scholarly journals Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1485 ◽  
Author(s):  
Suming Zhang ◽  
Gengxing Zhao ◽  
Kun Lang ◽  
Baowei Su ◽  
Xiaona Chen ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Satellite Imagery ◽  
Mean Squared Error ◽  
Multiple Scales ◽  
Yellow River ◽  
Eastern Coast ◽  
Inversion Method ◽  
Inversion Model ◽  
The Core ◽  
Sentinel 2A

Chlorophyll is the most important component of crop photosynthesis, and the reviving stage is an important period during the rapid growth of winter wheat. Therefore, rapid and precise monitoring of chlorophyll content in winter wheat during the reviving stage is of great significance. The satellite-UAV-ground integrated inversion method is an innovative solution. In this study, the core region of the Yellow River Delta (YRD) is used as a study area. Ground measurements data, UAV multispectral and Sentinel-2A multispectral imagery are used as data sources. First, representative plots in the Hekou District were selected as the core test area, and 140 ground sampling points were selected. Based on the measured SPAD values and UAV multispectral images, UAV-based SPAD inversion models were constructed, and the most accurate model was selected. Second, by comparing satellite and UAV imagery, a reflectance correction for satellite imagery was performed. Finally, based on the UAV-based inversion model and satellite imagery after reflectance correction, the inversion results for SPAD values in multi-scale were obtained. The results showed that green, red, red-edge and near-infrared bands were significantly correlated with SPAD values. The modeling precisions of the best inversion model are R2 = 0.926, Root Mean Squared Error (RMSE) = 0.63 and Mean Absolute Error (MAE) = 0.92, and the verification precisions are R2 = 0.934, RMSE = 0.78 and MAE = 0.87. The Sentinel-2A imagery after the reflectance correction has a pronounced inversion effect; the SPAD values in the study area were concentrated between 40 and 60, showing an increasing trend from the eastern coast to the southwest and west, with obvious spatial differences. This study synthesizes the advantages of satellite, UAV and ground methods, and the proposed satellite-UAV-ground integrated inversion method has important implications for real-time, rapid and precision SPAD values collected on multiple scales.

scholarly journals Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6521
Author(s):  
Guanghui Qi ◽  
Gengxing Zhao ◽  
Xue Xi
Keyword(s):  
Winter Wheat ◽  
Soil Salinity ◽  
Agricultural Production ◽  
Yellow River ◽  
Yellow River Delta ◽  
The Yellow River ◽  
Inversion Method ◽  
Optimal Model ◽  
The Yellow River Delta ◽  
Uav Images

Soil salinization is an important factor affecting winter wheat growth in coastal areas. The rapid, accurate and efficient estimation of soil salt content is of great significance for agricultural production. The Kenli area in the Yellow River Delta was taken as the research area. Three machine learning inversion models, namely, BP neural network (BPNN), support vector machine (SVM) and random forest (RF) were constructed using ground-measured data and UAV images, and the optimal model is applied to UAV images to obtain the salinity inversion result, which is used as the true salt value of the Sentinel-2A image to establish BPNN, SVM and RF collaborative inversion models, and apply the optimal model to the study area. The results showed that the RF collaborative inversion model is optimal, R2 = 0.885. The inversion results are verified by using the measured soil salt data in the study area, which is significantly better than the directly satellite remote sensing inversion method. This study integrates the advantages of multi-scale data and proposes an effective “Satellite-UAV-Ground” collaborative inversion method for soil salinity, so as to obtain more accurate soil information, and provide more effective technical support for agricultural production.

scholarly journals EXTRACTION OF BUILT-UP AREA USING HIGH RESOLUTION SENTINEL-2A AND GOOGLE SATELLITE IMAGERY

2018 ◽  
Vol XLII-4/W9 ◽  
pp. 165-169 ◽  
Author(s):  
S. Vigneshwaran ◽  
S. Vasantha Kumar
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Near Infrared ◽  
Accurate Information ◽  
Open Source Data ◽  
Kappa Value ◽  
Moderate Resolution ◽  
Sentinel 2A ◽  
Index Value ◽  
Normalized Difference Index

<p><strong>Abstract.</strong> Accurate information about the built-up area in a city or town is essential for urban planners for proper planning of urban infrastructure facilities and other basic amenities. The normalized difference indices available in literature for built-up area extraction are mostly based on moderate resolution images such as Landsat Thematic Mapper (TM) and enhanced TM (ETM+) and may not be applicable for high resolution images such as Sentinel-2A. In the present study, an attempt has been made to extract the built-up area from Sentinel-2A satellite data of Chennai, India using normalized difference index (NDI) with different band combinations. It was found that the built-up area was clearly distinguishable when the index value ranges between &amp;minus;0.29 and &amp;minus;0.09 in blue and near-infrared (NIR) band combination. Post extraction editing using Google satellite imagery was also attempted to improve the extraction results. The results showed an overall accuracy of 90% and Kappa value of 0.785. Same approach when applied for another area also yields good results with overall accuracy of 92% and Kappa value of 0.83. As the proposed approach is simple to understand, yields accurate results and requires only open source data, the same can be used for extracting the built-up area using Sentinel-2A and Google satellite imagery.</p>

scholarly journals KLASIFIKASI HABITAT PERAIRAN DANGKAL MENGGUNAKAN LOGIKA FUZZY DAN MAXIMUM LIKELIHOOD PADA CITRA SATELIT MULTISPEKTRAL

