Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection
Abstract
:1. Introduction
2. Methodology
2.1. Description of Flood Observation Satellite Sensor Selection Problem
2.2. OCEM Formalization
2.2.1. OCEM Framework
2.2.2. OCEM Formulation
2.3. OCEM Solving
2.3.1. Calculation of Observation Capability Factors
- SpCo: This can be calculated by using Equation (2), where is the sensor’s observation area and is the whole task area.
- TiCo: This can be calculated by using Equation (3), where is the sensor’s time window and is the task’s required time window.
- ThCo: This can be calculated by using Equations (4) and (5), where is the relevance degree of one specific observation parameter (e.g., land cover) to the flood, and is the number of the task’s required observation parameters. The relevance degrees are quantified into six values, which are sourced from the Observing System Capability Analysis and Review Tool (https://space.oscar.wmo.int/instruments (accessed on 1 September 2023)) released by the World Meteorological Organization.
- ReTi: This can be calculated by using Equation (6), where and are the start time and end time of the task, and is the time when the sensor can respond to the task.
- ReFc: This can be calculated by using Equation (7), where is the frequency of , and a higher frequency can result in a better observation performance theoretically.
- Observation quality: Observation quality is composed of four observation capability factors, namely, SpaRes, RadRes, SpeRes, and Pol. Each sensor only contains three of them because SpeRes only exists in optical sensors and Pol only exists in microwave sensors. Their detailed meanings and calculations are as follows:
- (1)
- SpaRes
This refers to the size of one pixel on the ground, which determines how detailed a satellite image is [38]. SpaRes can be calculated by using Equation (8), where is the task’s required spatial resolution, and is the spatial resolution of .- (2)
- RadRes
This reflects the capability of the sensor to recognize subtle changes in flood water radiation energy, which is represented by the radiometric quantization value of each image pixel [39]. RadRes can be obtained by using Equation (9), where is the radiometric quantization value of , and is the task’s required radiometric quantization value. A higher radiometric quantization value results in a better observation performance.- (3)
- SpeRes
This reflects the capability of the sensor to recognize spectral features of ground objects [40], and a higher spectral resolution (smaller wavelength interval) facilitates better observation of complex flood scenarios, such as flood water, vegetations, and houses. SpeRes can be calculated by using Equation (10), where is the task’s required spectral resolution, is the task’s required wavelength range, is the wavelength range of the sensor, and is the wavelength range intersection of the task and sensor.- (4)
- Pol
Different polarization modes cause different observation performance, for example, like polarization is more suitable for flood water identification when compared with cross-polarization [41,42], HH polarization is more effective than HV and VV polarization in recognizing the inundation area [43], and alternating polarization with co-polarization and cross-polarization is superior in flood mapping [44]. Consequently, on the basis of the review and analysis of related studies [43,45], Equation (11) is constructed to calculate Pol.
- EnIm: A flood event is always accompanied by bad weather (e.g., heavy rainfall and thick clouds), which can have a great impact on satellite sensors. Geographical environmental factors, such as atmospheric refraction and topography, also influence the observation quality to some extent [42]. However, the impacts caused by atmospheric refraction or topography can be reduced or eliminated by using some existing mathematical approaches and physical models [46,47]. The thick clouds can only be reduced by using external satellite images or ground observations [48,49], which conflicts with the goal of planning sensors to acquire observation data.
