An Assessment of Relations between Vegetation Green FPAR and Vegetation Indices through a Radiative Transfer Model.

The fraction of absorbed photosynthetically active radiation (FPAR) is widely used in remote sensing-based production models to estimate gross or net primary production. The forest canopy is composed primarily of photosynthetically active vegetation (PAV, green leaves) and non-photosynthetic vegetation (NPV e.g., branches), which absorb PAR but only the PAR absorbed by PAV is used for photosynthesis. Green FPAR (the fraction of PAR absorbed by PAV) is essential for the accurate estimation of GPP. In this study, the scattering by arbitrary inclined leaves (SAIL) model was reconfigured to partition the PAR absorbed by forest canopies. The characteristics of green FPAR and its relationships with spectral vegetation indices (NDVI, EVI, EVI2, and SAVI) were analyzed. The results showed that green FPAR varied with the canopy structure. In the forests with high coverage, the green FPAR was close to the total FPAR, while in the open forests, the green FPAR was far smaller than the total FPAR. Plant area index had more important impacts on the green FPAR than the proportion of PAV and optical properties of PAV. The significant relationships were found between spectral vegetation indices and the green FPAR, but EVI was more suitable to describe the variation of canopy green FPAR.

Assessment of GF3 Full-Polarimetric SAR Data for Dryland Crop Classification with Different Polarimetric Decomposition Methods.

Crop classification is one of the most important agricultural applications of remote sensing. Many studies have investigated crop classification using SAR data, while few studies have focused on the classification of dryland crops by the new Gaofen-3 (GF3) SAR data. In this paper, taking Hengshui city as the study area, the performance of the Freeman-Durden, Sato4, Singh4 and multi-component decomposition methods for dryland crop type classification applications are evaluated, and the potential of full-polarimetric GF3 data in dryland crop type classification are also investigated. The results show that the multi-component decomposition method produces the most accurate overall classifications (88.37%). Compared with the typical polarization decomposition techniques, the accuracy of the classification results using the new decomposition method is improved. In addition, the Freeman method generally yields the third-most accurate results, and the Sato4 (87.40%) and Singh4 (87.34%) methods yield secondary results. The overall classification accuracy of the GF3 data is very positive. These results demonstrate the great promising potential of GF3 SAR data for dryland crop monitoring applications.

Urban spring phenology in the middle temperate zone of China: dynamics and influence factors.

Urbanization and its resultant urban heat island provide a means for evaluating the impact of climate warming on vegetation phenology. To predict the possible response of vegetation phenology to rise of temperature, it is necessary to investigate factors influencing vegetation phenology in different climate zones. The start of growing season (SOS) in seven cities located in the middle temperate humid, semi-humid, semi-arid, and arid climate zones in China was extracted based on satellite-derived normalized difference vegetation index (NDVI) data. The dynamics of urban SOS from 2000 to 2009 and the correlations between urban SOS and land surface temperatures (LST), precipitation, and sunshine duration, respectively, were analyzed. The results showed that there were no obvious change trends for urban SOS, and the heat island induced by urbanization can make SOS earlier in urban areas than that in adjacent rural areas. And the impact of altitude on SOS was also not negligible in regions with obvious altitude difference between urban and adjacent rural areas. Precipitation and temperature were two main natural factors influencing urban SOS in the middle temperate zone, but their impacts varied with climate zones. Only in Harbin city with lower sunshine duration in spring, sunshine duration had more significant impact than temperature and precipitation. Interference of human activities on urban vegetation was non-negligible, which can lower the dependence of urban SOS on natural climatic factors.

[Monitoring "green tide" in the Yellow Sea and the East China Sea using multi-temporal and multi-source remote sensing images].

Landsat-TM (Theme Mapper) and EOS (Earth Observing System)-MODIS (MODerate resolution Imaging Spectrora-diometer) Terra/Aqua images were used to monitor the macro-algae (Ulva prolifera) bloom since 2007 at the Yellow Sea and the East China Sea. At the turbid waters of Northern Jiangsu Shoal, there is strong spectral mixing behavior, and satellite images with finer spatical resolution are more effective in detection of macro-algae patches. Macro-algae patches were detected by the Landsat images for the first time at the Sheyang estuary where is dominated by very turbid waters. The MODIS images showed that the macro-algae from the turbid waters near the Northern Jiangsu Shoal drifted southwardly in the early of May and affected the East China Sea waters; with the strengthening east-asian Summer Monsoon, macro-algae patches mainly drifted in a northward path which was mostly observed at the Yellow Sea. Macro-algae patches were also found to drift eastwardly towards the Korea Peninsular, which are supposed to be driven by the sea surface wind.