Vegetation Masking of Remote Sensing Data Aids Machine Learning for Soil Fertility Prediction
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Soil Sampling and Laboratory Analysis
2.3. Data Analysis and Model Development
2.3.1. Geospatial Data
2.3.2. Model Development and Assessment
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Soil Fertility Attributes | Mean | Std | Min | 25% | 50% | 75% | Max |
---|---|---|---|---|---|---|---|
pH | 6.97 | 0.37 | 5.66 | 6.76 | 7.01 | 7.22 | 8.07 |
----------------------- ppm ----------------------- | |||||||
P | 43.6 | 24.3 | 5.0 | 29 | 37 | 52 | 240 |
K | 222 | 75.4 | 86 | 156 | 209 | 287 | 421 |
Mg | 613 | 414 | 162 | 284 | 392 | 1039 | 1609 |
Ca | 2821 | 1711 | 758 | 1440 | 2031 | 4758 | 6849 |
S | 119 | 47.4 | 60 | 81 | 95 | 171 | 222 |
Zn | 3.25 | 2.04 | 0.80 | 1.80 | 2.80 | 4.20 | 26.4 |
Masked Imagery | |||||||
---|---|---|---|---|---|---|---|
Site Image | Mean | Std | Min | 25% | 50% | 75% | Max |
Median B2 | 884 | 147 | 527 | 771 | 876 | 991 | 1445 |
Median B3 | 1207 | 217 | 738 | 1024 | 1216 | 1370 | 1844 |
Median B4 | 1476 | 232 | 900 | 1299 | 1484 | 1650 | 2188 |
Median B5 | 1857 | 321 | 1245 | 1578 | 1909 | 2098 | 2773 |
Median B6 | 2212 | 475 | 1303 | 1805 | 2213 | 2544 | 3541 |
Median B7 | 2416 | 510 | 1398 | 1974 | 2426 | 2781 | 3839 |
Median B8 | 2539 | 529 | 1495 | 2064 | 2562 | 2891 | 3912 |
Median B11 | 3499 | 515 | 2349 | 2930 | 3667 | 3895 | 4512 |
Median B12 | 2700 | 475 | 1779 | 2253 | 2755 | 3086 | 3946 |
IQR B2 | 461 | 157 | 189 | 353 | 437 | 520 | 1046 |
IQR B3 | 575 | 208 | 213 | 417 | 573 | 667 | 1343 |
IQR B4 | 741 | 247 | 194 | 560 | 724 | 867 | 1566 |
IQR B5 | 769 | 277 | 173 | 539 | 770 | 935 | 1716 |
IQR B6 | 921 | 400 | 268 | 592 | 837 | 1153 | 2185 |
IQR B7 | 976 | 415 | 301 | 648 | 872 | 1243 | 2485 |
IQR B8 | 976 | 383 | 242 | 653 | 918 | 1235 | 2297 |
IQR B11 | 1080 | 347 | 360 | 797 | 1067 | 1310 | 2158 |
IQR B12 | 1171 | 475 | 267 | 721 | 1181 | 1455 | 2765 |
Not-masked imagery | |||||||
Site Image | Mean | Std | Min | 25% | 50% | 75% | Max |
Median B2 | 569 | 96 | 364 | 502 | 565 | 620 | 986 |
Median B3 | 844 | 120 | 607 | 752 | 831 | 919 | 1398 |
Median B4 | 854 | 187 | 393 | 750 | 852 | 955 | 1594 |
Median B5 | 1368 | 190 | 994 | 1225 | 1347 | 1512 | 2300 |
Median B6 | 2731 | 475 | 1517 | 2387 | 2793 | 3054 | 3858 |
Median B7 | 3166 | 610 | 1681 | 2674 | 3260 | 3625 | 4528 |
Median B8 | 3257 | 621 | 1743 | 2736 | 3396 | 3731 | 4564 |
Median B11 | 2800 | 254 | 2248 | 2589 | 2795 | 2993 | 3854 |
Median B12 | 1799 | 275 | 1239 | 1590 | 1782 | 1985 | 3123 |
