As a crucial parameter in land surface systems, soil moisture plays an important role in surface energy balance studies, environmental detection, and global climate change research. Remotely sensed data have been used for estimating soil moisture through different approaches, which has resulted in many achievements. Previous studies showed that the land surface temperature (LST) vegetation index method (LST-VI method) can obtain surface soil moisture with remote sensing sources, and it is relatively simple and easy to operate at a regional scale. However, one thorny difficulty is the dry edge determination from the LST-VI feature space. In this study, a remote sensing method is proposed to determine the theoretical dry edge from the LST-VI scatter plots, which do not require any ground measured auxiliary data. Based on the surface energy balance principle, this method derived the maximum LSTs for bare soil and full vegetation cover using MODIS products. The air temperature is parameterized by the LST using a semiempirical formula as the theoretical wet edge. The estimated soil moisture is validated by in situ measurements at a comprehensive weather station of Yucheng. The coefficient of determination is ∼0.60, and the root mean square error is about 0.08 m3 / m3. The relevant key parameters in determining the dry edge are also validated from the meteorological observation. The air temperature and net surface shortwave radiation flux all reach a very high level, with an RMSE of 3.75 k and 49.3 W m − 2, respectively. The results demonstrated that the proposed method can derive the accurate dry edge to estimate soil moisture from the remote sensing data, which will provide great help for future studies of soil moisture estimation using remote sensing techniques.
KEYWORDS: Cameras, Imaging systems, 3D acquisition, 3D image processing, Error analysis, Detection and tracking algorithms, Sensors, 3D vision, Optical engineering, Visual process modeling
This paper presents a three-dimensional (3-D) pose estimation algorithm based on monocular vision. The algorithm relies on the circle target whose radius is known, with the scale condition given, the depth information of the circle can be recovered incompletely, and finally the pose of the target can be estimated by single projection only. First, the circle target was mapped to be an upright elliptic cone in the pinhole imaging model. Second, radius constraint was applied to recover partial depth of the circle target based on the upright elliptic cone. Experimental work concerning the validity and accuracy of this method is presented; furthermore, an application case for robot-aided positioning is introduced.
Soil moisture (SM) is a key variable that has been widely used in many environmental studies. Land surface temperature versus vegetation index (LST-VI) space becomes a common way to estimate SM in optical remote sensing applications. Normalized LST-VI space is established by the normalized LST and VI to obtain the comparable SM in Zhang et al. (Validation of a practical normalized soil moisture model with in situ measurements in humid and semiarid regions [J]. International Journal of Remote Sensing, DOI: 10.1080/01431161.2015.1055610). The boundary conditions in the study were set to limit the point A (the driest bare soil) and B (the wettest bare soil) for surface energy closure. However, no limitation was installed for point D (the full vegetation cover). In this paper, many vegetation types are simulated by the land surface model - Noah LSM 3.2 to analyze the effects on soil moisture estimation, such as crop, grass and mixed forest. The locations of point D are changed with vegetation types. The normalized LST of point D for forest is much lower than crop and grass. The location of point D is basically unchanged for crop and grass.
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