversion model and satellite imagery after reflectance correction, the inversion results for SPAD values in multi-scale were obtained. The results showed that green, red, red-edge and near-infrared bands were significantly correlated with SPAD values. The modeling precisions of the best inversion model are R2 = 0.926, Root Mean Squared Error (RMSE) = 0.63 and Mean Absolute Error (MAE) = 0.92, and the verification precisions are R2 = 0.934, RMSE = 0.78 and MAE = 0.87. The Sentinel-2A imagery after the reflectance correction has a pronounced inversion effect; the SPAD values in the study area were concentrated between 40 and 60, showing an increasing trend from the eastern coast to the southwest and west, with obvious spatial differences. This study synthesizes the advantages of satellite, UAV and ground methods, and the proposed satellite-UAV-ground integrated inversion method has important implications for real-time, rapid and precision SPAD values collected on multiple scales.

Combination of different satellite data will provide increased opportunities for more frequent cloud-free surface observations due to variable cloud cover at the different satellite overpass times and dates. Satellite data from the polar-orbiting Landsat-8 (launched 2013), Sentinel-2A (launched 2015) and Sentinel-2B (launched 2017) sensors offer 10 m to 30 m multi-spectral global coverage. Together, they advance the virtual constellation paradigm for mid-resolution land imaging. In this study, a global analysis of Landsat-8, Sentinel-2A and Sentinel-2B metadata obtained from the committee on Earth Observation Satellite (CEOS) Visualization Environment (COVE) tool for 2016 is presented. A global equal area projection grid defined every 0.05° is used considering each sensor and combined together. Histograms, maps and global summary statistics of the temporal revisit intervals (minimum, mean, and maximum) and the number of observations are reported. The temporal observation frequency improvements afforded by sensor combination are shown to be significant. In particular, considering Landsat-8, Sentinel-2A, and Sentinel-2B together will provide a global median average revisit interval of 2.9 days, and, over a year, a global median minimum revisit interval of 14 min (±1 min) and maximum revisit interval of 7.0 days.

The main objective of this research is to investigate the potential combination of Sentinel-2A and ALOS-2 PALSAR-2 (Advanced Land Observing Satellite -2 Phased Array type L-band Synthetic Aperture Radar-2) imagery for improving the accuracy of the Aboveground Biomass (AGB) measurement. According to the current literature, this kind of investigation has rarely been conducted. The Hyrcanian forest area (Iran) is selected as the case study. For this purpose, a total of 149 sample plots for the study area were documented through fieldwork. Using the imagery, three datasets were generated including the Sentinel-2A dataset, the ALOS-2 PALSAR-2 dataset, and the combination of the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset (Sentinel-ALOS). Because the accuracy of the AGB estimation is dependent on the method used, in this research, four machine learning techniques were selected and compared, namely Random Forests (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MPL Neural Nets), and Gaussian Processes (GP). The performance of these AGB models was assessed using the coefficient of determination (R2), the root-mean-square error (RMSE), and the mean absolute error (MAE). The results showed that the AGB models derived from the combination of the Sentinel-2A and the ALOS-2 PALSAR-2 data had the highest accuracy, followed by models using the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset. Among the four machine learning models, the SVR model (R2 = 0.73, RMSE = 38.68, and MAE = 32.28) had the highest prediction accuracy, followed by the GP model (R2 = 0.69, RMSE = 40.11, and MAE = 33.69), the RF model (R2 = 0.62, RMSE = 43.13, and MAE = 35.83), and the MPL Neural Nets model (R2 = 0.44, RMSE = 64.33, and MAE = 53.74). Overall, the Sentinel-2A imagery provides a reasonable result while the ALOS-2 PALSAR-2 imagery provides a poor result of the forest AGB estimation. The combination of the Sentinel-2A imagery and the ALOS-2 PALSAR-2 imagery improved the estimation accuracy of AGB compared to that of the Sentinel-2A imagery only.

Soil organic carbon (SOC), as the largest carbon pool on the land surface, plays an important role in soil quality, ecological security and the global carbon cycle. Multisource remote sensing data-driven modeling strategies are not well understood for accurately mapping soil organic carbon. Here, we hypothesized that the Sentinel-2 Multispectral Sensor Instrument (MSI) data-driven modeling strategy produced superior outcomes compared to modeling based on Landsat 8 Operational Land Imager (OLI) data due to the finer spatial and spectral resolutions of the Sentinel-2A MSI data. To test this hypothesis, the Ebinur Lake wetland in Xinjiang was selected as the study area. In this study, SOC estimation was carried out using Sentinel-2A and Landsat 8 data, combining climatic variables, topographic factors, index variables and Sentinel-1A data to construct a common variable model for Sentinel-2A data and Landsat 8 data, and a full variable model for Sentinel-2A data, respectively. We utilized ensemble learning algorithms to assess the prediction performance of modeling strategies, including random forest (RF), gradient boosted decision tree (GBDT) and extreme gradient boosting (XGBoost) algorithms. The results show that: (1) The Sentinel-2A model outperformed the Landsat 8 model in the prediction of SOC contents, and the Sentinel-2A full variable model under the XGBoost algorithm achieved the best results R2 = 0.804, RMSE = 1.771, RPIQ = 2.687). (2) The full variable model of Sentinel-2A with the addition of the red-edge band and red-edge index improved R2 by 6% and 3.2% over the common variable Landsat 8 and Sentinel-2A models, respectively. (3) In the SOC mapping of the Ebinur Lake wetland, the areas with higher SOC content were mainly concentrated in the oasis, while the mountainous and lakeside areas had lower SOC contents. Our results provide a program to monitor the sustainability of terrestrial ecosystems through a satellite perspective.

