Estimating Fire Background Temperature at a Geostationary Scale—An Evaluation of Contextual Methods for AHI-8
"> Figure 1
<p>Examples of contextual temperature determination scenarios—(<b>a</b>) uniform contextual surroundings, with low spatial variance; (<b>b</b>) land cover change (yellow/green), with pixels of multiple land cover classes contributing to the estimate; (<b>c</b>) waterbodies (dark blue), which permanently obscure part of the contextual kernel; (<b>d</b>) cloud obscuration (hatched blue), which intermittently causes missing contextual data; and (<b>e</b>) smoke (grey), which provides directional partial obscuration of downwind pixels, and is less likely to be masked out of images than cloud.</p> "> Figure 2
<p>(<b>a</b>) land area of the full disk covered by the AHI sensor; (<b>b</b>) 500 × 500 image tiles with sufficient land surface processed for the full disk analysis. The horizontal banding of the full disk image in (<b>b</b>) also corresponds to the areas selected for the cloud analysis presented in Table 2.</p> "> Figure 3
<p>Case study areas selected for examination.</p> "> Figure 4
<p>(<b>a</b>) Mean brightness temperature difference between contextual estimates and the central pixel for the ring of pixels at the edge of each window across the full disk for 0500 UTC B07 AHI-8 images; (<b>b</b>) Standard deviation of contextual estimates derived from each window edge by percentage of available pixels in the window edge.</p> "> Figure 5
<p>Breakdown of the temperature estimation pass rate on pixels that have no solution in their 5 × 5 window. The percentage of pixels covered by each bar in this figure, as a portion of all pixels examined, is shown at the top of the figure. Each bar in the figure represents a minimum percentage level of valid contextual pixels for temperature calculation, and each coloured section represents the portion of pixels that are successful in deriving an estimate at each window size. The balance of exhausted pixels with no solution at each assessed percentage is also shown.</p> "> Figure 6
<p>Mean difference between contextual estimates and the central pixel for the selected period for each area. (<b>a</b>) south-eastern Australia (sea); (<b>b</b>) north-western Australia (nwa); (<b>c</b>) Borneo (bor); and (<b>d</b>) central Thailand (thl).</p> "> Figure 7
<p>Mean difference between contextual estimates and the central pixel for the selected period for each area. (<b>a</b>) eastern China (chn); (<b>b</b>) central Honshu (jpn); and (<b>c</b>) Siberia (sib).</p> "> Figure 8
<p>Changes in the spatial and statistical distribution of temperature estimates for the south-eastern Australia (sea) study area by window size. Window levels shown are (<b>a</b>) 5 × 5 window; (<b>b</b>) 7 × 7 window; (<b>c</b>) 9 × 9 window; and (<b>d</b>) 11 × 11 window.</p> "> Figure A1
