努沙登加拉群島 (NTI) 是印尼重要的農業生產地區，其經濟價值佔該地區國內生產總值 (GDP) 的 29。然而，這些島嶼經常面臨農業乾旱的挑戰，威脅到 NTIs 農業的永續發展。降水、向下表面輻射及表面溫度等氣象因素的互動可能影響植被 (SAVI)，並引發農業乾旱 (TVDI)。因此，本研究藉由時間滯後效應的整合，評估氣象因子如何影響植被及農業乾旱，並提供 NTIs 的乾旱預警。地表資訊來自於中解析度影像光譜儀 (MODIS)，而降水量則是利用 INSAT 多光譜降雨演算法 (IMSRA) 來估算 Himawari-8 的降水量。研究結果顯示，降雨量的估算與 8 天及每月的測量資料相當吻合。TVDI 分析證實 NTIs 經常遭受輕度至中度乾旱，其中耕地受影響最嚴重，導致 2019 至 2020 年生長季的稻米耕作延遲。此外，氣象驅動因素可解釋 NTIs 中 60% 以上的植被狀況與地表乾燥程度變化。地表溫度對大多數的植被變化和農業乾旱具有支配性影響和直接影。我們的 TVDI 估計模型以每月和 8 天的間隔應用，成功地捕捉到與 TVDI MODIS 觀測一致的乾旱空間模式，R²值高於 0.64。這些模型也顯示出誤差率低以及偵測空間乾旱分佈的強大能力，尤其是在空曠的土地區域，突顯其在 NTIs 農業乾旱預估的潛力。

Nusa Tenggara Islands (NTIs) are an important region for agricultural production in Indonesia, with their economic value accounting for 29% of the regional gross domestic product (GDP). However, these islands face the recurring challenge of agricultural drought, threatening the sustainability of agriculture in NTIs. The interactions of precipitation, downward surface radiation, and surface temperature as meteorological factors potentially affect the vegetation (SAVI) and trigger agricultural drought (TVDI). Therefore, through the integration of temporal lag effects, this study assesses how meteorological factor contributes to vegetation and agricultural drought, and provides early warning regarding drought for the NTIs. Surface information was obtained from the Moderate-resolution Imaging Spectroradiometer (MODIS), and precipitation was estimated from Himawari-8 using the INSAT Multi-Spectral Rainfall Algorithm (IMSRA). The findings show the rainfall estimation aligned well with gauge data on 8-day and monthly scales. Analysis of TVDI confirmed that NTIs are subject to frequent mild-to-moderate droughts, with cropland being the most impacted, leading to delays in rice cultivation in the 2019 to 2020 growing season. In addition, meteorological drivers explained more than 60% of the variation in vegetation condition and surface dryness in the NTIs. Surface temperature has a dominant influence and a direct impact on most of the vegetation changes and agricultural drought. Our TVDI estimation models, applied at monthly and 8-day time scales, successfully captured drought spatial patterns that align with TVDI MODIS observations, with R² values above 0.64. The models also demonstrated low error rates and a strong ability to detect spatial drought distribution, particularly in open land areas, highlighting their potential for agricultural drought estimation in the NTIs.

1. Naveendrakumar, G.; Vithanage, M.; Kwon, H.H.; Chandrasekara, S.S.K.; Iqbal, M.C.M.; Pathmarajah, S.; Fernando, W.C.D.K.; Obeysekera, J. South Asian Perspective on Temperature and Rainfall Extremes: A Review. Atmos Res 2019, 225, 110–120, doi:10.1016/J.ATMOSRES.2019.03.021.
2. Rafiq, M.; Li, Y.C.; Cheng, Y.; Rahman, G.; Ullah, I.; Ali, A. Spatial and Temporal Fluctuation of Rainfall and Drought in Balochistan Province, Pakistan. Arabian Journal of Geosciences 2022 15:2 2022, 15, 1–12, doi:10.1007/S12517-022-09514-4.
3. Kuswanto, H.; Hibatullah, F.; Soedjono, E.S. Perception of Weather and Seasonal Drought Forecasts and Its Impact on Livelihood in East Nusa Tenggara, Indonesia. Heliyon 2019, 5, e02360, doi:10.1016/J.HELIYON.2019.E02360.
4. Kirono, D.G.C.; Butler, J.R.A.; McGregor, J.L.; Ripaldi, A.; Katzfey, J.; Nguyen, K. Historical and Future Seasonal Rainfall Variability in Nusa Tenggara Barat Province, Indonesia: Implications for the Agriculture and Water Sectors. Clim Risk Manag 2016, 12, 45–58, doi:10.1016/J.CRM.2015.12.002.
5. BPS. Statistik Pertanian Provinsi Nusa Tenggara Timur 2023 (Agricultural Statistics of East Nusa Tenggara Province 2023) Available online: https://ntt.bps.go.id/id/publication/2024/09/20/e571dcc6145f7795887626fd/statistik-pertanian-provinsi-nusa-tenggara-timur-2023.html (accessed on 5 April 2025).
