FY-3A is the second Chinese Polar Orbital Meteorological Satellite with global, three-dimensional, quantitative, and multispectral capabilities. Its missions include monitoring global disasters and environment changes. This study describes some basic parameters and major technical indicators of the FY-3A and evaluates data quality and drought monitoring capability of the
Drought is a normal, recurrent feature of climate. It occurs almost everywhere, although its features vary from region to region. In the most general sense, drought originates from a deficiency of precipitation over an extended period of time, resulting in a water shortage for some activity, group, or environmental sector (NDMC define). Consequently, it is required to demonstrate the distribution and degree of drought condition timely, which is crucial for drought warning and resisting effectively [
At present, remote sensing methods for drought monitoring are mainly classified into four categories: Vegetation Index-based [
The FY-3A satellite is the second Chinese polar orbital meteorological satellite which provides global, three-dimensional, high-quantified, and multispectral remote sensed data. The satellite weighs 2400 kg, and has in-orbit dimensions of
With multispectral and high-resolution features, MERSI can detect atmosphere, land, and ocean through the reflection of visible channels and thermal infrared radiation. There are three channels located in the water vapor absorption window (0.905
Spectral properties of MERSI (partial).
Channel | Wavelength (um) | Bandwidth (um) | Resolution (m) | Application | |
---|---|---|---|---|---|
3 | 0.650 | 0.05 | 250 | 0.4 | Vegetation monitoring |
4 | 0.865 | 0.05 | 250 | 0.45 | |
5 | 11.25 | 2.5 | 250 | 0.50 K | Temperature retrieve |
17 | 0.905 | 0.02 | 1000 | 0.10 | Atmospheric Water Vapor detection |
18 | 0.940 | 0.02 | 1000 | 0.10 | |
19 | 0.980 | 0.02 | 1000 | 0.10 |
It can be found in Table
Both FY-3A and TERRA are polar-orbiting satellites. Channels setting of MERSI, including the center wavelength and the wave width, is basically consistent with that of MODIS, in particular the bands used for monitoring surface vegetation, atmospheric water vapor, and surface temperature. MODIS data is calibrated on orbit and it uses the complicated recorrecting technology to locate when it scans. Because of high-quality and effective monitoring, MODIS has become a widely used data source in drought monitoring. Therefore, MODIS is optimal reference data in analyzing and evaluating radiometric calibration, relative geometric location, and drought monitoring results of MERSI.
In order to enlarge the remote sensing data source, the objective of the present study is to evaluate the data quality and the drought monitoring capability of FY-3A MERSI data with TERRA MODIS data as a reference.
The case study area is near Bohai Bay in North China (area inside the red frame, Figure
Study area in North China.
The data of the study were extracted from MERSI on
Original data
The orange lines in Figure
The Normalized Difference Vegetation Index is a satellite-derived global vegetation indicator obtained from the red, near-infrared (NIR) ratio of vegetation reflectance in the electromagnetic spectrum [
The parameters,
NDVI provides information on vegetation productivity and phenology over large temporal and spatial scales and has been widely used in the recent ecological studies as a proxy for vegetation productivity and phenology [
The Land Surface Temperature can be calculated from the Brightness Temperature (BT) of the thermal infrared channels based on the thermal transmission equation. Because FY-3A MERSI contains only one thermal infrared channel, LST is replaced by BT of the thermal infrared channel in this paper [
Sandholt and others extracted water stress indices (i.e., the index of temperature vegetation drought) on the basis of the simplified NDVI-LST feature space.
In fact,
In the experiment, a serial points (e.g., take
The normal equation can be deduced from the error matrix equation based on the Least-squares algorithm
The coefficients can be calculated based on the matrix operation:
Last, the relation square (
After calibration, MERSI and MODIS data are projected with the latitude and longitude information recorded in scan. The relative accuracy of geometric location and radiometric calibration of the two kinds of data are also analyzed. NDVI is obtained through red and near infrared channels, and LST in the clear sky or cloud top temperature can be retrieved through thermal infrared and vapor channels. Furthermore, it is necessary to compare and analyze the distribution character of the dry and wet sides of the two images in the NDVI-LST feature space. User’s accuracy, producer’s accuracy, and the overall accuracy are evaluated for TVDI with MERSI and MODIS data.
