Future approaches to facilitate large-scale adoption of thermal based images as key input in the production of dynamic irrigation management zones

2017 ◽  
Vol 8 (2) ◽  
pp. 546-550 ◽  
Author(s):  
Y. Cohen ◽  
N. Agam ◽  
I. Klapp ◽  
A. Karnieli ◽  
O. Beeri ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Large Scale ◽  
Near Infrared ◽  
Water Status ◽  
Irrigation Management ◽  
Atmospheric Conditions ◽  
Management Zones ◽  
Financial Investments ◽  
Coarse Spatial Resolution ◽  
Aerial Platforms

To use VRI systems, a field is divided into irrigation management zones (IMZs). While IMZs are dynamic in nature, most of IMZs prescription maps are static. High-resolution thermal images (TI) coupled with measured atmospheric conditions have been utilized to map the within-field water status variability and to delineate in-season IMZs. Unfortunately, spaceborne TIs have coarse spatial resolution and aerial platforms require substantial financial investments, which may inhibit their large-scale adoption. Three approaches are proposed to facilitate large-scale adoption of TI-based IMZs: 1) increase of the capacity of aerial TI by enhancing their spatial resolution; 2) sharpening the spatial resolution of satellite TI by fusing satellite multi-spectral images in the visible-near-infrared (VIS-NIR) range; 3) increase the capacity of aerial TI by fusing satellite multi-spectral images in the VIS-NIR range. The scientific and engineering basis of each of the approaches is described together with initial results.

scholarly journals Downscaling of GRACE-Derived Groundwater Storage Based on the Random Forest Model

2019 ◽  
Vol 11 (24) ◽  
pp. 2979 ◽  
Author(s):  
Li Chen ◽  
Qisheng He ◽  
Kun Liu ◽  
Jinyang Li ◽  
Chenlin Jing
Keyword(s):  
Random Forest ◽  
Spatial Resolution ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Water Storage ◽  
Research Area ◽  
Groundwater Storage ◽  
Coarse Spatial Resolution ◽  
Long Time ◽  
Local Water

Groundwater is an important part of water storage and one of the important sources of agricultural irrigation, urban living, and industrial water use. The recent launch of Gravity Recovery and Climate Experiment (GRACE) Satellite has provided a new way for studying large-scale water storage. The application of GRACE in local water resources has been greatly limited because of the coarse spatial resolution, and low temporal resolution. Therefore, it is of great significance to improve the spatial resolution of groundwater storage for regional water management. Based on the method of random forest (RF), this study combined six hydrological variables, including precipitation, evapotranspiration, runoff, soil moisture, snow water equivalent, and canopy water to conduct downscaling study, aiming at downscaling the resolution of the total water storage and groundwater storage from 1° (110 km) and to 0.25° (approximately 25 km). The results showed that, from the perspective of long time series, the prediction results of the RF model are ideal in the whole research area and the observations wells area. From the perspective of space, the detailed changes of water storage could be captured in greater detail after downscaling. The verification results show that, on the monthly scale and annual scale, the correlation between the downscaling results and the observation wells is 0.78 and 0.94, respectively, and they both reach the confidence level of 0.01. Therefore, the RF downscaling model has great potential for predicting groundwater storage.

scholarly journals Mapping Management Zones Based on Soil Apparent Electrical Conductivity and Remote Sensing for Implementation of Variable Rate Irrigation—Case Study of Corn under a Center Pivot

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3427
Author(s):  
João Serrano ◽  
Shakib Shahidian ◽  
José Marques da Silva ◽  
Luís Paixão ◽  
Francisco Moral ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Hydrological Cycle ◽  
Irrigation Management ◽  
Travel Speed ◽  
Variable Rate ◽  
Management Zones ◽  
Variable Rate Irrigation

Climate change, especially the trend towards global warming, will significantly affect the global hydrological cycle, leading to a general reduction of the water available for agriculture. In this scenario, it is essential that research should focus on the development of ‘water saving’ techniques and technologies. This work summarizes the methodology followed in a project for large scale implementation of variable rate irrigation (VRI) systems using center pivots in corn crop. This is based on technologies for monitoring (i) soil electrical conductivity (ECa) and altimetry, (ii) soil moisture content, (iii) vegetation indices (Normalized Difference Vegetation Index, NDVI) obtained from satellite images, and automatic pivot travel speed control technologies. ECa maps were the basis for the definition of first homogeneous management zones (HMZ) in an experimental corn field of 28 ha. NDVI time-series were used to establish the subsequent HMZ and the respective dynamic prescription irrigation maps. The main result of this study was the reduction of spatial yield variability with the VRI management in 2017 compared to the conventional irrigation management. This study demonstrates how a relatively simple approach could be designed and implemented on a large scale, which represents an important and sustainable contribution to the resolution of practical farmer issues.

scholarly journals Sentinel-2 Image Fusion Using a Deep Residual Network

2018 ◽  
Vol 10 (8) ◽  
pp. 1290 ◽  
Author(s):  
Frosti Palsson ◽  
Johannes Sveinsson ◽  
Magnus Ulfarsson
Keyword(s):  
Spatial Resolution ◽  
Near Infrared ◽  
State Of The Art ◽  
Residual Network ◽  
Fused Image ◽  
Coarse Spatial Resolution ◽  
Single Sensor ◽  
Shortwave Infrared ◽  
Sentinel 2