<p>Changes in the spatial and statistical distribution of temperature estimates for the eastern Kalimantan (bor) study area by window size. Window levels shown are (<b>a</b>) 5 × 5 window; (<b>b</b>) 7 × 7 window; (<b>c</b>) 9 × 9 window; and (<b>d</b>) 11 × 11 window.</p> "> Figure A2
<p>Changes in the spatial and statistical distribution of temperature estimates for the eastern China (chn) study area by window size. Window levels shown are (<b>a</b>) 5 × 5 window; (<b>b</b>) 7 × 7 window; (<b>c</b>) 9 × 9 window; and (<b>d</b>) 11 × 11 window.</p> "> Figure A3
<p>Changes in the spatial and statistical distribution of temperature estimates for the central Japan (jpn) study area by window size. Window levels shown are (<b>a</b>) 5 × 5 window; (<b>b</b>) 7 × 7 window; (<b>c</b>) 9 × 9 window; and (<b>d</b>) 11 × 11 window.</p> "> Figure A4
<p>Changes in the spatial and statistical distribution of temperature estimates for the north-western Australia (nwa) study area by window size. Window levels shown are (<b>a</b>) 5 × 5 window; (<b>b</b>) 7 × 7 window; (<b>c</b>) 9 × 9 window; and (<b>d</b>) 11 × 11 window.</p> "> Figure A5
<p>Changes in the spatial and statistical distribution of temperature estimates for the central Siberian (sib) study area by window size. Window levels shown are (<b>a</b>) 5 × 5 window; (<b>b</b>) 7 × 7 window; (<b>c</b>) 9 × 9 window; and (<b>d</b>) 11 × 11 window.</p> "> Figure A6
<p>Changes in the spatial and statistical distribution of temperature estimates for the central Thailand (thl) study area by window size. Window levels shown are (<b>a</b>) 5 × 5 window; (<b>b</b>) 7 × 7 window; (<b>c</b>) 9 × 9 window; and (<b>d</b>) 11 × 11 window.</p> ">
Abstract
:1. Introduction
2. Method
2.1. Data
2.2. AHI Disk Characterisation
2.3. Expanding the Window
2.4. Case Study Evaluation
3. Results
3.1. AHI Full Disk Characterisation
3.2. Expanding Window Analysis
3.3. Case Study Areas
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
AHI | Advanced Himawari Imager |
ABOM | Australian Bureau of Meteorology |
JMA | Japan Meteorological Agency |
NCI | National Computing Infrastructure |
FRP | fire radiative power |
MWIR | medium-wave infrared |
MODIS | Moderate Resolution Imaging Spectroradiometer |
VIIRS | Visible Infrared Imaging Radiometer Suite |
FIMMA | Fire Identification, Mapping and Monitoring Algorithm |
AVHRR | Advanced Very High Resolution Radiometer |
MSG-SEVIRI | Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager |
GOES | Geostationary Operational Environmental Satellite |
UTC | Coordinated Universal Time |
LEO | low earth orbit |
Appendix A