The visible channels of MERIS are directly calibrated with slope-intercept form, and revised solar altitude and Earth-Sun Distance at the imaging moment. Digital number (DN) of infrared channels is already radiation value, so the brightness temperature can be gained with Planck Formula.
The visible data of MODIS is calibrated with slope-intercept form, while the infrared channel is calibration with Planck Formula to get brightness temperature directly [
Both record the latitude and longitude values in imaging; they are translated into geographic coordinates in the corresponding projection through strict transformation calculation method [
Result of radiation calibration and projection (true color: red, near-infrared, red):
It is obvious in Figure
Compared analysis of calibration result.
Index | Red channel | Near-infrared channel | Thermal infrared channel | |||
MODIS (b01) | MERSI (b03) | MODIS (b02) | MERSI (b04) | MODIS (b31) | MERSI (b05) | |
Wavelength | 0.645 | 0.65 | 0.859 | 0.865 | 11.03 | 11.25 |
Mean | 0.141 | 0.192 | 0.238 | 0.303 | 290.76 | 290.55 |
Maximum | 1 | 1 | 1 | 1 | 304 | 301 |
Minimum | 0.02 | 0.05 | 0.01 | 0.02 | 226 | 234 |
Medium | 0.13 | 0.16 | 0.24 | 0.3 | 292 | 292 |
Std. Dev | 0.101 | 0.126 | 0.12 | 0.152 | 6.868 | 5.643 |
The population characteristic value different of red channel reflectivity between MERSI and MODIS data is 0.05 and the population variance is 0.02. In other words, red channels of two sensors have same sensitivity to different types of
MODIS data is of high orbital location accuracy, so it can serve as reference for analyzing geometric location accuracy of MERSI data. Fifteen control points (balanced distributed in study area) are selected manually in the study. The geometric location differences of these points are used to analyze geometry location accuracy of MERSI data. Figure
Geometric location accuracy of MERSI data (unit: meter).
Point no. | MODISX | MODISY | MERSIX | MERSIY | RMS | ||
---|---|---|---|---|---|---|---|
1 | 5645738 | 5524095 | 5645718 | 5524098 | 186 | 707.32 | |
2 | 5932799 | 5861170 | 5932795 | 5862104 | 637.26 | ||
3 | 6491754 | 5291080 | 6491693 | 5292060 | 205.46 | ||
4 | 6854745 | 5403135 | 6853753 | 5404095 | 121 | 139.76 | |
5 | 7000293 | 5764656 | 6998805 | 5765990 | 128 | 205.96 | |
6 | 6636750 | 6044083 | 6635740 | 6045110 | 353 | 364.98 | |
7 | 6371813 | 5412078 | 6371707 | 5413082 | 241 | 297.46 | |
8 | 6129717 | 6003122 | 6128731 | 6003088 | 202 | 705 | 734.52 |
9 | 5576752 | 6133008 | 5573799 | 6133098 | 233 | 15 | 233.74 |
10 | 5417760 | 5347094 | 5415739 | 5347072 | 504 | 522.30 | |
11 | 6884743 | 6095120 | 6882747 | 6097091 | 67 | 387.63 | |
12 | 6299718 | 5735132 | 6298756 | 5736088 | 986 | 11 | 986.91 |
13 | 6338741 | 6141127 | 6337732 | 6142106 | 137 | 198.29 | |
14 | 5409658 | 5642143 | 5406747 | 5642105 | 612 | 614.34 | |
15 | 5798758 | 5283109 | 5798707 | 5283129 | 232 | 326.44 | |
Total difference |
The distribution of the matching points.
It is shown in Table
Based on the analysis of calibration results, MERSI can retrieve similar reflectivity, LST, and cloud top temperature as MODIS does. The geometric location difference between MERSI and MODIS is less than half pixel, while difference of some areas reaches one pixel, but it is still within the tolerance. In other words, the geolocation of MERSI is acceptable. Therefore, the MERSI data has potential for drought monitoring on basis of calibration and relative geometric location analysis.