6. Fang, Y.; Xiong, L. General Mechanisms of Drought Response and Their Application in Drought Resistance Improvement in Plants. Cell Mol Life Sci 2015, 72, 673–689, doi:10.1007/S00018-014-1767-0.
7. Tjasyono, B.H.; Gernowo, R.; Woro H, S.B. The Character of Rainfall in the Indonesian Monsoon. International Symposium on Equatorial Monsoon System 2008.
8. Ardi, R.D.W.; Aswan; Maryunani, K.A.; Yulianto, E.; Putra, P.S.; Nugroho, S.H.; Istiana Last Deglaciation—Holocene Australian-Indonesian Monsoon Rainfall Changes Off Southwest Sumba, Indonesia. Atmosphere 2020, Vol. 11, Page 932 2020, 11, 932, doi:10.3390/ATMOS11090932.
9. Wheeler, M.C.; McBride, J.L. Australian-Indonesian Monsoon. Intraseasonal Variability in the Atmosphere-Ocean Climate System 2005, 125–173, doi:10.1007/3-540-27250-X_5.
10. Giarno; Hadi, M.P.; Suprayogi, S.; Herumurti, S. Impact of Rainfall Intensity, Monsoon and MJO to Rainfall Merging in the Indonesian Maritime Continent. Journal of Earth System Science 2020, 129, 1–20, doi:10.1007/S12040-020-01427-8/TABLES/7.
11. Matsumoto, J.; Wang, B.; Wu, G.; Li, J.; Wu, P.; Hattori, M.; Mori, S.; Yamanaka, M.D.; Ogino, S.Y.; Jun-Ichi, H.; et al. An Overview of the Asian Monsoon Years 2007-2012 (AMY) and Multi-Scale Interactions in the Extreme Rainfall Events over the Indonesian Maritime Continent. World Scientific Series on Asia-Pacific Weather and Climate 2017, Volume 9, 365–385, doi:10.1142/9789813200913_0029.
12. Dewi Galuh Condro Kirono Principal Component Analysis for Identifying Period of Seasons in Indonesia. Indonesian Journal of Geography 2004, doi:https://doi.org/10.22146/ijg.2211.
13. Kusmiyarti, T.B.; Adnyana, W.S.; Nuarsa, W.; Sudarma, M.; Antara, M.O.G. Drought Monitoring Using Remote Sensing Data in Nusa Tenggara Timur Province, Indonesia in Between 2018 and 2023. Ecological Engineering and Environmental Technology 2024, 25, 134–145, doi:10.12912/27197050/192472.
14. Rahman As-syakur, A.; Wayan Sandi Adnyana, I.; Sudiana Mahendra, M.; Wayan Arthana, I.; Nyoman Merit, I.; Wayan Kasa, I.; Wayan Ekayanti, N.; Wayan Nuarsa, I.; Nyoman Sunarta, I. Observation of Spatial Patterns on the Rainfall Response to ENSO and IOD over Indonesia Using TRMM Multisatellite Precipitation Analysis (TMPA). International Journal of Climatology Int. J. Climatol 2014, doi:10.1002/joc.3939.
15. Sanit, M.S.; Rachmawati, T.A.; Firdausiyah, N. Impact Of Climate Change On Meteorological Drought In Insana Barat District, Timor Tengah Utara, East Nusa Tenggara. Journal of Syntax Literate 2023, 8, 417–425, doi:10.36418/SYNTAX-LITERATE.V8I1.11239.
16. Setiawan, A.M.; Lee, W.S.; Rhee, J. Spatio-Temporal Characteristics of Indonesian Drought Related to El Niño Events and Its Predictability Using the Multi-Model Ensemble. International Journal of Climatology 2017, 37, 4700–4719, doi:10.1002/JOC.5117.
17. Matuszko, D.; Library, W.O. Influence of the Extent and Genera of Cloud Cover on Solar Radiation Intensity. International Journal of Climatology Int. J. Climatol 2012, 32, 2403–2414, doi:10.1002/joc.2432.
18. Zhao, M.; A, G.; Liu, Y.; Konings, A.G. Evapotranspiration Frequently Increases during Droughts. Nature Climate Change 2022 12:11 2022, 12, 1024–1030, doi:10.1038/s41558-022-01505-3.
19. Chen, Z.; Wang, W.; Fu, J. Vegetation Response to Precipitation Anomalies under Different Climatic and Biogeographical Conditions in China. Scientific Reports 2020 10:1 2020, 10, 1–16, doi:10.1038/s41598-020-57910-1.
20. Wu, D.; Zhao, X.; Zhao, W.; Tang, B.; Xu, W. Response of Vegetation to Temperature, Precipitation and Solar Radiation Time-Scales: A Case Study over Mainland Australia. International Geoscience and Remote Sensing Symposium (IGARSS) 2014, 855–858, doi:10.1109/IGARSS.2014.6946559.