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Case Study Area | Start Date | End Date | AHI Image Area | Time (UTC) | Local Time@Centroid |
---|---|---|---|---|---|
sea | 2016-03-30 | 2016-04-29 | [4400, 4600, 3050, 3250] | 3:50 | 13:49 |
nwa | 2016-10-23 | 2016-11-22 | [3600, 3800, 2000, 2200] | 5:00 | 13:32 |
bor | 2016-02-14 | 2016-03-15 | [2600, 2800, 1400, 1600] | 5:40 | 13:22 |
thl | 2016-02-28 | 2016-03-29 | [1800, 2000, 800, 1000] | 6:30 | 13:15 |
chn | 2016-08-27 | 2016-09-26 | [1000, 1200, 1600, 1800] | 5:10 | 12:56 |
jpn | 2016-05-03 | 2016-06-02 | [900, 1100, 2500, 2700] | 3:50 | 12:59 |
sib | 2016-05-10 | 2016-06-09 | [200, 400, 2000, 2200] | 5:00 | 12:43 |
AHI Image Rows | # of Land Pixels | Mean % Cloud | SD % Cloud |
---|---|---|---|
0–500 | 526,506 | 74.1 | 15.7 |
500–1000 | 714,119 | 69.1 | 15.0 |
1000–1500 | 663,172 | 68.1 | 13.7 |
1500–2000 | 420,460 | 49.2 | 23.0 |
2000–2500 | 184,404 | 54.2 | 19.3 |
2500–3000 | 366,370 | 62.7 | 10.4 |
3000–3500 | 248,687 | 55.3 | 12.4 |
3500–4000 | 643,030 | 28.6 | 14.0 |
4000–4500 | 793,030 | 37.3 | 16.7 |
4500–5000 | 103,387 | 58.1 | 19.4 |
Window Size | Percentage of Context Pixels Required for Assessment | ||||||
---|---|---|---|---|---|---|---|
>75% | >65% | >55% | >45% | >35% | >25% | >15% | |
103,801 | 74,712 | 46,141 | 18,523 | 10,918 | 4840 | 2389 | |
2.23% | 1.60% | 0.99% | 0.40% | 0.23% | 0.10% | 0.05% | |
136,747 | 97,771 | 54,351 | 25,771 | 13,842 | 7322 | 3873 | |
2.93% | 2.10% | 1.17% | 0.55% | 0.30% | 0.16% | 0.08% | |
165,592 | 110,470 | 61,786 | 31,008 | 17,290 | 9436 | 4544 | |
3.55% | 2.37% | 1.32% | 0.66% | 0.37% | 0.20% | 0.10% | |
192,298 | 129,744 | 73,595 | 37,000 | 21,033 | 11,510 | 5563 | |
4.12% | 2.78% | 1.58% | 0.79% | 0.45% | 0.25% | 0.12% | |
217,235 | 150,574 | 86,662 | 43,558 | 24,681 | 13,651 | 6794 | |
4.66% | 3.23% | 1.86% | 0.93% | 0.53% | 0.29% | 0.15% | |
240,738 | 165,472 | 97,107 | 49,446 | 28,451 | 15,689 | 7549 | |
5.16% | 3.55% | 2.08% | 1.06% | 0.61% | 0.34% | 0.16% | |
263,862 | 182,197 | 106,023 | 55,620 | 31,895 | 17,482 | 8466 | |
5.66% | 3.91% | 2.27% | 1.19% | 0.68% | 0.37% | 0.18% | |
286,131 | 195,443 | 114,230 | 60,973 | 35,605 | 19,496 | 9159 | |
6.14% | 4.19% | 2.45% | 1.31% | 0.76% | 0.42% | 0.20% | |
307,516 | 210,405 | 122,986 | 66,290 | 38,851 | 21,809 | 10,196 | |
6.59% | 4.51% | 2.64% | 1.42% | 0.83% | 0.47% | 0.22% | |
328,452 | 226,933 | 132,790 | 71,657 | 42,888 | 24,078 | 11,199 | |
7.04% | 4.87% | 2.85% | 1.54% | 0.92% | 0.52% | 0.24% | |
348,645 | 240,456 | 142,150 | 75,910 | 46,572 | 25,839 | 12,100 | |
7.48% | 5.16% | 3.05% | 1.63% | 1.00% | 0.55% | 0.26% |
Window Size | 5 × 5 | 7 × 7 | 9 × 9 | 11 × 11 | 13 × 13 | 15 × 15 |
mean (K) | 0.037 | 0.031 | 0.029 | 0.027 | 0.025 | 0.024 |
