Research Article | | Peer-Reviewed

Rainfall–Surface Temperature Coupling and Compound Dry-Hot Persistence Across Northwestern Bangladesh: A 25-Year Multi-Sensor Earth Observation Analysis

Received: 31 August 2026     Accepted: 10 September 2026     Published: 28 September 2026
Views:       Downloads:
Abstract

Understanding how rainfall variability regulates land-surface thermal conditions is essential for climate-sensitive regions where water availability, vegetation dynamics, and heat stress interact. However, the temporal persistence and spatial heterogeneity of rainfall–surface temperature relationships remain insufficiently characterized in northwestern Bangladesh. This study integrates 25 years (2001–2025) of multi-source Earth observation data, including Terra MODIS land-surface temperature (MOD11A2), MODIS vegetation index (MOD13A2), and CHIRPS precipitation, to quantify pre-monsoon hydrothermal coupling across 16 districts of the Rajshahi and Rangpur divisions. A total of 275 quality-controlled 8-day March–May composites were analyzed using Mann–Kendall trend tests, Sen's slope estimation, fixed-effects distributed lag models, and a locally standardized compound dry-hot persistence indicator. Mean daytime land-surface temperature ranged from 27.93°C in Kurigram to 31.38°C in Chapai Nawabganj. No district exhibited a significant daytime land-surface temperature trend after false-discovery-rate correction, whereas NDVI increased significantly across all districts. The distributed-lag model showed that 10 mm of concurrent rainfall was associated with a 0.164°C lower daytime land-surface temperature (95% CI: −0.224 to −0.103°C), while rainfall during the preceding 8–16 days was associated with a weaker effect (−0.093°C per 10 mm). The 16–24-day rainfall effect was not significant. Rainfall sensitivity varied considerably among districts, ranging from −0.538°C per 10 mm in Rajshahi to −0.123°C per 10 mm in Lalmonirhat. These findings demonstrate that satellite-derived hydrothermal responses are spatially heterogeneous and highlight the value of multi-sensor Earth observation frameworks for identifying location-specific climate adaptation priorities.

Published in American Journal of Remote Sensing (Volume 14, Issue 2)
DOI 10.11648/j.ajrs.20261402.17
Page(s) 118-133
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Land-Surface Temperature, Precipitation, Distributed-Lag Model, NDVI, MODIS, CHIRPS, Bangladesh, Compound Dry-Hot Events

