Automatic Sub-Pixel Co-Registration of LandSat-8 OLI and Sentinel-2A MSI Images Using Phase Correlation and Machine Learning Based Mapping

This study investigates misregistration issues between Landsat-8/OLI and Sentinel-2A/MSI at 30 m resolution, and between multi-temporal Sentinel-2A images at 10 m resolution using a phase correlation approach and multiple transformation functions. Co-registration of 45 Landsat-8 to Sentinel-2A pairs and 37 Sentinel-2A to Sentinel-2A pairs were analyzed. Phase correlation proved to be a robust approach that allowed us to identify hundreds and thousands of control points on images acquired more than 100 days apart. Overall, misregistration of up to 1.6 pixels at 30 m resolution between Landsat-8 and Sentinel-2A images, and 1.2 pixels and 2.8 pixels at 10 m resolution between multi-temporal Sentinel-2A images from the same and different orbits, respectively, were observed. The non-linear Random Forest regression used for constructing the mapping function showed best results in terms of root mean square error (RMSE), yielding an average RMSE error of 0.07+/-0.02 pixels at 30 m resolution, and 0.09+/-0.05 and 0.15+/-0.06 pixels at 10 m resolution for the same and adjacent Sentinel-2A orbits, respectively, for multiple tiles and multiple conditions. A simpler 1st order polynomial function (affine transformation) yielded RMSE of 0.08+/-0.02 pixels at 30 m resolution and 0.12+/-0.06 (same Sentinel-2A orbits) and 0.20+/-0.09 (adjacent orbits) pixels at 10 m resolution

Evaluation of Landsat-8 and Sentinel-2A Aerosol Optical Depth Retrievals Across Chinese Cities and Implications for Medium Spatial Resolution Urban Aerosol Monitoring

In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure and human population density variations. The recent availability of medium resolution Landsat-8 and Sentinel-2 satellite data provide the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. AOD retrieved from 30 m Landsat-8 and 10 m Sentinel-2A data using the Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network (AERONET) Version 3 AOD data for 20 Chinese cities in 2016. Stringent selection criteria were used to select contemporaneous data; only satellite and AERONET data acquired within 10 min were considered. The average satellite retrieved AOD over a 1470 m1470 m window centered on each AERONET site was derived to capture fine scale urban AOD variations. AERONET Level 1.5 (cloud-screened) and Level 2.0 (cloud-screened and also quality assured) data were considered. For the 20 urban AERONET sites in 2016 there were 106 (Level 1.5) and 67 (Level 2.0) Landsat-8 AERONET AOD contemporaneous data pairs, and 118 (Level 1.5) and 89 (Level 2.0) Sentinel-2A AOD data pairs. The greatest AOD values (>1.5) occurred in Beijing, suggesting that the Chinese capital was one of the most polluted cities in China in 2016. The LaSRC Landsat-8 and Sentinel-2A AOD retrievals agreed well with the AERONET AOD data (linear regression slopes > 0.96; coefficient of determination r(exp 2) > 0.90; root mean square deviation < 0.175) and demonstrate that the LaSRC is an effective and applicable medium resolution AOD retrieval algorithm over urban environments. The Sentinel-2A AOD retrievals had better accuracy than the Landsat-8 AOD retrievals, which is consistent with previously published research.The implications of the research and the potential for urban aerosol monitoring by combining the freely available Landsat-8 and Sentinel-2 satellite data are discussed

Examination of Sentinel-2A Multi-spectral Instrument (MSI) Reflectance Anisotropy and the Suitability of a General Method to Normalize MSI Reflectance to Nadir BRDF Adjusted Reflectance

