Cai Chongfa

Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data

Crop identification is the basis of crop monitoring using remote sensing. Remote sensing the extent and distribution of individual crop types has proven useful to a wide range of users, including policy-makers, farmers, and scientists. Northern China is not merely the political, economic, and cultural centre of China, but also an important base for grain production. Its main grains are wheat, maize, and cotton. By employing the Fourier analysis method, we studied crop planting patterns in the Northern China plain. Then, using time-series EOS-MODIS NDVI data, we extracted the key parameters to discriminate crop types. The results showed that the estimated area and the statistics were correlated well at the county-level. Furthermore, there was little difference between the crop area estimated by the MODIS data and the statistics at province-level. Our study shows that the method we designed is promising for use in regional spatial scale crop mapping in Northern China using the MODIS NDVI time-series. # 2007 Elsevier B.V. All rights reserved.

Availability and residual effects of phosphate rocks and inorganic P fractionation in a red soil of Central China

The agronomic effectiveness, phosphorus utilization efficiency and inorganic fractions of phosphorus, applied once to an Ultisol in Xianning, Hubei province, and then cropped 9 seasons, were measured. The results showed: (1)forall crops, the relative agronomic effectiveness (RAE) of three phosphate rocks (PRs), compared with triple superphosphate (TSP), reached 103.1–154.9% at three rates of P applied; (2) phosphorus utilization ratios during 5 years were 33.7–59.3% with the average of 35.0–52.7% for three PRs, greater than TSP ; (3)the total amountof inorganic phosphorus in soils which received phosphate fertilizers were 395.5–492.8 mg/kg, however, it was 387.4 mg/kg in soil given no phosphorus fertilizer. The most remarkable increases were occluded P(O-P) and calcium P(Ca-P) fractions at P 35 level (i.e. soil extractable P reached 35 mg/kg). (4) after 9 crops were harvested, the balance of phosphorus in the soils of control (CK) and all P 15 level treatments was negative, however, for soils treated with P 25 level and P 35 level, the balance was positive.

Assessing soil erosion hazard -a raster based GIS approach with spatial principal component analysis (SPCA)

Soil erosion is one of the most serious environmental problems affecting the quality of soil, land, and water resources upon which humans depend for their sustenance. A soil erosion hazard map is highly useful for environmental planning, soil conservation and management in soil erosion prone areas. In order to assess the soil erosion hazard, remote sensing (RS) and geographical information system (GIS) technologies were adopted, and a numerical model was developed using spatial principal component analysis (SPCA). Here, an integrated soil erosion hazard index (SEHI) was computed and classified into four levels of soil erosion hazard viz. low, average, high, and very high. In the process, nine factors were selected together with the degree of importance of the factors in hazard of the soil erosion. Integrated RS and GIS techniques and models were applied to generate the necessary factors for the SPCA approach. In addition, erosion hazard and calculated rate of soil erosion were used to find out the risk of soil erosion. Soil erosion risk was identified as actual occurrence and potential risk. Results show that, in general, an average hazardous condition of soil erosion was found in the area. The potential risk was more extensive in terms of involved area compared to the actual occurrence, and both actual occurrence and potential risk were higher at the mid-level elevation of the area. This study highlighted that priority should be given where the actual occurrence is high to very high and the probability of potential risk is average to high for protecting the land at present and in the future as well. Therefore, the application of SPCA combined with RS and GIS provided an effective methodology to solve the complex decisional problem for soil erosion hazard and risk assessment.