References
[1] Li, Z., Wu, H., Duan, S., Zhao, W., Ren, H., Liu, X., Leng, P., Tang, R., Ye, X., Zhu, J., Sun, Y., Si, M., Liu, M., Li, J., Zhang, X., Shang, G., Tang, B., Yan, G., Zhou, C. Satellite Remote Sensing of Global Land Surface Temperature: Definition, Methods, Products, and Applications. Reviews of Geophysics. 2023, 61(1).
[2] Lin, L., Di, L., Zhang, C., Guo, L. The Global Land Surface Temperature Change in the 21st Century — A Satellite Remote Sensing Based Assessment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2024, 17, 1756–1764.
[3] Yu, Y., Privette, J. P., Liu, Y., Sun, D. Land Surface Temperature: Remote Sensing. 2020, pp. 33–38.
[4] Keune, J., Schumacher, D. L., Dirmeyer, P., Miralles, D. G. Drought self-propagation in drylands through moisture recycling. EGU General Assembly 2022, Vienna, Austria, 2022.
[5] Kroll, J., Stephan, R., Feldman, A. F., Miralles, D. G., Orth, R. Increased heating of the land surface as hot-dry events persist. EGUsphere [preprint]. 2025.
[6] Yoon, D., Chen, J.-H., Hsu, H., Findell, K. Different Roles of Land-atmosphere Coupling in Compound Drought-heatwave Events. EGU General Assembly 2025, Vienna, Austria, 2025.
[7] Mangan, M. R. Evapotranspiration: Atmospheric boundary layer interactions across scales of surface heterogeneity. PhD thesis, Wageningen University, Wageningen, the Netherlands, 2025.
[8] Tabari, H. Contrasting responses of drought and floods to background aridity in a changing climate across global terrestrial ecosystems. EGU General Assembly 2024, Vienna, Austria, 2024.
[9] Xiang, T., Vivoni, E. R., Gochis, D. J. Seasonal evolution of ecohydrological controls on land surface temperature over complex terrain. Water Resources Research. 2014, 50(5), 3852–3874.
[10] Guillod, B. P., Orlowsky, B., Miralles, D., Teuling, A. J., Blanken, P. D., Buchmann, N., Ciais, P., Ek, M., Findell, K. L., Gentine, P., Lintner, B. R., Scott, R. L., Van den Hurk, B., Seneviratne, S. I. Land-surface controls on afternoon precipitation diagnosed from observational data: uncertainties and confounding factors. Atmospheric Chemistry and Physics. 2014, 14, 8343–8367.
[11] McColl, K. A., He, Q., Lu, H., Entekhabi, D. Short-Term and Long-Term Surface Soil Moisture Memory Time Scales Are Spatially Anticorrelated at Global Scales. Journal of Hydrometeorology. 2019, 20(6), 1165–1182.
[12] Akter, T., Gazi, M. Y., Mia, M. B. Assessment of land cover dynamics, land surface temperature, and heat island growth in northwestern Bangladesh using satellite imagery. Environmental Processes. 2021, 8, 661–690.
[13] Brunsell, N. A. Characterization of land-surface precipitation feedback regimes with remote sensing. Remote Sensing of Environment. 2006, 100(2), 200–211.
[14] Wan, Z., Hook, S., Hulley, G. MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1 km SIN Grid V061 [Dataset]. NASA EOSDIS Land Processes Distributed Active Archive Center, 2021.
[15] Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., Ferreira, L. G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment. 2002, 83(1–2), 195–213.
[16] Weng, Q., Lu, D., Schubring, J. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment. 2004, 89(4), 467–483.
[17] Tahasin, A., Haydar, M., Hossen, Md. S., Sadia, H. Drought vulnerability assessment and its severe impact on crop production and livelihood of people: An empirical analysis of Barind Tract. Research Square [preprint]. 2023.