The Sentinel-2A multi-spectral instrument (MSI) acquires multi-spectral reflective wavelength observations with directional effects due to surface reflectance anisotropy and changes in the solar and viewing geometry. Directional effects were examined by considering two ten day periods of Sentinel-2A data acquired close to the solar principal and orthogonal planes over approximately 20° × 10° of southern Africa. More than 6.6 million (January 2016) and 10.6 million (April 2016) pairs of reflectance observations sensed 3 or 7 days apart in the forward and backscatter directions in overlapping Sentinel-2A orbit swaths were considered. The Sentinel-2A data were projected into the MODIS sinusoidal projection but first had to be registered due to a misregistration issue evident in the overlapping orbits. The top of atmosphere reflectance data were corrected to surface reflectance using the SEN2COR atmospheric correction software. Only pairs of forward and backward reflectance values that were cloud and snow-free, unsaturated, and had no significant change in their 3 or 7 day separation, were considered. The maximum observed Sentinel-2A view zenith angle was 11.93°. Greater BRDF effects were apparent in the January data (acquired close to the solar principal plane) than the April data (acquired close to the orthogonal plane) and at higher view zenith angle. For the January data the average difference between the surface reflectance in the forward and backward scatter directions at the Sentinel-2A scan edges increased with wavelength from 0.035 (blue), 0.047 (green), 0.057 (red), 0.078 (NIR), to about 0.1 (SWIR). These differences may constitute a significant source of noise for certain applications. The suitability of a recently published methodology developed to generate Landsat nadir BRDF-adjusted reflectance (NBAR) was examined for Sentinel-2A application. The methodology uses fixed MODIS BRDF spectral parameters and is attractive because it has little sensitivity to the land cover type, condition, or surface disturbance and can be derived in a computationally efficient manner globally. It was applied to the southern Africa Sentinel- 2A data and shown to reduce Sentinel-2A BRDF effects. The average difference between the reflectance in the forward and backward scatter directions at the Sentinel-2A scan edges was smaller in the NBAR data than in the corresponding surface reflectance data. Residual BRDF effects in the Sentinel-2A NBAR data occurred likely because of atmospheric correction and sensor calibration errors and inadequacies in the NBAR derivation approach. These issues are discussed with recommendations for future research including global and red-edge Sentinel-2A NBAR derivation that were not considered in this study

Perbandingan Indeks NDVI Tanaman Mangrove di Muara Sungai Kalimireng, Gresik Menggunakan Kamera Multispektral dan Citra Sentinel-2

Hutan mangrove memiliki fungsi penting sebagai penyerap karbon cukup baik di bumi, peran ini memiliki dampak positif bagi ekosistem kehidupan. Informasi hutan mangrove dapat diperoleh melalui data penginderaan jauh. Teknologi penginderaan jauh memiliki efisiensi yang tinggi untuk keperluan monitoring hutan mangrove, akan tetapi reso-lusi spasial citra yang rendah kurang efektif untuk pemantauan wilayah kurang dari 100 hektar. Penelitian ini bertujuan untuk memanfaatkan teknologi kamera multispektral pada hutan mangrove wilayah perairan Kalimireng, Gresik sebagai moni-toring hutan mangrove yang selanjutnya dibandingkan dengan citra satelit Sentinel-2A. Dengan teknologi penginderaan jauh ini, jenis dan kerapatan mangrove dapat diestimasi berdasar-kan nilai NDVI. Berdasarkan hasil pengambilan foto udara menggunakan kamera MAPIR 3 Survey nilai indeks NDVI berkisar di antara -0,140704−0,985896, sedangkan nilai NDVI pada Sentinel-2A berkisar di antara -0,196931–0,64069. rentang tersebut diklasifikasikan menjadi 5 kelas untuk MAPIR dan 4 kelas untuk Sentinel-2A. Klasifikasi dibagi menjadi lahan tidak bervegetasi, kehijauan sangat rendah, kehijauan rendah, kehi-jauan sedang, kehijauan tinggi. Hasil akurasi dari kedua citra memiliki OA 75 % untuk kamera MAPIR sedangkan 73,33% untuk Sentinel-2. Hasil korelasi dengan 50 titik menunjukkan korelasi positif antara kamera multispektral dan citra satelit Sentinel-2A. Nilai uji akurasi menentukan bahwa luas mangrove didapatkan dari kelas kehijauan tinggi untuk MAPIR dan kelas kehijauan sedang untuk Sentinel-2A, masing-masing luas mangrove adalah 41.065 ha untuk kamera MAPIR dan 36.097 ha untuk Sentinel-2A

Exploitation of SAR and optical Sentinel data to detect rice crop and estimate seasonal dynamics of leaf area index

This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R^2>0.93) and good accuracies (RMSE<0.83, rRMSE_m<23.6% and rRMSE_r<16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring

Assessing the Capability of Sentinel-2A Data for Mapping Seagrass Percent Cover in Jerowaru, East Lombok

Remote sensing technology has been widely used in various applications related to natural resources and environment monitoring. In this paper, we evaluated the capability of new Sentinel-2A image to map the distribution and percent cover of seagrass in optically shallow water of Jerowaru coastal area, East Lombok. Seagrass distribution map was produced from radiometrically and geometrically corrected Sentinel-2A image with overall accuracy of 61.9%. Using empirical model, seagrass percent cover was predicted with maximum coefficient of determination (R2) of 0.51 and standard error of estimate (SE) of 19.4%. The results suggest that Sentinel-2A image can be used to perform seagrass mapping time and cost-effectively and can be further improved by incorporating more robust empirical modeling technique

An Automated Approach for Sub-Pixel Registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery

