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Publikováno v:
IGARSS
The Water Cloud Model (WCM) has been widely used for estimation of Leaf Area Index (LAI) from Synthetic Aperture Radar (SAR). In different studies, it was demonstrated that this model performs well if it is calibrated well. However, calibration of th
Autor:
Li Pan, Haojie Pei, Changchun Li, Fuqin Yang, Haikuan Feng, Guijun Yang, Riqiang Chen, Peng Chen
Publikováno v:
IGARSS
Rapid and accurate estimation of the winter wheat leaf area index (LAI) is important for evaluating its growth and estimating yield. In this paper, Optimal Index (OI) was used to screen the best combination of hyperspectral bands in the flag stage an
Autor:
Pengfei Chen
Publikováno v:
IGARSS
The objective of this study is to estimating cotton leaf area index (LAI) using multi-spectral images acquired by Unmanned Aerial Vehicle (UAV). For this purpose, cotton nitrogen and irrigation experiment was conducted with four N treatments and four
Autor:
Menghao Ji, Bo-Hui Tang
Publikováno v:
IGARSS
The Fraunhofer line discrimination (FLD) principle is widely used for retrieving solar-induced chlorophyll fluorescence (SIF), which assumes that the spectral reflectance is smooth and can be modeled using simply mathematical function. However, the c
Autor:
Yonggang Qian, Yongguang Zhao, Chuan-Rong Li, Ning Wang, Shi Qiu, Lingli Tang, Ling-Ling Ma, Kun Li
Publikováno v:
IGARSS
Web of Science
Web of Science
This paper describes a four-component parameterized directional thermal radiance model for row crops, which consists of the thermal radiance of the sunlit/shaded soil and sunlit/shaded leaf, multiple scattering effects of canopy and sensor field of v
Publikováno v:
IGARSS
In this work, land surface temperature (LST) was downscaled by statistical regression model based on the nonlinear relationship with environment variables, including land surface reflectance, spectral indices, terrain factors, land cover type, reanal
Publikováno v:
IGARSS
This study applies the two-level model to predict stem volume (VOL), presented as wall-to-wall rasters. The SAR data were acquired with the TanDEM-X system and 518 scenes covered the entire Sweden. For comparison, a multiple linear regression model i
Autor:
Emanuele Santi, Giovanni Cuozzo, Mohammed Dabboor, Claudia Notarnicola, Felix Greifeneder, Simone Pettinato, Simonetta Paloscia
Publikováno v:
IGARSS
This research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture (SMC) from SAR acquisitions at C- and X-band.The study was conducted on an alpine test area in Italy and two agri