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 CO2 efflux from winter wheat field is -0.03~1.38μmolm-2s-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 CO2 efflux significantly correlated with the temperature of the air (Tair), the soil surface (Tsur), the averaged thermal (Tinf) temperature respectively at the p-level<0.001.
The relation between soil respiration and canopy thermal temperature (Tinf) and soil surface temperature (Tsur) was modeled with equations from Fang and Moncrieff (2001) respectively. On the whole, the performance of models with Tinf as independent is better than one with Tsur as independent for the data on May 8. The max multiple correlation coefficient (MCC) of the former is 0.95118 large than the MCC 0.92338 of the later, which provide a better fundament for the hypothesis above. The result of model analysis shows that the one of Schlentner and Van Cleve (1985) is best candidate in this study because of its high coefficient of determination and its principle.
However some problem should be improved in the future. Firstly, soil respiration was measured with CID photosynthesis system and chamber which demand to consider the disturbance of chamber and the precision of the instrument. Secondly, the research focus on a point not on whole area comparing with the resolution remote sensing image, such as NOAA/AVHRR, TM, MODIS, since the result can not be directly applied to satellite image, that is, the experiment on a large spatial scale should be done for satellite image application.
The primary purpose of this study was to estimate the boundary between vegetated and non-vegetated areas and to assess the condition of desertification in central Asia and western China located in arid and semiarid regions. Remote sensing data used in this study are a time-series of 10-day maximum Normalized Difference Vegetation Index (NDVI) composites derived from Global Area Coverage of Advanced Very High Resolution Radiometer (AVHRR) from 1982 to 2000. Taking place and development of desertification in the arid and semiarid regions directly influence the density and growth status of vegetation, making surface vegetation a most important indicator to desertification assessment. Vegetation is very sparse in desert and therefore onset of green-up in the desert was undetectable with AVHRR NDVI data. The occurrence of onset of green-up, as determined with time series NDVI data was used to identify desert and non-desert areas. The coefficient of variation (CoV) of the monthly NDVI (maximum-value composite) is used as a parameter to characterize the changes of vegetation in this work. The CoV can be used to compare the amount of variation in different sets of samples data. Changes in the value of the pixel-level CoV over time can be interpreted as a measure of vegetative biomass change over that time. The method to detect and quantify changes in CoV values for each pixel over 20-year period for which data was available is based on linear regression. If the CoV values exhibit a statistically significant decrease over time, it is possible to conclude that the area imaged in that pixel is under desertification.