A review of downscaling methods for remote sensing-based irrigation management: part I

Ha, Wonsook; Gowda, Prasanna H.; Howell, Terry A.

doi:10.1007/s00271-012-0331-7

Cited by 74 publications

(43 citation statements)

References 97 publications

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“…Recently, however, the development of both cheaper image acquisition systems and user-friendly, powerful data image processing packages has substantially increased the potential of the method for irrigation scheduling in commercial orchards [55,56]. Thermal readings can be made both at the plant level (ground-based imagery) [55,57] and from above the crop (airborne imagery), after installing the sensors on towers or cranes [58,59], on unmanned aerial vehicles (UAVs), also known as remote piloted aerial systems (RPAS) [60], planes [61] or satellites [62,63]. Ground-based and airborne thermal images can be combined to assess within-orchard spatial heterogeneity in water status, as demonstrated with grape [64] and olive plants [65].…”

Section: Thermal Sensingmentioning

confidence: 99%

“…Images from some satellites are free of charge, but both the return interval, about two weeks or more in some cases, and the spatial resolution, over 15 m for most satellites, are limiting for precision irrigation. The image downscaling method can be used to improve spatial resolution, such that ET maps for irrigation scheduling purposes can be derived [62]. Image fusion is also being proposed as a method to obtain higher spatial and spectral resolution images useful for irrigation management [63].…”

Section: Airborne Imagerymentioning

confidence: 99%

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Plant-Based Methods for Irrigation Scheduling of Woody Crops

Fernández

2017

Horticulturae

139

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Abstract:The increasing world population and expected climate scenarios impel the agricultural sector towards a more efficient use of water. The scientific community is responding to that challenge by developing a variety of methods and technologies to increase crop water productivity. Precision irrigation is intended to achieve that purpose, through the wise choice of the irrigation system, the irrigation strategy, the method to schedule irrigation, and the production target. In this review, the relevance of precision irrigation for a rational use of water in agriculture, and methods related to the use of plant-based measurements for both the assessment of plant water stress and irrigation scheduling, are considered. These include non-automated, conventional methods based on manual records of plant water status and gas exchange, and automated methods where the related variable is recorded continuously and automatically. Thus, the use of methodologies based on the Scholander chamber and portable gas analysers, as well as those of systems for measuring sap flow, stem diameter variation and leaf turgor pressure, are reviewed. Other methods less used but with a potential to improve irrigation are also considered. These include those based on measurements related to the stem and leaf water content, and to changes in electrical potential within the plant. The use of measurements related to canopy temperature, both for direct assessment of water stress and for defining zones with different irrigation requirements, is also addressed. Finally, the importance of choosing the production target wisely, and the need for economic analyses to obtain maximum benefit of the technology related to precision irrigation, are outlined.

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Section: Thermal Sensingmentioning

confidence: 99%

Section: Airborne Imagerymentioning

confidence: 99%

Plant-Based Methods for Irrigation Scheduling of Woody Crops

Fernández

2017

Horticulturae

139

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“…Therefore, larger spatial-scale errors occur when these remotely sensed models are applied to calculate the regional ET using satellite data. In previous studies, researchers have coupled high-and low-resolution satellite data and statistically quantified the inhomogeneity of mixed pixels to correct the scale error in ET estimations using (1) temperature downscaling, which converts images from a lower (coarser) to higher (finer) spatial resolution using statistical-based models with regression or stochastic relationships among parameters Norman et al, 2003;Cammalleri et al, 2013;Ha et al, 2013); (2) the correction-factor method, which uses subpixel landscapes information to determine the correction factor of scale bias (Maayar and Chen, 2006); and (3) the areaweighting method, which calculates roughness length and sensible heat flux based on subpixel landscapes (Xin et al, 2012). These correction methods mainly focus on two problems: inhomogeneity of landscapes and inhomogeneity of surface variables.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing algorithm for surface evapotranspiration considering landscape and statistical effects on mixed pixels

Peng

Xin

Jiao

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Evapotranspiration (ET) plays an important role in surface-atmosphere interactions and can be monitored using remote sensing data. However, surface heterogeneity, including the inhomogeneity of landscapes and surface variables, significantly affects the accuracy of ET estimated from satellite data. The objective of this study is to assess and reduce the uncertainties resulting from surface heterogeneity in remotely sensed ET using Chinese HJ-1B satellite data, which is of 30 m spatial resolution in VIS/NIR bands and 300 m spatial resolution in the thermal-infrared (TIR) band. A temperature-sharpening and flux aggregation scheme (TSFA) was developed to obtain accurate heat fluxes from the HJ-1B satellite data. The IPUS (input parameter upscaling) and TRFA (temperature resampling and flux aggregation) methods were used to compare with the TSFA in this study. The three methods represent three typical schemes used to handle mixed pixels from the simplest to the most complex. IPUS handles all surface variables at coarse resolution of 300 m in this study, TSFA handles them at 30 m resolution, and TRFA handles them at 30 and 300 m resolution, which depends on the actual spatial resolution. Analyzing and comparing the three methods can help us to get a better understanding of spatial-scale errors in remote sensing of surface heat fluxes. In situ data collected during HiWATER-MUSOEXE (Multi-Scale Observation Experiment on Evapotranspiration over heterogeneous land surfaces of the Heihe Watershed Allied Telemetry Experimental Research) were used to validate and analyze the methods. ET estimated by TSFA exhibited the best agreement with in situ observations, and the footprint validation results showed that the R 2 , MBE, and RMSE values of the sensible heat flux (H ) were 0.61, 0.90, and 50.99 W m −2 , respectively, and those for the latent heat flux (LE) were 0.82, −20.54, and 71.24 W m −2 , respectively. IPUS yielded the largest errors in ET estimation. The RMSE of LE between the TSFA and IPUS methods was 51.30 W m −2 , and the RMSE of LE between the TSFA and TRFA methods was 16.48 W m −2 . Furthermore, additional analysis showed that the TSFA method can capture the subpixel variations of land surface temperature and the influences of various landscapes within mixed pixels.

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“…However, irrigation (44), fertilization (89), weed detection (110,133), and yield mapping (157) are just some crop management practices that are being transformed by remote sensing technologies. As on-board processing in UAV-based technologies and sensor networking become more advanced (39), researchers will widely utilize remote sensing technologies to detect and quantify insect densities and their distributions.…”

Section: Discussionmentioning

confidence: 99%

Remote Sensing and Reflectance Profiling in Entomology

Nansen

Elliott

2016

Annu. Rev. Entomol.

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Remote sensing describes the characterization of the status of objects and/or the classification of their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remote sensing can be benchtop based, and therefore acquired at a high spatial resolution, or airborne at lower spatial resolution to cover large areas. Despite important challenges, airborne remote sensing technologies will undoubtedly be of major importance in optimized management of agricultural systems in the twenty-first century. Benchtop remote sensing applications are becoming important in insect systematics and in phenomics studies of insect behavior and physiology. This review highlights how remote sensing influences entomological research by enabling scientists to nondestructively monitor how individual insects respond to treatments and ambient conditions. Furthermore, novel remote sensing technologies are creating intriguing interdisciplinary bridges between entomology and disciplines such as informatics and electrical engineering.

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A review of downscaling methods for remote sensing-based irrigation management: part I

Cited by 74 publications

References 97 publications

Plant-Based Methods for Irrigation Scheduling of Woody Crops

Plant-Based Methods for Irrigation Scheduling of Woody Crops

Remote sensing algorithm for surface evapotranspiration considering landscape and statistical effects on mixed pixels

Remote Sensing and Reflectance Profiling in Entomology

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