21. van der Graaf, S.C.; J Janssen, T.A.; Erisman, J.W.; -, al; Salvador, C.; Nieto, R.; Kapwata, T.; Wang, T.; She, D.; Bao, Z.; et al. Exposure to Drought: Duration, Severity and Intensity (Java, Bali and Nusa Tenggara). IOP Conf Ser Earth Environ Sci 2017, 58, 012040, doi:10.1088/1755-1315/58/1/012040.
22. Ma’Rufah, U.; Hidayat, R.; Prasasti, I. Analysis of Relationship between Meteorological and Agricultural Drought Using Standardized Precipitation Index and Vegetation Health Index. IOP Conf Ser Earth Environ Sci 2017, 54, 012008, doi:10.1088/1755-1315/54/1/012008.
23. Bessho, K.; Date, K.; Hayashi, M.; Ikeda, A.; Imai, T.; Inoue, H.; Kumagai, Y.; Miyakawa, T.; Murata, H.; Ohno, T.; et al. An Introduction to Himawari-8/9— Japan’s New-Generation Geostationary Meteorological Satellites. Journal of the Meteorological Society of Japan. Ser. II 2016, 94, 151–183, doi:10.2151/JMSJ.2016-009.
24. Varma, A.K. Measurement of Precipitation from Satellite Radiometers (Visible, Infrared, and Microwave): Physical Basis, Methods, and Limitations. Remote Sensing of Aerosols, Clouds, and Precipitation 2018, 223–248, doi:10.1016/B978-0-12-810437-8.00011-6.
25. Sandholt, I.; Rasmussen, K.; Andersen, J. A Simple Interpretation of the Surface Temperature/Vegetation Index Space for Assessment of Surface Moisture Status. Remote Sens Environ 2002, 79, 213–224, doi:10.1016/S0034-4257(01)00274-7.
26. Gao, Z.L.; Qin, Q.M.; Sun, Y.J.; Zheng, X.P.; Wu, L.; Wang, N. Improvement of TVDI for Soil Moisture Estimation. International Geoscience and Remote Sensing Symposium (IGARSS) 2014, 3255–3258, doi:10.1109/IGARSS.2014.6947173.
27. Yan, H.; Zhou, G.; Yang, F.; Lu, X. DEM Correction to the TVDI Method on Drought Monitoring in Karst Areas. Int J Remote Sens 2019, 40, 2166–2189, doi:10.1080/01431161.2018.1500732.
28. Shashikant, V.; Shariff, A.R.M.; Wayayok, A.; Kamal, R.; Lee, Y.P.; Takeuchi, W. Utilizing TVDI and NDWI to Classify Severity of Agricultural Drought in Chuping, Malaysia. Agronomy 2021, Vol. 11, Page 1243 2021, 11, 1243, doi:10.3390/AGRONOMY11061243.
29. Li, Y.; Wang, X.; Wang, F.; Feng, K.; Li, H.; Han, Y.; Chen, S.; Li, Y.; Wang, X.; Wang, F.; et al. Temporal and Spatial Characteristics of Agricultural Drought Based on the TVDI in Henan Province, China. Water 2024, Vol. 16, Page 1010 2024, 16, 1010, doi:10.3390/W16071010.
30. Van Hoek, M.; Jia, L.; Zhou, J.; Zheng, C.; Menenti, M.; Su, Z.; Vekerdy, Z.; Gloaguen, R.; Thenkabail, P.S. Early Drought Detection by Spectral Analysis of Satellite Time Series of Precipitation and Normalized Difference Vegetation Index (NDVI). Remote Sensing 2016, Vol. 8, Page 422 2016, 8, 422, doi:10.3390/RS8050422.
31. Tian, Q.; Lu, J.; Chen, X. A Novel Comprehensive Agricultural Drought Index Reflecting Time Lag of Soil Moisture to Meteorology: A Case Study in the Yangtze River Basin, China. Catena (Amst) 2022, 209, 105804, doi:10.1016/J.CATENA.2021.105804.
32. Schneider, U.B.A.F.P.M.-C.A.R.B.Z.M. GPCC Full Data Reanalysis Version 7.0 at 0.5°: Monthly Land-Surface Precipitation from Rain-Gauges Built on GTS-Based and Historic Data. 2015, doi:10.5676/DWD_GPCC/FD_M_V7_050.
33. Kuswanto, H.; Puspa, A.W.; Ahmad, I.S.; Hibatullah, F. Drought Analysis in East Nusa Tenggara (Indonesia) Using Regional Frequency Analysis. The Scientific World Journal 2021, 2021, 6626102, doi:10.1155/2021/6626102.
34. Huete, A.R. A Soil-Adjusted Vegetation Index (SAVI). Remote Sens Environ 1988, 25, 295–309, doi:10.1016/0034-4257(88)90106-X.
