Abstract
Abstract. Leaf area index (LAI) is one of the key structural variables in terrestrial vegetation ecosystems. Remote sensing offers an opportunity to accurately derive LAI at regional scales. The anisotropy of canopy reflectance, variations in background characteristics, and variability in atmospheric conditions constitute three factors that can strongly constrain the accuracy of retrieved LAI. Based on a hybrid canopy reflectance model, a new hyperspectral directional second derivative method (DSD) is proposed in this paper. This method can estimate LAI accurately through analyzing the canopy anisotropy. The effect of the background can also be effectively removed. With the aid of a widely-accepted atmospheric model, the influence of atmospheric conditions can be minimized as well. Thus the inversion precision and the dynamic range can be markedly improved, which has been proved by numerical simulations. As the derivative method is very sensitive to random noise, we put forward an innovative filtering approach, by which the data can be de-noised in spectral and spatial dimensions synchronously. It shows that the filtering method can remove random noise effectively; therefore, the method can be applied to hyperspectral images. The study region was situated in Zhangye, Gansu Province, China; hyperspectral and multi-angular images of the study region were acquired via the Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA), on 4 June 2008. After the pre-processing procedures, the DSD method was applied, and the retrieved LAI was validated by ground reference data at 11 sites. Results show that the new LAI inversion method is accurate and effective with the aid of the innovative filtering method.
Highlights
Leaf area index (LAI) is one of the important geometricstructure parameters in terrestrial vegetation ecosystems
The first category is experiential inversion method based on Vegetation Index (VI), such as retrieving LAI according to the correlation between the Normalized Difference Vegetation Index (NDVI) and LAI (Turner, 1999; Brown et al, 2000; Haboudane et al, 2004; Wang et al, 2005)
We have found that through the use of the proper filtering method which can remove noises brought about by factors other than the variation of LAI or Chlorophyll concentration, the directional second derivative (DSD) method is more reliable and accurate
Summary
Leaf area index (LAI) is one of the important geometricstructure parameters in terrestrial vegetation ecosystems. The first category is experiential inversion method based on Vegetation Index (VI), such as retrieving LAI according to the correlation between the Normalized Difference Vegetation Index (NDVI) and LAI (Turner, 1999; Brown et al, 2000; Haboudane et al, 2004; Wang et al, 2005) Such an approach is simple and widely-used, its accuracy is very low, and some VI can get saturated. CHRIS acquires a set of images with five view angles (−55◦, −36◦, 0◦, 36◦ and 55◦) of each target during each acquisition sequence in 2.5 min (Verrelst et al, 2007; Kamalesh et al, 2008; CHRIS/PROBA Website), and the 62-band images are collected in the visible and near-infrared regions from 400 nm to 1050 nm This new hyperspectral sensor offers a new opportunity to effectively improve the inversion accuracy of LAI. The main objective of this paper is to establish a useful method that can best estimate LAI by multi-angular hyperspectral data, as such an approach can constrain the effect of background variations and the canopy anisotropic reflectance
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
