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

Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series

Greenhouse mapping through remote sensing has received extensive attention over the last decades. In this article, the innovative goal relies on mapping greenhouses through the combined use of very high resolution satellite data (WorldView-2) and Landsat 8 Operational Land Imager (OLI) time series within a context of an object-based image analysis (OBIA) and decision tree classification. Thus, WorldView-2 was mainly used to segment the study area focusing on individual greenhouses. Basic spectral information, spectral and vegetation indices, textural features, seasonal statistics and a spectral metric (Moment Distance Index, MDI) derived from Landsat 8 time series and/or WorldView-2 imagery were computed on previously segmented image objects. In order to test its temporal stability, the same approach was applied for two different years, 2014 and 2015. In both years, MDI was pointed out as the most important feature to detect greenhouses. Moreover, the threshold value of this spectral metric turned to be extremely stable for both Landsat 8 and WorldView-2 imagery. A simple decision tree always using the same threshold values for features from Landsat 8 time series and WorldView-2 was finally proposed. Overall accuracies of 93.0% and 93.3% and kappa coefficients of 0.856 and 0.861 were attained for 2014 and 2015 datasets, respectively

Perbandingan Model Estimasi Kandungan Nitrogen Padi Menggunakan Citra Hiperspektral dan Multispektral Sebagian Wilayah Kabupaten Sleman

Penelitian ini memiliki tujuan untuk mengetahui kemampun citra hipersepektral dan multispektral dalam mengestimasi kandungan nitrogen padi di sebagian wilayah Kabupaten Sleman menggunakan pendekatan semi-empiris. Bersamaan dengan itu juga menghitung besarnya akurasi pemetaan yang didapatkan. Diukur pula kemampuan data penginderaan multisensor untuk memetakan penggunaan lahan, sawah irigasi, yang menjadi dasar pengambilan data. Citra Hyperion dan citra Landsat 8 OLI digunakan sebagai data rujukan untuk pembuatan model estimasi.Hasil penelitian menunjukkan bahwa citra Hyperion dapat menyajikan informasi penggunaan lahan lebih baik dibandingkan dengan citra Landsat 8 OLI dengan akurasi pemetaan sebesar 87,78%. Model estimasi nitrogen padi terbaik dimiliki oleh NDNI (Hyperion) dengan nilai RMSE 0,45 dan r 0,53. Diikuti oleh OSAVI (Hyperion) RMSE=0,50, OSAVI (Landsat 8 OLI) RMSE=0,67 dan NDNI (Landsat 8 OLI) RMSE=0,80. Berdasarkan informasi tersebut, citra hiperspektral mampu menggambarkan informasi nitrogen padi di wilayah kajian lebih baik dibandingkan dengan citra Landsat 8 OLI. Hal ini disebabkan oleh karakteristik spektral citra

Pemanfaatan Citra Landsat 8 untuk Pemetaan Kekeringan Pertanian dengan Transformasi Temperature Vegetation Dryness Index (Tvdi) di Kabupaten Sukoharjo Tahun 2013 - 2014

Kekeringan diyakini oleh sebagian besar orang sebagai bencana alam yang paling komplek. Dampak dari kekeringan bervariasi dari perseorangan sampai dengan mengancam keamanan nasional. Kekeringan merupakan hal yang normal, bagian dari iklim yang dapat terjadi secara berulang dan bisa terjadi di setiap belahan bumi. Sektor pertanian sering merupakan bagian pertama yang mengalami dampak kekeringan karena ketergantungannya pada lengas tanah. Perkembangan teknologi penginderaan jauh memberikan solusi baru bagi masalah-masalah lingkungan serta bencana alam (kekeringan). Teknik penginderaan jauh yang mengkombinasikan pantulan spektral dan pantulan panas objek memberikan dimensi baru dalam menyediakan informasi mengenai lengas tanah. Penelitian ini mencoba untuk menilai kekeringan pertanian berdasarkan teknik penginderaan jauh yaitu dengan transformasi Temperature Vegetation Dryness Index (TVDI). TVDI merupakan indek kekeringan berdasarkan data penginderaan jauh yang mengkombinasikan pantulan spektral, dalam hal ini adalah Normalized Differrence Vegetation Index (NDVI), dan pantulan panas (LST) dari citra Landsat 8. Hasil pengolahan citra Landsat 8 menunjukkan bahwa musim kering mulai dialami lahan pertanian di Kabupaten Sukoharjo pada bulan Agustus hingga Oktober 2014. Kondisi tersebut sesuai dengan kondisi vegetasi yang mengalami penurunan tingkat kesehatan serta peningkatan suhu permukaan tanah pada periode waktu yang sama

Computationally Inexpensive Landsat 8 Operational Land Imager (OLI) Pansharpening

