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The synergic use of satellite data at moderate spatial resolution (i.e., 20–30 m) from the new Collection 2 (C2) Landsat-8/9 (L8/9) Operational Land Imager (OLI) and Sentinel-2 (S2) Multispectral Instrument (MSI) provides a new perspective in the remote sensing applications for gas flaring (GF) identification and monitoring, thanks to a significant improvement in the revisiting time (up to ~3 days). In this study, the daytime approach for gas flaring investigation (DAFI), recently developed for identifying, mapping and monitoring GF sites on a global scale using the L8 infrared radiances, has been ported on a virtual constellation (VC) (formed by C2 L8/9 + S2) to assess its capability in understanding the GF characteristics in the space-time domain. The findings achieved for the regions of Iraq and Iran, ranked at the second and third level among the top 10 gas flaring countries in 2022, demonstrate the reliability of the developed system, with improved levels of accuracy and sensitivity (+52%). As an outcome of this study, a more realistic picture of GF sites and their behavior is achieved. A new step aimed at quantifying the GFs radiative power (RP) has been added in the original DAFI configuration. The preliminary analysis of the daily OLI- and MSI-based RP, provided for all the sites by means of a modified RP formulation, revealed their good matching. An agreement of 90% and 70% between the annual RPs computed in Iraq and Iran and both their gas-flared volumes and carbon dioxide emissions were also recorded. Being that gas flaring is one of the main sources of greenhouse gases (GHG) worldwide, the RP products may concur to infer globally the GHGs GF emissions at finer spatial scales. For the presented achievements, DAFI can be seen as a powerful satellite tool able to automatically assess the gas flaring dimension on a global scale.

In September 2014, Kashmir witnessed a catastrophic flood resulting in a significant loss of lives and property. Such massive losses could have been avoided if any structural support such as dams were constructed in the Jhelum basin, which has a history of devastating floods. The GIS-based multicriteria analysis (MCA) model provided three suitability zones for dam locations. The final suitable dam sites were identified within the highest suitability zone based on topography (cross-sections), stream order, high suitable zone, minimum dam site interval, distance from roads, and protected area distance to the dam site. It was discovered that 10.98% of the total 4347.74 km2 area evaluated falls in the high suitability zone, 28.88% of the area falls in the medium suitability zone, and 60.14% of the area falls in the low suitability zone. Within the study area, four viable reservoir sites with a holding capacity of 4,489,367.55 m3 were revealed.

This study proposes a new water body classification method using top-of-atmosphere (TOA) reflectance and water indices (WIs) of the Landsat 8 Operational Land Imager (OLI) sensor and its corresponding random forest classifiers. In this study, multispectral images from the OLI sensor are represented as TOA reflectance and WI values because a classification result using two measures is better than raw spectral images. Two types of boosted random forest (BRF) classifiers are learned using TOA reflectance and WI values, respectively, instead of the heuristic threshold or unsupervised methods. The final probability is summed linearly using the probabilities of two different BRFs to classify image pixels to water class. This study first demonstrates that the Landsat 8 OLI sensor has higher classification rate because it provides improved signal-to-ratio radiometric by using 12-bit quantization of the data instead of 8-bit as available from other sensors. In addition, we prove that the performance of the proposed combination of two BRF classifiers shows robust water body classification results, regardless of topology, river properties, and background environment.

Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location and dispersed nature of these water bodies limits typical methods of monitoring, such as with gauges; fortunately, remote sensing offers a quick and cost-effective means of regularly measuring surface water extent in these isolated regions. Dozens of operational methods exist to use remote sensing to identify waterbodies, however, their performance when identifying surface water in the semi-arid Sahel has not been well-documented and the limitations of these methods for the region are not well understood. Here, we evaluate two global dynamic surface water datasets, fifteen spectral indices developed to classify surface water extent, and three simple decision tree methods created specifically to identify surface water in semi-arid environments. We find that the existing global surface water datasets effectively minimize false positives, but greatly underestimate the presence and extent of smaller, more turbid water bodies that are essential to local livelihoods, an important limitation in their use for monitoring water availability. Three of fifteen spectral indices exhibited both high accuracy and threshold stability when evaluated over different areas and seasons. The three simple decision tree methods had mixed performance, with only one having an overall accuracy that compared to the best performing spectral indices. We find that while global surface water datasets may be appropriate for analysis at the global scale, other methods calibrated to the local environment may provide improved performance for more localized water monitoring needs.

