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    μ‹œκ³΅κ°„ 해상도 ν–₯상을 ν†΅ν•œ 식생 λ³€ν™” λͺ¨λ‹ˆν„°λ§

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    ν•™μœ„λ…Όλ¬Έ(박사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : ν™˜κ²½λŒ€ν•™μ› ν˜‘λ™κ³Όμ • μ‘°κ²½ν•™, 2023. 2. λ₯˜μ˜λ ¬.μœ‘μƒ μƒνƒœκ³„μ—μ„œ λŒ€κΈ°κΆŒκ³Ό μƒλ¬ΌκΆŒμ˜ μƒν˜Έ μž‘μš©μ„ μ΄ν•΄ν•˜κΈ° μœ„ν•΄μ„œλŠ” 식생 λ³€ν™”μ˜ λͺ¨λ‹ˆν„°λ§μ΄ ν•„μš”ν•˜λ‹€. 이 λ•Œ, μœ„μ„±μ˜μƒμ€ μ§€ν‘œλ©΄μ„ κ΄€μΈ‘ν•˜μ—¬ 식생지도λ₯Ό μ œκ³΅ν•  수 μžˆμ§€λ§Œ, μ§€ν‘œλ³€ν™”μ˜ μƒμ„Έν•œ μ •λ³΄λŠ” κ΅¬λ¦„μ΄λ‚˜ μœ„μ„± μ΄λ―Έμ§€μ˜ 곡간 해상도에 μ˜ν•΄ μ œν•œλ˜μ—ˆλ‹€. λ˜ν•œ μœ„μ„±μ˜μƒμ˜ μ‹œκ³΅κ°„ 해상도가 식생지도λ₯Ό ν†΅ν•œ κ΄‘ν•©μ„± λͺ¨λ‹ˆν„°λ§μ— λ―ΈμΉ˜λŠ” 영ν–₯은 μ™„μ „νžˆ λ°ν˜€μ§€μ§€ μ•Šμ•˜λ‹€. λ³Έ λ…Όλ¬Έμ—μ„œλŠ” 고해상도 식생 지도λ₯Ό μΌλ‹¨μœ„λ‘œ μƒμ„±ν•˜κΈ° μœ„μ„± μ˜μƒμ˜ μ‹œκ³΅κ°„ 해상도λ₯Ό ν–₯μƒμ‹œν‚€λŠ” 것을 λͺ©ν‘œλ‘œ ν•˜μ˜€λ‹€. 고해상도 μœ„μ„±μ˜μƒμ„ ν™œμš©ν•œ 식생 λ³€ν™” λͺ¨λ‹ˆν„°λ§μ„ μ‹œκ³΅κ°„μ μœΌλ‘œ ν™•μž₯ν•˜κΈ° μœ„ν•΄ 1) 정지ꢀ도 μœ„μ„±μ„ ν™œμš©ν•œ μ˜μƒμœ΅ν•©μ„ 톡해 μ‹œκ°„ν•΄μƒλ„ ν–₯상, 2) μ λŒ€μ μƒμ„±λ„€νŠΈμ›Œν¬λ₯Ό ν™œμš©ν•œ 곡간해상도 ν–₯상, 3) μ‹œκ³΅κ°„ν•΄μƒλ„κ°€ 높은 μœ„μ„±μ˜μƒμ„ 토지피볡이 κ· μ§ˆν•˜μ§€ μ•Šμ€ κ³΅κ°„μ—μ„œ 식물 κ΄‘ν•©μ„± λͺ¨λ‹ˆν„°λ§μ„ μˆ˜ν–‰ν•˜μ˜€λ‹€. 이처럼, μœ„μ„±κΈ°λ°˜ μ›κ²©νƒμ§€μ—μ„œ μƒˆλ‘œμš΄ 기술이 λ“±μž₯함에 따라 ν˜„μž¬ 및 과거의 μœ„μ„±μ˜μƒμ€ μ‹œκ³΅κ°„ 해상도 μΈ‘λ©΄μ—μ„œ ν–₯μƒλ˜μ–΄ 식생 λ³€ν™”μ˜ λͺ¨λ‹ˆν„°λ§ ν•  수 μžˆλ‹€. 제2μž₯μ—μ„œλŠ” μ •μ§€κΆ€λ„μœ„μ„±μ˜μƒμ„ ν™œμš©ν•˜λŠ” μ‹œκ³΅κ°„ μ˜μƒμœ΅ν•©μœΌλ‘œ μ‹λ¬Όμ˜ 광합성을 λͺ¨λ‹ˆν„°λ§ ν–ˆμ„ λ•Œ, μ‹œκ°„ν•΄μƒλ„κ°€ ν–₯상됨을 λ³΄μ˜€λ‹€. μ‹œκ³΅κ°„ μ˜μƒμœ΅ν•© μ‹œ, ꡬ름탐지, μ–‘λ°©ν–₯ λ°˜μ‚¬ ν•¨μˆ˜ μ‘°μ •, 곡간 등둝, μ‹œκ³΅κ°„ μœ΅ν•©, μ‹œκ³΅κ°„ 결츑치 보완 λ“±μ˜ 과정을 κ±°μΉœλ‹€. 이 μ˜μƒμœ΅ν•© μ‚°μΆœλ¬Όμ€ κ²½μž‘κ΄€λ¦¬ λ“±μœΌλ‘œ 식생 μ§€μˆ˜μ˜ μ—°κ°„ 변동이 큰 두 μž₯μ†Œ(농경지와 λ‚™μ—½μˆ˜λ¦Ό)μ—μ„œ ν‰κ°€ν•˜μ˜€λ‹€. κ·Έ κ²°κ³Ό, μ‹œκ³΅κ°„ μ˜μƒμœ΅ν•© μ‚°μΆœλ¬Όμ€ 결츑치 없이 ν˜„μž₯관츑을 μ˜ˆμΈ‘ν•˜μ˜€λ‹€ (R2 = 0.71, μƒλŒ€ 편ν–₯ = 5.64% 농경지; R2 = 0.79, μƒλŒ€ 편ν–₯ = -13.8%, ν™œμ—½μˆ˜λ¦Ό). μ‹œκ³΅κ°„ μ˜μƒμœ΅ν•©μ€ 식생 μ§€λ„μ˜ μ‹œκ³΅κ°„ 해상도λ₯Ό μ μ§„μ μœΌλ‘œ κ°œμ„ ν•˜μ—¬, 식물 생μž₯κΈ°λ™μ•ˆ μœ„μ„±μ˜μƒμ΄ ν˜„μž₯ 관츑을 κ³Όμ†Œ 평가λ₯Ό μ€„μ˜€λ‹€. μ˜μƒμœ΅ν•©μ€ 높은 μ‹œκ³΅κ°„ ν•΄μƒλ„λ‘œ κ΄‘ν•©μ„± 지도λ₯Ό μΌκ°„κ²©μœΌλ‘œ μƒμ„±ν•˜κΈ°μ— 이λ₯Ό ν™œμš©ν•˜μ—¬ μœ„μ„± μ˜μƒμ˜ μ œν•œλœ μ‹œκ³΅κ°„ ν•΄μƒλ„λ‘œ λ°ν˜€μ§€μ§€ μ•Šμ€ μ‹λ¬Όλ³€ν™”μ˜ 과정을 λ°œκ²¬ν•˜κΈΈ κΈ°λŒ€ν•œλ‹€. μ‹μƒμ˜ 곡간뢄포은 정밀농업과 토지 피볡 λ³€ν™” λͺ¨λ‹ˆν„°λ§μ„ μœ„ν•΄ ν•„μˆ˜μ μ΄λ‹€. 고해상도 μœ„μ„±μ˜μƒμœΌλ‘œ 지ꡬ ν‘œλ©΄μ„ κ΄€μΈ‘ν•˜λŠ” 것을 μš©μ΄ν•˜κ²Œ ν•΄μ‘Œλ‹€. 