Towards Tree-level Evapotranspiration Estimation with Small UAVs in Precision Agriculture
okt 2022. · Springer Nature
E-knjiga
156
Broj stranica
reportOcjene i recenzije nisu potvrđene Saznajte više
O ovoj e-knjizi
Estimating evapotranspiration (ET) has been one of the most critical research areas in agriculture because of water scarcity, the growing population, and climate change. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, researchers use water balance, soil moisture, weighing lysimeters, or an energy balance approach, such as Bowen ratio or eddy covariance towers to estimate ET. However, these ET methods are point-specific or area-weighted measurements and cannot be extended to a large scale. On the other hand, while remote sensing is able to provide spatially distributed measurements, the spatial resolution of multispectral satellite images is often not enough for crops with clumped canopy structures, such as trees and vines. Unmanned aerial vehicles (UAVs) can mitigate these spatial and temporal limitations. Lightweight cameras and sensors can be mounted on the UAVs and take high-resolution images. Unlike satellite imagery, the spatial resolution of the UAV images can be at the centimeter-level. UAVs can also fly on-demand, which provides high temporal imagery.
This book examines the different UAV-based approaches of ET estimation. Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are discussed. It also covers the challenges and opportunities for UAVs in ET estimation, with the final chapters devoted to new ET estimation methods and their potential applications for future research.
This book examines the different UAV-based approaches of ET estimation. Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are discussed. It also covers the challenges and opportunities for UAVs in ET estimation, with the final chapters devoted to new ET estimation methods and their potential applications for future research.
O autoru
YangQuan Chen received his PhD degree in advanced control and instrumentation from the Nanyang Technological University in Singapore. Currently, he is a full professor at the University of California Merced. His Mechatronics, Embedded Systems and Automation (MESA) Lab at UC Merced is emerging as a widely known “drone lab” with the vision to build an “agriculture drone valley” in California’s Central Valley. The lab’s work on low-cost, reliably airworthy, multispectral UAV-based remote sensing systems helps create a new type of information services valuable not only for farming and growing, but also for environmental monitoring and assessment. Prof Chen has published over 300 peer-reviewed paper and more than 20 books/book chapters.
Ocijenite ovu e-knjigu
Recite nam šta mislite.
Informacije o čitanju
Pametni telefoni i tableti
Instalirajte aplikaciju Google Play Knjige za Android i iPad/iPhone uređaje. Aplikacija se automatski sinhronizira s vašim računom i omogućava vam čitanje na mreži ili van nje gdje god da se nalazite.
Laptopi i računari
Audio knjige koje su kupljene na Google Playu možete slušati pomoću web preglednika na vašem računaru.
Elektronički čitači i ostali uređaji
Da čitate na e-ink uređajima kao što su Kobo e-čitači, morat ćete preuzeti fajl i prenijeti ga na uređaj. Pratite detaljne upute Centra za pomoć da prenesete fajlove na podržane e-čitače.
