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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.
Hyperspectral Imagery For Various Crop Growth Information Extraction, Haibo Yao, Lei Tian, Marvin Paulsen, Amy L. Kaleita, Mukti Singh
Hyperspectral Imagery For Various Crop Growth Information Extraction, Haibo Yao, Lei Tian, Marvin Paulsen, Amy L. Kaleita, Mukti Singh
Amy L. Kaleita
Aerial hyperspectral imagery has potential for agriculture applications. The objective of this study is to identify significant wavelength ranges (image bands or band combinations) from hyperspectral imagery for different field information extraction. The field information include corn nitrogen content, plant population, yield, and grain quality such as oil, protein, and extractable starch. All the images were processed using the GA-SPCA (Genetic Algorithm based Selective Principal Component Analysis) method . T he GA-SPCA method can filter out significant image bands and reduce the image data dimension to only one principle component image through a cascade two-step dimension reduction process. It was …
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 …
Hyperspectral Imagery For Various Crop Growth Information Extraction, Haibo Yao, Lei Tian, Marvin Paulsen, Amy Kaleita, Mukti Singh
Hyperspectral Imagery For Various Crop Growth Information Extraction, Haibo Yao, Lei Tian, Marvin Paulsen, Amy Kaleita, Mukti Singh
Amy L. Kaleita
Aerial hyperspectral imagery has potential for agriculture applications. The objective of this study is to identify significant wavelength ranges (image bands or band combinations) from hyperspectral imagery for different field information extraction. The field information include corn nitrogen content, plant population, yield, and grain quality such as oil, protein, and extractable starch. All the images were processed using the GA-SPCA (Genetic Algorithm based Selective Principal Component Analysis) method . T he GA-SPCA method can filter out significant image bands and reduce the image data dimension to only one principle component image through a cascade two-step dimension reduction process. It was …
Avhrr Estimates Of Surface Temperature During The Southern Great Plains 1997 Experiment, Amy L. Kaleita, Praveen Kumar
Avhrr Estimates Of Surface Temperature During The Southern Great Plains 1997 Experiment, Amy L. Kaleita, Praveen Kumar
Amy L. Kaleita
In this study we aim to (1) explore the differences in the accuracy of satellitederived land-surface skin temperature for day and nighttime observations, (2) assess the effects of large solar zenith angles, and (3) develop an understanding of the spatial variability of the observed temperatures. Land-surface skin temperatures are obtained using the split-window technique from observations of the AVHRR instrument aboard the NOAA-12 and NOAA-14 satellites for the SGP97 (Southern Great Plains 1997) hydrology experiment. From the study of several days of observations we find that observed biases with respect to the ground temperature, both during day and night, are …