An Emulator Toolbox to Approximate Radiative Transfer Models with Statistical Learning

Physically-based radiative transfer models (RTMs) help in understanding the processes occurring on the Earth’s surface and their interactions with vegetation and atmosphere. When it comes to studying vegetation properties, RTMs allows us to study light interception by plant canopies and are used in the retrieval of biophysical variables through model inversion. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical models that approximate the functioning of RTMs. Emulators are advantageous in real practice because of the computational efficiency and excellent accuracy and flexibility for extrapolation. We hereby present an “Emulator toolbox” that enables analysing multi-output machine learning regression algorithms (MO-MLRAs) on their ability to approximate an RTM. The toolbox is included in the free-access ARTMO’s MATLAB suite for parameter retrieval and model inversion and currently contains both linear and non-linear MO-MLRAs, namely partial least squares regression (PLSR), kernel ridge regression (KRR) and neural networks (NN). These MO-MLRAs have been evaluated on their precision and speed to approximate the soil vegetation atmosphere transfer model SCOPE (Soil Canopy Observation, Photochemistry and Energy balance). SCOPE generates, amongst others, sun-induced chlorophyll fluorescence as the output signal. KRR and NN were evaluated as capable of reconstructing fluorescence spectra with great precision. Relative errors fell below 0.5% when trained with 500 or more samples using cross-validation and principal component analysis to alleviate the underdetermination problem. Moreover, NN reconstructed fluorescence spectra about 50-times faster and KRR about 800-times faster than SCOPE. The Emulator toolbox is foreseen to open new opportunities in the use of advanced RTMs, in which both consistent physical assumptions and data-driven machine learning algorithms live together

Decomposing reflectance spectra to track gross primary production in a subalpine evergreen forest

Photosynthesis by terrestrial plants represents the majority of CO₂ uptake on Earth, yet it is difficult to measure directly from space. Estimation of gross primary production (GPP) from remote sensing indices represents a primary source of uncertainty, in particular for observing seasonal variations in evergreen forests. Recent vegetation remote sensing techniques have highlighted spectral regions sensitive to dynamic changes in leaf/needle carotenoid composition, showing promise for tracking seasonal changes in photosynthesis of evergreen forests. However, these have mostly been investigated with intermittent field campaigns or with narrow-band spectrometers in these ecosystems. To investigate this potential, we continuously measured vegetation reflectance (400–900 nm) using a canopy spectrometer system, PhotoSpec, mounted on top of an eddy-covariance flux tower in a subalpine evergreen forest at Niwot Ridge, Colorado, USA. We