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Remote sensing is widely applied in the study of terrestrial primary production and the global carbon cycle. The researches on the spatial heterogeneity in images with different sensors and resolutions would improve the application of remote sensing. In this study two sites on alpine meadow grassland in Qinghai, China, which have distinct fractal vegetation cover, were used to test and analyze differences between Normalized Difference Vegetation Index (NDVI) and enhanced vegetation index (EVI) derived from the Huanjing (HJ) and Landsat Thematic Mapper (TM) sensors. The results showed that: 1) NDVI estimated from HJ were smaller than the corresponding values from TM at the two sites whereas EVI were almost the same for the two sensors. 2) The overall variance represented by HJ data was consistently about half of that of Landsat TM although their nominal pixel size is approximately 30m for both sensors. The overall variance from EVI is greater than that from NDVI. The difference of the range between the two sensors is about 6 pixels at 30m resolution. The difference of the range in which there is not more corrective between two vegetation indices is about 1 pixel. 3) The sill decreased when pixel size increased from 30m to 1km, and then decreased very quickly when pixel size is changed to 250m from 30m or 90m but slowly when changed from 250m to 500m. HJ can capture this spatial heterogeneity to some extent and this study provides foundations for the use of the sensor for validation of net primary productivity estimates obtained from ecosystem process models.

The spatial scaling of satellite data is faced widely and inevitably in remote sensing applications for the spatial heterogeneity of ecosystems. In this study variogram analysis was used to evaluate the spatial variability and the scale effects of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from Huanjing (that is, environment satellite sensor in Chinese, HJ-1A/B), Landsat-5 Thematic Mapper (TM), Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m, 500 m, 1 km, and the field sampled above-ground biomass (AGB). Results show that the overall spatial variance decreased when pixel size increased from 30 m (HJ and TM) to 1 km (MODIS) at the area of 10 km × 10 km. The value of 1 or 3×3 pixels approximately represent the above-ground biomass from the cyclic sampling design. This indicates that the HJ data can be used to retrieve the biomass and its scaling-up for its performance comparable with Landsat TM data, though both sensors were applicable than that of MODIS. Further the method to scale-up is a fundament approach to the validation and application of MODIS products and ecosystem model’s outputs on regional scale.

Monitoring crop growth status and yields using remote sensing data have been a challenges both in estimating the growing parameters and quantifying the seasonal changes. Traditionally, NOAA AVHRR data was applied to estimate and predict crop yields with statistical correlation methods. However, its spatial resolution of 8-km is not satisfying in monitoring crop growth on the site level. The launch of TERRA with moderate resolution imaging spectroradiometer (MODIS) instruments onboard began a new era in remote sensing of the Earth system which is providing a series of products of unparalleled quality and sophistication for the observation and biophysical monitoring of the terrestrial environment. Crop growth models simulate biophysical processes in the soil-crop-atmospheric system provide a continuous description of crop growth and development. Combining a growth model with the input parameters derived from remote sensing data provides spatial integrity as well as a real-time "calibration" of model parameters. A field study was conducted to evaluate the applicability of the 8-day MODIS leaf area index (LAI) data product in operational assessment of wheat growth condition and yields in the region of Yucheng, ShanDong Province, in China. The MODIS LAI product were used to compared with the DSSAT LAI--the output of crop simulation model (DSSAT) and the observed LAI. The MODIS LAI corresponded comparatively well with the DSSAT LAI in the early stage which have been tested well with the observed LAI, however in the later wheat growing stage, there are still some difference between the MODIS LAI and observed LAI. Limitations of this study and its conclusions are also discussed.

The determination of terrestrial ecosystem carbon source/sink spatial pattern is becoming one of the hottest problems and many environment politics focus on it. As a new tool for terrestrial ecosystem carbon modelling at large scale from field plot, to region, to global, remote sensing is applied to initialize, drive, and validate the model, combined with geophysics information system (GIS) and computer modelling. Carbon flux models with remote sensing data as input may be classified as light use efficiency model, process model, and eco-physiological model based on “big leaf” hypothesis. The model generally includes two parts: NPP and soil respiration model to estimate carbon flux based on the principle that the carbon flux of ecosystem equal NPP minus heterogeneity respiration (soil respiration). Remote sensing, however, is more applied in NPP modeling but little in soil respiration estimation. The latter mostly based on relationship between soil respiration and soil temperature and is highly developed. Since remote sensing is applied to retrieve land surface temperature (LST) with infrared waveband, a hypothesis was put forward, that is, land surface temperature retrieved from infrared waveband can substitute soil temperature to estimate soil respiration. The hypothesis was validated with a field experiment and result was given in this article. The experiment located in a winter wheat field at Quzhou experiment station, Hebei province, China, from Apr 19 to May 20, in 2002. The soil respiration rate was measured with CID photosynthesis system, and canopy infrared temperature, soil surface temperature were measured respectively at same time. The station provided us soil moisture content data of whole growth of winter wheat. The result shows that the soil CO 2 efflux from winter wheat field is -0.03~1.38μmolm -2 s -1 . Its diurnal variation is well fitted with univariate quartic curve. Its variation in winter wheat heading growth period well coincide with temperature and soil moisture content. The Pearson correlation analysis shows that, on the averaged sense, for a day, soil CO 2 efflux significantly correlated with the temperature of the air (T air ), the soil surface (T sur ), the averaged thermal (T inf ) temperature respectively at the p-level