[18] Wang, H., Xie, Q., Thompson, S. E., Moore, C. E., McGrath, G. S., Ruthrof, K. X. Subsurface constraints amplify vegetation stress during extreme heat and drought in a Mediterranean forest. Environmental Research Letters. 2025, 20(12), 124029.
[19] Sarker, T., Reza, S., Roy, R., Uddim, T., Alif, S. A., Tariq, A. Geospatial mapping of heatwaves and drought: Exploring their link to vegetation, soil moisture, and groundwater in the thirsty Barind Tract, Bangladesh. Environmental Earth Sciences. 2026, 85(3), 88.
[20] Alizadeh, O. Changes in the mean and variability of temperature and precipitation over global land areas. Environmental Research: Climate. 2023, 2(3), 035006.
[21] Bandari, A. Global precipitation varies with sea-surface temperature at different timescales. Scilight. 2019, 2019(25), 250012.
[22] Simon, J. M., Ángel, G. M., Bradfield, L., Madeleine, C. T. Climate Variability and Trends. 2018, pp. 89–124.
[23] Itzhak-Ben-Shalom, H., Alpert, P., Potchter, O., Samuels, R. MODIS Summer SUHI Cross-sections Anomalies over the Megacities of the Monsoon Asia Region and Global Trends. The Open Atmospheric Science Journal. 2017, 11(1), 121–136.
[24] Munawar, M., McNeil, R., Jani, R., Buya, S., Tarmizi, T. Variations in land surface temperature increase in South-East Asian Cities. Environmental Monitoring and Assessment. 2025, 197(2), 190.
[25] Shawky, M., Ahmed, M. R., Ghaderpour, E., Gupta, A., Achari, G., Dewan, A., Hassan, Q. K. Remote sensing-derived land surface temperature trends over South Asia. Ecological Informatics. 2023, 74, 101969.
[26] Dastour, H., Alam, M. M., Dewan, A., Hassan, Q. K. Evaluating climatic warming and the modulating effects of surface water and regional variables in western Bangladesh. Results in Engineering. 2025, 25, 103864.
[27] Rakib, Z. Characterization of climate change in southwestern Bangladesh: trend analyses of temperature, humidity, heat index, and rainfall. Climate Research. 2018, 76(3), 241–252.
[28] Suhan, M. S. I., Adhikary, S. K. Exploring Climate Change Trends in Temperature and Rainfall Across the Southeastern Coastal Area of Bangladesh. Journal of Engineering Science. 2025, 15(2), 51–67.
[29] Haque, M. R., Arman, Moniruzzaman, M., Roy, S. K., Islam, A. K. M. S. Spatiotemporal trends and urban-climate interactions of land surface temperature dynamics across Bangladesh. Anthropocene. 2026, 54, 100547.
[30] Liu, Y., Huang, Y., Yuan, J., Xie, Y., Zhou, C. Contribution of Surface Radiative Effects, Heat Fluxes and Their Interactions to Land Surface Temperature Variability. Journal of Geophysical Research: Atmospheres. 2024, 129(8).
[31] Brammer, H. Bangladesh's diverse and complex physical geography: implications for agricultural development. International Journal of Environmental Studies. 2016, 74(1), 1–27.
[32] Rashid, M. A., Ando, K., Tanaka, K., Kaida, Y. Village-Level Studies on Rice-Based Cropping Systems in the Low-Lying Areas of Bangladesh. II. Toposequence, hydrology, land classification and cropping patterns in the Bogra District of the Barind Tract. Japanese Journal of Crop Science. 1997, 66(1), 118–128.
[33] Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. 2017, 202, 18–27.
[34] Didan, K. MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid V061 [Dataset]. NASA EOSDIS Land Processes Distributed Active Archive Center, 2021.