analyzed driving spectral components in the measured canopy reflectance using both statistical and process-based approaches. The decomposed spectral components co-varied with carotenoid content and GPP, supporting the interpretation of the photochemical reflectance index (PRI) and the chlorophyll/carotenoid index (CCI). Although the entire 400–900 nm range showed additional spectral changes near the red edge, it did not provide significant improvements in GPP predictions. We found little seasonal variation in both normalized difference vegetation index (NDVI) and the near-infrared vegetation index (NIRv) in this ecosystem. In addition, we quantitatively determined needle-scale chlorophyll-to-carotenoid ratios as well as anthocyanin contents using full-spectrum inversions, both of which were tightly correlated with seasonal GPP changes. Reconstructing GPP from vegetation reflectance using partial least-squares regression (PLSR) explained approximately 87 % of the variability in observed GPP. Our results linked the seasonal variation in reflectance to the pool size of photoprotective pigments, highlighting all spectral locations within 400–900 nm associated with GPP seasonality in evergreen forests

근접 표면 원격 센싱 시스템들을 이용한 지속적 식물 계절 및 태양 유도 엽록소 형광물질 관측

학위논문(박사) -- 서울대학교대학원 : 환경대학원 협동과정 조경학, 2022.2. 류영렬.Monitoring phenology, physiological and structural changes in vegetation is essential to understand feedbacks of vegetation between terrestrial ecosystems and the atmosphere by influencing the albedo, carbon flux, water flux and energy. To this end, normalized difference vegetation index (NDVI) and solar-induced chlorophyll fluorescence (SIF) from satellite remote sensing have been widely used. However, there are still limitations in satellite remote sensing as 1) satellite imagery could not capture fine-scale spatial resolution of SIF signals, 2) satellite products are strongly influenced by condition of the atmosphere (e.g. clouds), thus it is challenging to know physiological and structural changes in vegetation on cloudy days and 3) satellite imagery captured a mixed signal from over- and understory, thus it is difficult to study the difference between overstory and understory phenology separately. Therefore, in order to more accurately understand the signals observed from the satellite, further studies using near-surface remote sensing system to collect ground-based observed data are needed. The main purpose of this dissertation is continuous observation of vegetation phenology and SIF using near-surface remote sensing system. To achieve the main goal, I set three chapters as 1) developing low-cost filter-based near-surface remote sensing system to monitor SIF continuously, 2) monitoring SIF in a temperate evergreen needleleaf forest continuously, and 3) understanding the relationships between phenology from in-situ multi-layer canopies and satellite products. In Chapter 2, I developed the filter-based smart surface sensing system (4S-SIF) to overcome the technical challenges of monitoring