특히 Planet Fusion은 μ΄ˆμ†Œν˜•μœ„μ„±κ΅° 데이터λ₯Ό μ΅œλŒ€ν•œ ν™œμš©ν•΄ 데이터 결츑이 μ—†λŠ” 3m 곡간 ν•΄μƒλ„μ˜ μ§€ν‘œ ν‘œλ©΄ λ°˜μ‚¬λ„μ΄λ‹€. κ·ΈλŸ¬λ‚˜ κ³Όκ±° μœ„μ„± μ„Όμ„œ(Landsat의 경우 30~60m)의 곡간 ν•΄μƒλ„λŠ” μ‹μƒμ˜ 곡간적 λ³€ν™”λ₯Ό 상세 λΆ„μ„ν•˜λŠ” 것을 μ œν•œν–ˆλ‹€. 제3μž₯μ—μ„œλŠ” Landsat λ°μ΄ν„°μ˜ 곡간 해상도λ₯Ό ν–₯μƒν•˜κΈ° μœ„ν•΄ Planet Fusion 및 Landsat 8 데이터λ₯Ό μ‚¬μš©ν•˜μ—¬ 이쀑 μ λŒ€μ  생성 λ„€νŠΈμ›Œν¬(the dual RSS-GAN)λ₯Ό ν•™μŠ΅μ‹œμΌœ, 고해상도 μ •κ·œν™” 식생 μ§€μˆ˜(NDVI)와 식물 근적외선 λ°˜μ‚¬(NIRv)도λ₯Ό μƒμ„±ν•˜λŠ” ν•œλ‹€. νƒ€μ›ŒκΈ°λ°˜ ν˜„μž₯ μ‹μƒμ§€μˆ˜(μ΅œλŒ€ 8λ…„)와 λ“œλ‘ κΈ°λ°˜ μ΄ˆλΆ„κ΄‘μ§€λ„λ‘œ the dual RSS-GAN의 μ„±λŠ₯을 λŒ€ν•œλ―Όκ΅­ λ‚΄ 두 λŒ€μƒμ§€(농경지와 ν™œμ—½μˆ˜λ¦Ό)μ—μ„œ ν‰κ°€ν–ˆλ‹€. The dual RSS-GAN은 Landsat 8 μ˜μƒμ˜ 곡간해상도λ₯Ό ν–₯μƒμ‹œμΌœ 곡간 ν‘œν˜„μ„ λ³΄μ™„ν•˜κ³  식생 μ§€μˆ˜μ˜ κ³„μ ˆμ  λ³€ν™”λ₯Ό ν¬μ°©ν–ˆλ‹€(R2> 0.96). 그리고 the dual RSS-GAN은 Landsat 8 식생 μ§€μˆ˜κ°€ ν˜„μž₯에 λΉ„ν•΄ κ³Όμ†Œ ν‰κ°€λ˜λŠ” 것을 μ™„ν™”ν–ˆλ‹€. ν˜„μž₯ 관츑에 λΉ„ν•΄ 이쀑 RSS-GANκ³Ό Landsat 8의 μƒλŒ€ 편ν–₯ κ°’ 각각 -0.8% μ—μ„œ -1.5%, -10.3% μ—μ„œ -4.6% μ˜€λ‹€. μ΄λŸ¬ν•œ κ°œμ„ μ€ Planet Fusion의 곡간정보λ₯Ό 이쀑 RSS-GAN둜 ν•™μŠ΅ν•˜μ˜€κΈ°μ— κ°€λŠ₯ν–ˆλ‹€. ν—€λ‹Ή 연ꡬ κ²°κ³ΌλŠ” Landsat μ˜μƒμ˜ 곡간 해상도λ₯Ό ν–₯μƒμ‹œμΌœ μˆ¨κ²¨μ§„ 곡간 정보λ₯Ό μ œκ³΅ν•˜λŠ” μƒˆλ‘œμš΄ μ ‘κ·Ό 방식이닀. κ³ ν•΄μƒλ„μ—μ„œ 식물 κ΄‘ν•©μ„± μ§€λ„λŠ” 토지피볡이 λ³΅μž‘ν•œ κ³΅κ°„μ—μ„œ νƒ„μ†Œ μˆœν™˜ λͺ¨λ‹ˆν„°λ§μ‹œ ν•„μˆ˜μ μ΄λ‹€. κ·ΈλŸ¬λ‚˜ Sentinel-2, Landsat 및 MODIS와 같이 νƒœμ–‘ 동쑰 ꢀ도에 μžˆλŠ” μœ„μ„±μ€ 곡간 해상도가 λ†’κ±°λ‚˜ μ‹œκ°„ 해상도 높은 μœ„μ„±μ˜μƒλ§Œ μ œκ³΅ν•  수 μžˆλ‹€. 졜근 λ°œμ‚¬λœ μ΄ˆμ†Œν˜•μœ„μ„±κ΅°μ€ μ΄λŸ¬ν•œ 해상도 ν•œκ³„μ„ 극볡할 수 μžˆλ‹€. 특히 Planet Fusion은 μ΄ˆμ†Œν˜•μœ„μ„± 자료의 μ‹œκ³΅κ°„ ν•΄μƒλ„λ‘œ μ§€ν‘œλ©΄μ„ κ΄€μΈ‘ν•  수 μžˆλ‹€. 4μž₯μ—μ„œ, Planet Fusion μ§€ν‘œλ°˜μ‚¬λ„λ₯Ό μ΄μš©ν•˜μ—¬ μ‹μƒμ—μ„œ λ°˜μ‚¬λœ 근적외선 볡사(NIRvP)λ₯Ό 3m 해상도 지도λ₯Ό μΌκ°„κ²©μœΌλ‘œ μƒμ„±ν–ˆλ‹€. 그런 λ‹€μŒ λ―Έκ΅­ μΊ˜λ¦¬ν¬λ‹ˆμ•„μ£Ό μƒˆν¬λΌλ©˜ν† -μƒŒ ν˜Έμ•„ν‚¨ λΈνƒ€μ˜ ν”ŒλŸ­μŠ€ νƒ€μ›Œ λ„€νŠΈμ›Œν¬ 데이터와 λΉ„κ΅ν•˜μ—¬ 식물 광합성을 μΆ”μ •ν•˜κΈ° μœ„ν•œ NIRvP μ§€λ„μ˜ μ„±λŠ₯을 ν‰κ°€ν•˜μ˜€λ‹€. μ „μ²΄μ μœΌλ‘œ NIRvP μ§€λ„λŠ” μŠ΅μ§€μ˜ μž¦μ€ μˆ˜μœ„ 변화에도 λΆˆκ΅¬ν•˜κ³  κ°œλ³„ λŒ€μƒμ§€μ˜ 식물 κ΄‘ν•©μ„±μ˜ μ‹œκ°„μ  λ³€ν™”λ₯Ό ν¬μ°©ν•˜μ˜€λ‹€. κ·ΈλŸ¬λ‚˜ λŒ€μƒμ§€ 전체에 λŒ€ν•œ NIRvP 지도와 식물 κ΄‘ν•©μ„± μ‚¬μ΄μ˜ κ΄€κ³„λŠ” NIRvP 지도λ₯Ό ν”ŒλŸ­μŠ€ νƒ€μ›Œ κ΄€μΈ‘λ²”μœ„μ™€ μΌμΉ˜μ‹œν‚¬ λ•Œλ§Œ 높은 상관관계λ₯Ό λ³΄μ˜€λ‹€. κ΄€μΈ‘λ²”μœ„λ₯Ό μΌμΉ˜μ‹œν‚¬ 경우, NIRvP μ§€λ„λŠ” 식물 광합성을 μΆ”μ •ν•˜λŠ” 데 μžˆμ–΄ ν˜„μž₯ NIRvP보닀 μš°μˆ˜ν•œ μ„±λŠ₯을 λ³΄μ˜€λ‹€. μ΄λŸ¬ν•œ μ„±λŠ₯ μ°¨μ΄λŠ” ν”ŒλŸ­μŠ€ νƒ€μ›Œ κ΄€μΈ‘λ²”μœ„λ₯Ό μΌμΉ˜μ‹œν‚¬ λ•Œ, 연ꡬ λŒ€μƒμ§€ κ°„μ˜ NIRvP-식물 κ΄‘ν•©μ„± κ΄€κ³„μ˜ κΈ°μšΈκΈ°κ°€ 