All Outputs (27)

Estimating peanut leaf chlorophyll content with dorsiventral leaf adjusted indices: minimizing the impact of spectral differences between adaxial and abaxial leaf surfaces (2019)
Journal Article
Xie, M., Wang, Z., Huete, A., Brown, L., Wang, H., Xie, Q., …Ding, Y. (2019). Estimating peanut leaf chlorophyll content with dorsiventral leaf adjusted indices: minimizing the impact of spectral differences between adaxial and abaxial leaf surfaces. Remote Sensing, 11(18), https://doi.org/10.3390/rs11182148

Relatively little research has assessed the impact of spectral differences among dorsiventral leaves caused by leaf structure on leaf chlorophyll content (LCC) retrieval. Based on reflectance measured from peanut adaxial and abaxial leaves and LCC me... Read More about Estimating peanut leaf chlorophyll content with dorsiventral leaf adjusted indices: minimizing the impact of spectral differences between adaxial and abaxial leaf surfaces.

GBOV (Ground-Based Observation for Validation): a copernicus service for validation of vegetation land products (2019)
Presentation / Conference
Bai, G., Dash, J., Brown, L., Meier, C., Lerebourg, C., Ronco, E., …Gobron, N. (2019, July). GBOV (Ground-Based Observation for Validation): a copernicus service for validation of vegetation land products. Presented at 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan

The Copernicus Ground-Based Observations for Validation (GBOV) service aims to develop and distribute robust in-situ datasets from a selection of ground-based monitoring sites for a systematic and quantitative validation of Earth Observation (EO) lan... Read More about GBOV (Ground-Based Observation for Validation): a copernicus service for validation of vegetation land products.

Estimating forest leaf area index and canopy chlorophyll content with Sentinel-2: an evaluation of two hybrid retrieval algorithms (2019)
Journal Article
Brown, L., Ogutu, B., & Dash, J. (2019). Estimating forest leaf area index and canopy chlorophyll content with Sentinel-2: an evaluation of two hybrid retrieval algorithms. Remote Sensing, 11(15), https://doi.org/10.3390/rs11151752

Estimates of biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC) are a fundamental requirement for effectively monitoring and managing forest environments. With its red-edge bands and high spatial... Read More about Estimating forest leaf area index and canopy chlorophyll content with Sentinel-2: an evaluation of two hybrid retrieval algorithms.

Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery (2019)
Journal Article
Xie, Q., Dash, J., Huete, A., Jiang, A., Yin, G., Ding, Y., …Huang, W. (2019). Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation, 80, 187-195. https://doi.org/10.1016/j.jag.2019.04.019

The red-edge bands place the recently available multispectral Sentinel-2 imagery at an advantage over other multispectral sensors, and hypothetically offer improved crop biophysical variable retrieval accuracy. In this study, Sentinel-2 data was test... Read More about Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery.

Synergetic exploitation of the sentinel-2 missions for validating the sentinel-3 ocean and land color instrument terrestrial chlorophyll index over a vineyard dominated Mediterranean environment (2019)
Journal Article
Brown, L., Dash, J., Lidon, A., Lopez-Baeza, E., & Dransfeld, S. (2019). Synergetic exploitation of the sentinel-2 missions for validating the sentinel-3 ocean and land color instrument terrestrial chlorophyll index over a vineyard dominated Mediterranean environment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7), 2244-2251. https://doi.org/10.1109/jstars.2019.2899998

Continuity to the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) will be provided by the Ocean and Land Color Instrument (OLCI) on-board the Sentinel-3 missions. To ensure its utility in a wide range of scientific... Read More about Synergetic exploitation of the sentinel-2 missions for validating the sentinel-3 ocean and land color instrument terrestrial chlorophyll index over a vineyard dominated Mediterranean environment.

Validation of the Sentinel-3 Ocean and Land Colour Instrument (OLCI) Terrestrial Chlorophyll Index (OTCI): synergetic exploitation of the Sentinel-2 missions (2018)
Presentation / Conference
Brown, L., Dash, J., Lidon, A., Lopez-Baeza, E., & Dransfeld, S. (2018, July). Validation of the Sentinel-3 Ocean and Land Colour Instrument (OLCI) Terrestrial Chlorophyll Index (OTCI): synergetic exploitation of the Sentinel-2 missions. Presented at IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain

Continuity to the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) will be provided by the Sentinel-3 Ocean and Land Colour Instrument (OLCI), and to ensure its utility in a wide range of operational applications, v... Read More about Validation of the Sentinel-3 Ocean and Land Colour Instrument (OLCI) Terrestrial Chlorophyll Index (OTCI): synergetic exploitation of the Sentinel-2 missions.

On the relationship between continuous measures of canopy greenness derived using near-surface remote sensing and satellite-derived vegetation products (2017)
Journal Article
Brown, L., Dash, J., Ogutu, B., & Richardson, A. (2017). On the relationship between continuous measures of canopy greenness derived using near-surface remote sensing and satellite-derived vegetation products. Agricultural and forest meteorology, 247, 280-292. https://doi.org/10.1016/j.agrformet.2017.08.012

Over the last two decades, satellite-derived estimates of biophysical variables have been increasingly used in operational services, requiring quantification of their accuracy and uncertainty. Evaluating satellite-derived vegetation products is chall... Read More about On the relationship between continuous measures of canopy greenness derived using near-surface remote sensing and satellite-derived vegetation products.