std (K) | 1.522 | 2.039 | 2.200 | 2.320 | 2.415 | 2.494 |
count | 76,023,810 | 75,858,159 | 75,871,580 | 75,880,469 | 75,888,096 | 75,893,762 |
Window Size | 17 × 17 | 19 × 19 | 21 × 21 | 23 × 23 | 25 × 25 | |
mean (K) | 0.023 | 0.023 | 0.023 | 0.024 | 0.024 | |
std (K) | 2.562 | 2.622 | 2.677 | 2.726 | 2.771 | |
count | 75,895,983 | 75,899,037 | 75,899,238 | 75,898,553 | 75,898,041 |
Window | 1.00 | Valid Portion of Total Context Pixels | |||||||
---|---|---|---|---|---|---|---|---|---|
0.99–0.75 | 0.99–0.65 | 0.99–0.55 | 0.99–0.45 | 0.99–0.35 | 0.99–0.25 | 0.99–0.15 | |||
5 × 5 | mean () | −0.029 | −0.083 | −0.036 | 0.006 | 0.063 | 0.086 | 0.100 | 0.111 |
std () | 1.090 | 1.603 | 1.699 | 1.759 | 1.839 | 1.867 | 1.885 | 1.898 | |
count | 40,958,274 | 18,106,490 | 24,041,100 | 27,144,999 | 30,622,724 | 32,199,899 | 33,351,687 | 34,480,202 | |
% avail | 53.88% | 23.82% | 31.62% | 35.71% | 40.28% | 42.36% | 43.87% | 45.35% | |
Total 5 × 5 success | 77.69% | 85.50% | 89.58% | 94.16% | 96.23% | 97.75% | 99.23% | ||
7 × 7 | mean () | – | 0.474 | 0.772 | 0.925 | 1.059 | 1.029 | 0.995 | 0.940 |
std () | – | 2.314 | 2.538 | 2.667 | 2.734 | 2.711 | 2.705 | 2.768 | |
count | N/A | 1,651,297 | 948,803 | 1,096,828 | 557,910 | 562,891 | 407,382 | 160,575 | |
% avail | N/A | 2.17% | 1.25% | 1.44% | 0.73% | 0.74% | 0.54% | 0.21% | |
9 × 9 | mean () | – | 0.704 | 1.007 | 1.143 | 1.293 | 1.270 | 1.193 | 1.143 |
std () | – | 2.592 | 2.778 | 2.874 | 2.932 | 2.908 | 2.914 | 2.999 | |
count | N/A | 502,591 | 369,619 | 289,271 | 182,700 | 127,933 | 134,785 | 53,782 | |
% avail | N/A | 0.66% | 0.49% | 0.38% | 0.24% | 0.17% | 0.18% | 0.07% | |
11 × 11 | mean () | – | 0.889 | 1.193 | 1.341 | 1.498 | 1.476 | 1.381 | 1.310 |
std () | – | 2.757 | 2.940 | 3.050 | 3.075 | 3.086 | 3.054 | 3.197 | |
count | N/A | 320,616 | 262,912 | 221,789 | 155,173 | 118,434 | 87,380 | 36,791 | |
% avail | N/A | 0.42% | 0.35% | 0.29% | 0.20% | 0.16% | 0.11% | 0.05% | |
13 × 13 | mean () | – | 1.024 | 1.321 | 1.491 | 1.615 | 1.611 | 1.521 | 1.471 |
std () | – | 2.860 | 3.055 | 3.161 | 3.200 | 3.228 | 3.221 | 3.348 | |
count | N/A | 228,249 | 199,477 | 177,211 | 130,197 | 102,158 | 63,145 | 27,398 | |
% avail | N/A | 0.30% | 0.26% | 0.23% | 0.17% | 0.13% | 0.08% | 0.04% | |
15 × 15 | mean () | – | 1.137 | 1.445 | 1.597 | 1.739 | 1.726 | 1.600 | 1.551 |
std () | – | 2.982 | 3.165 | 3.252 | 3.273 | 3.286 | 3.325 | 3.410 | |
count | N/A | 174,901 | 158,520 | 121,066 | 93,067 | 63,103 | 48,553 | 21,233 | |
% avail | N/A | 0.23% | 0.21% | 0.16% | 0.12% | 0.08% | 0.06% | 0.03% | |
17 × 17 | mean () | – | 1.224 | 1.585 | 1.702 | 1.830 | 1.804 | 1.765 | 1.626 |
std () | – | 3.032 | 3.283 | 3.333 | 3.371 | 3.436 | 3.437 | 3.449 | |
count | N/A | 139,247 | 108,539 | 105,588 | 70,645 | 58,638 | 38,539 | 14,115 | |