일관성을 λ³΄μ˜€κΈ° λ•Œλ¬Έμ΄λ‹€. λ³Έ 연ꡬ κ²°κ³ΌλŠ” μœ„μ„± 관츑을 ν”ŒλŸ­μŠ€ νƒ€μ›Œ κ΄€μΈ‘λ²”μœ„μ™€ μΌμΉ˜μ‹œν‚€λŠ” κ²ƒμ˜ μ€‘μš”μ„±μ„ 보여주고 높은 μ‹œκ³΅κ°„ ν•΄μƒλ„λ‘œ 식물 광합성을 μ›κ²©μœΌλ‘œ λͺ¨λ‹ˆν„°λ§ν•˜λŠ” μ΄ˆμ†Œν˜•μœ„μ„±κ΅° 자료의 잠재λ ₯을 보여쀀닀.Monitoring changes in terrestrial vegetation is essential to understanding interactions between atmosphere and biosphere, especially terrestrial ecosystem. To this end, satellite remote sensing offer maps for examining land surface in different scales. However, the detailed information was hindered under the clouds or limited by the spatial resolution of satellite imagery. Moreover, the impacts of spatial and temporal resolution in photosynthesis monitoring were not fully revealed. In this dissertation, I aimed to enhance the spatial and temporal resolution of satellite imagery towards daily gap-free vegetation maps with high spatial resolution. In order to expand vegetation change monitoring in time and space using high-resolution satellite images, I 1) improved temporal resolution of satellite dataset through image fusion using geostationary satellites, 2) improved spatial resolution of satellite dataset using generative adversarial networks, and 3) showed the use of high spatiotemporal resolution maps for monitoring plant photosynthesis especially over heterogeneous landscapes. With the advent of new techniques in satellite remote sensing, current and past datasets can be fully utilized for monitoring vegetation changes in the respect of spatial and temporal resolution. In Chapter 2, I developed the integrated system that implemented geostationary satellite products in the spatiotemporal image fusion method for monitoring canopy photosynthesis. The integrated system contains the series of process (i.e., cloud masking, nadir bidirectional reflectance function adjustment, spatial registration, spatiotemporal image fusion, spatial gap-filling, temporal-gap-filling). I conducted the evaluation of the integrated system over heterogeneous rice paddy landscape where the drastic land cover changes were caused by cultivation management and deciduous forest where consecutive changes occurred in time. The results showed that the integrated system well predict in situ measurements without data gaps (R2 = 0.71, relative bias = 5.64% at rice paddy site; R2 = 0.79, relative bias = -13.8% at deciduous forest site). The integrated system gradually improved the