2.3.2. Weight Assignment of Observation Capability Factors
3. Flood-Water-Observation-Oriented Satellite Sensor Selection Experiment
3.1. Flood Event and Observation Task Requirements
3.2. Available Sensor Resources
3.3. OCEM Calculation for Supporting Satellite Sensor Selection
3.3.1. Calculation and Results of Observation Capability Factors
3.3.2. Weights Calculation and Results
3.3.3. OCEM Calculation and Results
4. Discussion
4.1. Supporting the Optimum Satellite Sensor Selection for Flood Observation
4.2. Superiority of OCEM
5. Conclusions and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
List of Abbreviations
OCEM | Observation capability evaluation model |
EA | Experiment of application |
CA | Capability analysis |
SAR | Synthetic Aperture Radar |
XPAR | X-band phased-array meteorological radar |
DOCI | Dynamic observation capability index |
CWRC | Changjiang Water Resource Commission |
SpCo | Spatial coverage |
TiCo | Time coverage |
ThCo | Theme conformity |
ReTi | Response timeliness |
ReFc | Revisit frequency |
EnIm | Environment impact |
SpaRes | Spatial resolution conformity |
SpeRes | Spectral resolution conformity |
Pol | Polarization mode conformity |
RadRes | Radiation resolution conformity |
AHP | Analytic hierarchy process |
OSCAR | Observing System Capability Analysis and Review Tool |
WWO | World Weather Online |
SOCDB | Sensor observation capability database |
SSCI | Sensor static capability index |
SOCA Ontology | Sensor observation capability association ontology |
SOCO-Field | Sensor observation capability object field |
SSOCEM | Star sensor observation capability evaluation model |
EOPEM | Earth observation potential evaluation model |
SBSM | Sensor band selection method |
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Variable | Meaning |
---|---|
SpCo (spatial coverage) | The spatial coverage ratio of a sensor to the task area. |
TiCo (time coverage) | The time coverage ratio of a sensor to the task time window. |
ThCo (theme conformity) | The similarity between a sensor’s observation parameters and the task’s required parameters under a specific observation theme. |
ReTi (response timeliness) | The response timeliness of a sensor to the task. |
ReFc (revisit frequency) | The frequency of a sensor observing the task area within the task time window. |
SpaRes (spatial resolution conformity) | The coincidence rate of a sensor’s spatial resolution to the task’s required one. |
SpeRes (spectral resolution conformity) | The coincidence rate of a sensor’s spatial resolution to the task’s required one. |
Pol (polarization mode conformity) | The coincidence rate of a sensor’s polarization resolution to the task’s required one. |
RadRes (radiation resolution conformity) | The coincidence rate of a sensor’s radiation resolution to the task’s required one. |
EnIm (environment impact) | The extent to which geographical environmental factors influence the observation quality. |
Intensity of Importance | Definition | Explanation |
---|---|---|
1 | Equal importance | Two activities contribute equally to the object |
2 | Weak or slight | |
3 | Moderate importance | Experience and judgment favor slightly one activity over another |
