氣象因子與衛星觀測地表資訊和降水量在印尼努沙登加 拉群島的農業乾旱診斷;Meteorological Drivers and Agricultural Drought Diagnosis Based on Surface Information and Precipitation from Satellite Observations in Nusa Tenggara Islands, Indonesia

NCUIR > Research Center > Master of Science Program in Remote Sensing Science and Technology > Electronic Thesis & Dissertation > Item 987654321/98476

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Title:	氣象因子與衛星觀測地表資訊和降水量在印尼努沙登加拉群島的農業乾旱診斷;Meteorological Drivers and Agricultural Drought Diagnosis Based on Surface Information and Precipitation from Satellite Observations in Nusa Tenggara Islands, Indonesia
Authors:	光德;Krisnawan, Gede Dedy
Contributors:	遙測科技碩士學位學程
Keywords:	農業乾旱;氣象因素;TVDI;時間滯後;Agricultural drought;Meteorological factor;TVDI;Temporal Lag
Date:	2025-07-14
Issue Date:	2025-10-17 12:49:26 (UTC+8)
Publisher:	國立中央大學
Abstract:	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.
Appears in Collections:	[Master of Science Program in Remote Sensing Science and Technology ] Electronic Thesis & Dissertation

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