Land Sat Research Articles

Analysis of Land Use and Land Cover Dynamics in the Highlands of Northern Ethiopia

In the highlands of northern Ethiopia, many land use and land cover changes (LULCC) have occurred during different activities of the populations. Despite its coverage and unwise use of natural resources, these land uses and land covers are facing increasing or decreasing their area coverage. Information derived from land use and land cover change detection is important to land conservation, sustainable development, and management of Natural resources. This purpose of this study is therefore concerned with identifying the change in land use and land cover detection of the Wejic watershed. To identify land cover changes detection; remote sensing data, satellite imagery and image processing techniques had done within two dates of 1998 and 2017 using Land sat TM 30 m resolution images using Arc GIS 10.1. Hence, a field survey was carried out in 2017 to identify the major land cover types in the watershed. Detection Analysis of land use and land cover dynamics were calculated from the selected watershed in Southern Tigray, Northern Ethiopia. These land use types were cultivated, forest, bush, shrub, and homestead lands. Furthermore, a normalized difference vegetation index (NDVI) was developed for both years. The land use and land cover change results indicated that forest, cultivated land, and bush land decreased by 4.4%, 2.2%, and 2.4%, respectively. However, shrub land and homestead increased by 5.5% and 3.6%, respectively. Therefore The change detection analysis using GIS and remote sensing could deliver useful information to understand the seasonal patterns of land use dynamics for planners and decision makers; consequently, sustainable land management planning is possible.

Spatial variability of temperature inside atoll lagoons assessed with Landsat-8 satellite imagery

Sea Surface Temperature (SST) maps are necessary for managing marine resources in a climate change context, but are lacking for most of the 598 world's atolls. We assessed the feasibility of using the Landsat-8 (L8) satellite to infer SST maps for four French Polynesia atolls of aquaculture interest in Tuamotu Archipelago, namely Takaroa, Raroia, Tatakoto, and Reao. Specifically, we (1) used sensors to measure in situ the range of spatial temperature differences recorded in these four atoll lagoons; (2) calibrated and assessed the performances of SST algorithms to estimate lagoon temperature from L8 signals; (3) generated temperature maps for the lagoons and compared spatial patterns of temperature obtained from these maps with patterns highlighted by in situ sensors. Good agreements between satellite and in situ temperature data were obtained, with better results achieved when using an atoll-by-atoll optimization (average bias = −0.26 °C; RMSE = 0.55 °C). However, we also show that the range of temperature inside atoll lagoons is low, and of the same order of magnitude than RMSE achieved with SST algorithms. Because of the L8 overpass time (∼9 a.m.) and the revisit time (16 days), L8 SST could not capture the entire range of spatial differences measured in situ in the four lagoons, but could capture spatial gradients and fronts better than with few in situ sensors. Considering the achieved accuracies and the actual temperature differences at the four study sites, we discuss the usefulness of L8 derived SST maps to assist fishery and aquaculture management in atoll lagoons, as well as the possible generalization to other lagoons.

Harmonized Landsat and Sentinel-2 Data with Google Earth Engine

Continuous and dense time series of satellite remote sensing data are needed for several land monitoring applications, including vegetation phenology, in-season crop assessments, and improving land use and land cover classification. Supporting such applications at medium to high spatial resolution may be challenging with a single optical satellite sensor, as the frequency of good-quality observations can be low. To optimize good-quality data availability, some studies propose harmonized databases. This work aims at developing an ‘all-in-one’ Google Earth Engine (GEE) web-based workflow to produce harmonized surface reflectance data from Landsat-7 (L7) ETM+, Landsat-8 (L8) OLI, and Sentinel-2 (S2) MSI top of atmosphere (TOA) reflectance data. Six major processing steps to generate a new source of near-daily Harmonized Landsat and Sentinel (HLS) reflectance observations at 30 m spatial resolution are proposed and described: band adjustment, atmospheric correction, cloud and cloud shadow masking, view and illumination angle adjustment, co-registration, and reprojection and resampling. The HLS is applied to six equivalent spectral bands, resulting in a surface nadir BRDF-adjusted reflectance (NBAR) time series gridded to a common pixel resolution, map projection, and spatial extent. The spectrally corresponding bands and derived Normalized Difference Vegetation Index (NDVI) were compared, and their sensor differences were quantified by regression analyses. Examples of HLS time series are presented for two potential applications: agricultural and forest phenology. The HLS product is also validated against ground measurements of NDVI, achieving very similar temporal trajectories and magnitude of values (R2 = 0.98). The workflow and script presented in this work may be useful for the scientific community aiming at taking advantage of multi-sensor harmonized time series of optical data.

