Retrieval of Sea Surface Wind Fields Using Multi-Source Remote Sensing Data

Hu, Tangao; Li, Yue; Wu, Yiyue; Zhang, Dengrong

doi:10.3390/rs12091482

Cited by 6 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In July, citrus entered the fruit expansion stage, the fruit began to grow and mature, and the color gradually changed from green to mature color (orange or yellow), and the occlusion effect of fruit expansion on canopy leaves gradually increased. Since October, when citrus enters the fruit maturity stage and winter shoot stage, flower bud differentiation and fruit ripening occur at the same time, and the influence of fruit occlusion on the canopy spectrum of citrus orchards is gradually weakened [32] . Based on the above characteristics, this study proposed a scientific hypothesis from the perspective of phenological information, that is, the vegetation information of citrus orchards may weaken during fruit growth and expansion, while the vegetation information of citrus orchards may gradually recover during fruit ripening and winter shoot occurrence.…”

Section: Phenology and Spectral Characteristicsmentioning

confidence: 99%

Identification of citrus orchard under vegetation indexes using multi-temporal remote sensing

Liang,

Huang,

Wang

et al. 2022

Advan. Mod. Agr.

View full text Add to dashboard Cite

<p>Citrus is widely planted in southern China. Due to cloudy and rainy weather, complex planting types, and other factors, it is difficult to use spectral information to directly identify citrus orchard information. Based on the unique phenological characteristics of citrus, this study put forward the hypothesis that “the vegetation information of citrus orchards may be weakened during the growth and expansion of citrus fruit”. According to this feature, a method of citrus orchard information identification is proposed, and the threshold of the key time window is determined. Taking Wuming District, Nanning City, and Guangxi Zhuang Autonomous Region as the research area, an empirical study on remote sensing identification of citrus orchard information is carried out. First, multi-temporal Sentinel-2 remote sensing images of the study area in 2018 were obtained, and a normalized difference vegetation index was constructed. NDVI, Green Normalized Difference Vegetation Index (GNDVI), Difference Vegetation Index (DVI), Sentinel-derived red-edge spectral indices (RESI), and other vegetation spectral indices Secondly, according to the ground sample point information, the difference in remote sensing vegetation information of different vegetation types in different periods was compared, and then the optimal features of citrus orchard identification were determined. The results showed that there was no significant difference in spectral characteristics between citrus orchards and other major crop types in the study area (such as sugarcane, banana, corn, rice, etc.), but the multi-temporal remote sensing vegetation index of the study area showed that the NDVI of citrus orchards in October was 0.47 lower than that of November, which was significantly lower than that of other crop types. In October, the GNDVI of the citrus orchard also showed a low value of 0.43, but the difference was not obvious compared with other months. However, the dispersion degree of citrus orchard DVI was low, and the separation was not strong. According to the crop phenological calendar, the period of rapid expansion of citrus fruits was from September to October, which verified the scientific hypothesis proposed in this study that the vegetation information of citrus orchards would be weakened during this period. The dispersion degree of different vegetation indexes in the citrus fruit expansion stage was obviously different, and the dispersion degree of NDVI was the highest, and the difference was the strongest. According to the phenological characteristics of the citrus orchard NDVI in October, to further build the normalized index, by using the threshold value method to identify the spatial distribution of the citrus orchard, the identification method had an overall accuracy of 82.75%, better than other identification results of vegetation index. The results of the study for citrus orchard information and remote sensing identification research provide better support for theory and practice.</p>

show abstract

Section: Phenology and Spectral Characteristicsmentioning

confidence: 99%

Identification of citrus orchard under vegetation indexes using multi-temporal remote sensing

Liang,

Huang,

Wang

et al. 2022

Advan. Mod. Agr.