SIF in the field as well as to decrease sensor cost for more comprehensive spatial sampling. I verified the satisfactory spectral performance of the bandpass filters and confirmed that digital numbers (DN) from 4S-SIF exhibited linear relationships with the DN from the hyperspectral spectroradiometer in each band (R2 > 0.99). In addition, we confirmed that 4S-SIF shows relatively low variation of dark current value at various temperatures. Furthermore, the SIF signal from 4S-SIF represents a strong linear relationship with QEpro-SIF either changing the physiological mechanisms of the plant using DCMU (3-(3, 4-dichlorophenyl)-1, 1-dimethyurea) treatment. I believe that 4S-SIF will be a useful tool for collecting in-situ data across multiple spatial and temporal scales. Satellite-based SIF provides us with new opportunities to understand the physiological and structural dynamics of vegetation from canopy to global scales. However, the relationships between SIF and gross primary productivity (GPP) are not fully understood, which is mainly due to the challenges of decoupling structural and physiological factors that control the relationships. In Chapter 3, I reported the results of continuous observations of canopy-level SIF, GPP, absorbed photosynthetically active radiation (APAR), and chlorophyll: carotenoid index (CCI) in a temperate evergreen needleleaf forest. To understand the mechanisms underlying the relationship between GPP and SIF, I investigated the relationships of light use efficiency (LUE_p), chlorophyll fluorescence yield (Φ_F), and the fraction of emitted SIF photons escaping from the canopy (f_esc) separately. I found a strongly non-linear relationship between GPP and SIF at diurnal and seasonal time scales (R2 = 0.91 with a hyperbolic regression function, daily). GPP saturated with APAR, while SIF did not. In addition, there were differential responses of LUE_p and Φ_F to air temperature. While LUE_p reached saturation at high air temperatures, Φ_F did not saturate. I also found that the canopy-level chlorophyll: carotenoid index was strongly correlated to canopy-level Φ_F (R2 = 0.84) implying that Φ_F could be more closely related to pigment pool changes rather than LUE_p. In addition, I found that the f_esc contributed to a stronger SIF-GPP relationship by partially capturing the response of LUE_p to diffuse light. These findings can help refine physiological and structural links between canopy-level SIF and GPP in evergreen needleleaf forests. We do not fully understand what satellite NDVI derived leaf-out and full leaf dates actually observe because deciduous broadleaf forest consists of multi-layer canopies typically and mixed-signal from multi-layer canopies could affect satellite observation. Ultimately, we have the following question: What phenology do we actually see from space compared to ground observations on multi-layer canopy phenology? In Chapter 4, I reported the results of 8 years of continuous observations of multi-layer phenology and climate variables in