Extraction of Brightness Values of Different Object from IRS & Landsat Satellite Image : Comparative Study

The main aim of this research is to find optimum segmentation parameters for extracting different spectral values of a different object of Land sat and IRS image. A relatively new segmentation approach, multiresolution segmentation, is being examined using two data sets (Land sat and IRS).

Revealing irrigation uniformity with remote sensing: A comparative analysis of satellite-derived uniformity coefficient

This study investigates the use of satellite-derived Christiansen Uniformity Coefficient (SDCUC) values for evaluating irrigation uniformity. In the context of global water scarcity and the imperative for sustainable water management, we explore the potential of remote sensing methods to evaluate irrigation uniformity across large agricultural areas. The findings reveal a consistent tendency for SDCUC to overestimate irrigation uniformity, with an average overestimation rate of 7.83 %. However, accuracy improved with the appropriate method, vegetation index, or spectral band selection. Employing the entire satellite image for SDCUC (SDCUCTOT) assessment improved accuracy. For Sentinel-1 (S1), using the dual-band cross-polarization horizontal transmit/vertical receive band (VH), the bias confidence interval was −0.39–0.69 %, while for Sentinel-2 (S2), using the normalized difference red edge 3 index (NDRE3), it was −1.47–0.66 %, and for Landsat 8 (L8) and Landsat 9 (L9) using the shortwave infrared water stress index (SIWSI) it ranged from 0.36 % to 2.28 %. Improved results were also observed when the normalized difference vegetation index (NDVI) ranged between 0.4 and 0.8 or the evapotranspiration and potential evapotranspiration ratio (ET/PET) ranged between 0.30 and 0.55. In these conditions, SDCUCTOT for the S2, L8, and L9 using the simple ratio index (SR) ranged from 1.00 % to 2.33 %, 0.00–1.83 %, 0.23–2.00 %, respectively, and for S2, the normalized difference water index (NDWI) and NDRE3 ranged from −1.39–0.71 %, and −1.43–2.31 % respectively. These findings underscore the potential of remote sensing techniques to revolutionize water resource management and promote sustainable agriculture, emphasizing the synergistic role of ground-based measurements and the need for continued methodological refinements to improve accuracy. Further advancements and research are warranted to refine the methodology and enhance the accuracy and reliability of remote sensing-based irrigation uniformity assessment, ultimately contributing to more sustainable agricultural irrigation practices.

Improving the Spatiotemporal Resolution of Land Surface Temperature Using a Data Fusion Method in Haihe Basin, China