35. Hagenlocher, M.; Meza, I.; Anderson, C.C.; Min, A.; Renaud, F.G.; Walz, Y.; Siebert, S.; Sebesvari, Z. Drought Vulnerability and Risk Assessments: State of the Art, Persistent Gaps, and Research Agenda. Environmental Research Letters 2019, 14, 083002, doi:10.1088/1748-9326/AB225D.
36. Van Loon, A.F.; Van Lanen, H.A.J. A Process-Based Typology of Hydrological Drought. Hydrol Earth Syst Sci 2012, 16, 1915–1946, doi:10.5194/HESS-16-1915-2012.
37. Gholinia, A.; Abbaszadeh, P. Agricultural Drought Monitoring: A Comparative Review of Conventional and Satellite-Based Indices. Atmosphere 2024, Vol. 15, Page 1129 2024, 15, 1129, doi:10.3390/ATMOS15091129.
38. Kloos, S.; Yuan, Y.; Castelli, M.; Menzel, A. Agricultural Drought Detection with Modis Based Vegetation Health Indices in Southeast Germany. Remote Sens (Basel) 2021, 13, 3907, doi:10.3390/RS13193907/S1.
39. Bento, V.A.; Gouveia, C.M.; DaCamara, C.C.; Trigo, I.F. A Climatological Assessment of Drought Impact on Vegetation Health Index. Agric For Meteorol 2018, 259, 286–295, doi:10.1016/J.AGRFORMET.2018.05.014.
40. Chen, A.; Jiang, J.; Luo, Y.; Zhang, G.; Hu, B.; Wang, X.; Zhang, S. (TVDI) for Drought Monitoring in the Guangdong Province from 2000 to 2019. PeerJ 2023, 11, e16337, doi:10.7717/PEERJ.16337/SUPP-2.
41. Rawat, K.S.; Sehgal, V.K.; Singh, S.K.; Ray, S.S. Soil Moisture Estimation Using Triangular Method at Higher Resolution from MODIS Products. Physics and Chemistry of the Earth, Parts A/B/C 2022, 126, 103051, doi:10.1016/J.PCE.2021.103051.
42. Krishnan, S.; Indu, J. Assessing the Potential of Temperature/Vegetation Index Space to Infer Soil Moisture over Ganga Basin. J Hydrol (Amst) 2023, 621, 129611, doi:10.1016/j.jhydrol.2023.129611.
43. Dai, R.; Chen, S.; Cao, Y.; Zhang, Y.; Xu, X. A Modified Temperature Vegetation Dryness Index (MTVDI) for Agricultural Drought Assessment Based on MODIS Data: A Case Study in Northeast China. Remote Sens (Basel) 2023, 15, doi:10.3390/RS15071915.
44. Piao, S.; Wang, X.; Park, T.; Chen, C.; Lian, X.; He, Y.; Bjerke, J.W.; Chen, A.; Ciais, P.; Tømmervik, H.; et al. Characteristics, Drivers and Feedbacks of Global Greening. Nature Reviews Earth & Environment 2019 1:1 2019, 1, 14–27, doi:10.1038/s43017-019-0001-x.
45. Piao, S.; Cui, M.; Chen, A.; Wang, X.; Ciais, P.; Liu, J.; Tang, Y. Altitude and Temperature Dependence of Change in the Spring Vegetation Green-up Date from 1982 to 2006 in the Qinghai-Xizang Plateau. Agric For Meteorol 2011, 151, 1599–1608, doi:10.1016/J.AGRFORMET.2011.06.016.
46. Nemani, R.R.; Keeling, C.D.; Hashimoto, H.; Jolly, W.M.; Piper, S.C.; Tucker, C.J.; Myneni, R.B.; Running, S.W. Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999. Science (1979) 2003, 300, 1560–1563, doi:10.1126/SCIENCE.1082750/SUPPL_FILE/NEMANI.SOM.PDF.
47. Tao, Z.; Wang, H.; Liu, Y.; Xu, Y.; Dai, J. Phenological Response of Different Vegetation Types to Temperature and Precipitation Variations in Northern China during 1982–2012. Int J Remote Sens 2017, 38, 3236–3252, doi:10.1080/01431161.2017.1292070.
48. Ren, H.; Wen, Z.; Liu, Y.; Lin, Z.; Han, P.; Shi, H.; Wang, Z.; Su, T. Vegetation Response to Changes in Climate across Different Climate Zones in China. Ecol Indic 2023, 155, 110932, doi:10.1016/J.ECOLIND.2023.110932.
49. Hartmann, A.A.; Niklaus, P.A. Effects of Simulated Drought and Nitrogen Fertilizer on Plant Productivity and Nitrous Oxide (N2O) Emissions of Two Pastures. Plant Soil 2012, 361, 411–426, doi:10.1007/S11104-012-1248-X/METRICS.