Single sensor fusion is the fusion of two or more spectrally disjoint reflectance bands that have different spatial resolution and have been acquired by the same sensor. An example is Sentinel-2, a constellation of two satellites, which can acquire multispectral bands of 10 m, 20 m and 60 m resolution for visible, near infrared (NIR) and shortwave infrared (SWIR). In this paper, we present a method to fuse the fine and coarse spatial resolution bands to obtain finer spatial resolution versions of the coarse bands. It is based on a deep convolutional neural network which has a residual design that models the fusion problem. The residual architecture helps the network to converge faster and allows for deeper networks by relieving the network of having to learn the coarse spatial resolution part of the inputs, enabling it to focus on constructing the missing fine spatial details. Using several real Sentinel-2 datasets, we study the effects of the most important hyperparameters on the quantitative quality of the fused image, compare the method to several state-of-the-art methods and demonstrate that it outperforms the comparison methods in experiments.

scholarly journals Comparing Fully Deep Convolutional Neural Networks for Land Cover Classification with High-Spatial-Resolution Gaofen-2 Images

2020 ◽  
Vol 9 (8) ◽  
pp. 478 ◽  
Author(s):  
Zemin Han ◽  
Yuanyong Dian ◽  
Hao Xia ◽  
Jingjing Zhou ◽  
Yongfeng Jian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Near Infrared ◽  
High Spatial Resolution ◽  
Land Cover Classification ◽  
Quantitative Comparison ◽  
Remote Sensing Imagery

Land cover is an important variable of the terrestrial ecosystem that provides information for natural resources management, urban sprawl detection, and environment research. To classify land cover with high-spatial-resolution multispectral remote sensing imagery is a difficult problem due to heterogeneous spectral values of the same object on the ground. Fully convolutional networks (FCNs) are a state-of-the-art method that has been increasingly used in image segmentation and classification. However, a systematic quantitative comparison of FCNs on high-spatial-multispectral remote imagery was not yet performed. In this paper, we adopted the three FCNs (FCN-8s, Segnet, and Unet) for Gaofen-2 (GF2) satellite imagery classification. Two scenes of GF2 with a total of 3329 polygon samples were used in the study area and a systematic quantitative comparison of FCNs was conducted with red, green, blue (RGB) and RGB+near infrared (NIR) inputs for GF2 satellite imagery. The results showed that: (1) The FCN methods perform well in land cover classification with GF2 imagery, and yet, different FCNs architectures exhibited different results in mapping accuracy. The FCN-8s model performed best among the Segnet and Unet architectures due to the multiscale feature channels in the upsampling stage. Averaged across the models, the overall accuracy (OA) and Kappa coefficient (Kappa) were 5% and 0.06 higher, respectively, in FCN-8s when compared with the other two models. (2) High-spatial-resolution remote sensing imagery with RGB+NIR bands performed better than RGB input at mapping land cover, and yet the advantage was limited; the OA and Kappa only increased an average of 0.4% and 0.01 in the RGB+NIR bands. (3) The GF2 imagery provided an encouraging result in estimating land cover based on the FCN-8s method, which can be exploited for large-scale land cover mapping in the future.

scholarly journals An observation-based evaluation and ranking of historical Earth System Model simulations for regional downscaling in the northwest North Atlantic Ocean

2020 ◽  
Author(s):  
Arnaud Laurent ◽  
Katja Fennel ◽  
Angela Kuhn
Keyword(s):  
Spatial Resolution ◽  
North Atlantic ◽  
Large Scale ◽  
North Atlantic Ocean ◽  
Circulation Model ◽  
Biogeochemical Model ◽  
Earth System ◽  
Labrador Current ◽  
Coarse Spatial Resolution ◽  
Regional Downscaling

Abstract. Continental shelf regions in the ocean play an important role in the global cycling of carbon and nutrients but their responses to global change are understudied. Global Earth System Models (ESM), as essential tools for building understanding of ocean biogeochemistry, are used extensively and routinely for projections of future climate states; however, their relatively coarse spatial resolution is likely not appropriate for accurately representing the complex patterns of circulation and elemental fluxes on the shelves along ocean margins. Here, we compared 29 ESMs used in the IPCC’s Assessment Rounds (AR) 5 and 6 and a regional biogeochemical model for the northwest North Atlantic (NWA) shelf to assess their ability to reproduce observations of temperature, nitrate, and chlorophyll. The NWA region is biologically productive, influenced by the large-scale Gulf Stream and Labrador Current systems, and particularly sensitive to climate change. Most ESMs compare relatively poorly to observed nitrate and chlorophyll and show differences with observed temperature due to spatial mismatches in their large-scale circulation. Model-simulated nitrate and chlorophyll compare better with available observations in AR6 than in AR5, but none of the models performs equally well for all 3 parameters. The ensemble means of all ESMs, and of the five best performing ESMs, strongly underestimate observed chlorophyll and nitrate. The regional model has a much higher spatial resolution and reproduces the observations significantly better than any of the ESMs. It also simulates reasonably well vertically resolved observations from gliders and bi-monthly ship-based monitoring observations. A ranking of the ESMs suggests that the top 3 models are appropriate as boundary forcing for regional projections of future changes in the NWA region.