Land surface temperature (LST) serves as a pivotal component within the surface energy cycle, offering fundamental insights for the investigation of agricultural water environment, urban thermal environment, and land planning. However, LST monitoring at a point scale entails substantial costs and poses implementation challenges. Moreover, the existing LST products are constrained by their low spatiotemporal resolution, limiting their broader applicability. The fusion of multi-source remote sensing data offers a viable solution to enhance spatiotemporal resolution. In this study, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was used to estimate time series LST utilizing multi-temporal Landsat 8 (L8) and MOD21A2 within the Haihe basin in 2021. Validation of ESTARFM LST was conducted against L8 LST and in situ LST. The results can be summarized as follows: (1) ESTARFM was found to be effective in heterogeneous regions within the Haihe basin, yielding LST with a spatiotemporal resolution of 30 m and 8 d while retaining clear texture information; (2) the comparison between ESTARFM LST and L8 LST shows a coefficient determination (R2) exceeding 0.59, a mean absolute error (MAE) lower than 2.43 K, and a root mean square error (RMSE) lower than 2.63 K for most dates; (3) comparison between ESTARFM LST and in situ LST showcased high validation accuracy, revealing a R2 of 0.87, a MAE of 2.27 K, and a RMSE of 4.12 K. The estimated time series LST exhibited notable reliability and robustness. This study introduced ESTARFM for LST estimation, achieving satisfactory outcomes. The findings offer a valuable reference for other regions to generate LST data with a spatiotemporal resolution of 8 d and 30 m, thereby enhancing the application of data products in agriculture and hydrology contexts.

Repeat pass imaging-based velocity estimation of Siachen glacier in J&K Himalaya using Sentinel-2 and Landsat-8 data: A comparison with SAR observations

In this study optical data from satellites Sentinel-2 (S-2), Landsat-8 (L-8), and synthetic aperture radar (SAR) data from Sentinel-1(S-1) have been used to estimate the surface velocity of the Siachen Glacier in J&K Himalaya. Repeat-pass optical and SAR data have been processed employing sub-pixel offset tracking (SOT) approach to decipher surface velocity of glaciers during 2013–2018. It is observed that SOT derived 2-D surface velocity using S-2 and L-8 data pairs show more than 70 % correlation over the entire stretch of the glacier. High correlation among velocities from S-2 and L-8 over two years validates one sensor against another for glacier surface movement studies in the Himalaya. This study reveals that the velocity of the glacier varies from ∼120 ma−1 to ∼151 ma−1 during 2013–2018. This study results the movement of glacier using optical sensor and SAR sensor over same geographical zone and similar time zone to evaluate the feature extraction and sensor impact on feature extraction. We have found that all sensors have produced similar results, since data is collected near time frame.

Investigating the potential of remote sensing-based machine-learning algorithms to model Secchi-disk depth, total phosphorus, and chlorophyll-a in Lake Urmia

Many terminal lakes in agricultural basins are prone to eutrophication due to restricted inflows and receiving excess nutrients from their basin.The synergy of using satellite data andmachine learning models is a low-cost way to monitor the root-cause water quality variables (WQVs) of eutrophication. This study investigates the potential ofremote sensing-based machine learning algorithms to modelchlorophyll-a(Chl-a), total phosphorus (TP), Secchi disk depth (SD),and Carlson trophic state index (CTSI)in the north part of Lake Urmia (LU).The multiple linear regression (MLR) and artificial neural network (ANN) models were developed using Landsat-8 (L8) and Sentinel-2 (S2) data with nearly concurrent in-situ WQVs of the north part of LU from February 2016 to January 2017. Results showed that models based on L8 were superior to those with S2. Moreover, the ANN models based on L8 for Chl-a, SD, and TP having NSE = 0.75, 0.98, and 0.96, respectively, outperformed MLRs (with NSE = 0.74, 0.81, 0.58). Applying atmospheric correction (i.e., ACOLITE, C2RCC, and C2RCCX) enhances the models.The resultant Chl-aand SD maps indicated an inverse spatiotemporal pattern that agrees with the variation of the abiotic condition in the lake (e.g., surface temperature and total suspended sediments).According to the CTSI maps,the north part of LU was mesotrophic in February and March and eutrophic between June and October 2016. Our study indicates the promising application ofremote sensing-based machine learning algorithms to model the spatiotemporal variation of eutrophication in LU, which provides valuable insights into cost-effective lake monitoring.