4 | Moderate plus | |
5 | Strong importance | Experience and judgment strongly favor one activity over another |
6 | Strong plus | |
7 | Very strong or demonstrated importance | An activity is favored very strongly over another; its dominance is demonstrated in practice |
8 | Very, very strong | |
9 | Extreme importance | The evidence favoring one activity over another is of the highest possible order of affirmation |
Order | Phases | Time Window | Characteristics | Spatial Resolution | Spectral Resolution | Radiometric Resolution | Polarization Mode | Observation Parameters |
---|---|---|---|---|---|---|---|---|
1 | Preparedness | 13 July 0:00– 16 July 0:00 | Large-scale and periodic observation | 30 m | 0.07 Micrometer (optical) | 12 bits (radiometric quantization) | HH (microwave) |
|
2 | Response | 19 July 0:00–24:00 | Quick response | 10 m | ||||
3 | Recovery | 23 July 0:00–24:00 | High-quality observation | 1 m |
Flood Observation Task | #Available Optical Sensor | #Available Microwave Sensor |
---|---|---|
Preparedness phase | 13 | 3 |
Response phase | 13 | 2 |
Recovery phase | 12 | 2 |
Sensor Type | Satellite Sensor | SpCo | ThCo | EnIm | SpaRes | SpeRes | Pol | RadRes | TiCo | ReTi | ReFc |
---|---|---|---|---|---|---|---|---|---|---|---|
Optics | Worldview 4_SpaceView-110 | 0.2057 | 0.2667 | 0.2600 | 1 | 1 | / | 1 | 0.2607 | 0.6153 | 0.2500 |
Ziyuan1-02C_HRPC-2 | 0.4529 | 0 | 0.5400 | 1 | 0 | 1 | 0.2731 | 0.8539 | 0.2500 | ||
KazEOSat-1_NAOMI (KazEOSat) | 0.4836 | 0.2667 | 0.4700 | 1 | 1 | 1 | 0.2980 | 0.8449 | 0.2500 | ||
RapidEye-1_REIS | 0.3462 | 0.2667 | 0.4400 | 1 | 1 | 1 | 0.2731 | 0.5213 | 0.2500 | ||
GeoEye-1_GIS | 0.4081 | 0.2667 | 0.4700 | 1 | 1 | 1 | 0.2731 | 0.8413 | 0.2500 | ||
SSOT_NAOMI(SSOT) | 0.3771 | 0.2667 | 0.4700 | 1 | 1 | 1 | 0.2731 | 0.8437 | 0.2500 | ||
Kompsat-2_MSC-Multi-Spectral Camera | 0.4232 | 0.2667 | 0.3800 | 1 | 1 | 1 | 0.2855 | 0.5346 | 0.2500 | ||
RapidEye-2_REIS | 0.1573 | 0.2667 | 0.2500 | 1 | 1 | 1 | 0.1241 | 0.1893 | 0.2500 | ||
Gaofen9_PMS-2 | 0.0734 | 0.2667 | 0.2100 | 1 | 1 | 1 | 0.0497 | 0.3547 | 0.2500 | ||
Jilin-1_PMS-2 | 0.0891 | 0.2667 | 0.6100 | 1 | 1 | 1 | 0.0621 | 0.8624 | 0.2500 | ||
Skysat-7_Skysat | 0.2694 | 0.2667 | 0.2200 | 1 | 1 | 1 | 0.2607 | 0.6928 | 0.2500 | ||
Gaofen-1-03_PMS | 0.3846 | 0.2667 | 0.4700 | 1 | 1 | 1 | 0.2731 | 0.8411 | 0.2500 | ||
Skysat-14_Skysat | 0.2697 | 0.2667 | 0.5400 | 1 | 1 | 0.7500 | 0.2607 | 0.8496 | 0.2500 | ||
SAR | COSMO-Skymed 3_SAR-2000 | 0.3596 | 0.2000 | 1 | 1 | / | 0.8000 | 1 | 0.2607 | 0.9174 | 0.2500 |
COSMO-Skymed 4_SAR-2000 | 0.3941 | 0.2000 | 1 | 1 | 0.8000 | 1 | 0.2855 | 0.7432 | 0.2500 | ||
Coriolis_WindSat | 0.5061 | 0.2667 | 1 | 1 | 0.8000 | 1 | 0.2855 | 0.2373 | 0.2500 |
Sensor Type | Satellite Sensor | SpCo | ThCo | EnIm | SpaRes | SpeRes | Pol | RadRes | TiCo | ReTi | ReFc |
---|---|---|---|---|---|---|---|---|---|---|---|
Optics | Worldview4_SpaceView-110 | 0.2045 | 0.2667 | 0.0300 | 1 | 1 | / | 1 | 0.2510 | 0.8414 | 0.2582 |
Worldview2_WV110 | 0.4909 | 0.2667 | 0.0500 | 1 | 1 | 1 | 0.2869 | 0.5280 | 0.2582 | ||
Kompsat-2_MSC—Multi-Spectral Camera | 0.4245 | 0.2667 | 0 | 1 | 1 | 1 | 0.2749 | 0.6091 | 0.2582 | ||
