A review of potential image fusion methods for remote sensing-based irrigation management: part II

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

doi:10.1007/s00271-012-0340-6

Cited by 33 publications

(24 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

Plant-Based Methods for Irrigation Scheduling of Woody Crops

Fernández

2017

Horticulturae

131

View full text Add to dashboard Cite

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.

show abstract

Section: Thermal Sensingmentioning

confidence: 99%

“…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%

Plant-Based Methods for Irrigation Scheduling of Woody Crops

Fernández

2017

Horticulturae

131

View full text Add to dashboard Cite

show abstract

“…There are some previous studies of remote sensing data fusion methods using multiple optical sensors [20][21][22]. Considering different characteristics between the TIR band and the visible bands, traditional image fusion models, such as the widely used Principle Component Analysis (PCA) based fusion methods [23,24], Intensity-Hue-Saturation (HIS) transformation method [25,26] and wavelet-based image fusion methods [27,28], are more concentrating on the visual effects of the fused images, which may not be useful for quantitative remote sensing applications.…”

Section: Introductionmentioning

confidence: 99%

Advancing of Land Surface Temperature Retrieval Using Extreme Learning Machine and Spatio-Temporal Adaptive Data Fusion Algorithm

et al. 2015

View full text Add to dashboard Cite

As a critical variable to characterize the biophysical processes in ecological environment, and as a key indicator in the surface energy balance, evapotranspiration and urban heat islands, Land Surface Temperature (LST) retrieved from Thermal Infra-Red (TIR) images at both high temporal and spatial resolution is in urgent need. However, due to the limitations of the existing satellite sensors, there is no earth observation which can obtain TIR at detailed spatial-and temporal-resolution simultaneously. Thus, several attempts of image fusion by blending the TIR data from high temporal resolution sensor with data from high spatial resolution sensor have been studied. This paper presents a novel data fusion method by integrating image fusion and spatio-temporal fusion techniques, for deriving LST datasets at 30 m spatial resolution from daily MODIS image and Landsat ETM+ images. The Landsat ETM+ TIR data were firstly enhanced based on extreme learning machine (ELM) algorithm using neural network regression model, from 60 m to 30 m resolution. Then, the MODIS LST and enhanced Landsat ETM+ TIR data were fused by Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT) in order to derive high resolution synthetic data. The synthetic images were OPEN ACCESSRemote Sens. 2015, 7 4425 evaluated for both testing and simulated satellite images. The average difference (AD) and absolute average difference (AAD) are smaller than 1.7 K, where the correlation coefficient (CC) and root-mean-square error (RMSE) are 0.755 and 1.824, respectively, showing that the proposed method enhances the spatial resolution of the predicted LST images and preserves the spectral information at the same time.

show abstract

“…Earlier works on thermal data fusion used different terms, such as disaggregation [20][21][22], downscaling [23][24][25], enhancement [26][27][28], merging [28,29], and sharpening [30,31]. A few recent publications have presented a nice overview of the literature related to the remotely sensed LST disaggregation and its applications [32][33][34]. [32] broadly classified these techniques into two categories: downscaling, where images from a coarse spatial resolution is converted to fine resolution using statistically based models with regression or stochastic relationships among parameters without changing radiometric properties of the image; and fusion, where two or more images from the same sensor or different sensors are combined to obtain higher spectral and spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

On the Downscaling of Actual Evapotranspiration Maps Based on Combination of MODIS and Landsat-Based Actual Evapotranspiration Estimates

et al. 2014

View full text Add to dashboard Cite

Downscaling is one of the important ways of utilizing the combined benefits of the high temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) images and fine spatial resolution of Landsat images. We have evaluated the output regression with intercept method and developed the Linear with Zero Intercept (LinZI) method for downscaling MODIS-based monthly actual evapotranspiration (AET) maps to the Landsat-scale monthly AET maps for the Colorado River Basin for 2010. We used the 8-day MODIS land surface temperature product (MOD11A2) and 328 cloud-free Landsat images for computing AET maps and downscaling. The regression with intercept method does have limitations in downscaling if the slope and intercept are computed over a large area. A good agreement was obtained between downscaled monthly AET using the LinZI method and the eddy covariance measurements from seven flux sites within the Colorado River Basin. The mean bias ranged from −16 mm (underestimation) to 22 mm (overestimation) per month, and the coefficient of determination varied from 0.52 to 0.88. Some discrepancies between measured and downscaled monthly AET at two flux sites were found to be due to the prevailing flux OPEN ACCESS Remote Sens. 2014, 6 10484 footprint. A reasonable comparison was also obtained between downscaled monthly AET using LinZI method and the gridded FLUXNET dataset. The downscaled monthly AET nicely captured the temporal variation in sampled land cover classes. The proposed LinZI method can be used at finer temporal resolution (such as 8 days) with further evaluation. The proposed downscaling method will be very useful in advancing the application of remotely sensed images in water resources planning and management.

show abstract

A review of potential image fusion methods for remote sensing-based irrigation management: part II

Cited by 33 publications

References 66 publications

Plant-Based Methods for Irrigation Scheduling of Woody Crops

Plant-Based Methods for Irrigation Scheduling of Woody Crops

Advancing of Land Surface Temperature Retrieval Using Extreme Learning Machine and Spatio-Temporal Adaptive Data Fusion Algorithm

On the Downscaling of Actual Evapotranspiration Maps Based on Combination of MODIS and Landsat-Based Actual Evapotranspiration Estimates

Contact Info

Product

Resources

About