spatiotemporal resolution of vegetation maps, reducing the underestimation of in situ measurements, especially during peak growing season. Since the integrated system generates daily canopy photosynthesis maps for monitoring dynamics among regions of interest worldwide with high spatial resolution. I anticipate future efforts to reveal the hindered information by the limited spatial and temporal resolution of satellite imagery. Detailed spatial representations of terrestrial vegetation are essential for precision agricultural applications and the monitoring of land cover changes in heterogeneous landscapes. The advent of satellite-based remote sensing has facilitated daily observations of the Earths surface with high spatial resolution. In particular, a data fusion product such as Planet Fusion has realized the delivery of daily, gap-free surface reflectance data with 3-m pixel resolution through full utilization of relatively recent (i.e., 2018-) CubeSat constellation data. However, the spatial resolution of past satellite sensors (i.e., 30–60 m for Landsat) has restricted the detailed spatial analysis of past changes in vegetation. In Chapter 3, to overcome the spatial resolution constraint of Landsat data for long-term vegetation monitoring, we propose a dual remote-sensing super-resolution generative adversarial network (dual RSS-GAN) combining Planet Fusion and Landsat 8 data to simulate spatially enhanced long-term time-series of the normalized difference vegetation index (NDVI) and near-infrared reflectance from vegetation (NIRv). We evaluated the performance of the dual RSS-GAN against in situ tower-based continuous measurements (up to 8 years) and remotely piloted aerial system-based maps of cropland and deciduous forest in the Republic of Korea. The dual RSS-GAN enhanced spatial representations in Landsat 8 images and captured seasonal variation in vegetation indices (R2 > 0.95, for the dual RSS-GAN maps vs. in situ data from all sites). Overall, the dual RSS-GAN reduced Landsat 8 vegetation index underestimations compared with in situ measurements; relative bias values of NDVI ranged from βˆ’3.2% to 1.2% and βˆ’12.4% to βˆ’3.7% for the dual RSS-GAN and Landsat 8, respectively. This improvement was caused by spatial enhancement through the dual RSS-GAN, which captured fine-scale information from Planet Fusion. This study presents a new approach for the restoration