KazEOSat-2_KEIS | 0.3742 | 0.2667 | 0 | 1 | 1 | 1 | 0.2630 | 0.0403 | 0.2582 | ||
SkySat-2_SkySat | 0.2232 | 0.2667 | 0 | 1 | 1 | 1 | 0.1554 | 0.1681 | 0.2582 | ||
Worldview3_WV110 | 0.3887 | 0.2667 | 0 | 1 | 1 | 1 | 0.2749 | 0.0580 | 0.2582 | ||
Pléiades HR1A_HiRI | 0.4350 | 0.2667 | 0.0500 | 1 | 1 | 1 | 0.2749 | 0.5253 | 0.2582 | ||
Ziyuan1-02C_HRPC-2 | 0.4927 | 0 | 0.0200 | 1 | 0 | 0.7500 | 0.2869 | 0.5664 | 0.2582 | ||
Kompsat-3_AEISS | 0.4269 | 0.2667 | 0.0900 | 1 | 1 | 1 | 0.2749 | 0.4361 | 0.2582 | ||
Resurs-P1_ShMSA-VR | 0.1671 | 0.2667 | 0.0200 | 0.8333 | 1 | 1 | 0.1554 | 0.5729 | 0.2582 | ||
DubaiSat-2_HiRAIS | 0.3615 | 0.2667 | 0 | 1 | 1 | 1 | 0.2630 | 0.6138 | 0.2582 | ||
Gaofen-1-04_PMS | 0.4049 | 0.2667 | 0.0700 | 1 | 1 | 1 | 0.2630 | 0.4619 | 0.2582 | ||
Skysat-14_Skysat | 0.2692 | 0.2667 | 0 | 1 | 1 | 1 | 0.2510 | 0.0681 | 0.2582 | ||
SAR | COSMO-Skymed1_SAR-2000 | 0.3771 | 0.2000 | 1 | 1 | / | 0.8000 | 1 | 0.2630 | 0.7607 | 0.2582 |
Sentinel1A_SAR-C | 0.4525 | 0.1333 | 1 | 1 | 0.8000 | 1 | 0.2869 | 0.7175 | 0.2582 |
Sensor Type | Satellite Sensor | SpCo | ThCo | EnIm | SpaRes | SpeRes | Pol | RadRes | TiCo | ReTi | ReFc |
---|---|---|---|---|---|---|---|---|---|---|---|
Optics | SkySat-1_SkySat | 0.3547 | 0.2667 | 0.0500 | 1 | 1 | / | 1 | 0.2841 | 0.5304 | 0.2673 |
DubaiSat-2_HiRAIS | 0.3568 | 0.2667 | 0.2200 | 1 | 1 | 1 | 0.2841 | 0.1480 | 0.2673 | ||
UK-DMC-2_SLIM6 | 0.3945 | 0.2667 | 0.0600 | 0.0455 | 0 | 1 | 0.2841 | 0.7429 | 0.2673 | ||
Haiyang1C_CZI | 0.1849 | 0.2667 | 0.0500 | 0.0040 | 1 | 1 | 0.1162 | 0.5325 | 0.2673 | ||
Alsat2B_NAOMI | 0.4210 | 0.2667 | 0.0200 | 0.4000 | 1 | 1 | 0.2970 | 0.5491 | 0.2673 | ||
SkySat-13_SkySat | 0.2524 | 0.2667 | 0.2100 | 1 | 1 | 1 | 0.2583 | 0.9386 | 0.2673 | ||
SkySat-11_SkySat | 0.2687 | 0.2667 | 0.1100 | 0.0008 | 0 | 1 | 0.2712 | 0.4215 | 0.2673 | ||
Worldview3_WV110 | 0.3716 | 0.2667 | 0.0500 | 1 | 1 | 1 | 0.2841 | 0.5337 | 0.2673 | ||
Skysat-5_SkySat | 0.3068 | 0.2667 | 0.2400 | 1 | 1 | 1 | 0.2841 | 0.0804 | 0.2673 | ||
SkySat-6_SkySat | 0.1549 | 0.2667 | 0.2400 | 1 | 1 | 1 | 0.1550 | 0.0802 | 0.2673 | ||
Landsat7_ETM+ | 0.0010 | 0.3333 | 0.1200 | 0.0667 | 11 | 1 | 0.2841 | 0.7657 | 0.2673 | ||
RapidEye-2_REIS | 0.3537 | 0.2667 | 0.2500 | 0.1538 | 1 | 0.2841 | 0.0953 | 0.2673 | |||
SAR | COSMO-Skymed3_SAR-2000 | 0.3775 | 0.2000 | 1 | 1 | / | 0.8000 | 1 | 0.2841 | 0.7607 | 0.2673 |
Sentinel1B_SAR-C (Sentinel-1) | 0.4336 | 0.1333 | 1 | 0.2500 | 0.8000 | 1 | 0.2970 | 0.2474 | 0.2673 |
ReTi | TiCo | ReFc | SpaRes | SpeRes/Pol | RadRes | Weight | |
---|---|---|---|---|---|---|---|
ReTi | 1 | 1/5 | 1/9 | 1/3 | 1/3 | 1/3 | 0.032 |
TiCo | 5 | 1 | 1/5 | 5 | 5 | 5 | 0.236 |
ReFc | 9 | 5 | 1 | 7 | 7 | 7 | 0.534 |
SpaRes | 3 | 1/5 | 1/7 | 1 | 1 | 1 | 0.066 |
SpeRes/Pol | 3 | 1/5 | 1/7 | 1 | 1 | 1 | 0.066 |
RadRes | 3 | 1/5 | 1/7 | 1 | 1 | 1 | 0.066 |
= 6.340, CR = 0.055 < 0.1 |
ReTi | TiCo | ReFc | SpaRes | SpeRes/Pol | RadRes | Weight | |
---|---|---|---|---|---|---|---|
ReTi | 1 | 7 | 9 | 5 | 5 | 5 | 0.514 |
TiCo | 1/7 | 1 | 3 | 1/3 | 1/3 | 1/3 | 0.057 |
ReFc | 1/9 | 1/3 | 1 | 1/5 | 1/5 | 1/5 | 0.030 |
SpaRes | 1/5 | 3 | 5 | 1 | 1 | 1 | 0.133 |
SpeRes/Pol | 1/5 | 3 | 5 | 1 | 1 | 1 | 0.133 |
RadRes | 1/5 | 3 | 5 | 1 | 1 | 1 | 0.133 |