Moderate spatial resolution satellite data from the Landsat-8 OLI and Sentinel-2A MSI sensors together offer 10 m to 30 m multi-spectral reflective wavelength global coverage, providing the opportunity for improved combined sensor mapping and monitoring of the Earth’s surface. However, the standard geolocated Landsat-8 OLI L1T and Sentinel-2A MSI L1C data products are currently found to be misaligned. An approach for automated registration of Landsat-8 OLI L1T and Sentinel-2A MSI L1C data is presented and demonstrated using contemporaneous sensor data. The approach is computationally efficient because it implements feature point detection across four image pyramid levels to identify a sparse set of tie-points. Area-based least squares matching around the feature points with mismatch detection across the image pyramid levels is undertaken to provide reliable tie-points. The approach was assessed by examination of extracted tie-point spatial distributions and tie-point mapping transformations (translation, affine and second order polynomial), dense-matching prediction-error assessment, and by visual registration assessment. Two test sites over Cape Town and Limpopo province in South Africa that contained cloud and shadows were selected. A Landsat-8 L1T image and two Sentinel-2A L1C images sensed 16 and 26 days later were registered (Cape Town) to examine the robustness of the algorithm to surface, atmosphere and cloud changes, in addition to the registration of a Landsat-8 L1T and Sentinel-2A L1C image pair sensed 4 days apart (Limpopo province). The automatically extracted tie-points revealed sensor misregistration greater than one 30 m Landsat-8 pixel dimension for the two Cape Town image pairs, and greater than one 10 m Sentinel-2A pixel dimension for the Limpopo image pair. Transformation fitting assessments showed that the misregistration can be effectively characterized by an affine transformation. Hundreds of automatically located tie-points were extracted and had affine-transformation root-mean-square error fits of approximately 0.3 pixels at 10 m resolution and dense-matching prediction errors of similar magnitude. These results and visual assessment of the affine transformed data indicate that the methodology provides sub-pixel registration performance required for meaningful Landsat-8 OLI and Sentinel-2A MSI data comparison and combined data applications

Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model

A large proportion of the global land surface is covered by pasture. The advent of the Sentinel satellites program provides free datasets with good spatiotemporal resolution that can be a valuable source of information for monitoring pasture resources. We combined optical remote sensing data (proximal hyperspectral and Sentinel 2A) with a radiative transfer model (PROSAIL) to estimate leaf area index (LAI), and biomass, in a dairy farming context. Three sites in Southern England were used: two pasture farms that differed in pasture type and management, and a set of small agronomy trial plots with different mixtures of grasses, legumes and herbs, as well as pure perennial ryegrass. The proximal and satellite spectral data were used to retrieve LAI via PROSAIL model inversion, which were compared against field observations of LAI. The potential of bands of Sentinel 2A that corresponded with a 10 m resolution was studied by convolving narrow spectral bands (from a handheld hyperspectral sensor) into Sentinel 2A bands (10 m). Retrieved LAI, using these spectrally resampled S2A data, compared well with measured LAI, for all sites, even for those with mixed species cover (although retrieved LAI was somewhat overestimated for pasture mixtures with high LAI). This proved the suitability of 10 m Sentinel 2A spectral bands for capturing LAI dynamics for different types of pastures. We also found that inclusion of 20 m bands in the inversion scheme did not lead to any further improvement in retrieved LAI. Sentinel 2A image based retrieval yielded good agreement with LAI measurements obtained for a typical perennial ryegrass based pasture farm. LAI retrieved in this way was used to create biomass maps (that correspond to indirect biomass measurements by Rising Plate Meter (RPM)), for mixed-species paddocks for a farm for which limited field data were available. These maps compared moderately well with farmer-collected RPM measurements for this farm. We propose that estimates of paddock-averaged and within-paddock variability of biomass are more reliably obtained from a combined Sentinel 2A-PROSAIL approach, rather than by manual RPM measurements. The physically based radiative transfer model inversion approach outperformed the Normalised Difference Vegetation Index based retrieval method, and does not require site specific calibrations of the inversion scheme

Pemanfaatan Citra Sentinel-2A untuk Estimasi Produksi Pucuk Teh di Sebagian Kabupaten Karanganyar

