[Application and prospects of hyperspectral remote sensing in monitoring plant diversity in grassland]. / é«˜å ‰è°±é¥æ„Ÿåœ¨è‰åŽŸæ¤ç‰©å¤šæ ·æ€§ç›‘æµ‹ä¸­çš„åº”ç”¨ä¸Žå±•æœ›.

The biodiversity of grasslands is important for ecosystem function and health. The protection and mana-gement of grassland biodiversity requires the collection of the information on plant diversity. Hyperspectral remote sensing, with its unique advantages of extensive coverage and high spectral resolution, offers a new solution for long-term monitoring of plant diversity. We first reviewed the development history of hyperspectral remote sensing technology, emphasized its advantages in monitoring grassland plant diversity, and further analyzed its specific applications in this field. Finally, we discussed the challenges faced by hyperspectral remote sensing technology in its applications, such as the complexity of data processing, accuracy of algorithms, and integration with ground-based remote sensing data, and proposes prospects for future research directions. With the advancement of remote sensing technology and the integrated application of multi-source data, hyperspectral remote sensing would play an increasingly important role in grassland ecological monitoring and biodiversity conservation, which could provide scientific basis and technical support for global ecological protection and sustainable development.

[Estimating leaf area index by fusing MODIS and MISR data].

Moderate-resolution imaging spectrometer (MODIS) and multi-angle imaging spectroradiometer (MISR) are two important sensors on TERRA satellite. The authors can have more spectral and multi-angular observations on the land surface objects by combining these two datasets. In the present paper, both MODIS and MISR observations were combined to estimate leaf area index (LAI) of land surface. The adjoining model and trust-region optimal algorithm were introduced into the framework of physical model inversion to speed up the running of the model inversion algorithm. And the algorithm allows the prior knowledge on the retrieved parameters to be input into the inversion procedure. The uncertainty and sensitivity matrix (USM) based analysis is helpful for selecting the observed data subset with more information and less noise to retrieve LAI. The measured LAI in situ and estimated LAI from ETM data were scaling-up to MODIS/MISR LAI product scale, and were taken as the ground truth to evaluate the new approach. The result suggests that combining two sensors datasets can improve the accuracy of LAI estimation, and the developed inversion method in this paper can be applied to the large area remote sensed image data effectively.