% avail | N/A | 0.18% | 0.14% | 0.14% | 0.09% | 0.08% | 0.05% | 0.02% | |
19 × 19 | mean () | – | 1.328 | 1.694 | 1.818 | 1.953 | 1.875 | 1.834 | 1.702 |
std () | – | 3.177 | 3.358 | 3.414 | 3.445 | 3.450 | 3.507 | 3.610 | |
count | N/A | 113,322 | 93,057 | 79,027 | 54,876 | 46,985 | 31,733 | 12,024 | |
% avail | N/A | 0.15% | 0.12% | 0.10% | 0.07% | 0.06% | 0.04% | 0.02% | |
21 × 21 | mean () | – | 1.416 | 1.747 | 1.867 | 2.046 | 2.020 | 1.885 | 1.805 |
std () | – | 3.265 | 3.380 | 3.471 | 3.556 | 3.573 | 3.595 | 3.866 | |
count | N/A | 94,179 | 81,879 | 71,265 | 51,677 | 33,939 | 27,491 | 10,239 | |
% avail | N/A | 0.12% | 0.11% | 0.09% | 0.07% | 0.04% | 0.04% | 0.01% | |
23 × 23 | mean () | – | 1.422 | 1.817 | 1.951 | 2.043 | 2.040 | 1.948 | 1.911 |
std () | – | 3.288 | 3.502 | 3.572 | 3.591 | 3.657 | 3.646 | 3.883 | |
count | N/A | 80,631 | 73,046 | 63,430 | 48,480 | 36,557 | 23,016 | 9168 | |
% avail | N/A | 0.11% | 0.10% | 0.08% | 0.06% | 0.05% | 0.03% | 0.01% | |
25 × 25 | mean () | – | 1.547 | 1.877 | 2.025 | 2.079 | 2.110 | 1.988 | 2.024 |
std () | – | 3.342 | 3.548 | 3.549 | 3.575 | 3.661 | 3.556 | 3.886 | |
count | N/A | 70,008 | 64,301 | 51,988 | 40,127 | 27,803 | 20,150 | 8127 | |
% avail | N/A | 0.09% | 0.08% | 0.07% | 0.05% | 0.04% | 0.03% | 0.01% | |
Total failures | 13,584,005 | 8,664,283 | 5,643,074 | 3,057,960 | 1,687,196 | 831,675 | 231,882 | ||
17.87% | 11.40% | 7.42% | 4.02% | 2.22% | 1.09% | 0.31% |
Window | 1.00 | Valid Portion of Total Context Pixels | ||||
---|---|---|---|---|---|---|
0.99–0.45 | 0.99–0.35 | 0.99–0.25 | 0.99–0.15 | |||
5 × 5 | mean () | −0.029 | 0.076 | 0.086 | 0.100 | 0.111 |
std () | 1.090 | 1.856 | 1.867 | 1.885 | 1.898 | |
count | 40,958,274 | 31,473,186 | 32,199,899 | 33,351,687 | 34,480,202 | |
% avail | 53.88% | 41.40% | 42.36% | 43.87% | 45.35% | |
Total 5 × 5 success | 95.27% | 96.23% | 97.75% | 99.23% | ||
7 × 7 | mean () | – | 0.709 | 0.746 | 0.874 | 0.940 |
std () | – | 2.550 | 2.568 | 2.642 | 2.768 | |
count | N/A | 2,456,495 | 1,734,495 | 664,734 | 160,575 | |
% avail | N/A | 3.23% | 2.28% | 0.87% | 0.21% | |
9 × 9 | mean () | – | 0.623 | 0.628 | 0.703 | 0.996 |
std () | – | 2.639 | 2.640 | 2.673 | 2.928 | |
count | N/A | 591,757 | 589,044 | 531,807 | 97,775 | |
% avail | N/A | 0.78% | 0.77% | 0.70% | 0.13% | |
11 × 11 | mean () | – | 0.544 | 0.548 | 0.588 | 0.854 |
std () | – | 2.723 | 2.723 | 2.745 | 2.963 | |
count | N/A | 225,018 | 224,240 | 212,723 | 119,473 | |
% avail | N/A | 0.30% | 0.29% | 0.28% | 0.16% | |
13 × 13 | mean () | – | 0.485 | 0.487 | 0.518 | 0.701 |
std () | – | 2.789 | 2.792 | 2.816 | 2.971 | |
count | N/A | 108,023 | 107,653 | 103,138 | 66,691 | |
% avail | N/A | 0.14% | 0.14% | 0.14% | 0.09% | |