= 6.190, CR = 0.031 < 0.1 |
ReTi | TiCo | ReFc | SpaRes | SpeRes/Pol | RadRes | Weight | |
---|---|---|---|---|---|---|---|
ReTi | 1 | 2 | 3 | 1/5 | 1/5 | 1/5 | 0.067 |
TiCo | 1/2 | 1 | 2 | 1/7 | 1/7 | 1/7 | 0.042 |
ReFc | 1/3 | 1/2 | 1 | 1/9 | 1/9 | 1/9 | 0.027 |
SpaRes | 5 | 7 | 9 | 1 | 1 | 1 | 0.288 |
SpeRes/Pol | 5 | 7 | 9 | 1 | 1 | 1 | 0.288 |
RadRes | 5 | 7 | 9 | 1 | 1 | 1 | 0.288 |
= 6.051, CR = 0.008 < 0.1 |
Methods | Applicable Scenario | Evaluation Mode | Spatiotemporal Characteristics Considered | Main Content | |
---|---|---|---|---|---|
Spatiotemporal Process | Environmental Impact | ||||
OCEM | Flood | Quantitative | √ | √ | Quantitatively evaluate the performance of satellite sensors in flood water observation tasks, considering the spatiotemporal characteristics of the flood event. This enables the optimum selection and planning of sensors |
DOCI | General | Quantitative | × | √ | Quantitatively evaluate the observation performance of satellite sensors in various scenarios to support their optimal selection and planning |
SSCI | General | Quantitative | × | × | Cluster satellite sensors based on the evaluation of their static observation capability, enabling the recommendation of multiple sensors for different observation scenarios |
SOCA Ontology | General | Qualitative | × | × | Create a semantic web where each satellite sensor is semantically linked to its multidimensional observation capabilities (e.g., {SensorA, hasObservationSpace, SpaceA}), supporting the fast and semantic discovery of satellite sensors |
SOCO-Field | General | Qualitative | × | √ | Establish the linkage between geographical locations and sensor observation capabilities based on the GIS object field model. This enables location-based discovery and qualitative cognition of sensors |
SSOCEM | Astronomy | Quantitative | × | √ | Evaluate the observation stability and accuracy of star sensors in hypersonic aerothermal conditions |
EOPEM | General | Quantitative | × | × | Assess the degree of satisfaction of Earth observation sensor capabilities in meeting SGD demand, as well as its future potential |
SBSM | Flood | Quantitative | × | × | Use the Elimination and Choice Expressing Reality method to select the optimal bands of sensors for flood area detection and mapping |
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Duan, M.; Zhang, Y.; Liu, R.; Chen, S.; Deng, G.; Yi, X.; Li, J.; Yang, P. Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection. Appl. Sci. 2023, 13, 12482. https://doi.org/10.3390/app132212482
Duan M, Zhang Y, Liu R, Chen S, Deng G, Yi X, Li J, Yang P. Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection. Applied Sciences. 2023; 13(22):12482. https://doi.org/10.3390/app132212482
Chicago/Turabian StyleDuan, Mu, Yunbo Zhang, Ran Liu, Shen Chen, Guoquan Deng, Xiaowei Yi, Jie Li, and Puwei Yang. 2023. "Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection" Applied Sciences 13, no. 22: 12482. https://doi.org/10.3390/app132212482
APA StyleDuan, M., Zhang, Y., Liu, R., Chen, S., Deng, G., Yi, X., Li, J., & Yang, P. (2023). Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection. Applied Sciences, 13(22), 12482. https://doi.org/10.3390/app132212482