Gross primary production (GPP) is the total amount of atmospheric carbon (CO2) assimilated by vegetation. In this
article, a regional terrestrial ecosystem GPP estimation model REG-PEM(REGion Production Efficiency Model) is
developed based on light use efficiency theory, and 8-day composite and annual GPP are calculated using REG-PEM
model in Jiangxi province. The REG-PEM model was designed on the basis of the production efficiency concept in
which gross primary production is calculated from the products of the photosynthetically active radiation (PAR)
absorbed by the vegetation(APAR) and light use efficiency, and all the input data get from remote sensing method. GPP
are calculated using MODIS 8-day composite products and total ozone mapping spectrometer (TOMS) reflectance data
in Jiangxi province in 2003 and 2004. GPP increases in spring, reaches maximum in summer and decreases in autumn,
and fluctuates in the year. The results indicate that the REG-PEM model is capable of tracking seasonal dynamics and
interannual variations in GPP at a 8-day temporal resolution.
The policy of ecological return of cultivated land has been carried out for several years in China and the cultivated land is decreasing. The objective of this study is to explore the potential and the methodology for the cropland change detection with Discrete Fourier Transform (DFT) approach using high temporal resolution imagery and some ancillary data. The data used in this study are 10-day composite SPOT-4 VEGETATION (VGT) Normalized Difference Vegetation Index (NDVI) over the period from April to November in 1998 and 2002 respectively, and the ancillary data include the existing land cover dataset derived from TM images and agricultural phonological calendar. The DFT
method was applied to the NDVI data set on a per pixel basis. The magnitude of the difference of amplitudes in the first three harmonics was used to identify the areas where changes might occur, and then the unsupervised classification was used to determine the types of change. The methodology used in this study can minimize the influence of noise and phenology variance to the change detection. The result showed that the significant change of cropland and other land cover can be detected with this method.
During the past 20 years, China’s agro-ecosystems have great changes in response to changes in climate and agricultural management. Agricultural productivity is of vital importance to the national food security and sustainable development. So far, agricultural statistics are the only source of the data about changes in agricultural productivity in national scale, and there is little geo-spatial information on these changes. Remote sensing provides an important tool to monitor the spatial and temporal variations at high resolution, but it had yet to used fully at regional and national scales to assess the interannual and long-term changes in agricultural productivity. This study estimated agricultural net primary productivity (ANPP) at the national level using a remote sensing-based production efficiency model, GLO-PEM. In the study, the arable area has been derived from TM data. ANPP was calculated from 8m, 10-day composite Advanced Very High Resolution Radiometers (AVHRR) data from 1981 to 2000 using GLO-PEM model. Using the data we analyzed the spatial variations in agricultural productivity in China between the 1980s and the 1990s. A 3-level hierarchy regionalization system is used in analyzing the spatial pattern and its changes in the agricultural productivity. China’s average agricultural ANPP increased 59.8 million tons from the 1980s to the 1990s. The increment of ANPP mainly occurred in the major cereal-planting plains, especially HuangHuaiHai Plain. The characteristics of land resources are the dominating factors to cause the changes at 10 years scale. There were some decreases, which mainly caused by the degradation on fragile lands, the rapid expansion of rural industries, and the urban development from high-quality arable lands.