15 × 15 | mean () | – | 0.448 | 0.451 | 0.481 | 0.637 |
std () | – | 2.828 | 2.831 | 2.852 | 3.018 | |
count | N/A | 60,176 | 59,952 | 57,566 | 39,017 | |
% avail | N/A | 0.08% | 0.08% | 0.08% | 0.05% | |
17 × 17 | mean () | – | 0.413 | 0.414 | 0.435 | 0.584 |
std () | – | 2.844 | 2.845 | 2.869 | 3.019 | |
count | N/A | 37,688 | 37,596 | 36,118 | 24,821 | |
% avail | N/A | 0.05% | 0.05% | 0.05% | 0.03% | |
19 × 19 | mean () | – | 0.401 | 0.403 | 0.434 | 0.562 |
std () | – | 2.864 | 2.867 | 2.897 | 3.057 | |
count | N/A | 25,000 | 24,899 | 23,883 | 16,827 | |
% avail | N/A | 0.03% | 0.03% | 0.03% | 0.02% | |
21 × 21 | mean () | – | 0.439 | 0.441 | 0.464 | 0.607 |
std () | – | 2.996 | 3.000 | 3.031 | 3.226 | |
count | N/A | 17,483 | 17,419 | 16,712 | 12,002 | |
% avail | N/A | 0.02% | 0.02% | 0.02% | 0.02% | |
23 × 23 | mean () | – | 0.316 | 0.318 | 0.341 | 0.428 |
std () | – | 2.913 | 2.919 | 2.943 | 3.092 | |
count | N/A | 12,125 | 12,068 | 11,667 | 8478 | |
% avail | N/A | 0.02% | 0.02% | 0.02% | 0.01% | |
25 × 25 | mean () | – | 0.304 | 0.306 | 0.324 | 0.415 |
std () | – | 2.869 | 2.874 | 2.897 | 2.998 | |
count | N/A | 8910 | 8867 | 8596 | 6289 | |
% avail | N/A | 0.01% | 0.01% | 0.01% | 0.01% | |
Total failures | 49,675 | 49,404 | 46,905 | 33,386 | ||
0.07% | 0.06% | 0.06% | 0.04% |
Window Edge | 5 × 5 | 7 × 7 | 9 × 9 | 11 × 11 | ||||
---|---|---|---|---|---|---|---|---|
Case Study Area | Mean | SD | Mean | SD | Mean | SD | Mean | SD |
sea | 0.031 | 1.312 | 0.051 | 1.891 | 0.063 | 2.090 | 0.079 | 2.229 |
nwa | 0.059 | 1.031 | 0.024 | 1.440 | 0.022 | 1.570 | 0.021 | 1.658 |
bor | 0.089 | 0.856 | 0.089 | 1.231 | 0.097 | 1.360 | 0.101 | 1.454 |
thl | 0.022 | 1.481 | 0.021 | 2.202 | 0.023 | 2.469 | 0.024 | 2.673 |
chn | 0.023 | 0.942 | 0.024 | 1.348 | 0.020 | 1.494 | 0.014 | 1.605 |
jpn | 0.092 | 1.928 | 0.140 | 2.862 | 0.162 | 3.259 | 0.178 | 3.553 |
sib | 0.112 | 1.370 | 0.134 | 1.810 | 0.144 | 1.939 | 0.152 | 2.026 |
Valid pOrtion of Total Context Pixels | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
1.00 | 0.99–0.75 | 0.99–0.65 | 0.99–0.55 | 0.99–0.45 | 0.99–0.35 | 0.99–0.25 | 0.99–0.15 | All | ||
sea | mean () | −0.021 | −0.042 | −0.013 | 0.010 | 0.043 | 0.056 | 0.063 | 0.068 | 0.022 |
std () | 1.670 | 1.832 | 1.862 | 1.880 | 1.921 | 1.931 | 1.935 | 1.941 | 1.804 | |
count | 279,250 | 152,220 | 210,297 | 243,132 | 284,703 | 308,590 | 330,486 | 363,534 | 688,739 | |
% avail | 40.5% | 22.1% | 30.5% | 35.3% | 41.3% | 44.8% | 48.0% | 52.8% | ||
nwa | mean () | −0.051 | −0.134 | −0.035 | 0.034 | 0.127 | 0.171 | 0.198 | 0.218 | 0.061 |
std () | 1.377 | 2.576 | 2.769 | 2.856 | 2.953 | 2.992 | 3.014 | 3.029 | 2.216 | |
count | 548,125 | 258,015 | 339,147 | 382,837 | 438,487 | 470,677 | 499,270 | 538,353 | 1,129,978 | |