2018 ◽  
Vol 10 (3) ◽  
pp. 667-681
Author(s):  
Muhammad Siddiq Sangadji ◽  
Vincentius Paulus Siregar ◽  
Henry Munandar Manik
Keyword(s):  
Remote Sensing ◽  
Fuzzy Logic ◽  
Maximum Likelihood ◽  
Satellite Imagery ◽  
Data Retrieval ◽  
Classification Algorithm ◽  
Benthic Habitat ◽  
Fuzzy Logic Algorithm ◽  
Accuracy Level ◽  
Sentinel 2A

ABSTRAKLogika fuzzy memiliki aplikasi di berbagai bidang, namun memiliki arti khusus untuk penginderaan jarak jauh. Logika fuzzy memungkinkan keanggotaan parsial, bagian yang sangat penting dibidang penginderaan jarak jauh, karena keanggotaan parsial diterjemahkan secara dekat dengan masalah piksel campuran. Penelitian ini bertujuan untuk menerapkan algoritma klasifikasi logika fuzzy untuk memetakan habitat dasar Perairan dangkal pada Citra Satelit SPOT 7 dan Sentinel 2A, menguji tingkat akurasinya dan membandingkan algoritma klasifikasi logika fuzzy dengan maximum likelihood. Pengambilan data lapang berlokasi di gusung Karang Lebar dan Karang Congkak, Kepuluan Seribu pada tanggal 6 Desember sampai dengan 10 Desember 2017. Keseluruhan hasil uji akurasi menunjukan bahwa algoritma logika fuzzy masih memiliki tingkat akurasi yang baik dibandingkan dengan algoritma maximum likelihood. Perbedaan ukuran pixel (resolusi spasial) dari citra satelit juga mempengaruhi hasil akurasi, dimana citra satelit SPOT 7 memiliki tingkat akurasi yang lebih besar dibandingkan dengan Sentinel 2A.ABSTRACTFuzzy logic has applications in various fields, but has special meaning for remote sensing. Fuzzy logic allows partial membership, a very important property in the field of remote sensing, since partial membership is translated closely to the problem of mixed pixels. The aim of this research is to apply fuzzy logic classification algorithm to map benthic habitat in SPOT 7 and Sentinel 2A satellite imagery, test its accuracy level and compare fuzzy logic classification algorithm with maximum likelihood. Field data retrieval located in Karang Lebar and Karang Congkak, Kepulauan Seribu on 6 December until 10 December 2017. The overall accuracy test results show that fuzzy logic algorithm still has a good accuracy level compared to the maximum likelihood algorithm. Differences in pixel size (spatial resolution) of satellite imagery also affect accuracy results, where SPOT 7 satellite imagery has greater accuracy then Sentinel 2A. 

Hyperspectral characteristics and inversion model estimation of winter wheat under different elevated CO2 concentrations

2020 ◽  
Vol 42 (3) ◽  
pp. 1035-1053
Author(s):  
Chao Liu ◽  
Zhenghua Hu ◽  
A.R.M. Towfiqul Islam ◽  
Rui Kong ◽  
Lingfei Yu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Elevated Co2 ◽  
Model Estimation ◽  
Inversion Model ◽  
Co2 Concentrations ◽  
And Inversion

scholarly journals Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream

2020 ◽  
Vol 12 (20) ◽  
pp. 3376 ◽  
Author(s):  
Giovanni Romano ◽  
Giovanni Francesco Ricci ◽  
Francesco Gentile
Keyword(s):  
Winter Wheat ◽  
Satellite Imagery ◽  
Vegetation Cover ◽  
Riparian Vegetation ◽  
Deciduous Forests ◽  
Sensor Data ◽  
Mountain Stream ◽  
Landsat 8 ◽  
Area Index ◽  
Sentinel 2

