scholarly journals Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection

2021 ◽  
Vol 13 (2) ◽  
pp. 266
Author(s):  
Yiting Wang ◽  
Donghui Xie ◽  
Yinggang Zhan ◽  
Huan Li ◽  
Guangjian Yan ◽  
...  
Keyword(s):  
Input Data ◽  
Temporal Distribution ◽  
Optimal Combination ◽  
Growth Stages ◽  
Landsat 8 ◽  
Fusion Model ◽  
Modis Ndvi ◽  
Image Pairs ◽  
Limited Input Data ◽  
Fine Resolution

Despite its wide applications, the spatiotemporal fusion of coarse- and fine-resolution satellite images is limited primarily to the availability of clear-sky fine-resolution images, which are commonly scarce due to unfavorable weather, and such a limitation might cause errors in spatiotemporal fusion. Thus, the effective use of limited fine-resolution images, while critical, remains challenging. To address this issue, in this paper we propose a new phenological similarity strategy (PSS) to select the optimal combination of image pairs for a prediction date. The PSS considers the temporal proximity and phenological similarity between the base and prediction images and computes a weight for identifying the optimal combination of image pairs. Using the PSS, we further evaluate the influence of input data on the fusion accuracy by varying the number and temporal distribution of input images. The results show that the PSS (mean R = 0.827 and 0.760) outperforms the nearest date (mean R = 0.786 and 0.742) and highest correlation (mean R = 0.821 and 0.727) strategies in both the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the linear mixing growth model (LMGM), respectively, for fusing Landsat 8 OLI and MODIS NDVI datasets. Furthermore, base images adequately covering different growth stages yield better predictability than simply increasing the number of base images.

scholarly journals Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of “Super Resolution-Then-Blend” and “Blend-Then-Super Resolution” Approaches

2021 ◽  
Vol 13 (4) ◽  
pp. 606
Author(s):  
Tee-Ann Teo ◽  
Yu-Ju Fu
Keyword(s):  
Satellite Images ◽  
Traditional Approach ◽  
Spectral Band ◽  
Super Resolution ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Fusion Model ◽  
Image Blending ◽  
Fine Resolution

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion.

scholarly journals Simulation of Daily Mean Soil Temperatures for Agricultural Land Use Considering Limited Input Data

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 441
Author(s):  
Philipp Grabenweger ◽  
Branislava Lalic ◽  
Miroslav Trnka ◽  
Jan Balek ◽  
Erwin Murer ◽  
...  
Keyword(s):  
Soil Temperature ◽  
Input Data ◽  
Agricultural Land ◽  
Soil Column ◽  
Soil Conditions ◽  
Snow Layer ◽  
Time Step ◽  
Soil Temperatures ◽  
Dimensional Simulation ◽  
Limited Input Data

A one-dimensional simulation model that simulates daily mean soil temperature on a daily time-step basis, named AGRISOTES (AGRIcultural SOil TEmperature Simulation), is described. It considers ground coverage by biomass or a snow layer and accounts for the freeze/thaw effect of soil water. The model is designed for use on agricultural land with limited (and mostly easily available) input data, for estimating soil temperature spatial patterns, for single sites (as a stand-alone version), or in context with agrometeorological and agronomic models. The calibration and validation of the model are carried out on measured soil temperatures in experimental fields and other measurement sites with various climates, agricultural land uses and soil conditions in Europe. The model validation shows good results, but they are determined strongly by the quality and representativeness of the measured or estimated input parameters to which the model is most sensitive, particularly soil cover dynamics (biomass and snow cover), soil pore volume, soil texture and water content over the soil column.

scholarly journals Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

2020 ◽  
Vol 12 (12) ◽  
pp. 2015 ◽  
Author(s):  
Manuel Ángel Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando José Aguilar ◽  
Dilek Koc-San ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Remote Sensing Techniques ◽  
Image Pairs ◽  
Study Sites ◽  
High Level ◽  
Sentinel 2

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

Estimation of Evapotranspiration using Remote Sensing and Surface Energy Balance Algorithm for Land (SEBAL) in Canal Command

2021 ◽  
Vol 58 (03) ◽  
pp. 274-285
Author(s):  
H. V. Parmar ◽  
N. K. Gontia
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Surface Energy ◽  
Land Surface ◽  
Surface Energy Balance ◽  
Crop Growth ◽  
Growth Stages ◽  
Landsat 8 ◽  
Surface Temperatures ◽  
Canal Command

