GeoEye-1 satellite<i>versus</i>ground-based multispectral data for estimating nitrogen status of turfgrasses

Caturegli, Lisa; Casucci, Marco; Lulli, Filippo; Grossi, Nicola; Gaetani, Monica; Magni, Simone; Bonari, E.; Volterrani, Marco

doi:10.1080/01431161.2015.1035409

Cited by 27 publications

(29 citation statements)

References 44 publications

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“…These interferences explain the difference of 34% observed between the NDVI using the hyperspectral sensor those obtained using satellite images for the sugarcane in the two experimental areas ( Figure 5). Although several studies have shown a high correlation between the NDVI obtained using hyperspectral sensor and satellite image data [59,60], we suggest caution when choosing the spectral data acquisition platform to calculate the NDVI.…”

Section: Ndvi Acquisition By Satellite Image Vs Hyperspectral Sensormentioning

confidence: 81%

Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf-Tissue Nutrients in Fields Managed with Straw Removal

et al. 2018

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The total or partial removal of sugarcane (Saccharum spp. L.) straw for bioenergy production may deplete soil quality and consequently affect negatively crop yield. Plants with lower yield potential may present lower concentration of leaf-tissue nutrients, which in turn changes light reflectance of canopy in different wavelengths. Therefore, vegetation indexes, such as the normalized difference vegetation index (NDVI) associated with concentration of leaf-tissue nutrients could be a useful tool for monitoring potential sugarcane yield changes under straw management. Two sites in São Paulo state, Brazil were utilized to evaluate the potential of NDVI for monitoring sugarcane yield changes imposed by different straw removal rates. The treatments were established with 0%, 25%, 50%, and 100% straw removal. The data used for the NDVI calculation was obtained using satellite images (CBERS-4) and hyperspectral sensor (FieldSpec Spectroradiometer, Malvern Panalytical, Almelo, Netherlands). Besides sugarcane yield, the concentration of the leaf-tissue nutrients (N, P, K, Ca, and S) were also determined. The NDVI efficiently predicted sugarcane yield under different rates of straw removal, with the highest performance achieved with NDVI derived from satellite images than hyperspectral sensor. In addition, leaf-tissue N and P concentrations were also important parameters to compose the prediction models of sugarcane yield. A prediction model approach based on data of NDVI and leaf-tissue nutrient concentrations may help the Brazilian sugarcane sector to monitor crop yield changes in areas intensively managed for bioenergy production.

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Section: Ndvi Acquisition By Satellite Image Vs Hyperspectral Sensormentioning

confidence: 81%

Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf-Tissue Nutrients in Fields Managed with Straw Removal

et al. 2018

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“…Caturegli et al (2015) found that the GeoEye-1 NDVI was correlated to GS NDVI, with the conclusion that either could be used to estimate N status of turfgrasses. Several spectral bands from Quickbird satellite imagery and multi-spectral imagery from a Portable Field Spec Handheld Spectroradiometer (Analytical Spectral Devices, Inc., Boulder, CO) were highly correlated in winter wheat N rate experiments in Turkey (Genc et al, 2009).…”

Section: Comparison Of Vegetative Indexes From Satellite Imagery Compmentioning

confidence: 99%

Comparison of Satellite Imagery and Ground‐Based Active Optical Sensors as Yield Predictors in Sugar Beet, Spring Wheat, Corn, and Sunflower

Sharma

Denton

et al. 2017

Agronomy Journal

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Algorithms using active-optical (AO) sensors have been developed to direct in-season N application to crops. Many farmers in the United States have a large number of farm fi elds to manage. Farmers using AO technology must visit each fi eld and operate the sensor across the entire fi eld in order to conduct in-season N application. A fi eld might be driven over with an on-the-go N fertilizer applicator, but the application might not be required. Th e objective of this study was to determine whether satellite imagery might be used to predict yield in sugar beet, spring wheat, corn and sunfl ower similar to the yield prediction possible using AO sensors. If so, the algorithms produced could be used to select fi elds that would benefi t from in-season N application. Two N-rate studies in sugar beet, spring wheat, corn and sunfl ower, were conducted with experimental unit size of 9 by 9 m large enough to fi t a satellite pixel of 5 by 5 m size within each unit. Th e AO sensor and satellite imagery data were related to yield of sugar beet, spring wheat, corn and sunfl ower in some site-years. Th e problem is the ability to acquire the satellite imagery early enough in the season to be useful as a screening tool. Th ese results indicate that even though satellite imagery could be used as a fi eld screening tool, a better option may be to mount an AO sensor on a farm implement for an early season activity, or to explore the use of unmanned aerial vehicles (UAVs).

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“…Gomez-Candon et al [53] assessed the errors in input prescription maps based on high spatial resolution GeoEye-1 images. Caturegli et al [54] compared GeoEye-1 satellite and ground-based multispectral data for estimating nitrogen status of turfgrasses. Li et al [55] studied the feasibility of Huanglongbing (citrus greening) detection based on World-View-2 satellite images.…”

Section: Applications Of High Resolution Satellite Sensors In Precisimentioning

confidence: 99%

High resolution satellite imaging sensors for precision agriculture

Yang

2018

Front. Agr. Sci. Eng.

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The central concept of precision agriculture is to manage within-field soil and crop growth variability for more efficient use of farming inputs. Remote sensing has been an integral part of precision agriculture since the farming technology started developing in the mid to late 1980s. Various types of remote sensors carried on groundbased platforms, manned aircraft, satellites, and more recently, unmanned aircraft have been used for precision agriculture applications. Original satellite sensors, such as Landsat and SPOT, have commonly been used for agricultural applications over large geographic areas since the 1970s, but they have limited use for precision agriculture because of their relatively coarse spatial resolution and long revisit time. Recent developments in high resolution satellite sensors have significantly narrowed the gap in spatial resolution between satellite imagery and airborne imagery. Since the first high resolution satellite sensor IKONOS was launched in 1999, numerous commercial high resolution satellite sensors have become available. These imaging sensors not only provide images with high spatial resolution, but can also repeatedly view the same target area. The high revisit frequency and fast data turnaround time, combined with their relatively large aerial coverage, make high resolution satellite sensors attractive for many applications, including precision agriculture. This article will provide an overview of commercially available high resolution satellite sensors that have been used or have potential for precision agriculture. The applications of these sensors for precision agriculture are reviewed and application examples based on the studies conducted by the author and his collaborators are provided to illustrate how high resolution satellite imagery has been used for crop identification, crop yield variability mapping and pest management. Some challenges and future directions on the use of high resolution satellite sensors and other types of remote sensors for precision agriculture are discussed.

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GeoEye-1 satelliteversusground-based multispectral data for estimating nitrogen status of turfgrasses

Cited by 27 publications

References 44 publications

Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf-Tissue Nutrients in Fields Managed with Straw Removal

Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf-Tissue Nutrients in Fields Managed with Straw Removal

Comparison of Satellite Imagery and Ground‐Based Active Optical Sensors as Yield Predictors in Sugar Beet, Spring Wheat, Corn, and Sunflower

High resolution satellite imaging sensors for precision agriculture

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