Remote Estimation of Crop Chlorophyll Content by Means of High‐Spectral‐Resolution Reflectance Techniques

Abstract

Th e chlorophyll content (Chl) of plants is an important variable in assessing their physiological status and photosynthetic performance. High-spectral-resolution refl ectance measurements have shown a powerful capacity to detect the subtle variations in spectral absorption features related to changes in Chl. Th is study proposed a simple technique based on an empirical linear relationship between Chl and the normalized area over refl ectance curve (NAOC) index to remotely estimate the spatial distribution of Chl. Th e NAOC is based on the calculation of the Chl-sensitive spectral interval obtained by hyperspectral measurements. Th e data sets used came from four recent European Space Agency campaigns that aimed to study crop properties from space: SPARC (Spain, 2003-2004), AgriSAR (Germany, 2006), CEFLES2 (France, 2007), and Sen3Exp (Spain, 2009). Th e following steps were undertaken to investigate the proposed relationship: (i) calibrating the SPAD-502 chlorophyll meter, (ii) collecting spatially distributed Chl data across various crops during the Sen3Exp campaign by using the calibrated SPAD-502 m, and (iii) fi tting a NAOC with Chl fi eld data and tuning it using data from the other campaigns so that a broad variety of crops, climatic conditions, and soil types was covered. Th e proposed technique was applied to multitemporal hyperspectral images during June 2009. Th e Chl maps were generated with a RMSE accuracy of 10 μg/cm 2 . Mapping their diff erences revealed the spatial distribution of greening up and senescing across crop fi elds. Th is new technique shows great potential for remotely monitoring the physiological status of crops.

Asynchronism in leaf and wood production in tropical forests: a study combining satellite and ground-based measurements

“…In the near future, new techniques for the study of chlorophyll content and photosynthetic activity by remote sensing will be available from airborne and space-borne sensors as well (e.g. the ESA-FLEX scientific mission; European Space Agency, 2008; Meroni et al, 2010;Delegido et al, 2011) and should give direct measurements of the production of carbohydrates by trees. As current IPCC scenarios predict an intensification of the dry period for the Guiana Shield and the Amazon during the 21st century (Solomon et al, 2007(Solomon et al, , 2009Harris et al, 2008), further research is needed to decipher what shapes the forest productivity pattern.…”

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

“…Information in the red edge combined with the frequent revisit time of Sentinel 2 (5 days) is expected to increase the accuracy of leaf CC estimation. To date, algorithms to estimate CC based on Sentinel 2 simulated spectral bands are being revised, created, and validated in crops across Europe, showing promising results [93][94][95]. Therefore, there is much scope for the application of these algorithms to estimate CC in mangrove forests once Sentinel 2 is operational.…”

Vegetation Index to estimate chlorophyll content from multispectral remote sensing data

“…Several satellite missions have been launched with the specific objective of monitoring changes in the vegetation cover over the Earth surface [Delegido et al, 2011]. In this sense, recently much attention has been paid to the development of methods to derive spatially distributed maps of Chlorophyll content (Chl) in vegetation using remote sensing techniques [Delegido et al, 2010].…”

“…One such example is the NAOC (Normalized Area Over reflectance Curve) index, which is a spectral index based on the normalized integral of many spectral bands initially developed for the Chl mapping of heterogeneous cropping areas [Delegido et al, 2010]. The NAOC index was validated with experimental Chl data from a wide variety of vegetation types including grass, crops and fruit trees [Delegido et al, 2010[Delegido et al, , 2011. According to Delegido et al [2011], the experimental relationship between NAOC and Chl is given by:…”

“…The NAOC index was validated with experimental Chl data from a wide variety of vegetation types including grass, crops and fruit trees [Delegido et al, 2010[Delegido et al, , 2011. According to Delegido et al [2011], the experimental relationship between NAOC and Chl is given by:…”