scholarly journals Monitoring Hybrid Rice Phenology at Initial Heading Stage Based on Low-Altitude Remote Sensing Data

2020 ◽  
Vol 13 (1) ◽  
pp. 86
Author(s):  
Yi Ma ◽  
Qi Jiang ◽  
Xianting Wu ◽  
Renshan Zhu ◽  
Yan Gong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Hybrid Rice ◽  
Remote Sensing Data ◽  
Rice Cultivars ◽  
Sensing Data ◽  
Gauss Function ◽  
Low Altitude ◽  
Rice Phenology ◽  
Heading Stage

Accurate monitoring of hybrid rice phenology (RP) is crucial for breeding rice cultivars and controlling fertilizing amount. The aim of this study is to monitor the exact date of hybrid rice initial heading stage (IHSDAS) based on low-altitude remote sensing data and analyze the influence factors of RP. In this study, six field experiments were conducted in Ezhou city and Lingshui city from 2016 to 2019, which involved different rice cultivars and nitrogen rates. Three low-altitude remote sensing platforms were used to collect rice canopy reflectance. Firstly, we compared the performance of normalized difference vegetation index (NDVI) and red edge chlorophyll index (CIred edge) for monitoring RP. Secondly, double logistic function (DLF), asymmetric gauss function (AGF), and symmetric gauss function (SGF) were used to fit time-series CIred edge for acquiring phenological curves (PC), the feature: maximum curvature (MC) of PC was extracted to monitor IHSDAS. Finally, we analyzed the influence of rice cultivars, N rates, and air temperature on RP. The results indicated that CIred edge was more appropriate than NDVI for monitoring RP without saturation problem. Compared with DLF and AGF, SGF could fit CIred edge without over fitting problem. MC of SGF_CIred edge from all three platforms showed good performance in monitoring IHSDAS with good robustness, R2 varied between 0.82 and 0.95, RMSE ranged from 2.31 to 3.81. In addition, the results demonstrated that high air temperature might cause a decrease of IHSDAS, and the growth process of rice was delayed when more nitrogen fertilizer was applied before IHSDAS. This study illustrated that low-altitude remote sensing technology could be used for monitoring field-scale hybrid rice IHSDAS accurately.

Estimation of instantaneous air temperature using remote sensing data

2017 ◽  
Vol 39 (1) ◽  
pp. 258-275 ◽  
Author(s):  
Foroogh Golkar ◽  
Ali Akbar Sabziparvar ◽  
Reza Khanbilvardi ◽  
Mohammad Jafar Nazemosadat ◽  
Shahrokh Zand- Parsa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Remote Sensing Data ◽  
Sensing Data

Impact of Observational Environment Change on Air Temperature Based on High-Spatial-Resolution Satellite Remote Sensing Data

2020 ◽  
Vol 40 (10) ◽  
pp. 1028001
Author(s):  
陈世涵 Chen Shihan ◽  
李玲 Li Ling ◽  
蒋弘凡 Jiang Hongfan ◽  
居伟杰 Ju Weijie ◽  
张曼玉 Zhang Manyu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Air Temperature ◽  
Satellite Remote Sensing ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Environment Change ◽  
Sensing Data

Estimating basin-wide air temperature by partial integration of remote sensing data

2018 ◽  
Vol 55 (10) ◽  
pp. 1196-1206
Author(s):  
Vedran Ivezic ◽  
Damir Bekic ◽  
Igor Kerin
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Standard Methods ◽  
Sensing Data ◽  
Interpolation Methods ◽  
Partial Integration

A comparison of various methods that enable temporally continuous computation of basin-wide air temperature is presented. An approach that combines remote sensing data with measurements at meteorological stations for obtaining basin-wide air temperature is proposed and compared to the standard interpolation methods of point measurements. For a basin of over 1000 km2, the proposed approach provides significantly more reliable air temperature rasters (average Δ = 9%) than the standard interpolation methods (average Δ = 25%), all by using satellite images and measurements from only two meteorological stations in comparison to standard methods using measurements from 10 meteorological stations.

scholarly journals Air temperature estimation based on environmental parameters using remote sensing data

2018 ◽  
Vol 18 (48) ◽  
pp. 131-152
Author(s):  
Chenoor Mohammadi ◽  
Manouchehr Farajzadeh ◽  
Yousef Ghavdel Rahimi ◽  
Abbas Ali Aliakbar Bidokhti ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Remote Sensing Data ◽  
Environmental Parameters ◽  
Temperature Estimation ◽  
Sensing Data ◽  
Air Temperature Estimation

scholarly journals Model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s

2019 ◽  
Vol 11 (2) ◽  
pp. 183 ◽  
Author(s):  
Shangmin Zhao ◽  
Shifang Zhang ◽  
Weiming Cheng ◽  
Chenghu Zhou
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Remote Sensing Data ◽  
Data Interpretation ◽  
Qilian Mountains ◽  
Bench Mark ◽  
Sensing Data ◽  
Mountain Permafrost ◽  
Simulation Results ◽  
The Qilian Mountains

Based on the results of remote sensing data interpretation, this paper aims to simulate and predict the mountain permafrost distribution changes affected by the mean decadal air temperature (MDAT), from the 1990s to the 2040s, in the Qilian Mountains. A bench-mark map is visually interpreted to acquire a mountain permafrost distribution from the 1990s, based on remote sensing images. Through comparison and estimation, a logistical regression model (LRM) is constructed using the bench-mark map, topographic and land coverage factors and MDAT data from the 1990s. MDAT data from the 2010s to the 2040s are predicted according to survey data from meteorological stations. Using the LRM, MDAT data and the factors, the probabilities (p) of decadal mountain permafrost distribution from the 1990s to the 2040s are simulated and predicted. According to the p value, the permafrost distribution statuses are classified as ‘permafrost probable’ (p > 0.7), ‘permafrost possible’ (0.7 ≥ p ≥ 0.3) and ‘permafrost improbable’ (p < 0.3). From the 1990s to the 2040s, the ‘permafrost probable’ type mainly degrades to that of ‘permafrost possible’, with the total area degenerating from 73.5 × 103 km2 to 66.5 × 103 km2. The ‘permafrost possible’ type mainly degrades to that of ‘permafrost impossible’, with a degradation area of 6.5 × 103 km2, which accounts for 21.3% of the total area. Meanwhile, the accuracy of the simulation results can reach about 90%, which was determined by the validation of the simulation results for the 1990s, 2000s and 2010s based on remote sensing data interpretation results. This research provides a way of understanding the mountain permafrost distribution changes affected by the rising air temperature rising over a long time, and can be used in studies of other mountains with similar topographic and climatic conditions.

Air temperature retrieval from remote sensing data based on thermodynamics

2004 ◽  
Vol 80 (1) ◽  
pp. 37-48 ◽  
Author(s):  
Y.-J. Sun ◽  
J.-F. Wang ◽  
R.-H. Zhang ◽  
R. R. Gillies ◽  
Y. Xue ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Temperature Retrieval
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