a deciduous broadleaf forest, South Korea. Multi-channel spectrometers installed above and below overstory canopy allowed us to monitor over- and understory canopy phenology separately, continuously. I evaluated the widely used phenology detection methods, curvature change rate and threshold with NDVI observed above top of the canopy and compared leaf-out and full leaf dates from both methods to in-situ observed multi-layer phenology. First, I found that NDVI from the above canopy had a strong linear relationship with satellites NDVI (R2=0.95 for MODIS products and R2= 0.85 for Landsat8). Second, leaf-out dates extracted by the curvature change rate method and 10% threshold were well matched with understory leaf-out dates. Third, the full-leaf dates extracted by the curvature change rate method and 90% threshold were similar to overstory full-leaf dates. Furthermore, I found that overstory leaf-out dates were closely correlated to accumulated growing degree days (AGDD) while understory leaf-out dates were related to AGDD and also sensitive to the number of chill days (NCD). These results suggest that satellite-based leaf-out and full leaf dates represent understory and overstory signals in the deciduous forest site, which requires caution when using satellite-based phenology data into future prediction as overstory and understory canopy show different sensitivities to AGDD and NCD.식물 계절 및 식생의 생리학적, 구조적인 변화를 지속적으로 모니터링 하는 것은 육상생태계와 대기권 사이의 에너지, 탄소 순환 등의 피드백을 이해하는데 필수적이다. 이를 관측하기 위하여 위성에서 관측된 정규화 식생 지수 (NDVI) 태양 유도 엽록소 형광물질 (SIF)는 대중적으로 사용되고 있다. 그러나, 우주 위성 기반의 자료는 다음과 같은 한계점들이 존재한다. 1) 아직까지 고해상도의 위성 기반 SIF 자료는 없고, 2) 위성 자료들은 대기의 질 (예, 구름)에 영향을 받아, 흐린 날의 식생의 생리학적, 구조적 변화를 탐지하기 힘들다. 또한, 3) 위성 이미지는 상부 식생과 하부 식생이 혼합되어 섞인 신호를 탐지하기 때문에, 각 층의 식물 계절을 각각 연구하기에 어려움이 있다. 그러므로, 위성에서 탐지한 신호를 평가하고, 식생의 생리학적, 구조적 변화를 보다 정확히 이해하기 위해서는 근접 표면 원격 센싱 시스템을 이용한 실측 자료 기반의 연구들이 요구된다. 본 학위논문의 주 목적은 근접 표면 원격 센싱 시스템을 이용하여 식물 계절 및 SIF를 현장에서 지속적으로 실측하고, 위성 영상 기반의 연구가 갖고 있는 한계점 및 궁금증들을 해결 및 보완하는 것이다. 이 목적을 달성하기 위하여, 아래와 같은 세가지 Chapter: 1) SIF를 관측하기 위한 필터 기반의 저렴한 근접 표면 센싱 시스템 개발, 2)온대 침엽수림에서의 연속적인 SIF 관측, 3)위성 기반의 식물 계절과 실측한 다층 식생의 식물 계절 비교로 구성하고, 이를 진행하였다. SIF는 식생의 구조적, 생리학적 변화를 이해하고, 추정하는 인자로 사용될 수 있어, SIF를 현장에서 관측하기 위한 다양한 근접 표면 원격 센싱 시스템들이 최근 제시되어 오고 있다. 그러나, 아직까지 SIF를 관측하기 위한 상업적으로 유통되는 관측 시스템은 현저히 부족하며, 분광계의 구조적 특성상 현장에서 관측 시스템을 보정 및 관리하기가 어려워 높은 질의 SIF를 취득하는 것은 매우 도전 적인 분야이다. 본 학위 논문의 Chapter 2에서는 SIF를 현장에서 보다 손쉽게 관측하기 위한 필터 기반의 근접 표면 센싱 시스템(Smart Surface Sensing System, 4S-SIF)을 개발하였다. 센서는 대역 필터들과 포토다이오드가 결합되어 있으며, 서보 모터를 사용하여 대역 필터 및 관측 방향을 자동적으로 변경함으로써, 한 개의 포토다이오드가 3개의 파장 범위(757, 760, 770 nm)의 빛 및 태양으로부터 입사되는 광량과 식생으로 반사/방출된 광량을 관측할 수 있도록 고안되었다. 포토다이오드로부터 인식된 디지털 수치 값은 상업적으로 판매되는 초고해상도 분광계(QE Pro, Ocean Insight)와 뚜렷한 선형 관계를 보이는 것을 확인하였다 (R2 > 0.99). 추가적으로, 4S-SIF에서 관측된 SIF와 초고해상도 분광계를 이용하여 추출한 SIF가 선형적인 관계를 이루는 것을 확인하였다. 식생의 생리학적 변화를 일으키는 화학 물질인 DCMU(3-(3, 4-dichlorophenyl)-1, 1-dimethyurea)을 처리했음에도 불구하고, 산출된 SIF들은 선형 관계를 보였다. 본 센서는 기존 시스템들에 비해 사용하기 쉽고 간단하며, 저렴하기 때문에 다양한 시공간적 스케일의 SIF를 관측할 수 있다는 장점이 있다. 