[35] Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., Michaelsen, J. The climate hazards infrared precipitation with stations — a new environmental record for monitoring extremes. Scientific Data. 2015, 2, 150066.
[36] Food and Agriculture Organization of the United Nations. Global Administrative Unit Layers (GAUL) 2015, level 2 [Dataset]. Earth Engine Data Catalog, 2015. Available from:
[37] Nugraha, A. S. A., Kamal, M., Heru Murti, S., Widyatmanti, W. Accuracy assessment of land surface temperature retrievals from remote sensing imagery: pixel-based, single and multi-channel methods. Geomatics, Natural Hazards and Risk. 2024, 15(1).
[38] Duan, S.-B., Li, Z.-L., Li, H., Göttsche, F.-M., Wu, H., Zhao, W., Leng, P., Zhang, X., Coll, C. Validation of Collection 6 MODIS land surface temperature product using in situ measurements. Remote Sensing of Environment. 2019, 225, 16–29.
[39] Xu, S., Wang, D., Liang, S., Liu, Y., Jia, A. Assessing the Reliability of the MODIS LST Product to Detect Temporal Variability. IEEE Geoscience and Remote Sensing Letters. 2023, 20, 1–5.
[40] Yan, J., Ni, L., Li, X., Cheng, Y., Wu, H. A framework for reconstructing 1 km all-weather hourly LST from MODIS data. International Journal of Remote Sensing. 2023, 44(24), 7654–7677.
[41] Sen, P. K. Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association. 1968, 63(324), 1379–1389.
[42] Mann, H. B. Nonparametric tests against trend. Econometrica. 1945, 13(3), 245–259.
[43] Benjamini, Y., Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995, 57(1), 289–300.
[44] Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton, R. M., Van den Hurk, B., AghaKouchak, A., Jézéquel, A., Mahecha, M. D., Maraun, D., Ramos, A. M., Ridder, N. N., Thiery, W., Vignotto, E. A typology of compound weather and climate events. Nature Reviews Earth & Environment. 2020, 1(7), 333–347.
[45] Zscheischler, J., Westra, S., van den Hurk, B. J. J. M., Seneviratne, S. I., Ward, P. J., Pitman, A., AghaKouchak, A., Bresch, D. N., Leonard, M., Wahl, T., Zhang, X. Future climate risk from compound events. Nature Climate Change. 2018, 8, 469–477.
[46] Gao, Y., Guilloteau, C., Foufoula-Georgiou, E., Xu, C., Sun, X., Vrugt, J. A. Soil Moisture-Cloud-Precipitation Feedback in the Lower Atmosphere From Functional Decomposition of Satellite Observations. Geophysical Research Letters. 2024, 51(22), e2024GL110347.
[47] Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., Dolman, A. J. Global land-surface evaporation estimated from satellite-based observations. Hydrology and Earth System Sciences. 2011, 15, 453–469.
[48] Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., Teuling, A. J. Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Science Reviews. 2010, 99(3–4), 125–161.
[49] Ma, J., Shen, H., Jiang, M., Lin, L., Meng, C., Zeng, C., Li, H., Wu, P. A mechanism-guided machine learning method for mapping gapless land surface temperature. Remote Sensing of Environment. 2024, 303, 114001.
[50] Wang, T., Shi, J., Ma, Y., Husi, L., Comyn-Platt, E., Ji, D., Zhao, T., Xiong, C. Recovering Land Surface Temperature Under Cloudy Skies Considering the Solar-Cloud-Satellite Geometry: Application to MODIS and Landsat-8 Data. Journal of Geophysical Research: Atmospheres. 2019, 124(6), 3401–3416.
