Hyperspectral remote sensing estimation model for rape moisture content under different levels of water stress

2011 ◽  
Author(s):  
Zhang Xiaodong ◽  
Mao Hanping ◽  
Zuo Zhiyv ◽  
Sun Jun ◽  
Gao Hongyan
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Moisture Content ◽  
Hyperspectral Remote Sensing ◽  
Estimation Model ◽  
Different Levels

scholarly journals Monitoring Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae) Infestation in Soybean Using Hyperspectral Remote Sensing

2020 ◽  
Author(s):  
Pedro P. S. Barros ◽  
Inana X. Schutze ◽  
Fernando H. Iost Filho ◽  
Pedro Takao Yamamoto ◽  
Peterson Fiorio ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Bemisia Tabaci ◽  
Insect Pest ◽  
Remote Sensing Data ◽  
Management Program ◽  
Hyperspectral Remote Sensing ◽  
Negative Effects ◽  
Soybean Plants ◽  
Soybean Leaves ◽  
Different Levels

Although monitoring and observing insect pest populations in the fields is essential in crop management, it is still a laborious and sometimes ineffective process. High infestation levels may diminish the photosynthetic activity of soybean plants, affecting their development and reducing the yield. An imprecise decision making in integrated pest management program may lead to an ineffective control in infested areas or the excessive use of insecticides. In order to reach a more efficient control of arthropods population it is important to evaluate the infestation in time to mitigate its negative effects on the crop and remote sensing is an important tool for monitoring. It was proposed that infested soybean areas could be identified, and the arthropods quantified from non-infested areas in a field by hyperspectral remote sensing. Thus, the goals of this study were to investigate and discriminate the reflectance characteristics of soybean non-infested and infested with Bemisia tabaci using hyperspectral remote sensing data. Therefore, samples of infested and non-infested soybean leaves were collected and transported to the laboratory to obtain the hyperspectral curves. The results obtained allowed to discriminate the different levels of infestation and to separate healthy from whitefly infested soybean leaves based on their reflectance.

Long-term effects of water stress on hyperspectral remote sensing indicators in young radiata pine

2021 ◽  
Vol 502 ◽  
pp. 119707
Author(s):  
Michael S. Watt ◽  
Ellen Mae C. Leonardo ◽  
Honey Jane C. Estarija ◽  
Peter Massam ◽  
Dilshan de Silva ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Radiata Pine ◽  
Hyperspectral Remote Sensing ◽  
Long Term Effects

scholarly journals Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4056
Author(s):  
Lifei Wei ◽  
Haochen Pu ◽  
Zhengxiang Wang ◽  
Ziran Yuan ◽  
Xinru Yan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Efficiency ◽  
Hyperspectral Remote Sensing ◽  
Arsenic Content ◽  
Least Squares Regression ◽  
Arsenic Pollution ◽  
Estimation Model ◽  
Successive Projections Algorithm ◽  
Data Redundancy ◽  
Soil Arsenic

With the continuous application of arsenic-containing chemicals, arsenic pollution in soil has become a serious problem worldwide. The detection of arsenic pollution in soil is of great significance to the protection and restoration of soil. Hyperspectral remote sensing is able to effectively monitor heavy metal pollution in soil. However, due to the possible complex nonlinear relationship between soil arsenic (As) content and the spectrum and data redundancy, an estimation model with high efficiency and accuracy is urgently needed. In response to this situation, 62 samples and 27 samples were collected in Daye and Honghu, Hubei Province, respectively. Spectral measurement and physical and chemical analysis were performed in the laboratory to obtain the As content and spectral reflectance. After the continuum removal (CR) was performed, the stable competitive adaptive reweighting sampling algorithm coupled the successive projections algorithm (sCARS-SPA) was used for characteristic band selection, which effectively solves the problem of data redundancy and collinearity. Partial least squares regression (PLSR), radial basis function neural network (RBFNN), and shuffled frog leaping algorithm optimization of the RBFNN (SFLA-RBFNN) were established in the characteristic wavelengths to predict soil As content. These results show that the sCARS-SPA-SFLA-RBFNN model has the best universality and high prediction accuracy in different land-use types, which is a scientific and effective method for estimating the soil As content.

scholarly journals Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review

2019 ◽  
Vol 11 (10) ◽  
pp. 1240 ◽  
Author(s):  
Max Gerhards ◽  
Martin Schlerf ◽  
Kaniska Mallick ◽  
Thomas Udelhoven
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Water Status ◽  
Thermal Infrared ◽  
Hyperspectral Remote Sensing ◽  
Plant Responses ◽  
Plant Water ◽  
Stress Detection ◽  
Crop Water ◽  
Crop Water Stress

Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor technology currently in place for ground and airborne applications and either have satellite concepts under development (e.g., HySPIRI/SBG (Surface Biology and Geology), Sentinel-8, HiTeSEM in the TIR) or are subject to satellite missions recently launched or scheduled within the next years (i.e., EnMAP and PRISMA (PRecursore IperSpettrale della Missione Applicativa, launched on March 2019) in the VNIR/SWIR, Fluorescence Explorer (FLEX) in the SIF). Identification of plant water stress or drought is of utmost importance to guarantee global water and food supply. Therefore, knowledge of crop water status over large farmland areas bears large potential for optimizing agricultural water use. As plant responses to water stress are numerous and complex, their physiological consequences affect the electromagnetic signal in different spectral domains. This review paper summarizes the importance of water stress-related applications and the plant responses to water stress, followed by a concise review of water-stress detection through remote sensing, focusing on TIR without neglecting the comparison to other spectral domains (i.e., VNIR/SWIR and SIF) and multi-sensor approaches. Current and planned sensors at ground, airborne, and satellite level for the TIR as well as a selection of commonly used indices and approaches for water-stress detection using the main multi-/hyperspectral remote sensing imaging techniques are reviewed. Several important challenges are discussed that occur when using spectral emissivity, temperature-based indices, and physically-based approaches for water-stress detection in the TIR spectral domain. Furthermore, challenges with data processing and the perspectives for future satellite missions in the TIR are critically examined. In conclusion, information from multi-/hyperspectral TIR together with those from VNIR/SWIR and SIF sensors within a multi-sensor approach can provide profound insights to actual plant (water) status and the rationale of physiological and biochemical changes. Synergistic sensor use will open new avenues for scientists to study plant functioning and the response to environmental stress in a wide range of ecosystems.

scholarly journals On-line Fault Detection and Diagnosis for Greenhouse Environmental Control

2001 ◽  
Author(s):  
Ido Seginer ◽  
Louis D. Albright ◽  
Robert W. Langhans
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Fault Detection ◽  
Early Detection ◽  
Data Base ◽  
Crop Rotation ◽  
Hyperspectral Remote Sensing ◽  
Sugar Beets ◽  
Detection And Identification ◽  
Wide Range

Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.  

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