위성 기반의 SIF를 이용하여 총일차생산성(gross primary productivity, GPP)을 추정하는 연구는 최근 탄소 순환 연구 분야에서 각광받고 있는 연구 주제이다. 그러나, SIF와 GPP의 관계는 여전히 많은 불확실성을 지니고 있는데, 이는 SIF-GPP 관계를 조절하는 식생의 구조적 및 생리학적 요인을 따로 분리하여 고찰한 연구들이 부족하기 때문이다. 본 학위 논문의 Chapter 3에서는 지속적으로 SIF, GPP, 흡수된 광합성유효복사량 (absorbed photosynthetically active radiation, APAR), 그리고 클로로필과 카로티노이드의 비율 인자 (chlorophyll: carotenoid index, CCI)를 온대침엽수림에서 연속적으로 관측하였다. SIF-GPP 관계의 구체적인 메커니즘 관계를 밝히기 위하여, 광 이용효율 (light use efficiency, LUE_p), 엽록소 형광 수득률 (chlorophyll fluorescence yield, Φ_F) 그리고 SIF 광자가 군락으로부터 방출되는 비율 (escape fraction, f_esc)을 각각 도출하고 탐구하였다. SIF와 GPP의 관계는 뚜렷한 비 선형적인 관계를 보이는 것을 확인했으며(R2 = 0.91 with a hyperbolic regression function, daily), 일주기 단위에서 SIF는 APAR에 대해 선형적이었지만 GPP는 APAR에 대해 뚜렷한 포화 관계를 보이는 것을 확인하였다. 추가적으로 LUE_p 와 Φ_F 가 대기 온도에 따라 반응하는 정도가 다른 것을 확인하였다. LUE_p는 높은 온도에서 포화 되었지만, Φ_F는 포화 패턴을 확인할 수 없었다. 또한, 군락 수준에서의 CCI와 Φ_F가 뚜렷한 상관 관계를 보였다(R2 = 0.84). 이는 Φ_F가 엽록소 색소에 영향을 LUE_p에 비해 더 강한 관계가 있을 수 있음을 시사한다. 마지막으로, f_esc가 태양광의 산란된 정도에 따라 반응을 하여, Φ_F와 LUE_p의 강한 상관 관계를 형성하는데 기여하는 것을 확인하였다. 이러한 발견은 온대 침엽수림에서 군락 수준의 SIF-GPP관계를 생리학적 및 구조적 측면에서 이해하고 규명하는데 큰 도움이 될 것이다. 식물 계절은 식생이 철을 따라 주기적으로 나타내는 변화를 관측하는 반응이다. 식물 계절은 육상생태계와 대기권 사이의 물질 순환을 이해하는데 매우 중요하다. 위성 기반의 NDVI는 식물 계절을 탐지하고 연구하는데 가장 대중적으로 사용된다. 그러나, 활엽수림에서의 위성 NDVI 기반의 개엽 시기 및 성숙 시기가 실제 어느 시점을 탐지하는지는 불분명하다. 실제 활엽수림은 다층 식생 구조의 삼차원으로 이루어져 있는 반면, 위성 영상은 다층 식생의 신호가 섞여 있는 이차원의 결과물이기 때문이다. 따라서, 위성 NDVI 기반의 식물 계절이 다층 식생 구조를 이루고 있는 활엽수림에서 실제 현장 관측과 비교하였을 때 어느 시점을 탐지하는지에 대한 궁금증이 남는다. 본 학위 논문의 Chapter 4에서는 지속적으로 8년 동안 활엽수림내의 다층 식생의 식물 계절을 근접 표면 원격 센싱 시스템을 이용하여 관측하고, 위성 NDVI 기반의 식물 계절과 비교하였다. 다채널 분광계를 상부 식생의 위와 아래에 설치함으로써, 상부 식생과 하부 식생의 식물 계절을 각각 연속적으로 관측하였다. 식물 계절을 탐지하기 위하여 가장 많이 사용되는 방법인 1) 역치를 이용하는 방법과 2) 이계도함수를 이용하는 방법을 사용하여 개엽 시기 및 성숙 시기를 계산하고 이를 다층 식생의 식물 계절과 비교하였다. 본 연구 결과, 첫번째로, 군락의 상층부에서 실측한 NDVI와 위성 기반의 NDVI가 강한 선형 관계를 보이는 것을 확인했다 (R2=0.95 는 MODIS 영상들 및 R2= 0.85 는 Landsat8). 두번째로, 이계도함수 방법과 10%의 역치 값을 이용한 방법이 비슷한 개엽 시기를 추정하는 것을 확인하였으며, 하부 식생의 개엽 시기와 비슷한 시기임을 확인하였다. 세번째로, 이계도함수 방법과 90%의 역치 값을 이용한 방법이 비슷한 성숙 시기를 산출하였으며, 이는 상부 식생의 성숙 시기와 비슷하였다. 추가적으로 상부 식생의 개엽 시기와 하부 식생의 개엽 시기가 온도와 반응하는 정도가 뚜렷하게 차이가 나는 것을 확인할 수 있었다. 상부 식생의 개엽 시기는 적산 생장 온도 일수 (AGDD)와 강한 상관성을 보였고, 하부 식생의 개엽 시기는 AGDD와 연관성을 갖고 있을 뿐만 아니라 추위 일수(NCD)에도 민감하게 반응하는 것을 확인하였다. 이러한 결과는 위성 NDVI 기반의 개엽 시기는 하부 식생의 개엽 시기와 연관성이 높고, 성숙 시기는 상부 식생의 성숙 시기와 비슷하다는 것을 의미한다. 또한, 상부 식생과 하부 식생이 온도에 다른 민감성을 갖고 있어, 위성에서 산출된 식물 계절을 이용하여 기후변화를 이해하고자 할 때, 어떤 층의 식생이 위성 영상에 주된 영향을 미치는지 고려해야 한다는 것을 시사한다. 위성은 넓은 지역의 변화를 손쉽게 모니터링할 수 있어 많은 가능성을 갖고 있는 도구이지만, 보다 정확한 위성 관측 값을 이해하기 위해서는 현장에서 관측된 자료를 기반으로 한 검증이 요구된다. 본 학위 논문에서는 1) 근접 표면 센싱 시스템을 개발, 2) 근접 표면 센싱 시스템을 활용한 식생의 생리학적 구조적 변화의 지속적인 관측, 3) 다층 식생 구조에서 관측되는 식물 계절 및 위성에서 추정된 식물 계절의 연관성 평가를 수행하였다. 개발한 근접 표면 센서는 상업 센서들과 비교했을 때, 가격적으로 저렴하고 손 쉽게 사용할 수 있었으며, 성능적으로도 부족함이 없었다. 근접 표면 센싱 시스템을 이용하여 SIF를 온대 침엽수림에서 지속적으로 관측한 결과, 총일차생산성과 SIF는 비선형 관계를 갖는 것을 확인하였다. 이는 많은 선행 연구들에서 발표한 위성 기반의 SIF와 GPP가 선형적인 관계를 보인다는 것과는 다소 상반된 결과이다. 다측 식생의 봄철 식물 계절을 연속적으로 관측하고, 위성 기반의 식물 계절과 비교평가한 연구에서는 위성 기반의 개엽 시기는 하부 식생에 영향을 주로 받고, 성숙 시기는 상부 식생의 시기와 비슷한 것을 확인하였다. 