[51] Jiang, K., Pan, Z., Pan, F., Teuling, A. J., Han, G., An, P., Chen, X., Wang, J., Song, Y., Cheng, L., Zhang, Z., Huang, N., Ma, S., Gao, R., Zhang, Z., Men, J., Lv, X., Dong, Z. Combined influence of soil moisture and atmospheric humidity on land surface temperature under different climatic background. iScience. 2023, 26(6), 106837.
[52] Faisal, A.-A., Kafy, A.-A., Al Rakib, A., Akter, K. S., Jahir, D. M. A., Sikdar, M. S., Ashrafi, T. J., Mallik, S., Rahman, M. M. Assessing and predicting land use/land cover, land surface temperature and urban thermal field variance index using Landsat imagery for Dhaka Metropolitan area. Environmental Challenges. 2021, 4, 100192.
[53] Islam, M. T., Shahriar, M. A. Assessing the Effect of Land Use and Land Cover Changes on Land Surface Temperature in Jessore District, Bangladesh using Remote Sensing Techniques. Research Square [preprint]. 2023.
[54] Marufuzzaman, M., Khanam, M. M., Hasan, M. K. Monitoring the Land Cover Change and Its Impact on the Land Surface Temperature of Rajshahi City, Bangladesh using GIS and Remote Sensing Techniques. Journal of Geography, Environment and Earth Science International. 2021, 1–19.
[55] Ali, Y., Miah, M. D. Temporal and Spatial Variations in Surface Water and Its Interrelations with Land Surface Temperature and Rainfall Patterns in Chattogram City, Bangladesh. The Chittagong University Journal of Science. 2025, 45(1), 54–74.
[56] Dewan, A., Kiselev, G., Botje, D., Mahmud, G. I., Bhuian, M. H., Hassan, Q. K. Surface urban heat island intensity in five major cities of Bangladesh: Patterns, drivers and trends. Sustainable Cities and Society. 2021, 71, 102926.
[57] Hossain, R. B., Ahmed, R., Sharmin, T., Refat, A., Moni, U. H. Spatiotemporal Analysis of Urban Expansion and Its Impact on Agricultural Land Degradation and Vegetation Health in Narayanganj District, Bangladesh. Acadlore Transactions on Geosciences. 2024, 3(4), 197–209.
[58] Goswami, M. M., Mujumdar, M., Singh, B. B., Ingale, M., Ganeshi, N., Ranalkar, M., Franz, T. E., Srivastav, P., Niyogi, D., Krishnan, R., Patil, S. N. Understanding the soil water dynamics during excess and deficit rainfall conditions over the core monsoon zone of India. Environmental Research Letters. 2023, 18(11), 114011.
[59] Taylor, C. M., Klein, C., Harris, B. L. Multiday Soil Moisture Persistence and Atmospheric Predictability Resulting From Sahelian Mesoscale Convective Systems. Geophysical Research Letters. 2024, 51(20).
[60] Meng, Q., Chen, S., Zhang, L., Zhu, X., Zhang, Y., Atkinson, P. M. GLOSTFM: A global spatiotemporal fusion model integrating multi-source satellite observations to enhance land surface temperature resolution. Remote Sensing of Environment. 2025, 319, 114640.
[61] Zarakas, C. M., Swann, A. L. S., Battisti, D. S. Land-Atmosphere Feedbacks Dampen Surface Evapotranspiration Fluxes in Wet Regions. ESS Open Archive [preprint]. 2024.
[62] Khanna, J., Cook, K. H., Vizy, E. K. Opposite spatial variability of climate change-induced surface temperature trends due to soil and atmospheric moisture in tropical/subtropical dry and wet land regions. International Journal of Climatology. 2020, 40(14), 5887–5905.
[63] Pal, P., Bhattacharya, P. Soil physical properties: A crucial component of ecosystem health and productivity. International Journal of Agriculture and Nutrition. 2024, 6(2), 75–77.
Cite This Article
  • APA Style