SENTINEL-2, HERRAMIENTA EN LA SEGURIDAD AGROALIMENTARIA Y AGRICULTURA DE PRECISIÓN

<p><strong>Background</strong>. Food production as the world population increases is an issue of global importance, resulting in uncertainty around agri-food security (AS). It is essential to apply new technology that monitors and makes crop management more efficient to achieve sustainable development (SD). One technology responsible for improving conventional farmers' practices is precision agriculture (PA) using tools such as big data analytics, artificial intelligence and geospatial analysis. In this sense, the monitoring of markers through satellite images requires knowledge for adequate interpretations. Some satellites have multispectral images, monitoring with these has demonstrated high precision in predicting crop yields. There are several satellites, Landsat 8 (L8) and Sentinel-2 (S2) are the most advanced and open access. <strong>Objective</strong>. To present the spectrophotometric fundamentals and current state of remote sensing with S2 multispectral images in relation to problems facing agriculture. It is concluded that remote sensing is a tool that contributes to the proper management of agricultural production, showing high reliability. <strong>Methodology</strong>. This review was carried out in accordance with the PRISMA statement (Preferred Reporting Items for Systematic reviews and Meta-Analyses), a systematic analysis of literature in the databases was carried out. MDPI, Springer Link, Science Direct, Taylor and Francis and Google Scholar were consulted. The key words were “Sentinel-2, R2, Salinity, Pest, Nitrogen, Drought”. Zotero (www.zotero.org) was used as a free access bibliography manager; articles that were not related to the research objectives were excluded. <strong>Results</strong>. Articles related to the objective of this review were identified in the databases; the study was structured based on the needs of precision agriculture (PA). The importance of adequately managing the water demand of crops, salinity stress, pest detection and the movement of nitrogen (N) in the soil-plant-atmosphere interaction. <strong>Implications</strong>. Remote perception of phenomena at the field level represents an area for improvement in crop management. Increasing resource efficiency is crucial to achieving agri-food security (AS) and thus part of the sustainable development goals (SDGs). <strong>Conclusion</strong>. The S2 has multispectral images that, when processed, allow obtaining information about the crop, i.e. monitoring stress, humidity in plants and optimization of plant nutrition. With PA, decisions are made for crop management, costs are reduced by reducing losses due to crop stress, and resource efficiency increases.<strong> </strong></p>

Harmonized NDVI time-series from Landsat and Sentinel-2 reveal phenological patterns of diverse, small-scale cropping systems in East Africa

Capturing the phenological responses of crops in diverse, small-scale agricultural systems is one of the most notable remote sensing research gaps in Eastern Africa. Because of the heterogeneous and complex agricultural environments, remote sensing methodologies successfully implemented elsewhere yield poor results in Eastern Africa. The reliance on local rainfall and dry spells can lead to alterations in planting patterns, impacting growing season timing and yield, underscoring the importance of high temporal resolution for capturing crop phenology. We generated a high-resolution land use/land cover (LULC) classification map of 10 m using Sentinel-1 and Sentinel-2 imagery together with a large set of reference data acquired in situ in 2022. The LULC classification yielded an overall accuracy of 87.9% and Cohen's kappa value of 0.83. Based on the LULC map, we investigated the phenological metrics of the most important crops in the Kamuli district of Eastern Uganda by combining a multi-sensor harmonization approach, thresholding method, and cloud computing. Previously unavailable Landsat 9 band adjustment coefficients were derived. By harmonizing Landsat (LS) 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 and 9 Operational Land Imager (OLI), with Sentinel-2 Multispectral Instrument (MSI), the phenological patterns during the March to May growing season were captured using a dense temporal Normalized Difference Vegetation Index (NDVI) time series. The seasonal onset for most crop types was in the end of March to middle of April while the end of the growing season occurred from the end of June to middle of July. Investigation of the 13 crop types using a high-resolution LULC map and dense, harmonized NDVI time series signifies the first field-level phenological metrics estimation of smallholder farming systems in Eastern Africa. Future research should focus on expanding the methodology in space and time to provide insights into past and current growing season development.