Pansharpening algorithms fuse higher spatial resolution panchromatic with lower spatial resolution multispectral imagery to create higher spatial resolution multispectral images. The free-availability and systematic global acquisition of Landsat 8 data indicate an expected need for global coverage and so computationally efficient Landsat 8 pansharpening. This study adapts and evaluates the established, and relatively computationally inexpensive, Brovey and context adaptive Gram Schmidt component substitution (CS) pansharpening methods for application to the Landsat 8 15 m panchromatic and 30 m red, green, blue, and near-infrared bands. The intensity images used by these CS pansharpening methods are derived as a weighted linear combination of the multispectral bands in three different ways using band spectral weights set (i) equally as the reciprocal of the number of bands; (ii) using fixed Landsat 8 spectral response function based (SRFB) weights derived considering laboratory spectra; and (iii) using image specific spectral weights derived by regression between the multispectral and the degraded panchromatic bands. The spatial and spectral distortion and computational cost of the different methods are assessed using Landsat 8 test images acquired over agricultural scenes in South Dakota, China, and India. The results of this study indicate that, for global Landsat 8 application, the context adaptive Gram Schmidt pansharpening with an intensity image defined using the SRFB spectral weights is appropriate. The context adaptive Gram Schmidt pansharpened results had lower distortion than the Brovey results and the least distortion was found using intensity images derived using the SRFB and image specific spectral weights but the computational cost using the image specific weights was greater than the using the SRFB weights. Recommendations for large area Landsat 8 pansharpening application are described briefly and the SRFB spectral weights are provided so users may implement computationally inexpensive Landsat 8 pansharpening themselves

Temporal changes in mediterranean pine forest biomass using synergy models of ALOS PALSAR-Sentinel 1-Landsat 8 Sensors

Currently, climate change requires the quantification of carbon stored in forest biomass. Synthetic aperture radar (SAR) data offers a significant advantage over other remote detection measurement methods in providing structural and biomass-related information about ecosystems. This study aimed to develop non-parametric Random Forest regression models to assess the changes in the aboveground forest biomass (AGB), basal area (G), and tree density (N) of Mediterranean pine forests by integrating ALOS-PALSAR, Sentinel 1, and Landsat 8 data. Variables selected from the Random Forest models were related to NDVI and optical textural variables. For 2015, the biomass models with the highest performance integrated ALS-ALOS2-Sentinel 1-Landsat 8 data (R2 = 0.59) by following the model using ALS data (R2 = 0.56), and ALOS2-Sentinel 1-Landsat 8 (R2 = 0.50). The validation set showed that R2 values vary from 0.55 (ALOS2-Sentinel 1-Landsat 8) to 0.60 (ALS-ALOS2-Sentinel 1-Landsat 8 model) with RMSE below 20 Mg ha−1. It is noteworthy that the individual Sentinel 1 (R2 = 0.49). and Landsat 8 (R2 = 0.47) models yielded equivalent results. For 2020, the AGB model ALOS2-Sentinel 1-Landsat 8 had a performance of R2 = 0.55 (validation R2 = 0.70) and a RMSE of 9.93 Mg ha−1. For the 2015 forest structural variables, Random Forest models, including ALOS PAL-SAR 2-Sentinel 1 Landsat 8 explained between 30% and 55% of the total variance, and for the 2020 models, they explained between 25% and 55%. Maps of the forests’ structural variables were generated for 2015 and 2020 to assess the changes during this period using the ALOS PALSAR 2-Sentinel 1-Landsat 8 model. Aboveground biomass (AGB), diameter at breast height (dbh), and dominant height (Ho) maps were consistent throughout the entire study area. However, the Random Forest models underestimated higher biomass levels (>100 Mg ha−1) and overestimated moderate biomass levels (30–45 Mg ha−1). The AGB change map showed values ranging from gains of 43.3 Mg ha−1 to losses of −68.8 Mg ha−1 during the study period. The integration of open-access satellite optical and SAR data can significantly enhance AGB estimates to achieve consistent and long-term monitoring of forest carbon dynamics

Landsat 8 Observation of the Internal Solitary Waves in the Lombok Strait

Landsat  8,  Landsat  Data  Continuity  Mission  (LDCM)  satellite,  was  launched  on  11 February 2013 with Operation Land Imager (OLI) sensors. Tis sensor has better radiometric performance than the previous mission, which is quantized in the 12-bit dynamic range due to an increase in the signal-to-noise (SNR) ratio. In this analysis, the spatio-temporal distribution of the propagation of the internal solitary wave (ISW) in the Lombok Strait was extracted from the Landsat 8 images described for the first time.  Tere were 14 ISW events studied for period 2014  -  2015  using  Landsat  8.  Te  manifestations  of  ISW  recorded  on  Landsat  8  images  were then extracted using digitization method to investigate and measure several parameters and ISW distribution in the Lombok Strait. Te estimation results of the average ISW phase velocity in this study are 2.05 ms-1 with the direction of propagation heading north at an average angle of 19.08°. Tis study has shown that Landsat 8 can be used to monitor and analyze several internal wave parameters in the ocean