Remote sensing based various land surface and bio-physical variables like Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), surface albedo, transmittance and surface emissivity are useful for the estimation of spatio-temporal variations in evapotranspiration (ET) using Surface Energy Balance Algorithm for Land (SEBAL) method. These variables were estimated under the present study for Ozat-II canal command in Junagadh district, Gujarat, India, using Landsat-7 and Landsat-8 images of summer season of years 2014 and 2015. The derived parameters were used in SEBAL to estimate the Actual Evapotranspiration (AET) of groundnut and sesame crops. The lower values NDVI observed during initial (March) and end (May) stages of crop growth indicated low vegetation cover during these periods. With full canopy coverage of the crops, higher value of NDVI (0.90) was observed during the mid-crop growth stage. The remote sensing-based LST was lower for agricultural areas and the area near banks of the canal and Ozat River, while higher surface temperatures were observed for rural settlements, road and areas with exposed dry soil. The maximum surface temperatures in the cropland were observed as 311.0 K during March 25, 2014 and 315.8 K during May 31, 2015. The AET of summer groundnut increased from 3.75 to 7.38 mm.day-1, and then decreased to 3.99 mm.day-1 towards the end stage of crop growth. The daily AET of summer sesame ranged from 1.06 to 7.72 mm.day-1 over different crop growth stages. The seasonal AET of groundnut and sesame worked out to 358.19 mm and 346.31 mm, respectively. The estimated AET would be helpful to schedule irrigation in the large canal command.

Evaluating a method for reconstruction of global, zonal and regional mean temperatures from sparse proxy data

2021 ◽  
Author(s):  
Maximilian May ◽  
Nils Weitzel ◽  
Lukas Jonkers ◽  
Kira Rehfeld
Keyword(s):  
Surface Temperature ◽  
Input Data ◽  
Temporal Distribution ◽  
Weighted Sums ◽  
Glacial Cycle ◽  
Science Data ◽  
Proxy Data ◽  
Data Synthesis ◽  
Temperature Reconstructions ◽  
Global Mean

&lt;p&gt;Global mean surface temperature is a fundamental measure for climate evolution in both past and present and a key quantity to evaluate climate simulations. However, for paleoclimate periods, its calculation hinges on proxy data distributed sparsely and inhomogeneously in both space and time. Thus, large sets of different proxy records need to be combined in order to obtain global mean temperature reconstructions, but there is no widely accepted method to perform this task. Building on the work of [1], we suggest and evaluate an algorithm to reconstruct spatially averaged surface temperatures on centennial to orbital timescales. As the most abundant archive for continuous temperature reconstructions, we focus on marine sediment records as input data. Our implementation is applicable to any compilation of sea-surface temperature reconstructions and capable of calculating global, hemispherical and regional temperature. Major steps of the reconstruction algorithm are interpolation to a common timescale, zonal normalization and calculation of spatially weighted sums, including uncertainty propagation via Monte Carlo methods. We assess the applicability of the algorithm by employing it to the PalMod130k marine palaeoclimate data synthesis [2] and to pseudo-proxy data generated from transient simulations of the last glacial cycle. Our results suggest that the algorithm is capable of calculating average temperatures mostly consistent with expectations, however capturing centennial-scale variability is limited due to the low spatio-temporal distribution of the input data. This underlines the importance of both increasing the amount, resolution and age control of proxy data as well as extending the algorithm such that it also incorporates other types of paleoclimate archives.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;References:&lt;/p&gt;&lt;p&gt;[1] &amp;#160;C. W. Snyder, &amp;#8220;Evolution of global temperature over the past two million years,&amp;#8221; Nature, vol. 538, no. 7624, pp. 226&amp;#8211;228, 2016&lt;/p&gt;&lt;p&gt;[2] &amp;#160;L. Jonkers, O. Cartapanis, M. Langner, N. McKay, S. Mulitza, A. Strack, and M. Kucera, &amp;#8220;Integrating palaeoclimate time series with rich metadata for uncertainty modelling: &amp;#160;Strategy and documentation of the PALMOD 130k marine palaeoclimate data synthesis,&amp;#8221; Earth System Science Data, vol. 12, no. 2, pp. 1053&amp;#8211;1081, 2020&lt;/p&gt;

scholarly journals Using Remote Sensing to Monitor the Spring Phenology of Acadia National Park across Elevational Gradients

2019 ◽  
Author(s):  
Yan Liu ◽  
Caitlin McDonough MacKenzie ◽  
Richard B. Primack ◽  
Michael J. Hill ◽  
Xiaoyang Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
National Park ◽  
Vegetation Index ◽  
Landsat 8 ◽  
Mountainous Area ◽  
Acadia National Park ◽  
Fusion Model ◽  
Elevational Gradients ◽  
Spring Phenology