Report on Landsat 8 and Sentinel-2B observations of the Nord Stream 2 pipeline methane leak

Abstract. In late September 2022, explosions of the Nord Stream pipelines caused what could be the largest anthropogenic methane leak ever recorded. We report on Landsat 8 (L8) and Sentinel-2B (S-2B) observations of the sea-foam patch produced by the Nord Stream 2 (NS2) leak located close to Bornholm island, acquired on 29 and 30 September, respectively. Usually, reflected sunlight over sea is insufficient for these Earth imagers to observe any methane signal in nadir-viewing geometry. However, the NS2 foam patch observed here is bright enough to possibly allow the detection of methane above it. We apply the multi-band single-pass (MBSP) method to infer methane enhancement above the NS2 foam patch and then use the integrated mass enhancement (IME) method in a Monte Carlo ensemble approach to estimate methane leak rates and their uncertainties. This very specific NS2 observation case challenges some of MBSP and IME implicit assumptions and thus calls for customized calibrations: (1) for MBSP, we perform an empirical calibration of sea-foam albedo spectral dependence by using sea-foam observations in ship trails, and (2) for IME, we yield a tailored effective wind speed calibration that accounts for a partial plume observation, as methane enhancement may only be seen above the NS2 sea-foam patch. Our comprehensive uncertainty analysis yields large methane leak rate uncertainty ranges that include zero for single overpasses and, assuming they are independent, a best estimate of 502 ± 464 t h−1 for the combined averaged L8 and S-2B emission rate. Within all our Monte Carlo ensembles, positive methane leak rates have higher probabilities (80 %–88 %) than negative ones (12 %–20 %), thus indicating that L8 and S-2B likely captured a methane-related signal. Overall, we see our work both as a nuanced analysis of L8 and S-2B contributions to quantifying the NS2 leak emissions and as a methodological cautionary tale that builds insight into MBSP and IME sensitivities.

A longitudinal analysis of soil salinity changes using remotely sensed imageries

Soil salinization threatens agricultural productivity, leading to desertification and land degradation. Given the challenges of conducting labor-intensive and expensive field studies and laboratory analyses on a large scale, recent efforts have focused on leveraging remote sensing techniques to study soil salinity. This study assesses the importance of soil salinity indices’ derived from remotely sensed imagery. Indices derived from Landsat 8 (L8) and Sentinel 2 (S2) imagery are used in Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Decision Tree (DT), and Support Vector Machine (SVR) are associated with the electrical (EC) conductivity of 280 soil samples across 24,000 hectares in Northeast Iran. The results indicated that the DT is the best-performing method (RMSE = 12.25, MAE = 2.15, R2 = 0.85 using L8 data and RMSE = 10.9, MAE = 2.12, and R2 = 0.86 using S2 data). Also, the results showed that Multi-resolution Valley Bottom Flatness (MrVBF), moisture index, Topographic Wetness Index (TWI), and Topographic Position Indicator (TPI) are the most important salinity indices. Subsequently, a time series analysis indicated a reduction in salinity and sodium levels in regions with installed drainage networks, underscoring the effectiveness of the drainage system. These findings can assist decision-making about land use and conservation efforts, particularly in regions with high soil salinity.