50. Hinge, G.; Mohamed, M.M.; Long, D.; Hamouda, M.A. Meta-Analysis in Using Satellite Precipitation Products for Drought Monitoring: Lessons Learnt and Way Forward. Remote Sens (Basel) 2021, 13, doi:10.3390/RS13214353.
51. Immerzeel, W.W.; Rutten, M.M.; Droogers, P. Spatial Downscaling of TRMM Precipitation Using Vegetative Response on the Iberian Peninsula. Remote Sens Environ 2009, 113, 362–370, doi:10.1016/J.RSE.2008.10.004.
52. Zhong, R.; Chen, X.; Lai, C.; Wang, Z.; Lian, Y.; Yu, H.; Wu, X. Drought Monitoring Utility of Satellite-Based Precipitation Products across Mainland China. J Hydrol (Amst) 2019, 568, 343–359, doi:10.1016/J.JHYDROL.2018.10.072.
53. Huffman, G.J.; Bolvin, D.T.; Braithwaite, D.; Hsu, K.L.; Joyce, R.J.; Kidd, C.; Nelkin, E.J.; Sorooshian, S.; Stocker, E.F.; Tan, J.; et al. Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG). Advances in Global Change Research 2020, 67, 343–353, doi:10.1007/978-3-030-24568-9_19/FIGURES/3.
54. Huffman, G.J.; Bolvin, D.T. Real-Time TRMM Multi-Satellite Precipitation Analysis Data Set Documentation. 2018.
55. Levizzani, V.; Schmetz, J.; Lutz, H.J.; Kerkmann, J.; Alberoni, P.P.; Cervino, M. Precipitation Estimations from Geostationary Orbit and Prospects for METEOSAT Second Generation. Meteorological Applications 2001, 8, 23–41, doi:10.1017/S1350482701001037.
56. Chen, Y.; Mu, X.; McVicar, T.R.; Wang, Y.; Guo, Y.; Yan, K.; Lai, Y.; Xie, D.; Yan, G. Using an Improved Radiative Transfer Model to Estimate Leaf Area Index, Fractional Vegetation Cover and Leaf Inclination Angle from Himawari-8 Geostationary Satellite Data. Remote Sens Environ 2025, 318, doi:10.1016/j.rse.2024.114595.
57. Liu, M.; Zhai, H.; Zhang, X.; Dong, X.; Hu, J.; Ma, J.; Sun, W. Time-Lag and Accumulation Responses of Vegetation Growth to Average and Extreme Precipitation and Temperature Events in China between 2001 and 2020. Science of The Total Environment 2024, 945, 174084, doi:10.1016/J.SCITOTENV.2024.174084.
58. Rhee, J.; Im, J.; Carbone, G.J. Monitoring Agricultural Drought for Arid and Humid Regions Using Multi-Sensor Remote Sensing Data. Remote Sens Environ 2010, 114, 2875–2887, doi:10.1016/J.RSE.2010.07.005.
59. Prabowo, M.A.; Soekirno, S.; Ananda, N.; Asri, D.P.; Yulizar, D.; Adi, S.P.; Martarizal; Suhandi, N. Drought Prediction Based Standardized Precipitation Index Using Multilayer Perceptron Model. 2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024 2024, 262–267, doi:10.1109/SIML61815.2024.10578245.
60. Katul, G.G.; Oren, R.; Manzoni, S.; Higgins, C.; Parlange, M.B. Evapotranspiration: A Process Driving Mass Transport and Energy Exchange in the Soil-Plant-Atmosphere-Climate System. Reviews of Geophysics 2012, 50, doi:10.1029/2011RG000366.
61. Li, D.; Wang, L. Sensitivity of Surface Temperature to Land Use and Land Cover Change-Induced Biophysical Changes: The Scale Issue. Geophys Res Lett 2019, 46, 9678–9689, doi:10.1029/2019GL084861.
62. Li, Z.L.; Wu, H.; Duan, S.B.; Zhao, W.; Ren, H.; Liu, X.; Leng, P.; Tang, R.; Ye, X.; Zhu, J.; et al. Satellite Remote Sensing of Global Land Surface Temperature: Definition, Methods, Products, and Applications. Reviews of Geophysics 2023, 61, e2022RG000777, doi:10.1029/2022RG000777.
63. Jiménez-Munoz, J.C.; Sobrino, J.A. A Generalized Single-Channel Method for Retrieving Land Surface Temperature from Remote Sensing Data. Journal of Geophysical Research: Atmospheres 2003, 108, 4688, doi:10.1029/2003JD003480.
64. Sun, D.; Pinker, R.T. Estimation of Land Surface Temperature from a Geostationary Operational Environmental Satellite (GOES-8). Journal of Geophysical Research: Atmospheres 2003, 108, 4326, doi:10.1029/2002JD002422.