    Muntakim, N., Hossain, M. I., Ahmed, Z., Rahman, M. M. (2026). Rainfall–Surface Temperature Coupling and Compound Dry-Hot Persistence Across Northwestern Bangladesh: A 25-Year Multi-Sensor Earth Observation Analysis. American Journal of Remote Sensing, 14(2), 118-133. https://doi.org/10.11648/j.ajrs.20261402.17

    Copy | Download

    ACS Style

    Muntakim, N.; Hossain, M. I.; Ahmed, Z.; Rahman, M. M. Rainfall–Surface Temperature Coupling and Compound Dry-Hot Persistence Across Northwestern Bangladesh: A 25-Year Multi-Sensor Earth Observation Analysis. Am. J. Remote Sens. 2026, 14(2), 118-133. doi: 10.11648/j.ajrs.20261402.17

    Copy | Download

    AMA Style

    Muntakim N, Hossain MI, Ahmed Z, Rahman MM. Rainfall–Surface Temperature Coupling and Compound Dry-Hot Persistence Across Northwestern Bangladesh: A 25-Year Multi-Sensor Earth Observation Analysis. Am J Remote Sens. 2026;14(2):118-133. doi: 10.11648/j.ajrs.20261402.17

    Copy | Download

  • @article{10.11648/j.ajrs.20261402.17,
      author = {Nafia Muntakim and Md. Imran Hossain and Zihad Ahmed and Md. Mizanoor Rahman},
      title = {Rainfall–Surface Temperature Coupling and Compound Dry-Hot Persistence Across Northwestern Bangladesh: 
    A 25-Year Multi-Sensor Earth Observation Analysis},
      journal = {American Journal of Remote Sensing},
      volume = {14},
      number = {2},
      pages = {118-133},
      doi = {10.11648/j.ajrs.20261402.17},
      url = {https://doi.org/10.11648/j.ajrs.20261402.17},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20261402.17},
      abstract = {Understanding how rainfall variability regulates land-surface thermal conditions is essential for climate-sensitive regions where water availability, vegetation dynamics, and heat stress interact. However, the temporal persistence and spatial heterogeneity of rainfall–surface temperature relationships remain insufficiently characterized in northwestern Bangladesh. This study integrates 25 years (2001–2025) of multi-source Earth observation data, including Terra MODIS land-surface temperature (MOD11A2), MODIS vegetation index (MOD13A2), and CHIRPS precipitation, to quantify pre-monsoon hydrothermal coupling across 16 districts of the Rajshahi and Rangpur divisions. A total of 275 quality-controlled 8-day March–May composites were analyzed using Mann–Kendall trend tests, Sen's slope estimation, fixed-effects distributed lag models, and a locally standardized compound dry-hot persistence indicator. Mean daytime land-surface temperature ranged from 27.93°C in Kurigram to 31.38°C in Chapai Nawabganj. No district exhibited a significant daytime land-surface temperature trend after false-discovery-rate correction, whereas NDVI increased significantly across all districts. The distributed-lag model showed that 10 mm of concurrent rainfall was associated with a 0.164°C lower daytime land-surface temperature (95% CI: −0.224 to −0.103°C), while rainfall during the preceding 8–16 days was associated with a weaker effect (−0.093°C per 10 mm). The 16–24-day rainfall effect was not significant. Rainfall sensitivity varied considerably among districts, ranging from −0.538°C per 10 mm in Rajshahi to −0.123°C per 10 mm in Lalmonirhat. These findings demonstrate that satellite-derived hydrothermal responses are spatially heterogeneous and highlight the value of multi-sensor Earth observation frameworks for identifying location-specific climate adaptation priorities.},
     year = {2026}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Rainfall–Surface Temperature Coupling and Compound Dry-Hot Persistence Across Northwestern Bangladesh: 
    A 25-Year Multi-Sensor Earth Observation Analysis
    AU  - Nafia Muntakim
    AU  - Md. Imran Hossain
    AU  - Zihad Ahmed
    AU  - Md. Mizanoor Rahman
    Y1  - 2026/09/28
    PY  - 2026
    N1  - https://doi.org/10.11648/j.ajrs.20261402.17
    DO  - 10.11648/j.ajrs.20261402.17
    T2  - American Journal of Remote Sensing
    JF  - American Journal of Remote Sensing
    JO  - American Journal of Remote Sensing
    SP  - 118
    EP  - 133
    PB  - Science Publishing Group
    SN  - 2328-580X
    UR  - https://doi.org/10.11648/j.ajrs.20261402.17
    AB  - Understanding how rainfall variability regulates land-surface thermal conditions is essential for climate-sensitive regions where water availability, vegetation dynamics, and heat stress interact. However, the temporal persistence and spatial heterogeneity of rainfall–surface temperature relationships remain insufficiently characterized in northwestern Bangladesh. This study integrates 25 years (2001–2025) of multi-source Earth observation data, including Terra MODIS land-surface temperature (MOD11A2), MODIS vegetation index (MOD13A2), and CHIRPS precipitation, to quantify pre-monsoon hydrothermal coupling across 16 districts of the Rajshahi and Rangpur divisions. A total of 275 quality-controlled 8-day March–May composites were analyzed using Mann–Kendall trend tests, Sen's slope estimation, fixed-effects distributed lag models, and a locally standardized compound dry-hot persistence indicator. Mean daytime land-surface temperature ranged from 27.93°C in Kurigram to 31.38°C in Chapai Nawabganj. No district exhibited a significant daytime land-surface temperature trend after false-discovery-rate correction, whereas NDVI increased significantly across all districts. The distributed-lag model showed that 10 mm of concurrent rainfall was associated with a 0.164°C lower daytime land-surface temperature (95% CI: −0.224 to −0.103°C), while rainfall during the preceding 8–16 days was associated with a weaker effect (−0.093°C per 10 mm). The 16–24-day rainfall effect was not significant. Rainfall sensitivity varied considerably among districts, ranging from −0.538°C per 10 mm in Rajshahi to −0.123°C per 10 mm in Lalmonirhat. These findings demonstrate that satellite-derived hydrothermal responses are spatially heterogeneous and highlight the value of multi-sensor Earth observation frameworks for identifying location-specific climate adaptation priorities.
    VL  - 14
    IS  - 2
    ER  - 

    Copy | Download

Author Information
  • Sections