IQR B2 | 491 | 125 | 208 | 390 | 483 | 586 | 1096 |
IQR B3 | 553 | 165 | 224 | 417 | 543 | 672 | 1248 |
IQR B4 | 975 | 232 | 342 | 794 | 977 | 1147 | 1736 |
IQR B5 | 800 | 255 | 289 | 592 | 776 | 968 | 1541 |
IQR B6 | 1287 | 326 | 407 | 1049 | 1292 | 1502 | 2249 |
IQR B7 | 1865 | 486 | 726 | 1542 | 1804 | 2126 | 3491 |
IQR B8 | 1796 | 446 | 612 | 1510 | 1740 | 2021 | 3315 |
IQR B11 | 1165 | 374 | 314 | 813 | 1195 | 1429 | 2130 |
IQR B12 | 1405 | 411 | 467 | 1072 | 1409 | 1703 | 2738 |
| ||||||||
Training dataset (n = 1716) | Test dataset (n = 429) | |||||||
Fertility attribute | R2 | R2 std | RMSE | RMSE std | MAE | R2 | RMSE | MAE |
pH | 0.34 | 0.08 | 0.30 | 0.02 | 0.04 | −0.65 | 0.31 | 0.23 |
P | 0.28 | 0.09 | 20.3 | 2.95 | 5.26 | −1.05 | 20.1 | 13.2 |
K | 0.70 | 0.05 | 40.8 | 3.9 | 3.58 | 0.62 | 40.1 | 29.6 |
Mg | 0.95 | 0.01 | 95.4 | 10.26 | 0.57 | 0.95 | 86.5 | 59.1 |
Ca | 0.93 | 0.01 | 445 | 40.62 | 67.15 | 0.94 | 404 | 303 |
S | 0.95 | 0.01 | 10.4 | 0.89 | 0.89 | 0.95 | 9.71 | 7.35 |
Zn | 0.35 | 0.09 | 1.66 | 0.33 | 0.57 | −0.48 | 1.41 | 0.77 |
| ||||||||
Training dataset (n = 1716) | Test dataset (n = 429) | |||||||
Fertility attribute | R2 | R2 std | RMSE | RMSE std | MAE | R2 | RMSE | MAE |
pH | 0.39 | 0.07 | 0.29 | 0.02 | 0.07 | −0.28 | 0.29 | 0.21 |
P | 0.34 | 0.10 | 19.3 | 3.18 | 5.14 | −0.47 | 20.1 | 13.1 |
K | 0.74 | 0.04 | 38.3 | 2.94 | 11.7 | 0.65 | 37.9 | 28.4 |
Mg | 0.95 | 0.01 | 94.9 | 9.48 | 6.50 | 0.96 | 81.1 | 58.8 |
Ca | 0.93 | 0.01 | 438 | 25.3 | 63.0 | 0.94 | 381 | 293 |
S | 0.96 | 0.01 | 10.0 | 0.63 | 1.53 | 0.95 | 10.1 | 7.38 |
Zn | 0.38 | 0.12 | 1.62 | 0.35 | 0.47 | −0.20 | 1.41 | 0.82 |
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Winzeler, H.E.; Mancini, M.; Blackstock, J.M.; Libohova, Z.; Owens, P.R.; Ashworth, A.J.; Miller, D.M.; Silva, S.H.G. Vegetation Masking of Remote Sensing Data Aids Machine Learning for Soil Fertility Prediction. Remote Sens. 2024, 16, 3297. https://doi.org/10.3390/rs16173297
Winzeler HE, Mancini M, Blackstock JM, Libohova Z, Owens PR, Ashworth AJ, Miller DM, Silva SHG. Vegetation Masking of Remote Sensing Data Aids Machine Learning for Soil Fertility Prediction. Remote Sensing. 2024; 16(17):3297. https://doi.org/10.3390/rs16173297
Chicago/Turabian StyleWinzeler, Hans Edwin, Marcelo Mancini, Joshua M. Blackstock, Zamir Libohova, Phillip R. Owens, Amanda J. Ashworth, David M. Miller, and Sérgio H. G. Silva. 2024. "Vegetation Masking of Remote Sensing Data Aids Machine Learning for Soil Fertility Prediction" Remote Sensing 16, no. 17: 3297. https://doi.org/10.3390/rs16173297