65. Becker, F.; Li, Z.L. Towards a Local Split Window Method over Land Surfaces. Remote Sens (Basel) 1990, 11, 369–393, doi:10.1080/01431169008955028.
66. Wan, Z. A Generalized Split-Window Algorithm for Retrieving Land-Surface Temperature from Space. IEEE Transactions on Geoscience and Remote Sensing 1996, 34, 892–905, doi:10.1109/36.508406.
67. Wan, Z.; Zhang, Y.; Zhang, Q.; Li, Z. liang Validation of the Land-Surface Temperature Products Retrieved from Terra Moderate Resolution Imaging Spectroradiometer Data. Remote Sens Environ 2002, 83, 163–180, doi:10.1016/S0034-4257(02)00093-7.
68. Wan, Z.; Li, Z.L. Radiance‐based Validation of the V5 MODIS Land‐surface Temperature Product. Int J Remote Sens 2008, 29, 5373–5395, doi:10.1080/01431160802036565.
69. Wan, Z. New Refinements and Validation of the Collection-6 MODIS Land-Surface Temperature/Emissivity Product. Remote Sens Environ 2014, 140, 36–45, doi:10.1016/J.RSE.2013.08.027.
70. Braswell, B.H.; Schimel, D.S.; Linder, E.; Moore, B. The Response of Global Terrestrial Ecosystems to Interannual Temperature Variability. Science (1979) 1997, 278, 870–872, doi:10.1126/SCIENCE.278.5339.870/ASSET/C3D36592-CD3F-453B-9F2A-542182B032DE/ASSETS/GRAPHIC/SE4375892003.JPEG.
71. Piao, S.; Cui, M.; Chen, A.; Wang, X.; Ciais, P.; Liu, J.; Tang, Y. Altitude and Temperature Dependence of Change in the Spring Vegetation Green-up Date from 1982 to 2006 in the Qinghai-Xizang Plateau. Agric For Meteorol 2011, 151, 1599–1608, doi:10.1016/J.AGRFORMET.2011.06.016.
72. Ullah, W.; Ahmad, K.; Ullah, S.; Tahir, A.A.; Javed, M.F.; Nazir, A.; Abbasi, A.M.; Aziz, M.; Mohamed, A. Analysis of the Relationship among Land Surface Temperature (LST), Land Use Land Cover (LULC), and Normalized Difference Vegetation Index (NDVI) with Topographic Elements in the Lower Himalayan Region. Heliyon 2023, 9, e13322, doi:10.1016/J.HELIYON.2023.E13322.
73. Qian, Z.; Sun, Y.; Chen, Z.; Ji, F.; Feng, G.; Ma, Q. Analysis of Land Surface Temperature Sensitivity to Vegetation in China. Remote Sens (Basel) 2023, 15, 4544, doi:10.3390/RS15184544/S1.
74. Choudhury, A. Drought Trend and Its Association with Land Surface Temperature (LST) over Homogeneous Drought Regions of India (2001–2019). Discover Water 2024 4:1 2024, 4, 1–13, doi:10.1007/S43832-024-00115-8.
75. Monteith, J.; Unsworth, M. Principles of Environmental Physics: Plants, Animals, and the Atmosphere: Fourth Edition. Principles of Environmental Physics: Plants, Animals, and the Atmosphere: Fourth Edition 2013, 1–401, doi:10.1016/C2010-0-66393-0.
76. Li, X.; Qu, Y. Evaluation of Vegetation Responses to Climatic Factors and Global Vegetation Trends Using GLASS LAI from 1982 to 2010. Canadian Journal of Remote Sensing 2018, 44, 357–372, doi:10.1080/07038992.2018.1526064.
77. Li, Z.; Chen, Z. Remote Sensing Indicators for Crop Growth Monitoring at Different Scales. International Geoscience and Remote Sensing Symposium (IGARSS) 2011, 4062–4065, doi:10.1109/IGARSS.2011.6050124.
78. Rahnama, S.; Shahidi, A.; Yaghoobzadeh, M.; Mehran, A.A.; Rahnama, S.; Shahidi, A.; Yaghoobzadeh, M.; Mehran, A.A. Comparison of Different Drought Monitoring Indices in Different Climatic Conditions in Iran. Atmósfera 2024, 38, 507–529, doi:10.20937/ATM.53319.
79. Wang, C.; Qi, S.; Niu, Z.; Wang, J. Evaluating Soil Moisture Status in China Using the Temperature–Vegetation Dryness Index (TVDI). Canadian Journal of Remote Sensing 2004, 30, 671–679, doi:10.5589/M04-029.
80. Goetz, S.J. Multi-Sensor Analysis of NDVI, Surface Temperature and Biophysical Variables at a Mixed Grassland Site. Int J Remote Sens 1997, 18, 71–94, doi:10.1080/014311697219286.