즉, 근접 표면 센싱 시스템을 이용하여 현장에서 실측한 결과는 위성 영상을 활용한 연구들과는 다른 결과를 보일 수도 있으며, 위성 영상을 평가 및 이해하는데 사용될 수 있다. 따라서, 보다 정확한 식생의 구조적, 생리학적 메커니즘을 이해하기 위해서는 근접 표면 센싱을 활용한 현장에서 구축한 자료 기반의 더 많은 연구들이 필요하다는 것을 시사한다.Abstract i Chapter 1. Introduction 2 1. Background 2 2. Purpose 5 Chapter 2. Monitoring SIF using a filter-based near surface remote sensing system 9 1. Introduction 9 2. Instrument desing and technical spefications of the filter-based smart surface sensing system (4S-SIF) 12 2.1. Ultra-narrow band pass filter 14 2.2. Calibration of 4S-SIF 15 2.3. Temperature and humidity response 16 2.4. Evaluate SIF quality from 4S-SIF in the field 17 3. Results 20 4. Discussion 23 Chapter 3. SIF is non-linearly related to canopy photosynthesis in a temperate evergreen needleleaf forest during fall transition 27 1. Introduction 27 2. Methods and Materials 31 2.1. Study site 31 2.2. Leaf-level fluorescence measurement 32 2.3. Canopy-level SIF and spectral reflectance measurement 34 2.4. SIF retrieval 37 2.5. Canopy-level photosynthesis estimates 38 2.6. Meteorological variables and APAR 39 2.7. Statistical analysis 40 3. Results 41 4. Discussion 48 4.1. Non-linear relationships between SIF and GPP 49 4.2. Role of f_esc in SIF-GPP relationship 53 4.3. Implications of non-linear SIF-GPP relationship in temperate ENF 54 5. Conclusion 57 6. Appendix 59 Chapter 4. Monitoring spring phenology of multi-layer canopy in a deciduous broadleaf forest: What signal do satellites actually see in space 65 1. Introduction 65 2. Materials and Methods 69 2.1. Study site 69 2.2. Multi-layer spectral reflectance and transmittance measurement 70 2.3. Phenometrics detection 72 2.4. In-situ multi-layer phenology 74 2.5. Satellite remote sensing data 75 2.6. Meteorological variables 75 3. Results 76 3.1. Seasonal to interannual variations of NDVI, 1-transmittance, and air temperature 76 3.2. Inter-annual variation of leaf-out and full-leaf dates 78 3.3. The relationships between dates calculated according tothreshold and in-situ multi-layer phenology 80 3.4. The relationship between multi-layer phenology, AGDD and NCD 81 4. Discussion 82 4.1. How do satellite-based leaf-out and full-leaf dates differ from in-situ multi-layer phenology 83 4.2. Are the 10 % and 90 % thresholds from satellite-basedNDVI always well matched with the leaf-out and full-leaf dates calculated by the curvature change rate 86 4.3. What are the implications of the difference between satellite-based and multi-layer phenology 87 4.4. Limitations and implications for future studies 89 5. Conclusion 91 6. Appendix 92 Chapter 5. Conclusion 114 Abstract in Korean 115박

Performance of solar-induced chlorophyll fluorescence in estimating water-use efficiency in a temperate forest