of hidden sub-pixel spatial information in Landsat images. Mapping canopy photosynthesis in both high spatial and temporal resolution is essential for carbon cycle monitoring in heterogeneous areas. However, well established satellites in sun-synchronous orbits such as Sentinel-2, Landsat and MODIS can only provide either high spatial or high temporal resolution but not both. Recently established CubeSat satellite constellations have created an opportunity to overcome this resolution trade-off. In particular, Planet Fusion allows full utilization of the CubeSat data resolution and coverage while maintaining high radiometric quality. In Chapter 4, I used the Planet Fusion surface reflectance product to calculate daily, 3-m resolution, gap-free maps of the near-infrared radiation reflected from vegetation (NIRvP). I then evaluated the performance of these NIRvP maps for estimating canopy photosynthesis by comparing with data from a flux tower network in Sacramento-San Joaquin Delta, California, USA. Overall, NIRvP maps captured temporal variations in canopy photosynthesis of individual sites, despite changes in water extent in the wetlands and frequent mowing in the crop fields. When combining data from all sites, however, I found that robust agreement between NIRvP maps and canopy photosynthesis could only be achieved when matching NIRvP maps to the flux tower footprints. In this case of matched footprints, NIRvP maps showed considerably better performance than in situ NIRvP in estimating canopy photosynthesis both for daily sum and data around the time of satellite overpass (R2 = 0.78 vs. 0.60, for maps vs. in situ for the satellite overpass time case). This difference in performance was mostly due to the higher degree of consistency in slopes of NIRvP-canopy photosynthesis relationships across the study sites for flux tower footprint-matched maps. Our results show the importance of matching satellite observations to the flux tower footprint and demonstrate the potential of CubeSat constellation imagery to monitor canopy photosynthesis remotely at high spatio-temporal resolution.Chapter 1. Introduction 2 1. Background 2 1.1 Daily gap-free surface reflectance using geostationary satellite products 2 1.2 Monitoring past vegetation changes with high-spatial-resolution 3 1.3 High spatiotemporal resolution vegetation photosynthesis maps 4 2. Purpose of Research 4 Chapter 