Spatial Heterogeneity and Climate Correlates of Vegetation Greenness across Indonesian Provinces: Evidence from Multi-Source Remote Sensing and Panel Data (2018–2024)
Authors
-
Aman Suyadi 
Department ofAgrotechnology, Faculty ofAgriculture and Fisheries, Universitas Muhammadiyah Purwokerto, Purwokerto
53182, Indonesia
-
Marga Surya Mudhari
Research Center for Photonics, National Research and Innovation Agency (BRIN), Jakarta 10340, Indonesia
DOI:
https://doi.org/10.30564/re.v8i5.117286Received:6 August 2026 |Revised:15 September 2026 |Accepted:21 September 2026 |Published Online:16 October 2026
Abstract
Strong spatial associations between land surface temperature (LST) and vegetation greenness are often interpreted as evidence of vegetation responses to temperature. We examined whether that interpretation is supported by annual observations from 34 Indonesian provinces during 2018–2024 (238 province-year records). The Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and daytime land surface temperature (LST) were combined with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall, TerraClimate Palmer Drought Severity Index (PDSI), and Soil Moisture Active Passive (SMAP) surface soil moisture. Pooled and fixed-effects (FE) regressions compared the national association with changes within provinces, while Moran’s I assessed remaining spatial structure. NDVI and LST were strongly inversely correlated (r = −0.845). After adjustment for rainfall, the LST coefficient was −0.03296 in pooled OLS and −0.00420 under province FE (p = 0.081), an 87.3% reduction in magnitude. Adding year effects gave a coefficient of −0.01035. Evidence for this within-province association was suggestive: province-clustered p = 0.021, but restricted wild-cluster bootstrap p = 0.076. Driscoll–Kraay inference gave p < 0.001, although its reliability is limited by the seven-year panel. One-year-lag coefficients were not distinguishable from zero. Two-way FE residuals remained spatially autocorrelated (Moran’s I = 0.227, p < 0.001). The main contribution is an empirical limit on using Indonesia’s provincial thermal–greenness gradient to infer interannual vegetation responses. Province means combine land-cover composition and canopy condition, and FE does not separate their changing contributions or resolve reciprocal LST–NDVI feedbacks. NDVI saturation and the absence of EVI or land-cover-stratified checks further restrict the ecological interpretation.
Keywords:
Vegetation Greenness, Remote Sensing, Normalized Difference Vegetation Index (NDVI), Land Surface Temperature, Panel Data, Spatial Autocorrelation, IndonesiaReferences
[1]Wang, X., Ou, T., Zhang, W., et al., 2022. An Overview of Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate. Remote Sensing. 14(20), 5275. DOI: https://doi.org/10.3390/rs14205275
[2]Zeng, Y., Hao, D., Huete, A., et al., 2022. Optical vegetation indices for monitoring terrestrial ecosystems globally. Nature Reviews Earth & Environment. 3(7), 477–493. DOI: https://doi.org/10.1038/s43017-022-00298-5
[3]Dronova, I., Taddeo, S., 2022. Remote sensing of phenology: Towards the comprehensive indicators of plant community dynamics from species to regional scales. Journal of Ecology. 110(7), 1460–1484. DOI: https://doi.org/10.1111/1365-2745.13897
[4]Yan, K., Gao, S., Yan, G., et al., 2025. A global systematic review of the remote sensing vegetation indices. International Journal of Applied Earth Observation and Geoinformation. 139, 104560. DOI: https://doi.org/10.1016/j.jag.2025.104560
[5]Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment. 8(2), 127–150. DOI: https://doi.org/10.1016/0034-4257(79)90013-0
[6]Huete, A., Didan, K., Miura, T., et al., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment. 83(1–2), 195–213. DOI: https://doi.org/10.1016/S0034-4257(02)00096-2
[7]Gao, S., Zhong, R., Yan, K., et al., 2023. Evaluating the saturation effect of vegetation indices in forests using 3D radiative transfer simulations and satellite observations. Remote Sensing of Environment. 295, 113665. DOI: https://doi.org/10.1016/j.rse.2023.113665
[8]Aldrian, E., Dwi Susanto, R., 2003. Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature. International Journal of Climatology. 23(12), 1435–1452. DOI: https://doi.org/10.1002/joc.950
[9]Furusawa, T., Koera, T., Siburian, R., et al., 2023. Time-series analysis of satellite imagery for detecting vegetation cover changes in Indonesia. Scientific Reports. 13(1), 8437. DOI: https://doi.org/10.1038/s41598-023-35330-1
[10]Ramdani, F., Setiani, P., Sianturi, R., 2024. Towards understanding climate change impacts: monitoring the vegetation dynamics of terrestrial national parks in Indonesia. Scientific Reports. 14(1), 18257. DOI: https://doi.org/10.1038/s41598-024-69276-9
[11]Agustiyara, A., Mutiarin, D., Nurmandi, A., et al., 2025. Mapping Urban Green Spaces in Indonesian Cities Using Remote Sensing Analysis. Urban Science. 9(2), 23. DOI: https://doi.org/10.3390/urbansci9020023