Abstract. Greenup dates of the mountainous Acadia National Park, were monitored using remote sensing data (including Landsat 8 surface reflectances (at a 30 m spatial resolution) and VIIRS reflectances adjusted to a nadir view (gridded at a 500 m spatial resolution)) during the 2013–2016 growing seasons. Ground-level leaf-out monitoring in the areas alongside the north-south-oriented hiking trails on three of the park's tallest mountains (466 m, 418 m, and 380 m) was used to evaluate satellite derived greenup dates in this study. While the 30 m resolution would be expected to provide a better scale for phenology detection in this mountainous region than the 500 m resolution, the daily temporal resolution of the 500 m data would be expected to offer vastly superior monitoring of the rapid variations experienced during vegetation greenup along elevational gradients. Therefore, the greenup dates derived from the Landsat 8 Enhanced Vegetation Index (EVI) data, augmented with Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) simulated EVI values, does provide more spatial details than VIIRS data alone and agree well with field monitored leaf out dates. Satellite derived greenup dates from the 30 m of Acadia National Park vary among different elevational zones, although the date of greenup is not always the most advanced at the lowest elevation. This indicates that the spring phenology is not only determined by microclimates associated with different elevations in this mountainous area, but is also possibly affected by the species mixture, localized temperatures, and other factors in Acadia.

scholarly journals SPATIAL-TEMPORAL DISTRIBUTION OF CHLOROPHYLL-A IN SOUTHERN PART OF THE MAKASSAR STRAIT

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Wasir Samad Daming ◽  
Muhammad Anshar Amran ◽  
Amir Hamzah Muhiddin ◽  
Rahmadi Tambaru
Keyword(s):  
Chlorophyll A ◽  
Satellite Data ◽  
Chlorophyll Concentration ◽  
Temporal Distribution ◽  
Fishing Ground ◽  
Landsat 8 ◽  
Trade Winds ◽  
Chl A ◽  
June July ◽  
Potential Area

Surface chlorophyll-a (Chl-a) distribution have been analyzed with seasonal variation during southeast monsoon in southern part of Makassar Strait and Flores Sea. Satellite data of Landsat-8 is applied to this study to formulate the distribution of chlorophyll concentration during monsoonal wind period. The distribution of chlorophyll concentration was normally peaked condition in August during southeast monsoon. Satellite data showed that a slowdown in the rise of the distribution of chlorophyll in September with a lower concentration than normal is likely due to a weakening the strength of southeast trade winds during June – July – August 2016. Further analysis shows that the southern part of the Makassar strait is likely occurrence of upwelling characterized by increase in surface chlorophyll concentrations were identified as the potential area of fishing ground.

scholarly journals Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 3038
Author(s):  
Dhahi Al-Shammari ◽  
Ignacio Fuentes ◽  
Brett M. Whelan ◽  
Patrick Filippi ◽  
Thomas F. A. Bishop
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Google Earth ◽  
Growth Stages ◽  
Landsat 8 ◽  
Crop Type ◽  
Eastern Australia ◽  
Normalised Difference Vegetation Index ◽  
Cotton Fields

A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.

scholarly journals Spatial and Temporal Distribution of Biomass Open Burning Emissions in the Greater Mekong Subregion

Climate ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 90
Author(s):  
Agapol Junpen ◽  
Jirataya Roemmontri ◽  
Athipthep Boonman ◽  
Penwadee Cheewaphongphan ◽  
Pham Thi Bich Thao ◽  
...  
Keyword(s):  
Fine Particulate Matter ◽  
Temporal Distribution ◽  
Landsat 8 ◽  
Total Biomass ◽  
Burnt Area ◽  
Fire Emissions ◽  
Vegetation Fires ◽  
Agricultural Burning ◽  
Burnt Areas ◽  
Greater Mekong Subregion

Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area products are widely used to assess the damaged area after wildfires and agricultural burning have occurred. This study improved the accuracy of the assessment of the burnt areas by using the MCD45A1 and MCD64A1 burnt area products with the finer spatial resolution product from the Landsat-8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) surface reflectance data. Thus, more accurate wildfires and agricultural burning areas in the Greater Mekong Subregion (GMS) for the year 2015 as well as the estimation of the fire emissions were reported. In addition, the results from this study were compared with the data derived from the fourth version of the Global Fire Emissions Database (GFED) that included small fires (GFED4.1s). Upon analysis of the data of the burnt areas, it was found that the burnt areas obtained from the MCD64A1 and MCD45A1 had lower values than the reference fires for all vegetation fires. These results suggested multiplying the MCD64A1 and MCD45A1 for the GMS by the correction factors of 2.11−21.08 depending on the MODIS burnt area product and vegetation fires. After adjusting the burnt areas by the correction factor, the total biomass burnt area in the GMS during the year 2015 was about 33.3 million hectares (Mha), which caused the burning of 109 ± 22 million tons (Mt) of biomass. This burning emitted 178 ± 42 Mt of CO2, 469 ± 351 kilotons (kt) of CH4, 18 ± 3 kt of N2O, 9.4 ± 4.9 Mt of CO, 345 ± 206 kt of NOX, 46 ± 25 kt of SO2, 147 ± 117 kt of NH3, 820 ± 489 kt of PM2.5, 60 ± 32 kt of BC, and 350 ± 205 kt of OC. Furthermore, the emission results of fine particulate matter (PM2.5) in all countries were slightly lower than GFED4.1s in the range between 0.3 and 0.6 times.

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