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing 10 (2018): 796, doi:10.3390/rs10050796.Water-use efficiency (WUE) is a critical variable describing the interrelationship between carbon uptake and water loss in land ecosystems. Different WUE formulations (WUEs) including intrinsic water use efficiency (WUEi), inherent water use efficiency (IWUE), and underlying water use efficiency (uWUE) have been proposed. Based on continuous measurements of carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) at a temperate forest, we analyze the correlations between SIF emission and the different WUEs at the canopy level by using linear regression (LR) and Gaussian processes regression (GPR) models. Overall, we find that SIF emission has a good potential to estimate IWUE and uWUE, especially when a combination of different SIF bands and a GPR model is used. At an hourly time step, canopy-level SIF emission can explain as high as 65% and 61% of the variances in IWUE and uWUE. Specifically, we find that (1) a daily time step by averaging hourly values during daytime can enhance the SIF-IWUE correlations, (2) the SIF-IWUE correlations decrease when photosynthetically active radiation and air temperature exceed their optimal biological thresholds, (3) a low Leaf Area Index (LAI) has a negative effect on the SIF-IWUE correlations due to large evaporation fluxes, (4) a high LAI in summer also reduces the SIF-IWUE correlations most likely due to increasing scattering and (re)absorption of the SIF signal, and (5) the observation time during the day has a strong impact on the SIF-IWUE correlations and SIF measurements in the early morning have the lowest power to estimate IWUE due to the large evaporation of dew. This study provides a new way to evaluate the stomatal regulation of plant-gas exchange without complex parameterizations.This research was supported by U.S. Department of Energy Office of Biological and Environmental Research Grant DE-SC0006951, National Science Foundation Grants DBI 959333 and AGS-1005663, and the University of Chicago and the MBL Lillie Research Innovation Award to Jianwu Tang. This study was also supported by the open project grant (LBKF201701) of Key Laboratory of Land Surface Pattern and Simulation, Chinese Academy of Sciences

Modelling, Monitoring and Validation of Plant Phenology Products

Phänologie, die Lehre der periodisch wiederkehrenden Entwicklungserscheinungen in der Natur, hat sich in den letzten Jahrzehnten zu einem wichtigen Teilgebiet der Klimaforschung entwickelt. Einer der Haupteffekte der globalen Erwärmung ist die Veränderung der Wachstumsmuster und Fortpflanzungsgewohnheiten von Pflanzen, und somit veränderte Phänologie. Um die Auswirkungen der Klimaveränderung auf wildwachsende sowie Kulturpflanzen vorherzusagen, werden phänologische Modelle angewendet, verbessert und validiert. Dabei ist Wissen über den aktuellen Stand der Vegetation notwendig, welches aus Beobachtungen und fernerkundliche Messungen gewonnen wird. Die hier präsentierte Arbeit befasst sich mit dem Verständnis der Zusammenhänge zwischen fernerkundlichen Messungen und phänologischen Stadien und somit den Herausforderungen der modernen phänologischen Forschung: Der Vorhersage der Phänologie durch Modellierungsansätze, der Beobachtung der Phänologie mit optischen boden- und