Simultaneous usage of optic and thermal hyperspectral sensors for crop water stress characterization

Pipia, Luca; Pérez, Fernando; Tardà, Anna; Martinez, L.; Arbiol, Román

doi:10.1109/igarss.2012.6352071

Cited by 6 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many approaches have been developed to ameliorate the ill-posed semiblind source estimation problem in land surface temperature (LST) and land surface emissivity (LSE) estimation. The TES algorithm originally proposed for ASTER sensor is the most popular and has been suggested for many other sensors, such as AHS [13,22], TASI [14,23], Multispectral Thermal Imager (MTI) [24], The Moderate Resolution Imaging Spectroradiometer (MODIS) [25], and Spinning Enhanced Visible and Infrared Imager (SEVIRI) [26].…”

Section: Temperature and Emissivity Separation Algorithmmentioning

confidence: 99%

Temperature and Emissivity Smoothing Separation with Nonlinear Relation of Brightness Temperature and Emissivity for Thermal Infrared Sensors

Miao

Zhang

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Aiming at low spectral contrast materials, the Optimized Smoothing for Temperature Emissivity Separation (OSTES) method was developed to improve the Temperature and Emissivity Separation (TES) algorithm based on the linear relationship between brightness temperature and emissivity features, but there was little smoothing improvement for higher spectral contrast materials. In this paper, a new nonlinear-relationship based algorithm is presented, focusing on improving the performance of the OSTES method for materials with middle or high spectral contrast. This novel approach is a two-step procedure. Firstly, by introducing atmospheric impact factor, the nonlinear relationship is mathematically proved using first-order Taylor series approximation. Moreover, it is proven that nonlinear model has stronger universality than linear model. Secondly, a new method named Temperature and Emissivity Separation with Nonlinear Constraint (TESNC) is proposed based on the nonlinear model for smoothing temperature and emissivity retrieval. The key procedure of TESNC is the lowest emissivity smoothing estimation based on nonlinear model and retrieved by minimizing the reconstruction error of the Planck radiance. TESNC was tested on a series of synthetic data with different kinds of natural materials representing several multispectral and hyperspectral infrared sensors. It is shown that, especially for materials with higher spectral contrast, the proposed method is less sensitive to changes in atmospheric conditions and sample temperatures. Furthermore, the standard Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) products in different kind of atmospheric conditions were used for verifying the improvement. TESNC is more accurate and stable with the decrease of emissivity and changes of atmospheric conditions compared with TES, Adjusted Normalized Emissivity Method (ANEM), and OSTES methods.

show abstract

Section: Temperature and Emissivity Separation Algorithmmentioning

confidence: 99%

Temperature and Emissivity Smoothing Separation with Nonlinear Relation of Brightness Temperature and Emissivity for Thermal Infrared Sensors

Miao

Zhang

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…[4]This thought proposes a novel strategy for sharp developing by interfacing a canny recognizing structure and sagacious irrigator system through remote correspondence innovation. [5].…”

Section: Literature Surveymentioning

confidence: 99%

An Integrated Heterogeneous Smart Agriculture System

Rajasekhar*,

Singh,

Mohammed

2019

IJRTE

View full text Add to dashboard Cite

It is very essential to use smart agriculture in present days. This will solve various issues occur in agriculture. With Internet of Things (IoT), wireless sensors and fog computing an integrated system providing the smart agriculture is running in present villages. Various issues are identified on this smart agriculture. Parameters such as irrigation scheduling and inefficient utilization of water resources are two of several ubiquitous parameters restricting production in many agricultural regions. To solve these issues, energy consumption of the sensors plays major role to send and receive the data on various parameters. In this paper, an integrated energy efficient sensors by using thermal imaging to maintain the constant data flow from sensors to fog and cloud server.

show abstract