Satellite Time-Series Analysis for Thermal Anomaly Detection in the Naples Urban Area, Italy

Naples is the most densely populated Italian city (7744 inhabitants per km2). It is located in a particular geological context: the presence of Mt Vesuvius characterizes the eastern part, and the western part is characterized by the presence of the Phlegrean Fields, making Naples a high-geothermal-gradient region. This endogenous heat, combined with the anthropogenic heat due to intense urbanization, has defined Naples as an ideal location for Surface Urban Heat Island (SUHI) analysis. SUHI analysis was effectuated by acquiring the Land Surface Temperature (LST) over Naples municipality by processing Landsat 8 (L8) Thermal Infrared Sensor (TIRS) images in the 2013–2023 time series by employing Google Earth Engine (GEE). In GEE, two different approaches have been followed to analyze thermal images, starting from the Statistical Mono Window (SMW) algorithm, which computes the LST based on the brightness temperature (Tb), the emissivity value, and the atmospheric correction coefficients. The first one is used for the LST retrieval from daytime images; here, the emissivity component is derived using, firstly, the Normalized Difference Vegetation Index (NDVI) and then the Vegetation Cover Method (VCM), defining the Land Surface Emissivity (LSɛ), which considers solar radiation as the main source of energy. The second approach is used for the LST retrieval from nighttime images, where the emissivity is directly estimated from the Advance Spaceborne Thermal Emission Radiometer database (ASTER-GED), as, during nighttime without solar radiation, the main source of energy is the energy emitted by the Earth’s surface. From these two different algorithms, 123 usable daytime and nighttime LST images were downloaded from GEE and analyzed in Quantum GIS (QGIS). The results show that the SUHI is more concentrated in the eastern part, characterized by intense urbanization, as shown by the Corine Land Cover (CLC). At the same time, lower SUHI intensity is detected in the western part, defined by the Land Cover (LC) vegetated class. Also, in the analysis, we highlighted 40 spots (10 hotspots and 10 coldspots, both for daytime and nighttime collection) that present positive or negative temperature peaks for all the time series. Due to the huge amount of data, this work considered only the five representative spots that were most representative for SUHI analysis and determination of thermal anomalies in the urban environment.

Validating Digital Earth Australia NBART for the Landsat 9 Underfly of Landsat 8

In recent years, Geoscience Australia has undertaken a successful continental-scale validation program, targeting Landsat and Sentinel analysis-ready data surface reflectance products. The field validation model used for this program was successfully built upon earlier studies, and the measurement uncertainties associated with these protocols have been quantified and published. As a consequence, the Australian earth observation community was well-prepared to respond to the United States Geological Survey (USGS) call for collaborators with the 2021 Landsat 8 (L8) and Landsat 9 (L9) underfly. Despite a number of challenges, seven validation datasets were captured across five sites. As there was only a single 100% overlap transit across Australia, and the country was amidst a strong La Niña climate cycle, it was decided to deploy teams to the two available overpasses with only 15% side lap. The validation sites encompassed rangelands, chenopod shrublands, and a large inland lake. Apart from instrument problems at one site, good weather enabled the capture of high-quality field data allowing for meaningful comparisons between the radiometric performance of L8 and L9, as well as the USGS and Australian Landsat analysis-ready data processing models. Duplicate (cross-calibration) spectral sampling at different sites provides evidence of the field protocol reliability, while the off-nadir view of L9 over the water site has been used to better compare the performance of different water and atmospheric correction processing models.

Above‐ground biomass retrieval with multi‐source data: Prediction and applicability analysis in Eastern Mongolia

AbstractGrassland aboveground biomass (AGB) is a key variable to measure grassland productivity, and accurate assessment of AGB is important for optimizing grassland resource management and understanding carbon, water, and energy fluxes. Current approaches on large scales such as the Mongolian Steppe Ecosystem often combine field measurements with optical and/or synthetic aperture radar (SAR) data. Meanwhile, especially the representativeness of the field measurements for large‐scale analysis have seldom been accounted for. Therefore, we provide the first remotely sensed AGB product for central and Eastern Mongolia which (1) uses random forest (RF), (2) is fully validated against over 600 field samples, and (3) applies a novel method, dissimilarity index (DI), to derive the area of applicability of the model with respect to the training data. Therefore, different remote sensing data sources such as multi‐scale and multi‐temporal optical images—Worldview 2 (WV2), Sentinel 2 (S2), and Landsat 8 (L8) in combination with SAR data are tested for their suitability to provide an area‐wide estimation on large scale. The results showed that the AGB prediction by combining Sentinel 1 (S1) and S2 using RF had the highest accuracy. Furthermore, the model was applicable to at least 72.61% of the steppe area. Areas where the model was not applicable are mostly distributed along the edges of grassland. This study demonstrates the potential of combining Sentinel‐derived indices and machine learning to provide a reliable AGB prediction for grassland for extremely large ecosystems with strong climatic gradients.