[12]Mehmood, K., Anees, S.A., Rehman, A., et al., 2024. Exploring spatiotemporal dynamics of NDVI and climate-driven responses in ecosystems: Insights for sustainable management and climate resilience. Ecological Informatics. 80, 102532. DOI: https://doi.org/10.1016/j.ecoinf.2024.102532
[13]Kliengchuay, W., Phonphan, W., Niampradit, S., et al., 2025. Variation of vegetation cover and the relationship with land surface temperature across Thailand (2007 to 2022). Scientific Reports. 15(1), 27823. DOI: https://doi.org/10.1038/s41598-025-13018-y
[14]Wan, Z., Zhang, Y., Zhang, Q., et al., 2002. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment. 83(1–2), 163–180. DOI: https://doi.org/10.1016/S0034-4257(02)00093-7
[15]Li, Z.-L., Wu, H., Duan, S.-B., et al., 2023. Satellite Remote Sensing of Global Land Surface Temperature: Definition, Methods, Products, and Applications. Reviews of Geophysics. 61(1), e2022RG000777. DOI: https://doi.org/10.1029/2022RG000777
[16]Funk, C., Peterson, P., Landsfeld, M., et al., 2015. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data. 2(1), 150066. DOI: https://doi.org/10.1038/sdata.2015.66
[17]Abatzoglou, J.T., Dobrowski, S.Z., Parks, S.A., et al., 2018. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data. 5(1), 170191. DOI: https://doi.org/10.1038/sdata.2017.191
[18]Alley, W.M., 1984. The Palmer Drought Severity Index: Limitations and Assumptions. Journal of Climate and Applied Meteorology. 23(7), 1100–1109. DOI: https://doi.org/10.1175/1520-0450(1984)023%253C1100:TPDSIL%253E2.0.CO;2
[19]Reichle, R.H., Liu, Q., Koster, R.D., et al., 2019. Version 4 of the SMAP Level‐4 Soil Moisture Algorithm and Data Product. Journal of Advances in Modeling Earth Systems. 11(10), 3106–3130. DOI: https://doi.org/10.1029/2019MS001729
[20]Robinson, W.S., 1950. Ecological Correlations and the Behavior of Individuals. American Sociological Review. 15(3), 351–357. DOI: https://doi.org/10.2307/2087176
[21]Fotheringham, A.S., Wong, D.W.S., 1991. The Modifiable Areal Unit Problem in Multivariate Statistical Analysis. Environment and Planning A: Economy and Space. 23(7), 1025–1044. DOI: https://doi.org/10.1068/a231025
[22]O’brien, R.M., 2007. A Caution Regarding Rules of Thumb for Variance Inflation Factors. Quality & Quantity. 41(5), 673–690. DOI: https://doi.org/10.1007/s11135-006-9018-6
[23]Hausman, J.A., 1978. Specification Tests in Econometrics. Econometrica. 46(6), 1251–1271. DOI: https://doi.org/10.2307/1913827
[24]Mundlak, Y., 1978. On the Pooling of Time Series and Cross Section Data. Econometrica. 46(1), 69–85. DOI: https://doi.org/10.2307/1913646
[25]Moran, P.A.P., 1950. Notes On Continuous Stochastic Phenomena. Biometrika. 37(1–2), 17–23. DOI: https://doi.org/10.1093/biomet/37.1-2.17
[26]Jolliffe, I.T., Cadima, J., 2016. Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 374(2065), 20150202. DOI: https://doi.org/10.1098/rsta.2015.0202
[27]Lloyd, S., 1982. Least squares quantization in PCM. IEEE Transactions on Information Theory. 28(2), 129–137. DOI: https://doi.org/10.1109/TIT.1982.1056489
[28]Arthur, D., Vassilvitskii, S., 2007. k-means++: the advantages of careful seeding. In proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, USA, 7–9 January 2007; pp. 1027–1035.
[29]Rousseeuw, P.J., 1987. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics. 20, 53–65. DOI: https://doi.org/10.1016/0377-0427(87)90125-7
[30]Pedregosa, F., Varoquaux, G., Gramfort, A., et al., 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 12, 2825–2830.
[31]Feng, S., Zhang, Z., Zhao, S., et al., 2023. Time lag effect of vegetation response to seasonal precipitation in the Mara River Basin. Ecological Processes. 12(1), 49. DOI: https://doi.org/10.1186/s13717-023-00461-w
[32]Karnieli, A., Agam, N., Pinker, R.T., et al., 2010. Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations. Journal of Climate. 23(3), 618–633. DOI: https://doi.org/10.1175/2009JCLI2900.1
[33]Zhang, Y., Wang, X., Lian, X., et al., 2024. Asymmetric impacts of forest gain and loss on tropical land surface temperature. Nature Geoscience. 17(5), 426–432. DOI: https://doi.org/10.1038/s41561-024-01423-3
[34]Gong, Z., Ge, W., Guo, J., et al., 2024. Satellite remote sensing of vegetation phenology: Progress, challenges, and opportunities. ISPRS Journal of Photogrammetry and Remote Sensing. 217, 149–164. DOI: https://doi.org/10.1016/j.isprsjprs.2024.08.011
[35]Anwar, R.P., Irfan, M., Akil, A., et al., 2026. Multi-Scale Drivers of Urban Vegetation Moisture Stress: A Comparative OLS and GWR Analysis in Makassar City, Indonesia. Land. 15(2), 267. DOI: https://doi.org/10.3390/land15020267
[36]Dong, F., Qin, F., Zhang, T., et al., 2026. kNDVI reveals vegetation dynamics and hydro–edaphic controls in inner Mongolia (2000–2024). Scientific Reports. 16(1), 5244. DOI: https://doi.org/10.1038/s41598-026-35762-5
[37]Iheaturu, C.J., Curatola Fernández, G.F., Wingate, V.R., et al., 2026. Remote sensing of tropical forest recovery: A review and decision-support framework for multi-sensor integration. Remote Sensing of Environment. 335, 115257. DOI: https://doi.org/10.1016/j.rse.2026.115257
Downloads
Issue
Article Type
License
Copyright © 2026 Aman Suyadi, Marga Surya Mudhari
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.