Integration of wireless sensor network and remote sensing for monitoring and determining irrigation demand in Cyprus

2009 ◽  
Author(s):  
Athos Agapiou ◽  
George Papadavid ◽  
Diofantos G. Hadjimitsis
Keyword(s):  
Remote Sensing ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Irrigation Demand

scholarly journals Validation of Leaf Area Index Measurement System Based on Wireless Sensor Network

2021 ◽  
Author(s):  
Rongjin Yang ◽  
Lu Liu ◽  
Qiang Liu ◽  
Xiuhong Li ◽  
Lizeyan Yin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Wireless Sensor Network ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Sensor Network ◽  
Measurement System ◽  
Wireless Sensor ◽  
Area Index ◽  
The Real ◽  
Real Value

Abstract Accurate measurement of leaf area index (LAI) is important for agricultural analysis such as the estimation of crop yield, which makes its measurement work important. There are mainly two ways to obtain LAI: ground station measurement and remote sensing satellite monitoring. Recently, reliable progress has been made in long-term automatic LAI observation using wireless sensor network (WSN) technology under certain conditions. We developed and designed an LAI measurement system (LAIS) based on a wireless sensor network to select and improve the appropriate algorithm according to the image collected by the sensor, to get a more realistic leaf area index. The corn LAI was continuously observed from May 30 to July 16, 2015. Research on hardware has been published, this paper focuses on improved system algorithm and data verification. By improving the finite length average algorithm, the data validation results are as follows: 1. The slope of the fitting line between LAIS measurement data and the real value is 0.944, and the root means square error (RMSE) is 0.264 (absolute error ~ 0-0.6), which has high consistency with the real value. 2. The measurement error of LAIS is less than LAI2000, although the result of our measurement method will be higher than the actual value, it is due to the influence of weeds on the ground. 3. LAIS data can be used to support the retrieval of remote sensing products. We find a suitable application situation of our LAIS system data, and get our application value as ground monitoring data by the verification with remote sensing product data, which supports its application and promotion in similar research in the future.

scholarly journals Assessment of Cornfield LAI Retrieved from Multi-Source Satellite Data Using Continuous Field LAI Measurements Based on a Wireless Sensor Network

2020 ◽  
Vol 12 (20) ◽  
pp. 3304
Author(s):  
Lihong Yu ◽  
Jiali Shang ◽  
Zhiqiang Cheng ◽  
Zebin Gao ◽  
Zixin Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Satellite Data ◽  
Wireless Sensor ◽  
Landsat 8 ◽  
Area Index ◽  
Landsat 7 ◽  
Satellite Sensors ◽  
Continuous Field

Accurate and continuous monitoring of leaf area index (LAI), a widely-used vegetation structural parameter, is crucial to characterize crop growth conditions and forecast crop yield. Meanwhile, advancements in collecting field LAI measurements have provided strong support for validating remote-sensing-derived LAI. This paper evaluates the performance of LAI retrieval from multi-source, remotely sensed data through comparisons with continuous field LAI measurements. Firstly, field LAI was measured continuously over periods of time in 2018 and 2019 using LAINet, a continuous LAI measurement system deployed using wireless sensor network (WSN) technology, over an agricultural region located at the Heihe watershed at northwestern China. Then, cloud-free images from optical satellite sensors, including Landsat 7 the Enhanced Thematic Mapper Plus (ETM+), Landsat 8 the Operational Land Imager (OLI), and Sentinel-2A/B Multispectral Instrument (MSI), were collected to derive LAI through inversion of the PROSAIL radiation transfer model using a look-up-table (LUT) approach. Finally, field LAI data were used to validate the multi-temporal LAI retrieved from remote-sensing data acquired by different satellite sensors. The results indicate that good accuracy was obtained using different inversion strategies for each sensor, while Green Chlorophyll Index (CIgreen) and a combination of three red-edge bands perform better for Landsat 7/8 and Sentinel-2 LAI inversion, respectively. Furthermore, the estimated LAI has good consistency with in situ measurements at vegetative stage (coefficient of determination R2 = 0.74, and root mean square error RMSE = 0.53 m2 m−2). At the reproductive stage, a significant underestimation was found (R2 = 0.41, and 0.89 m2 m−2 in terms of RMSE). This study suggests that time-series LAI can be retrieved from multi-source satellite data through model inversion, and the LAINet instrument could be used as a low-cost tool to provide continuous field LAI measurements to support LAI retrieval.

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