2. Generating daily gap-filled BRDF adjusted surface reflectance product at 10 m resolution using geostationary satellite product for monitoring daily canopy photosynthesis 6 1. Introduction 6 2. Methods 11 2.1 Study sites 11 2.2 In situ measurements 13 2.3 Satellite products 14 2.4 Integrated system 17 2.5 Canopy photosynthesis 21 2.6 Evaluation 23 3. Results and discussion 24 3.1 Comparison of STIF NDVI and NIRv with in situ NDVI and NIRv 24 3.2 Comparison of STIF NIRvP with in situ NIRvP 28 4. Conclusion 31 Chapter 3. Super-resolution of historic Landsat imagery using a dual Generative Adversarial Network (GAN) model with CubeSat constellation imagery for monitoring vegetation changes 32 1. Introduction 32 2. Methods 38 2.1 Real-ESRGAN model 38 2.2 Study sites 40 2.3 In situ measurements 42 2.4 Vegetation index 44 2.5 Satellite data 45 2.6 Planet Fusion 48 2.7 Dual RSS-GAN via fine-tuned Real-ESRGAN 49 2.8 Evaluation 54 3. Results 57 3.1 Comparison of NDVI and NIRv maps from Planet Fusion, Sentinel 2 NBAR, and Landsat 8 NBAR data with in situ NDVI and NIRv 57 3.2 Comparison of dual RSS-SRGAN model results with Landsat 8 NDVI and NIRv 60 3.3 Comparison of dual RSS-GAN model results with respect to in situ time-series NDVI and NIRv 63 3.4 Comparison of the dual RSS-GAN model with NDVI and NIRv maps derived from RPAS 66 4. Discussion 70 4.1 Monitoring changes in terrestrial vegetation using the dual RSS-GAN model 70 4.2 CubeSat data in the dual RSS-GAN model 72 4.3 Perspectives and limitations 73 5. Conclusion 78 Appendices 79 Supplementary material 82 Chapter 4. Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates 85 1. Introduction 85 2. Methods 89 2.1 Study sites 89 2.2 In situ measurements 92 2.3 Planet Fusion NIRvP 94 2.4 Flux footprint model 98 2.5 Evaluation 98 3. Results 105 3.1 Comparison of Planet Fusion NIRv and NIRvP with in situ NIRv and NIRvP 105 3.2 Comparison of instantaneous Planet Fusion NIRv and NIRvP with against tower GPP estimates 108 3.3 Daily GPP estimation from Planet Fusion -derived NIRvP 114 4. Discussion 118 4.1 Flux tower footprint matching and effects of spatial and temporal resolution on GPP estimation 118 4.2 Roles of radiation component in GPP mapping 123 4.3 Limitations and perspectives 126 5. Conclusion 133 Appendix 135 Supplementary Materials 144 Chapter 5. Conclusion 153 Bibliography 155 Abstract in Korea 199 Acknowledgements 202λ°•