NDVI and LST, the two key parameters for TVDI method, cannot be retrieved when remote sensing data is covered by clouds, so TVDI is inapplicable for the cloud-covered areas. Consequently, to identify whether an image is qualified for drought monitoring, cloud detection should be performed before deducing parameters [
Cloud in visible band shows high reflectance, and remote sensing image with 0.66 um is ideal for distinguishing borders between land and cloud [
As cloud refection spectrum has reflection characteristics at 0.66 um and its absorption window at 0.936 um is influenced by water vapor, the CDI is positive (CDI > 0), because soil reflection spectra has little difference in reflection properties between 0.66 um and 0.936 um, the CDI is close to 0; vegetation reflection spectra has low reflection at 0.66 um and high reflection at 0.936 um, the CDI is negative (CDI < 0).
As the reflectance of water is higher than vegetation near the red band and the reflectance of vegetation is obviously higher than water round the NIR band (0.841 um–0.876 um) [
Based on the cloud detection and water extraction algorithms discussed above, it can be concluded that MERSI and MODIS data can have cloud and water detection with the same algorithm and the results is shown in Figure
Result of cloud detection:
Figure
For MERSI and MODIS, NDVI is usually calculated by red band and NIR band. Meanwhile, MERSI has only one TIR band (10
From previous studies, it is known that the distribution of the wet and dry sides is significant in the two-dimensional feature space composed by NDVI and LST from MERSI. It is necessary to analyze the characteristics of the NDVI-LST feature space and determine the wet and dry sides.
LST is calculated with the NDVI datasets and single-window algorithm,
The wet and dry sides in feature space:
It is shown in Figure
The fitting equations of wet and dry sides based on MERSI are as follows.
Dry side equation:
Wet side equation:
The above formulas demonstrate that if the slope of dry side is less than zero,
The fitting
It is found through the above analysis that the wet and dry sides possess high-fitting relevance in the NDVI-LST feature space of MERSI and it means that MERSI is suitable for drought monitoring. TVDI is used to analyze the drought monitoring capability of FY-3A MERSI in the study. MODIS has been proved to be one of the most successful data for drought monitoring and is naturally utilized as reference data. The monitoring results and comparison of MERSI and MODIS are as shown in Figure
Table
TVDI confusion matrices between MERSI and MODIS.
TERRA | FY-3A | ||||||
High | Middle | Low | Normal | Wet | Cloud | Producer accuracy | |
High | 0.1281 | 0.1949 | 0.056 | 0.0287 | 0.1276 | 0.097 | 20.235 |
Middle | 0.105 | 8.0814 | 7.7763 | 0.4183 | 0.3291 | 0.602 | 46.6786 |
Low | 0.1833 | 1.3909 | 18.945 | 4.3751 | 1.1041 | 0.533 | 71.4042 |
Normal | 0.110 | 0.1072 | 2.3245 | 7.5212 | 2.4723 | 0.239 | 58.8742 |
Wet | 0.2405 | 0.0579 | 0.3669 | 2.3293 | 21.2561 | 0.266 | 86.6994 |
Cloud | 0.204 | 0.4602 | 0.5915 | 0.31 | 0.4686 | 16.195 | 88.8404 |
User accuracy | 13.1908 | 78.517 | 63.024 | 50.199 | 82.523 | 90.296 | Correct: 72.1274 |
Acceptable: 93.8306 |
The following conclusions can be reached on the basis of above comparison and analysis.
FY-3A MERSI data enjoys a high quality. The relative difference of calibration results between MERSI and MODIS is negligible, and temperature retrieving capability of MERSI is as good as MODIS. In addition, it is also proved that their cloud detection results have strong consistency. Furthermore, the overall difference of geo-location between MERSI and MODIS is about 0.4666 pixels, in other words, MERSI data possess wonderful geo-location capability.
The
The TVDI confusion matrices indicate that FY-3A MERSI possesses the similar capability to MODIS, with absolute accuracy of 72.1274 and acceptable accuracy of 93.8306. Therefore, it is possible for us to obtain the drought products in operation with 250 m spatial resolution and global scale with MERSI as a new kind of data.
The authors are grateful to the members of the MODIS Data Receiving Station, Wuhan University, China. This paper is granted by FY-3A Satellite Application in Drought Monitoring and Early Warning in Northwest China which is subtask of FY-3A Satellite Application Projection from China Meteorological Administration (CMA). Meanwhile, this paper is also granted the AMD University Plan.