81. Ali, S.; Tong, D.; Xu, Z.T.; Henchiri, M.; Wilson, K.; Siqi, S.; Zhang, J. Characterization of Drought Monitoring Events through MODIS- and TRMM-Based DSI and TVDI over South Asia during 2001–2017. Environmental Science and Pollution Research 2019, 26, 33568–33581, doi:10.1007/S11356-019-06500-4/METRICS.
82. Huete, A.R.; Liu, H.Q. An Error and Sensitivity Analysis of the Atmospheric and Soil-Correcting Variants of the NDVI for the MODISEOS. IEEE Transactions on Geoscience and Remote Sensing 1994, 32, 897–905, doi:10.1109/36.298018.
83. Kumparan Kondisi Geografis Pulau Bali dan Nusa Tenggara Berdasarkan Peta | kumparan.com Available online: https://kumparan.com/kabar-harian/kondisi-geografis-pulau-bali-dan-nusa-tenggara-berdasarkan-peta-22d0HEcXhkq/full (accessed on 7 April 2025).
84. BPS Luas Panen Dan Produksi Padi Di Nusa Tenggara Barat 2023. 2024, 6.
85. Rustiana, S.; Trismidianto; Satyawardhana, H. The Influence of ENSO and IOD during Mesoscale Convective Complex (MCC) to Rainfall in Indonesia. IOP Conf Ser Earth Environ Sci 2019, 303, doi:10.1088/1755-1315/303/1/012006.
86. Hanifa, R.; Wiratmo, J. ENSO and IOD Influence on Extreme Rainfall in Indonesia: Historical and Future Analysis. Agromet 2024, 38, 78–87, doi:10.29244/J.AGROMET.38.2.78-87.
87. Krisnawan G.; Chang Y.; Tsai F.; Tseng K.; Lin T. Meteorological Drivers and Agricultural Drought Diagnosis Based on Surface Information and Precipitation from Satellite Observations in Nusa Tenggara Islands, Indonesia. Remote Sens (Basel), accepted for publication, 2025, 17.
88. Liang, D.; Zuo, Y.; Huang, L.; Zhao, J.; Teng, L.; Yang, F. Evaluation of the Consistency of MODIS Land Cover Product (MCD12Q1) Based on Chinese 30 m GlobeLand30 Datasets: A Case Study in Anhui Province, China. ISPRS International Journal of Geo-Information 2015, Vol. 4, Pages 2519-2541 2015, 4, 2519–2541, doi:10.3390/IJGI4042519.
89. Sharma, R.C.; Hara, K.; Hirayama, H. Improvement of Countrywide Vegetation Mapping over Japan and Comparison to Existing Maps. Advances in Remote Sensing 2018, 07, 163–170, doi:10.4236/ARS.2018.73011.
90. Wan, Z. New Refinements and Validation of the MODIS Land-Surface Temperature/Emissivity Products. Remote Sens Environ 2008, 112, 59–74, doi:10.1016/J.RSE.2006.06.026.
91. Wang, D.; Liang, S.; Zhang, Y.; Gao, X.; Brown, M.G.L.; Jia, A. A New Set of Modis Land Products (Mcd18): Downward Shortwave Radiation and Photosynthetically Active Radiation. Remote Sens (Basel) 2020, 12, doi:10.3390/RS12010168.
92. Prakash, S.; Mishra, A.; Gairola, R.; Varma, A.; Pal, P. Combined Use of Microwave and IR Data for The Study of Indian Monsoon Rainfall-2009. ISPRS Archives XXXVIII-8/W3 Workshop Proceedings: Impact of Climate Change on Agriculture 2009.
93. A. Sobajima Rapidly Developing Cumulus Areas Derivation Algorithm Theoretical Basis Document; 2012;
94. WU Li Classification of Drought Grades Based on Temperature Vegetation Drought Index Using the MODIS Data. Research of Soil and Water Conservation 2017, 24, 130–135.
95. He, Y.; Chen, F.; Jia, H.; Wang, L.; Bondur, V.G. Different Drought Legacies of Rain-Fed and Irrigated Croplands in a Typical Russian Agricultural Region. Remote Sens (Basel) 2020, 12, doi:10.3390/RS12111700.
96. Wu, D.; Zhao, X.; Liang, S.; Zhou, T.; Huang, K.; Tang, B.; Zhao, W. Time-Lag Effects of Global Vegetation Responses to Climate Change. Glob Chang Biol 2015, 21, 3520–3531, doi:10.1111/GCB.12945.
97. Apriyana, Y.; Surmaini, E.; Estiningtyas, W.; Pramudia, A.; Ramadhani, F.; Suciantini, S.; Susanti, E.; Purnamayani, R.; Syahbuddin, H. The Integrated Cropping Calendar Information System: A Coping Mechanism to Climate Variability for Sustainable Agriculture in Indonesia. Sustainability 2021, Vol. 13, Page 6495 2021, 13, 6495, doi:10.3390/SU13116495.