Penginderaan jauh saat ini telah berkembang sangat pesat, mulai dari bertambahnya citra baru dan gratis untuk diakses siapa saja hingga pemanfaatanya yang kian bervariasi untuk diaplikasian dalam berbagai bidang tak terkecuali bidang perkebunan. Tujuan dari penelitian ini ialah 1) mengkaji akurasi pemodelan hasil estimasi produksi pucuk teh berbasis transformasi indeks vegetasi (NDVI, SAVI, ARVI), 2) mengkaji kemampuan Citra Sentinel- 2A dalam mengkorelasikan antara variabel estimasi produksi (kerapatan tajuk dan pantulan spektral tanaman teh) dengan beberapa transformasi indeks vegetasi dan 3) mengestimasi produksi pucuk teh di perkebunan teh Kemuning tahun 2016. Hasil akurasi perhitungan estimasi produksi pucuk teh yang didapatkan dari pemanfaatan citra Sentinel-2A tahun 2016 ialah sebesar 58.84% dengan RMSE sebesar 201,105 kg/ha. Citra Sentinel-2A mampu mengkorelasikan hasil estimasi produksi pucuk teh dengan beberapa transformasi indeks vegetasi. Korelasi regresi tertinggi yakni pada produktivitas pucuk teh di lapangan dengan transformasi indeks vegetasi ARVI dengan korelasi regresi sebesar 63% yang mengindikasikan hubungan yang kuat antar dua variabel tersebut. Produksi total atau keseluruhan estimasi produksi pucuk teh perkebunan Kemuning bulan oktober tahun 2016 ialah sebesar 215,041 ton

ГЕОЛОГИЧЕСКИЕ ПРИЗНАКИ, ВЫЯВЛЕННЫЕ ПО ДАННЫМ ПОЛЕВЫХ НАБЛЮДЕНИЙ И ДИСТАНЦИОННОГО ЗОНДИРОВАНИЯ В РАЙОНЕ УМ ТАГИР, ВОСТОЧНАЯ ПУСТЫНЯ, ЕГИПЕТ

The current study presents the integration between field observations and remotely sensed data for detection and extraction of geological structural features using Sentinel-2A and Aster DEM images. The area under investigation is represented by the Neoproterozoic East African Orogeny encompassing a part of the Arabian Nubian Shield. All the rock units studied belong to the Late Cryogenian-Ediacaran periods that are divided into two large terrains (continental arc terrain and oceanic arc terrain). The metagabbro and metavolcaniclastic rocks make up the oceanic terrain, while the gabbro, tonalite, granodiorite, dokhan volcanic, monzogranite and alkali feldspar granite comprise continental arc terrain. The Sentinel-2A remote sensing and ASTER DEM data have meaningful application in respect of geological interpretation. Lineament analysis is one of the most useful tools in geological mapping and mineral exploration. Several methods of processing and extracting lithological information and lineaments were applied to the Sentinel-2A and ASTER DEM data covering the present study. The methods include various image enhancements (FCC, MNF and PCA) and the application of directional filters (Sobel). The study results show that the area was subjected to stresses of various directions (WNW – ESE, NW-SE, NE-SW, N-S, NNE – SSW, and E-W). There occurred some important structure-related and mineralization events like migmatization (in granodiorites) and bearing mineralization (in gabbro), which are associated with major elements of evidence-based structural control of the area and with the proximity of the Quena Safaga shear zone-related mineralization.Темой исследования является интеграция данных полевых наблюдений и дистанционного зондирования с целью обнаружения и извлечения геолого-структурных признаков с помощью спутниковых снимков Sentinel-2A и цифровой модели рельефа ASTER DEM. Исследуемый район представляет собой неопротерозойскую восточно-африканскую орогению, охватывающую часть Нубийско-Аравийского щита. Изучаемые толщи пород относятся к позднему криогению – эдиакарию и образуют два террейна (континентальный дуговой террейн и океанический дуговой террейн). Океанический террейн сложен метагаббро и метавулканокластическими породами, а габбро, тоналиты, гранодиориты, доханские вулканиты, монцониты и щелочные полевошпатовые граниты слагают континентальный дуговой террейн. Данные со спутника Sentinel-2A, а также данные ASTER DEM используются для геологической интерпретации. Линеаментный анализ является одним из наиболее полезных инструментов геологического картирования и разведки полезных ископаемых. В рамках исследования были использованы методы для обработки данных Sentinel-2A и ASTER DEM, извлечения литологической информации из снимков и проведения линеаментного анализа снимков. Это различные методы увеличения изображения (FCC, MNF и PCA) и применения направленных фильтров (Sobel). Результаты исследования указывают на то, что в данной области имели место напряжения различной направленности (ЗСЗ – ВЮВ, СЗ-ЮВ, СВ-ЮЗ, С-Ю, ССВ – ЮЮЗ и В-З). Образовалось несколько крупных сопутствующих структур и произошло несколько событий в процессе минерализации, таких как мигматизация (в гранодиоритах) и минерализация по простиранию (в габбро), обусловленных главными элементами научно обоснованного структурного контроля территории и близостью нахождения минерализованной зоны сдвига Кена-Сафага