Optical remote sensing is one of the most attractive options for generating crop cover maps because it enables computation of vegetation indices, which are useful for assessing the condition of vegetation. The Sentinel-2A Multispectral Instrument (MSI), which is a multispectral sensor with 13 bands covering the visible, near infrared and short-wave infrared (SWIR) wavelength regions, offers a vast number of vegetation indices. Spectral indices, which are combinations of spectral measurements at different wavelengths, have been used in the previous studies and they sometimes contributed to improve classification accuracies. In this study, 91 published spectral indices were calculated from the MSI data. Additionally, classification algorithms are essential for generating accurate maps and the random forests classifier is one of which possesses the five hyperparameters were applied. The improvements in classification accuracies were confirmed achieving an overall accuracy of 93.1% based on the reflectance at 4 bands and 8 spectral indices.

In this research work, a multi-index-based support vector machine (SVM) classification approach has been proposed to determine the complex and morphologically heterogeneous land cover/use (LCU) patterns of cities, with a special focus on separating bare lands and built-up regions, using Istanbul, Turkey as the main study region, and Ankara and Konya (in Turkey) as the independent test regions. The multi-index approach was constructed using three-band combinations of spectral indices, where each index represents one of the three major land cover categories, green areas, water bodies, and built-up regions. Additionally, a shortwave infrared-based index, the Normalized Difference Tillage Index (NDTI), was proposed as an alternative to existing built-up indices. All possible index combinations and the original ten-band Sentinel-2A image were classified with the SVM algorithm, to map seven LCU classes, and an accuracy assessment was performed to determine the multi-index combination that provided the highest performance. The SVM classification results revealed that the multi-index combination of the normalized difference tillage index (NDTI), the red-edge-based normalized vegetation index (NDVIre), and the modified normalized difference water index (MNDWI) improved the mapping accuracy of the heterogeneous urban areas and provided an effective separation of bare land from built-up areas. This combination showed an outstanding overall performance with a 93% accuracy and a 0.91 kappa value for all LCU classes. The results of the test regions provided similar findings and the same index combination clearly outperformed the other approaches, with 92% accuracy and a 0.90 kappa value for Ankara, and an 84% accuracy and a 0.79 kappa value for Konya. The multi-index combination of the normalized difference built-up index (NDBI), the NDVIre, and the MNDWI, ranked second in the assessment, with similar accuracies to that of the ten-band image classification.

Remote sensing analyses frequently use feature selection methods to remove non-beneficial feature variables from the input data, which often improve classification accuracy and reduce the computational complexity of the classification. Many remote sensing analyses report the results of the feature selection process to provide insights on important feature variable for future analyses. Are these feature selection results generalizable to other classification models, or are they specific to the input dataset and classification model they were derived from? To investigate this, a series of radial basis function (RBF) support vector machines (SVM) supervised machine learning land cover classifications of Sentinel-2A Multispectral Instrument (MSI) imagery were conducted to assess the transferability of recursive feature elimination (RFE)-derived feature sets between different classification models using different training sets acquired from the same remotely sensed image, and to classification models of other similar remotely sensed imagery. Feature selection results for various training sets acquired from the same image and different images widely varied on small training sets (n = 108). Variability in feature selection results between training sets acquired from different images was reduced as training set size increased; however, each RFE-derived feature set was unique, even when training sample size was increased over 10-fold (n = 1895). The transferability of an RFE-derived feature set from a high performing classification model was, on average, slightly more accurate in comparison to other classification models of the same image, but provided, on average, slightly lower accuracies when generalized to classification models of other, similar remotely sensed imagery. However, the effects of feature set transferability on classification accuracy were inconsistent and varied per classification model. Specific feature selection results in other classification models or remote sensing analyses, while useful for providing general insights on feature variables, may not always generalize to provide comparable accuracies for other classification models of the same dataset, or other, similar remotely sensed datasets. Thus, feature selection should be individually conducted for each training set within an analysis to determine the optimal feature set for the classification model.

Sentinel-2A MSI is the Landsat-like spatial resolution (10–60 m) super-spectral instrument of the European Space Agency (ESA), aimed at additional data continuity for global land surface monitoring with Landsat and Satellite Pour l’Observation de la Terre (SPOT) missions. Several simulation studies have been conducted in the last several years to show the potential of Sentinel-2A MSI (MultiSpectral Instrument). Now that real data are available, the first confirmations of this potential and comparisons with other operational systems are being made. This paper aims at evaluating Sentinel-2A MSI band ratio products that are relevant for geological remote sensing. A Sentinel-2A MSI and a Landsat 8 OLI (Operational Land Imager) scene were processed from their respective levels L1C and L1T to level L2A (bottom of atmosphere reflectance). Then, three band ratios originally defined for Landsat TM (Thematic Mapper) were used to map mineralogy associated with a hydrothermal alteration system in southeast Spain. The results obtained with Sentinel-2A MSI were compared with those obtained with Landsat 8 OLI and a simulated Sentinel-2A MSI dataset that was used before actual data were released. Results show that the images appear similar to the human eye having a correlation of approximately 0.8 and higher, but that the associated data ranges differ significantly. The resulting products are also compared to a published geologic map of the study area, and it is shown that the resulting maps correspond with the conceptual geologic model of the epithermal deposit.