98. Hayashi, K.; Bugayong, I.; Siregar, I.H.; Jonharnas; Wirajaswadi, L.; Hadiawati, L.; Agustiani, N.; Orden, M.E. Appraisal of Rainfed Rice Production and Management Practices through Case Studies in North Sumatera and West Nusa Tenggara, Indonesia. Tropical Agriculture and Development 2018, 62, 43–54, doi:10.11248/JSTA.62.43.
99. Meliani, F.; Sulistyowati, R.; Permata, Z.D.O.; Sumargana, L.; Amaliyah, R.; Purwandani, A.; Putrantijo, N.; Ramadhan, A.R. Preliminary Study on Validation of HIMAWARI-8 Data with Ground Based Rainfall Data at South Sumatera, Indonesia. IOP Conf Ser Earth Environ Sci 2020, 500, doi:10.1088/1755-1315/500/1/012031.
100. Ismanto, R.D.; Prasasti, I.; Fitriana, H.L. Comparison Analysis of Himawari 8, CHIRPS and GSMaP Data to Detect Rain in Indonesia. Jurnal Meteorologi dan Geofisika 2023, 24, 9–17, doi:10.31172/JMG.V24I1.863.
101. Mishra, A.; Gairola, R.; Varma, A.; Agarwal, V.K. Study of Intense Rainfall Events over India Using Kalpana-IR and TRMM-Precipitation Radar Observations; 2014;
102. Lei, H.; Li, H.; Zhao, H.; Ao, T.; Li, X. Comprehensive Evaluation of Satellite and Reanalysis Precipitation Products over the Eastern Tibetan Plateau Characterized by a High Diversity of Topographies. Atmos Res 2021, 259, 105661, doi:10.1016/J.ATMOSRES.2021.105661.
103. Bhattarai, S.; Talchabhadel, R. Comparative Analysis of Satellite-Based Precipitation Data across the CONUS and Hawaii: Identifying Optimal Satellite Performance. Remote Sensing 2024, Vol. 16, Page 3058 2024, 16, 3058, doi:10.3390/RS16163058.
104. Nasrollahi, N. Reducing False Rain in Satellite Precipitation Products Using Cloudsat Cloud Classification Maps and Modis Multi-Spectral Images. 2015, 21–32, doi:10.1007/978-3-319-12081-2_4.
105. Vicente-Serrano, S.M.; Gouveia, C.; Camarero, J.J.; Beguería, S.; Trigo, R.; López-Moreno, J.I.; Azorín-Molina, C.; Pasho, E.; Lorenzo-Lacruz, J.; Revuelto, J.; et al. Response of Vegetation to Drought Time-Scales across Global Land Biomes. Proc Natl Acad Sci U S A 2013, 110, 52–57, doi:10.1073/PNAS.1207068110/-/DCSUPPLEMENTAL.
106. Wang, C.; Chen, J.; Lee, S.C.; Xiong, L.; Su, T.; Lin, Q.; Xu, C.Y. Response and Recovery Times of Vegetation Productivity under Drought Stress: Dominant Factors and Relationships. J Hydrol (Amst) 2025, 655, 132945, doi:10.1016/J.JHYDROL.2025.132945.
107. Ashouri, H.; Hsu, K.L.; Sorooshian, S.; Braithwaite, D.K.; Knapp, K.R.; Cecil, L.D.; Nelson, B.R.; Prat, O.P. PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies. Bull Am Meteorol Soc 2015, 96, 69–83, doi:10.1175/BAMS-D-13-00068.1.
108. Funk, C.; Peterson, P.; Landsfeld, M.; Pedreros, D.; Verdin, J.; Shukla, S.; Husak, G.; Rowland, J.; Harrison, L.; Hoell, A.; et al. The Climate Hazards Infrared Precipitation with Stations—a New Environmental Record for Monitoring Extremes. Scientific Data 2015 2:1 2015, 2, 1–21, doi:10.1038/sdata.2015.66.
109. Suriadi, A.; Mulyani, A.; Hadiawati, L.; Suratman Biophysical Characteristics of Dry-Climate Upland and Agriculture Development Challenges in West Nusa Tenggara and East Nusa Tenggara Provinces. IOP Conf Ser Earth Environ Sci 2021, 648, doi:10.1088/1755-1315/648/1/012014.
110. Karuniasa, M.; Pambudi, P.A. The Analysis of the El Niño Phenomenon in the East Nusa Tenggara Province, Indonesia. Journal of Water and Land Development 2022, 52, 180–185, doi:10.24425/JWLD.2022.140388.
111. Selvaraj, J.; Marimuthu, P.D. Modeling Vegetation Dynamics: Insights from Distributed Lag Model and Spatial Interpolation of Satellite Derived Environmental Data. Lecture Notes in Networks and Systems 2024, 963, 41–51, doi:10.1007/978-981-97-2069-9_4/TABLES/5.