% avail | 48.5% | 22.8% | 30.0% | 33.9% | 38.8% | 41.7% | 44.2% | 47.6% | ||
bor | mean () | −0.106 | −0.096 | −0.038 | 0.003 | 0.051 | 0.071 | 0.086 | 0.096 | 0.061 |
std () | 1.121 | 1.472 | 1.585 | 1.651 | 1.719 | 1.746 | 1.764 | 1.777 | 1.681 | |
count | 90,734 | 250,567 | 343,181 | 392,122 | 451,781 | 485,259 | 515,552 | 559,085 | 702,114 | |
% avail | 12.9% | 35.7% | 48.9% | 55.8% | 64.3% | 69.1% | 73.4% | 79.6% | ||
thl | mean () | −0.033 | 0.000 | 0.047 | 0.079 | 0.109 | 0.118 | 0.122 | 0.125 | 0.016 |
std () | 1.679 | 1.874 | 1.920 | 1.941 | 1.961 | 1.965 | 1.967 | 1.970 | 1.776 | |
count | 683,361 | 224,582 | 281,720 | 310,807 | 346,989 | 367,880 | 386,865 | 415,359 | 1,134,791 | |
% avail | 60.2% | 19.8% | 24.8% | 27.4% | 30.6% | 32.4% | 34.1% | 36.6% | ||
chn | mean () | −0.032 | −0.041 | 0.006 | 0.039 | 0.079 | 0.092 | 0.100 | 0.104 | 0.021 |
std () | 1.159 | 1.310 | 1.345 | 1.370 | 1.407 | 1.418 | 1.424 | 1.428 | 1.272 | |
count | 428,453 | 176,040 | 232,020 | 262,412 | 301,287 | 324,324 | 346,985 | 384,005 | 868,807 | |
% avail | 49.3% | 20.3% | 26.7% | 30.2% | 34.7% | 37.3% | 39.9% | 44.2% | ||
jpn | mean () | −0.019 | −0.151 | −0.134 | −0.056 | 0.079 | 0.102 | 0.116 | 0.125 | 0.046 |
std () | 2.061 | 2.246 | 2.269 | 2.332 | 2.460 | 2.479 | 2.486 | 2.490 | 2.265 | |
count | 120,759 | 54,546 | 74,758 | 86,968 | 103,879 | 114,110 | 124,201 | 141,136 | 288,787 | |
% avail | 41.8% | 18.9% | 25.9% | 30.1% | 36.0% | 39.5% | 43.0% | 48.9% | ||
sib | mean () | −0.057 | −0.073 | −0.017 | 0.020 | 0.066 | 0.080 | 0.088 | 0.092 | 0.037 |
std () | 1.120 | 1.746 | 1.814 | 1.859 | 1.947 | 1.969 | 1.980 | 1.996 | 1.745 | |
count | 86,220 | 66,918 | 97,011 | 117,111 | 149,287 | 173,672 | 202,360 | 260,949 | 478,458 | |
% avail | 18.0% | 14.0% | 20.3% | 24.5% | 31.2% | 36.3% | 42.3% | 54.5% |
© 2018 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 (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hally, B.; Wallace, L.; Reinke, K.; Jones, S.; Engel, C.; Skidmore, A. Estimating Fire Background Temperature at a Geostationary Scale—An Evaluation of Contextual Methods for AHI-8. Remote Sens. 2018, 10, 1368. https://doi.org/10.3390/rs10091368
Hally B, Wallace L, Reinke K, Jones S, Engel C, Skidmore A. Estimating Fire Background Temperature at a Geostationary Scale—An Evaluation of Contextual Methods for AHI-8. Remote Sensing. 2018; 10(9):1368. https://doi.org/10.3390/rs10091368
Chicago/Turabian StyleHally, Bryan, Luke Wallace, Karin Reinke, Simon Jones, Chermelle Engel, and Andrew Skidmore. 2018. "Estimating Fire Background Temperature at a Geostationary Scale—An Evaluation of Contextual Methods for AHI-8" Remote Sensing 10, no. 9: 1368. https://doi.org/10.3390/rs10091368