In recent decades, technological advancements in sensors have generated increasing interest in remote sensing data for the study of vegetation features. Image pixel resolution can affect data analysis and results. This study evaluated the potential of three satellite images of differing resolution (Landsat 8, 30 m; Sentinel-2, 10 m; and Pleiades 1A, 2 m) in assessing the Leaf Area Index (LAI) of riparian vegetation in two Mediterranean streams, and in both a winter wheat field and a deciduous forest used to compare the accuracy of the results. In this study, three different retrieval methods—the Caraux-Garson, the Lambert-Beer, and the Campbell and Norman equations—are used to estimate LAI from the Normalized Difference Vegetation Index (NDVI). To validate sensor data, LAI values were measured in the field using the LAI 2200 Plant Canopy Analyzer. The statistical indices showed a better performance for Pleiades 1A and Landsat 8 images, the former particularly in sites characterized by high canopy closure, such as deciduous forests, or in areas with stable riparian vegetation, the latter where stable reaches of riparian vegetation cover are almost absent or very homogenous, as in winter wheat fields. Sentinel-2 images provided more accurate results in terms of the range of LAI values. Considering the different types of satellite imagery, the Lambert-Beer equation generally performed best in estimating LAI from the NDVI, especially in areas that are geomorphologically stable or have a denser vegetation cover, such as deciduous forests.

scholarly journals Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China

2020 ◽  
Vol 12 (21) ◽  
pp. 3539
Author(s):  
Haifeng Tian ◽  
Jie Pei ◽  
Jianxi Huang ◽  
Xuecao Li ◽  
Jian Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Satellite Imagery ◽  
Remote Sensing Data ◽  
Northern China ◽  
Google Earth ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Winter Crops ◽  
Sentinel 2

Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery can be used to monitor spatial and temporal changes in croplands such as winter wheat and maize. However, to our knowledge, few studies are focusing on garlic area mapping. Here, we proposed a method for coupling active and passive satellite imagery for the identification of both garlic and winter wheat in Northern China. First, we used passive satellite imagery (Sentinel-2 and Landsat-8 images) to extract winter crops (garlic and winter wheat) with high accuracy. Second, we applied active satellite imagery (Sentinel-1 images) to distinguish garlic from winter wheat. Third, we generated a map of the garlic and winter wheat by coupling the above two classification results. For the evaluation of classification, the overall accuracy was 95.97%, with a kappa coefficient of 0.94 by eighteen validation quadrats (3 km by 3 km). The user’s and producer’s accuracies of garlic are 95.83% and 95.85%, respectively; and for the winter wheat, these two accuracies are 97.20% and 97.45%, respectively. This study provides a practical exploration of targeted crop identification in mixed planting areas using multisource remote sensing data.

scholarly journals Combined Use of Landsat-8 and Sentinel-2A Images for Winter Crop Mapping and Winter Wheat Yield Assessment at Regional Scale

2017 ◽  
Vol 3 (2) ◽  
pp. 163-186 ◽  
Author(s):  
Sergii Skakun ◽  
◽  
Eric Vermote ◽  
Jean-Claude Roger ◽  
Belen Franch ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Regional Scale ◽  
Wheat Yield ◽  
Landsat 8 ◽  
Combined Use ◽  
Winter Crop ◽  
Winter Wheat Yield ◽  
Crop Mapping ◽  
Sentinel 2A

scholarly journals GRID-SITES: Gridded Solar Iterative Temperature Emission Solver for Fast DEM Inversion

Solar Physics ◽  
2019 ◽  
Vol 294 (10) ◽  
Author(s):  
James Pickering ◽  
Huw Morgan
Keyword(s):  
Emission Measure ◽  
Inversion Method ◽  
Multiple Channels ◽  
Differential Emission Measure ◽  
The Core ◽  
Data Inversion ◽  
Core Method ◽  
Input Dataset ◽  
Order Of Magnitude ◽  
General Measurement

Abstract The increasing size of solar datasets demands highly efficient and robust analysis methods. This paper presents an approach that can increase the computational efficiency of differential emission measure (DEM) inversions by an order of magnitude or higher, with the efficiency factor increasing with the size of the input dataset. The method, named the Gridded Solar Iterative Temperature Emission Solver (Grid-SITES) is based on grouping pixels according to the similarity of their intensities in multiple channels, and solving for one DEM per group. This is shown to be a valid approach, given a sufficiently high number of grid bins for each channel. The increase in uncertainty arising from the quantisation of the input data is small compared to the general measurement and calibration uncertainties. In this paper, we use the Solar Iterative Temperature Emission Solver (SITES) as the core method for the DEM inversion, although Grid-SITES provides a general framework which may be used with any DEM inversion method, or indeed any large multi-dimensional data inversion problem. The method is particularly efficient for processing larger images, offering a factor of 30 increase in speed for a 10 megapixel image. For a time series of observations, the gridded results can be passed sequentially to each new image, with new populated bins added as required. This process leads to increasing efficiency with each new image, with potential for a ${\approx\,}100$≈100 increase in efficiency dependent on the size of the images.

Sign in / Sign up

Export Citation Format

Share Document