    Data fusion in agriculture: resolving ambiguities and closing data gaps.

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    Abstract. Acquiring useful data from agricultural areas has always been somewhat of a challenge, as these are often expansive, remote, and vulnerable to weather events. Despite these challenges, as technologies evolve and prices drop, a surge of new data are being collected. Although a wealth of data are being collected at different scales (i.e., proximal, aerial, satellite, ancillary data), this has been geographically unequal, causing certain areas to be virtually devoid of useful data to help face their specific challenges. However, even in areas with available resources and good infrastructure, data and knowledge gaps are still prevalent, because agricultural environments are mostly uncontrolled and there are vast numbers of factors that need to be taken into account and properly measured for a full characterization of a given area. As a result, data from a single sensor type are frequently unable to provide unambiguous answers, even with very effective algorithms, and even if the problem at hand is well defined and limited in scope. Fusing the information contained in different sensors and in data from different types is one possible solution that has been explored for some decades. The idea behind data fusion involves exploring complementarities and synergies of different kinds of data in order to extract more reliable and useful information about the areas being analyzed. While some success has been achieved, there are still many challenges that prevent a more widespread adoption of this type of approach. This is particularly true for the highly complex environments found in agricultural areas. In this article, we provide a comprehensive overview on the data fusion applied to agricultural problems; we present the main successes, highlight the main challenges that remain, and suggest possible directions for future research.Article number: 2285

    Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

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    Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

    Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

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    Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

    Remote Sensing

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    This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

    Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

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    This work was supported by the projects: "VIRTUOUS" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181, "SUSTAINABLE" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702 and the "Project of Excellence" from Junta de Andalucia 2020. Ref. P18-H0-4700. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of β€œzero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.European Commission 101007702 872181Junta de Andalucia P18-H0-470

    Remote Sensing of Plant Biodiversity

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    At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imageryβ€”but global coverageβ€”of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally. This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plantsβ€”primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution. The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity. Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely. Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understandingβ€”that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON). This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earthβ€”just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequateβ€”and globalβ€”measures of what we are losing

    Remote Sensing of Plant Biodiversity

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    This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluatedβ€”focusing particularly on plantsβ€”using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale

    Deep Learning-Based Machinery Fault Diagnostics

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    This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

    A Range of Earth Observation Techniques for Assessing Plant Diversity

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    AbstractVegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS
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