Image Quality Assessment of Multi-Satellite Pan-Sharpening Approach: A Case Study using Sentinel-2 Synthetic Panchromatic Image and Landsat-8

INTRODUCTION: The satellite's physical and technical capabilities limit high spectral and spatial resolution image acquisition. In Remote Sensing (RS), when high spatial and spectral resolution data is essential for specific Geographic Information System (GIS) applications, Pan Sharpening (PanS) becomes imperative in obtaining such data. 
 OBJECTIVES: Study aims to enhance the spatial resolution of the multispectral Landsat-8 (L8) images using a synthetic panchromatic band generated by averaging four fine-resolution bands in the Sentinel-2 (S2) images. 
 METHODS: Evaluation of the proposed multi-satellite PanS approach, three different PanS techniques, Smoothed Filter Intensity Modulation (SFIM), Gram-Schmidt (GS), and High Pass Filter Additive (HPFA) are used for two different study areas. The techniques' effectiveness was evaluated using well-known Image Quality Assessment Metrics (IQAM) such as Root Mean Square Error (RMSE), Correlation Coefficient (CC), Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS), and Relative Average Spectral Error (RASE). This study leveraged the GEE platform for datasets and implementation.
 RESULTS: The promising values were provided by the GS technique, followed by the SFIM technique, whereas the HPFA technique produced the lowest quantitative result. 
 CONCLUSION: In this study, the spectral bands of the MS image’s performance show apparent variation with respect to that of the different PanS techniques used.

Landsat 9 Thermal Infrared Sensor-2 (TIRS-2) Pre- and Post-Launch Spatial Response Performance

The launch of Landsat 9 (L9) on 27 September 2021 marks the ongoing commitment of the Landsat mission to delivering users with calibrated Earth observations for fifty years. The two imaging sensors on L9 are the Thermal Infrared Sensor-2 (TIRS-2) and the Operational Land Imager-2 (OLI-2). Shortly after launch, the image data from OLI-2 and TIRS-2 were evaluated for both radiometric and geometric quality. This paper provides a synopsis of the evaluation of the spatial response of the TIRS-2 instrument. The assessment focuses on determining the instrument’s ability to detect a perfect knife edge. The spatial response was evaluated both pre- and post-launch. Pre-launch testing was performed at NASA Goddard Space Flight Center (GSFC) under flight-like thermal vacuum (TVAC) conditions. On orbit, coastline targets were identified to evaluate the spatial response and compared against Landsat 8 (L8). The pre-launch results indicate that the spatial response of the TIRS-2 sensor is consistent with its predecessor on board L8, with no noticeable decline in image quality to compromise any TIRS science objectives. Similarly, the post-launch analysis shows no apparent degradation of the TIRS-2 focus during the launch and the initial operational timeframe.

Field Evaluation of Land Deterioration in Al - Mohammadi Basin Valley at Western Desert

The recurrent study was in including lots scientific details on land deterioration for Almehmedi valley basin. This basin is represented as distinguish sites because it possesses different morphology, geological properties, climate pattern, various land resources such as soils, vegetation and water resources. Conceptualization of this study was entirely prepared then results were different to discover the range of land degradation using field evolution on this basin, which its area was 1971.49 km2. Therefore basin lands were divided into six land unites via geomorphological terms. These terms were done using optical explanation on Land sat 8 satellite image. These unites are high plateaus, flat plateaus, unbath, valleys, alluvial fans, flood plain. Moreover field investigation was applied using GLASOD physical, biological, chemical and saline deterioration. Thus, their cause was explained and measured the deterioration amounts within land units using tables maps. The research study could clouded basin field spatial differences resulted in differing the valley basin from other lands. Furthermore, it resulted in spatial relations amongst human iturimm and natural in this area.

