Open Access. Powered by Scholars. Published by Universities.®
Bioresource and Agricultural Engineering Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Publication
Articles 1 - 5 of 5
Full-Text Articles in Bioresource and Agricultural Engineering
Narrow-Band And Derivative-Based Vegetation Indices For Hyperspectral Data, Kelly Robert Thorp, Lei Tian, Haibo Yao, Lie Tang
Narrow-Band And Derivative-Based Vegetation Indices For Hyperspectral Data, Kelly Robert Thorp, Lei Tian, Haibo Yao, Lie Tang
Lie Tang
Hyperspectral remote sensing imagery was collected over a soybean field in central Illinois in mid-June 2001 before canopy closure. Estimates of percent vegetation cover were generated through the processing of RGB (red, green, blue) digital images collected on the ground with an automated crop mapping system. A comparative study was completed to test the ability of broad-band, narrow-band, and derivative-based vegetation indices to predict percent soybean cover at levels less than 70%. Though remote sensing imagery is commonly analyzed using reference data collected at random points over a scene of interest, the analysis of the hyperspectral imagery in this research …
Soil Moisture Estimation From Remotely Sensed Hyperspectral Data, Amy L. Kaleita, Lei F. Tian, Haibo Yao
Soil Moisture Estimation From Remotely Sensed Hyperspectral Data, Amy L. Kaleita, Lei F. Tian, Haibo Yao
Amy L. Kaleita
A methodology for mapping surface soil moisture content across an agricultural field from optical remote sensing and ground sampling is developed. This study uses both ground-based and remotely sensed spectral measurements of soil reflectance in visible and near-infrared wavelengths and concurrent measurements of volumetric soil moisture within the top 6 cm. After determining appropriate wavelengths for soil moisture estimation from spectral reflectance, a cokriging scheme was used to generate soil moisture maps. Results indicate that combining reflectance and ground measurements can yield more detailed maps of soil moisture than ground measurement alone.
Soil Moisture Estimation From Remotely Sensed Hyperspectral Data, Amy L. Kaleita, Lei Tian, Haibo Yao
Soil Moisture Estimation From Remotely Sensed Hyperspectral Data, Amy L. Kaleita, Lei Tian, Haibo Yao
Amy L. Kaleita
A methodology for mapping surface soil moisture content across an agricultural field from optical remote sensing and ground sampling is developed. This study uses both ground-based and remotely sensed spectral measurements of soil reflectance in visible and near-infrared wavelengths and concurrent measurements of volumetric soil moisture within the top 6 cm. After determining appropriate wavelengths for soil moisture estimation from spectral reflectance, a cokriging scheme was used to generate soil moisture maps. Results indicate that combining reflectance and ground measurements can yield more detailed maps of soil moisture than ground measurement alone.
Remote Sensing Of Site-Specific Soil Characteristics For Precision Farming, Amy L. Kaleita, Lei F. Tian
Remote Sensing Of Site-Specific Soil Characteristics For Precision Farming, Amy L. Kaleita, Lei F. Tian
Amy L. Kaleita
A methodology for assessing distributed surface soil moisture content from optical remote sensing is developed. This study uses both ground-based and remotely sensed spectral measurements of soil reflectance in visible and near-infrared wavelengths and concurrent measurements of volumetric soil moisture within the top 6 cm to establish a relationship between spectral response and moisture. Various approaches, including principal component analyses and regression techniques are investigated to determine the potential for quantifying soil moisture from the spectral reflection data. Preliminary investigations have yielded R 2 values as high as 0.62 when comparing predictions to actual moisture values. Investigation of predicting soil …
Remote Sensing Of Site-Specific Soil Characteristics For Precision Farming, Amy L. Kaleita, Lei Tian
Remote Sensing Of Site-Specific Soil Characteristics For Precision Farming, Amy L. Kaleita, Lei Tian
Amy L. Kaleita
A methodology for assessing distributed surface soil moisture content from optical remote sensing is developed. This study uses both ground-based and remotely sensed spectral measurements of soil reflectance in visible and near-infrared wavelengths and concurrent measurements of volumetric soil moisture within the top 6 cm to establish a relationship between spectral response and moisture. Various approaches, including principal component analyses and regression techniques are investigated to determine the potential for quantifying soil moisture from the spectral reflection data. Preliminary investigations have yielded R 2 values as high as 0.62 when comparing predictions to actual moisture values. Investigation of predicting soil …