Water bodies are essential to humans and other forms of life. Identification of water bodies can be useful in various ways, including estimation of water availability, demarcation of flooded regions, change detection, and so on. In past decades, Landsat satellite sensors have been used for land use classification and water body identification. Due to the introduction of a New Operational Land Imager (OLI) sensor on Landsat 8 with a high spectral resolution and improved signal-to-noise ratio, the quality of imagery sensed by Landsat 8 has improved, enabling better characterization of land cover and increased data size. Therefore, it is necessary to explore the most appropriate and practical water identification methods that take advantage of the improved image quality and use the fewest inputs based on the original OLI bands. The objective of the study is to explore the potential of a J48 decision tree (JDT) in identifying water bodies using reflectance bands from Landsat 8 OLI imagery. J48 is an open-source decision tree. The test site for the study is in the Northern Han River Basin, which is located in Gangwon province, Korea. Training data with individual bands were used to develop the JDT model and later applied to the whole study area. The performance of the model was statistically analysed using the kappa statistic and area under the curve (AUC). The results were compared with five other known water identification methods using a confusion matrix and related statistics. Almost all the methods showed high accuracy, and the JDT was successfully applied to the OLI image using only four bands, where the new additional deep blue band of OLI was found to have the third highest information gain. Thus, the JDT can be a good method for water body identification based on images with improved resolution and increased size.

Soil salinity and sodicity can significantly reduce the value and the productivity of affected lands, posing degradation, and threats to sustainable development of natural resources on earth. This research attempted to map soil salinity/sodicity via disentangling the relationships between Landsat 8 Operational Land Imager (OLI) imagery and in-situ measurements (EC, pH) over the west Jilin of China. We established the retrieval models for soil salinity and sodicity using Partial Least Square Regression (PLSR). Spatial distribution of the soils that were subjected to hybridized salinity and sodicity (HSS) was obtained by overlay analysis using maps of soil salinity and sodicity in geographical information system (GIS) environment. We analyzed the severity and occurring sizes of soil salinity, sodicity, and HSS with regard to specified soil types and land cover. Results indicated that the models’ accuracy was improved by combining the reflectance bands and spectral indices that were mathematically transformed. Therefore, our results stipulated that the OLI imagery and PLSR method applied to mapping soil salinity and sodicity in the region. The mapping results revealed that the areas of soil salinity, sodicity, and HSS were 1.61 × 106 hm2, 1.46 × 106 hm2, and 1.36 × 106 hm2, respectively. Also, the occurring area of moderate and intensive sodicity was larger than that of salinity. This research may underpin efficiently mapping regional salinity/sodicity occurrences, understanding the linkages between spectral reflectance and ground measurements of soil salinity and sodicity, and provide tools for soil salinity monitoring and the sustainable utilization of land resources.

This paper discusses the pre-launch spectral characterization of the Operational Land Imager (OLI) at the component, assembly and instrument levels and relates results of those measurements to artifacts observed in the on-orbit imagery. It concludes that the types of artifacts observed and their magnitudes are consistent with the results of the pre-launch characterizations. The OLI in-band response was characterized both at the integrated instrument level for a sampling of detectors and by an analytical stack-up of component measurements. The out-of-band response was characterized using a combination of Focal Plane Module (FPM) level measurements and optical component level measurements due to better sensitivity. One of the challenges of a pushbroom design is to match the spectral responses for all detectors so that images can be flat-fielded regardless of the spectral nature of the targets in the imagery. Spectral variability can induce striping (detector-to-detector variation), banding (FPM-to-FPM variation) and other artifacts in the final data products. Analyses of the measured spectral response showed that the maximum discontinuity between FPMs due to spectral filter differences is 0.35% for selected targets for all bands except for Cirrus, where there is almost no signal. The average discontinuity between FPMs is 0.12% for the same targets. These results were expected and are in accordance with the OLI requirements. Pre-launch testing identified low levels (within requirements) of spectral crosstalk amongst the three HgCdTe (Cirrus, SWIR1 and SWIR2) bands of the OLI and on-orbit data confirms this crosstalk in the imagery. Further post-launch analyses and simulations revealed that the strongest crosstalk effect is from the SWIR1 band to the Cirrus band; about 0.2% of SWIR1 signal leaks into the Cirrus. Though the total crosstalk signal is only a few counts, it is evident in some scenes when the in-band cirrus signal is very weak. In moist cirrus-free atmospheres and over typical land surfaces, at least 30% of the cirrus signal was due to the SWIR1 band. In the SWIR1 and SWIR2 bands, crosstalk accounts for no more than 0.15% of the total signal.

