Name
Abstract | ||
|---|---|---|
Early detection of potential drought is important for the local government and people to take actions to protect their crop. The drought monitoring relies on the monitoring of soil moisture. Traditionally measurement of soil moisture is a time-consuming job and only limited samples could be collected. Many problems would be results from extending those point measurements to 2D space, especially for a regional area with heterogeneous soil characteristics. The emergency of remote-sensing technology makes it possible to rapidly monitor drought on a regional scale because of their spatially explicit measurements, almost real-time detection as well as their relatively low cost. In Northern China, drought is a severe problem. Northern China Plain often suffers from drought in the spring because of seasonal wind influence. The earlier detection of soil moisture in Northern China Plain is very important for local and national governments and farmers. In this paper, the monitoring of the drought is based on the monitoring of the soil moisture that was retrieved from the relationship of the soil moisture and the thermal inertia. The thermal inertia was derived using a real thermal inertia model using MODIS data. Before using MODIS data, some pre-processing should be done such as radiometric correction, geometric rectification, and water body masking. MODIS data after processing can be used to retrieve the thermal inertia. Thermal inertia is one of the typical parameters for describing the thermal characteristic of a material, it is a physical parameter representing the ability of a material to conduct and store heat, and in the context of planetary science, it is a measure of the subsurface's ability to store heat during the day and reradiate it during the night. It is defined as c k P U � , where P(Jm -2 S -1/2 ) |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IGARSS.2008.4778861 | IGARSS |
Keywords | Field | DocType |
drought monitoring,modis,remote sensing,soil moisture,thermal inertia,agriculture,data models,remote monitoring,meteorology,hydrology,real time,information systems,moisture,environmental economics,seasonality,geographic information systems,remote sensing technology,thermal conductivity,natural disaster,disasters | Longitude,Geographic information system,Moisture,Computer science,Remote sensing,Natural disaster,Agriculture,Latitude,Water content,Beijing | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Wu | 1 | 71 | 6.69 |
| Guoyin Cai | 2 | 25 | 11.55 |
| Mingyi Du | 3 | 4 | 8.65 |
| Yong Xue | 4 | 118 | 57.61 |