Plastic-covered greenhouses mapping in Morocco with Google Earth engine: Comparing Sentinel-2 and Landsat-8 data using pixel- and object-based methods

With the ever-increasing demand for food production, monitoring plastic-covered greenhouse (PCG) spatiotemporal distribution accurately holds significant importance for monitoring agricultural expansion and environmental impacts. For this purpose, in this paper, we used a random forest (RF) algorithm to produce multi-temporal PCGs maps in three different intensive agricultural Moroccan areas (Loukkos (LS), Gharb (GB), and Souss-Mass (SM)). Two classification approaches, object-based (OB) and pixel-based (PB), were applied to Landsat-8 (L8) and Sentinel-2 (S2) imagery via the Google Earth Engine (GEE) platform. Additionally, the RF performance is compared across five feature scenarios, combining spectral bands, spectral indices, and texture information. Further, we estimated the PCGs' spatiotemporal dynamics from 2018 to 2023. Findings indicated that the Blue and SWIR bands, Normalized Difference Tillage Index (NDTI), Plastic-Mulched Landcover Index (PMLI), variance, and sum average texture were the most significant variables that contributed to accuracy in PCGs classification. The 6-year average overall accuracy achieved >93.10% in mapping PCGs. Notably, the band combination “All” (Bands + Indices + Texture) produced high overall accuracy (>94.9%) and F1-score (>91.8%) compared to other feature combinations. Nevertheless, we found considerably improved classification accuracy by combining only spectral indices with texture (OA >94.6%). It was noticed that both OB and PB approaches reveal their similar performance in most cases, with slight differences. Besides, the PCGs estimated area analysis showed that SM area had the largest PCG coverage, approximately three times larger than the other areas, and it exhibited an overall growth of +8.45% between 2018 and 2023. Thus, this research demonstrates the RF and OB-PB's capability to accurately identify PCGs in GEE. In addition, these results can serve as valuable insights for policymakers and agricultural planners to efficiently monitor and manage PCGs expansion in intensive agricultural areas.

Reconstructing Snow Cover under Clouds and Cloud Shadows by Combining Sentinel-2 and Landsat 8 Images in a Mountainous Region

Snow cover is a sensitive indicator of global climate change, and optical images are an important means for monitoring its spatiotemporal changes. Due to the high reflectivity, rapid change, and intense spatial heterogeneity of mountainous snow cover, Sentinel-2 (S2) and Landsat 8 (L8) satellite imagery with both high spatial resolution and spectral resolution have become major data sources. However, optical sensors are more susceptible to cloud cover, and the two satellite images have significant spectral differences, making it challenging to obtain snow cover beneath clouds and cloud shadows (CCSs). Based on our previously published approach for snow reconstruction on S2 images using the Google Earth Engine (GEE), this study introduces two main innovations to reconstruct snow cover: (1) combining S2 and L8 images and choosing different CCS detection methods, and (2) improving the cloud shadow detection algorithm by considering land cover types, thus further improving the mountainous-snow-monitoring ability. The Babao River Basin of the Qilian Mountains in China is chosen as the study area; 399 scenes of S2 and 35 scenes of L8 are selected to analyze the spatiotemporal variations of snow cover from September 2019 to August 2022 in GEE. The results indicate that the snow reconstruction accuracies of both images are relatively high, and the overall accuracies for S2 and L8 are 80.74% and 88.81%, respectively. According to the time-series analysis of three hydrological years, it is found that there is a marked difference in the spatial distribution of snow cover in different hydrological years within the basin, with fluctuations observed overall.