Plant invasion imposes significant threats to biodiversity and ecosystem function. Thus, monitoring the spatial pattern of invasive plants is vital for effective ecosystem management. Spartina alterniflora (S. alterniflora) has been one of the most prevalent invasive plants along the China coast, and its spread has had severe ecological consequences. Here, we provide new observation from Landsat operational land imager (OLI) images. Specifically, 43 Landsat-8 OLI images from 2014 to 2016, a combination of object-based image analysis (OBIA) and support vector machine (SVM) methods, and field surveys covering the whole coast were used to construct an up-to-date dataset for 2015 and investigate the spatial variability of S. alterniflora in the coastal zone of mainland China. The classification results achieved good estimation, with a kappa coefficient of 0.86 and 96% overall accuracy. Our results revealed that there was approximately 545.80 km2 of S. alterniflora distributed in the coastal zone of mainland China in 2015, from Hebei to Guangxi provinces. Nearly 92% of the total area of S. alterniflora was distributed within four provinces: Jiangsu, Shanghai, Zhejiang, and Fujian. Seven national nature reserves invaded by S. alterniflora encompassed approximately one-third (174.35 km2) of the total area of S. alterniflora over mainland China. The Yancheng National Nature Reserve exhibited the largest area of S. alterniflora (115.62 km2) among the reserves. Given the rapid and extensive expansion of S. alterniflora in the 40 years since its introduction and its various ecological effects, geospatially varied responding decisions are needed to promote sustainable coastal ecosystems.

Detecting spatial patterns of land surface phenology (LSP) with high spatial and temporal resolutions is crucial for accurately estimating phenological response and feedback to climate change and biogeochemical cycles. Numerous studies have revealed LSP across the Qinghai–Tibet Plateau (QTP) using a variety of coarse-resolution satellite data. However, detailed phenological spatial patterns along with changes of mountain topography remain poorly understood, which greatly limits efforts to predict the impacts of climate change on vegetation growth and ecosystem productivity in complex terrain regions. Combining Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat 8 OLI (Operational Land Imager) observations in overlapping zones of adjacent images, this study detected Normalized Difference Vegetation Index (NDVI)-based LSP metrics at a spatial resolution of 30 m, and explored how LSP varied with topographic factors along a 2600 km belt transect of the central QTP. The results show that the greenup onset date showed a delayed tendency with the increase of elevation at a mean rate of 1.52 days/100 m, while the dormancy onset date indicated an advanced tendency at a mean rate of −0.59 days/100 m. In general, greenup onset date was later but dormancy onset date was earlier on shaded slopes than on sunlit slopes in the meadow area. By contrast, greenup onset date did not significantly depend on aspect in the steppe area, while dormancy onset date indicated a similar response to aspect in the steppe area with that in the meadow area. With regard to the effect of slope on vegetation phenology in the meadow area, greenup onset date was significantly delayed but dormancy onset date significantly advanced with the increase of slope on both north and south slopes. In the steppe area, however, the influence pattern of slope on vegetation phenology was the opposite. Essentially, effects of topographical parameters on LSP were controlled by temperature and moisture combinations in complex terrain.

Dissolved oxygen is one of the parameters to detect whether a water body is experiencing interference or pollution due to the influence of pollutants and the presence of dirt from outside such as garbage discharges from local communities. Dissolved oxygen plays an important role in the life of biota in the aquatic environment, especially in coastal areas because with sufficient oxygen levels in water bodies, living things will be able to grow and multiply properly. This study wants to try to analyze dissolved oxygen levels in the coastal area of Tuban, where this coast is good for study because the morphology of the beach is relatively gentle and close to the recreation area of Boom Tuban beach. The method used in analyzing the distribution of dissolved oxygen is by remote sensing technology to obtain reflectance values from satellite images used to compile algorithms where later the most optimal mathematical model of dissolved oxygen can be obtained on the coast of Boom. Furthermore, it will also be modeled with a polynomial interpolation approach to obtain the best results from the reflectance relationship of the image with dissolved oxygen levels. The results obtained show that the polynomial approach of degrees 2 and 3 does not provide a significant increase in the correlation value besides that dissolved oxygen levels are still within normal limits, and have not experienced significant pollution so that it does not require expensive coastal handling, only needs monitoring every year to get a healthy water situation to support coastal areas intended for regional tourism.