Combining optical remote sensing data with in-situ measurements in order to estimate vegetation parameters on agricultural fields and corresponding uncertainties

Katharina Heupel; Daniel Spengler; Cornelia Weltzien

doi:10.1117/12.2280409

2 November 2017 Combining optical remote sensing data with in-situ measurements in order to estimate vegetation parameters on agricultural fields and corresponding uncertainties

Katharina Heupel, Daniel Spengler, Cornelia Weltzien

Author Affiliations +

Proceedings Volume 10421, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX; 1042124 (2017) https://doi.org/10.1117/12.2280409
Event: SPIE Remote Sensing, 2017, Warsaw, Poland

Abstract

The estimation and quantification of vegetation parameters on field-scale is necessary to make statements about potential yield and the heterogeneity of its spatial distribution. The ESA satellite mission Sentinel-2 provides optical remote sensing data with a high temporal resolution allowing for an extensive monitoring of agricultural fields. In order to quantify the vegetation parameters as well as to calibrate and validate regression models, additional in-situ measurements are essential. Comprehensive field measurements in two study areas in Germany have been conducted in the growing season 2017 parallel to Sentinel-2 image acquisitions. All ground truth data form a dense time series of the vegetation parameters crop height, crop coverage, chlorophyll content, leaf area index, and wet and dry biomass. First results show a strong linear relation between dry and wet biomass, whereas the slope of the regression line changes with increasing phenological growth stage. Furthermore, there is a clear relationship between in-situ measured wet and dry biomass and NDVI in the early vegetation period, but a saturation occurs in later growth stages. The paper represents a status report of current work in progress, reports first results and gives an outlook of future work.

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Katharina Heupel, Daniel Spengler, and Cornelia Weltzien "Combining optical remote sensing data with in-situ measurements in order to estimate vegetation parameters on agricultural fields and corresponding uncertainties", Proc. SPIE 10421, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX, 1042124 (2 November 2017); https://doi.org/10.1117/12.2280409

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KEYWORDS

Data modeling

Vegetation

Agriculture

Remote sensing

Statistical analysis

Biological research

Optical sensing

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