Monitoring Crop Water Status and Irrigation Needs Using Multispectral Airborne Sensors

HortScience ◽  
1995 ◽  
Vol 30 (4) ◽  
pp. 905D-905
Author(s):  
Thomas R. Clarke ◽  
M. Susan Moran
Keyword(s):  
Water Stress ◽  
Near Infrared ◽  
Water Status ◽  
Canopy Cover ◽  
Stress Index ◽  
Crop Water ◽  
Vegetative Cover ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Subsurface Drip

Water application efficiency can be improved by directly monitoring plant water status rather than depending on soil moisture measurements or modeled ET estimates. Plants receiving sufficient water through their roots have cooler leaves than those that are water-stressed, leading to the development of the Crop Water Stress Index based on hand-held infrared thermometry. Substantial error can occur in partial canopies, however, as exposed hot soil contributes to deceptively warm temperature readings. Mathematically comparing red and near-infrared reflectances provides a measure of vegetative cover, and this information was combined with thermal radiance to give a two-dimensional index capable of detecting water stress even with a low percentage of canopy cover. Thermal, red, and near-infrared images acquired over subsurface drip-irrigated cantaloupe fields demonstrated the method's ability to detect areas with clogged emitters, insufficient irrigation rate, and system water leaks.

scholarly journals Line observations of the 30-Dor complex and N159A5 with the MPE imaging NIR spectrometer FAST

1991 ◽  
Vol 148 ◽  
pp. 205-206 ◽  
Author(s):  
A. Krabbe ◽  
J. Storey ◽  
V. Rotaciuc ◽  
S. Drapatz ◽  
R. Genzel
Keyword(s):  
Spatial Resolution ◽  
Molecular Hydrogen ◽  
Near Infrared ◽  
Large Magellanic Cloud ◽  
Magellanic Cloud ◽  
Emission Lines ◽  
Resolving Power ◽  
Max Planck ◽  
First Time ◽  
Infrared Array

Images with subarcsec spatial resolution in the light of near-infrared atomic (Bry) and molecular hydrogen H2 (S(1) v=1-0) emission lines were obtained for some extended, pointlike objects in the Large Magellanic Cloud (LMC) for the first time. We used the Max-Planck-Institut für extraterrestrische Physik (MPE) near-infrared array spectrometer FAST (image scale 0.8”/pix, spectral resolving power 950) at the ESO/MPI 2.2m telescope, La Silla. We present some results on the 30-Dor complex and N159A5.

scholarly journals An optical observational cluster mass function at z ∼ 1 with the ORELSE survey

2021 ◽  
Vol 502 (3) ◽  
pp. 3942-3954
Author(s):  
D Hung ◽  
B C Lemaux ◽  
R R Gal ◽  
A R Tomczak ◽  
L M Lubin ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Large Scale ◽  
Near Infrared ◽  
Function Analysis ◽  
Voronoi Tessellation ◽  
Cosmological Parameters ◽  
Mass Range ◽  
Mass Function ◽  
Cluster Mass ◽  
Theoretical Mass

ABSTRACT We present a new mass function of galaxy clusters and groups using optical/near-infrared (NIR) wavelength spectroscopic and photometric data from the Observations of Redshift Evolution in Large-Scale Environments (ORELSE) survey. At z ∼ 1, cluster mass function studies are rare regardless of wavelength and have never been attempted from an optical/NIR perspective. This work serves as a proof of concept that z ∼ 1 cluster mass functions are achievable without supplemental X-ray or Sunyaev-Zel’dovich data. Measurements of the cluster mass function provide important contraints on cosmological parameters and are complementary to other probes. With ORELSE, a new cluster finding technique based on Voronoi tessellation Monte Carlo (VMC) mapping, and rigorous purity and completeness testing, we have obtained ∼240 galaxy overdensity candidates in the redshift range 0.55 < z < 1.37 at a mass range of 13.6 < log (M/M⊙) < 14.8. This mass range is comparable to existing optical cluster mass function studies for the local universe. Our candidate numbers vary based on the choice of multiple input parameters related to detection and characterization in our cluster finding algorithm, which we incorporated into the mass function analysis through a Monte Carlo scheme. We find cosmological constraints on the matter density, Ωm, and the amplitude of fluctuations, σ8, of $\Omega _{m} = 0.250^{+0.104}_{-0.099}$ and $\sigma _{8} = 1.150^{+0.260}_{-0.163}$. While our Ωm value is close to concordance, our σ8 value is ∼2σ higher because of the inflated observed number densities compared to theoretical mass function models owing to how our survey targeted overdense regions. With Euclid and several other large, unbiased optical surveys on the horizon, VMC mapping will enable optical/NIR cluster cosmology at redshifts much higher than what has been possible before.

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