View full text Add to dashboard Cite

show abstract

“…As sea surface states differ over time [5], it is necessary to ensure that the acquisition time of the CyGNSS measurement is close to that of CCMP wind speed. The spatial and temporal windows are the collocation criteria for ensuring the consistency of ocean winds observed by different sources.…”

Section: Spatial and Temporal Collocationmentioning

confidence: 99%

“…In the process of ocean dynamics, sea surface wind is the most influential factor in the generation of ocean hydroynamic phenomena such as wind waves. It not only plays a role in global and regional ocean circulation, but also in weather forecasting and other atmospheric and oceanographic science studies [5][6][7][8]. Consequently, the adequate and accurate description of sea surface wind fields is important for related research [9].…”

Section: Introductionmentioning

confidence: 99%

Validation of NOAA CyGNSS Wind Speed Product with the CCMP Data

Yang

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CyGNSS) mission was launched in December 2016, which can remotely sense sea surface wind with a relatively high spatio-temporal resolution for tracking tropical cyclones. In recent years, with the gradual development of the geophysical model function (GMF) for CyGNSS wind retrieval, different versions of CyGNSS Level 2 products have been released and their performance has gradually improved. This paper presents a comprehensive evaluation of CyGNSS wind product v1.1 produced by the National Oceanic and Atmospheric Administration (NOAA). The Cross-Calibrated Multi-Platform (CCMP) analysis wind (v02.0 and v02.1 near real time) products produced by Remote Sensing Systems (RSS) were used as the reference. Data pairs between the NOAA CyGNSS and RSS CCMP products were processed and evaluated by the bias and standard deviation SD. The CyGNSS dataset covers the period between May 2017 and December 2020. The statistical comparisons show that the bias and SD of CyGNSS relative to CCMP-nonzero collocations when the flag of CCMP winds is nonzero are –0.05 m/s and 1.19 m/s, respectively. The probability density function (PDF) of the CyGNSS winds coincides with that of CCMP-nonzero. Furthermore, the average monthly bias and SD show that CyGNSS wind is consistent and reliable generally. We found that negative deviation mainly appears at high latitudes in both hemispheres. Positive deviation appears in the China Sea, the Arabian Sea, and the west of Africa and South America. Spatial–temporal analysis demonstrates the geographical anomalies in the bias and SD of the CyGNSS winds, confirming that the wind speed bias shows a temporal dependency. The verification and comparison show that the remotely sensed wind speed measurements from NOAA CyGNSS wind product v1.1 are in good agreement with CCMP winds.

show abstract

“…In addition, using accurate inputs of ocean surface wind is crucial in wind resource assessment [4], hurricane center determination [5], and ocean surface heat flux product development [6]. To better understand the impact of winds on oceans and improve weather forecasting, satellite remote sensing, both active (such as scatterometer) and passive (such as radiometer), has been providing a significant understanding of the ocean by providing global observations [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Improving GNSS-R Ocean Wind Speed Retrieval for the BF-1 Mission Using Satellite Platform Attitude Measurements

Chen

Wang

Zhao

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

The receive antenna gain is needed to accurately calibrate the normalized bi-statistic radar cross section (NBRCS) measured by the BF-1 mission, which is a global navigation satellite system reflectometry (GNSS-R) constellation of two microsatellites and the first Chinese GNSS-R satellite mission. The instability of the satellite platform is the main cause of receive antenna gain errors. To obtain a high precision gain value, a calibration method that remaps the ocean surface detection location to the receive antenna pattern using satellite platform attitude measurements is proposed in this paper. Thirty-two orbits of Delay Doppler maps (DDMs) data, which were greatly disturbed by the attitude, are selected to test the effectiveness of the proposed algorithm. The accuracy of wind speed retrieval is analyzed, and results show that the data calibration algorithm is effective in reducing the wind speed retrieval error. Compared with the un-calibrated data, the data subjected to the calibration algorithm show a significant improvement of 19.33% in correlation coefficient and average decreases of 30.91% and 42.57% in root mean square error (RMSE) and mean bias error (MBE), respectively. Moreover, the comparison highlights that the influence of the satellite platform attitude disturbance on wind speed retrieval is abated significantly. The proposed approach can effectively improve the quality of GNSS-R measurements, allowing for a better understanding of global weather abnormalities and generally improving weather forecasting.

show abstract

Retrieval of Sea Surface Wind Fields Using Multi-Source Remote Sensing Data

Cited by 6 publications

References 20 publications

Identification of citrus orchard under vegetation indexes using multi-temporal remote sensing

Identification of citrus orchard under vegetation indexes using multi-temporal remote sensing

Validation of NOAA CyGNSS Wind Speed Product with the CCMP Data

Improving GNSS-R Ocean Wind Speed Retrieval for the BF-1 Mission Using Satellite Platform Attitude Measurements

Contact Info

Product

Resources

About