satellitengestützten Sensoren und der Validierung phänologischer Produkte.Phenology, the study of recurring life cycle events of plants and animals has emerged as an important part of climate change research within the last decades. One of the main effects of global warming on vegetation is altered phenology, since plants have to modify their growth patterns and reproduction habits as reaction to changing environmental conditions. Forecasting phenology, thus phenological modelling, is a timely challenge given the necessity to predict the impact of global warming on wild-growing species and agricultural crops. However, assessing the present state of vegetation, thus phenological monitoring, is essential to update and validate model results. An improved comprehension of the relationships between plant phenology and remotely sensed products is crucial to interpret these results. Consequently, the presented thesis deals with the main challenges faced in modern phenology research, covering phenological forecasting with a modelling approach, satellite-based phenology extraction, and near-surface long-term monitoring of phenology

시공간 해상도 향상을 통한 식생 변화 모니터링

학위논문(박사) -- 서울대학교대학원 : 환경대학원 협동과정 조경학, 2023. 2. 류영렬.육상 생태계에서 대기권과 생물권의 상호 작용을 이해하기 위해서는 식생 변화의 모니터링이 필요하다. 이 때, 위성영상은 지표면을 관측하여 식생지도를 제공할 수 있지만, 지표변화의 상세한 정보는 구름이나 위성 이미지의 공간 해상도에 의해 제한되었다. 또한 위성영상의 시공간 해상도가 식생지도를 통한 광합성 모니터링에 미치는 영향은 완전히 밝혀지지 않았다. 본 논문에서는 고해상도 식생 지도를 일단위로 생성하기 위성 영상의 시공간 해상도를 향상시키는 것을 목표로 하였다. 고해상도 위성영상을 활용한 식생 변화 모니터링을 시공간적으로 확장하기 위해 1) 정지궤도 위성을 활용한 영상융합을 통해 시간해상도 향상, 2) 적대적생성네트워크를 활용한 공간해상도 향상, 3) 시공간해상도가 높은 위성영상을 토지피복이 균질하지 않은 공간에서 식물 광합성 모니터링을 수행하였다. 이처럼, 위성기반 원격탐지에서 새로운 기술이 등장함에 따라 현재 및 과거의 위성영상은 시공간 해상도 측면에서 향상되어 식생 변화의 모니터링 할 수 있다. 제2장에서는 정지궤도위성영상을 활용하는 시공간 영상융합으로 식물의 광합성을 모니터링 했을 때, 시간해상도가 향상됨을 보였다. 시공간 영상융합 시, 구름탐지, 양방향 반사 함수 조정, 공간 등록, 시공간 융합, 시공간 결측치 보완 등의 과정을 거친다. 이 영상융합 산출물은 경작관리 등으로 식생 지수의 연간 변동이 큰 두 장소(농경지와 낙엽수림)에서 평가하였다. 그 결과, 시공간 영상융합 산출물은 결측치 없이 현장관측을 예측하였다 (R2 = 0.71, 상대 편향 = 5.64% 농경지; R2 = 0.79, 상대 편향 = -13.8%, 활엽수림). 시공간 영상융합은 식생 지도의 시공간 해상도를 점진적으로 개선하여, 식물 생장기동안 위성영상이 현장 관측을 과소 평가를 줄였다. 영상융합은 높은 시공간 해상도로 광합성 지도를 일간격으로 생성하기에 이를 활용하여 위성 영상의 제한된 시공간 해상도로 밝혀지지 않은 식물변화의 과정을 발견하길 기대한다. 식생의 공간분포은 정밀농업과 토지 피복 변화 모니터링을 위해 필수적이다. 고해상도 위성영상으로 지구 표면을 관측하는 것을 용이하게 해졌다. 특히 Planet Fusion은 초소형위성군 데이터를 최대한 활용해 데이터 결측이 없는 3m 공간 해상도의 지표 표면 반사도이다. 그러나 과거 위성 센서(Landsat의 경우 30~60m)의 공간 해상도는 식생의 공간적 변화를 상세 분석하는 것을 제한했다. 제3장에서는 Landsat 데이터의 공간 해상도를 향상하기 위해 Planet Fusion 및 Landsat 8 데이터를 사용하여 이중 적대적 생성 네트워크(the dual RSS-GAN)를 학습시켜, 고해상도 정규화 식생 지수(NDVI)와 식물 근적외선 반사(NIRv)도를 생성하는 한다. 타워기반 현장 식생지수(최대 8년)와 드론기반 초분광지도로 the dual RSS-GAN의 성능을 대한민국 내 두 대상지(농경지와 활엽수림)에서 평가했다. The dual RSS-GAN은 Landsat 8 영상의 공간해상도를 향상시켜 공간 표현을 보완하고 식생 지수의 계절적 변화를 포착했다(R2> 0.96). 그리고 the dual RSS-GAN은 Landsat 8 식생 지수가 현장에 비해 과소 평가되는 것을 완화했다. 현장 관측에 비해 이중 RSS-GAN과 Landsat 8의 상대 편향 값 각각 -0.8% 에서 -1.5%, -10.3% 에서 -4.6% 였다. 이러한 개선은 Planet Fusion의 공간정보를 이중 RSS-GAN로 학습하였기에 가능했다. 헤당 연구 결과는 Landsat 영상의 공간 해상도를 향상시켜 숨겨진 공간 정보를 제공하는 새로운 접근 방식이다. 고해상도에서 식물 광합성 지도는 토지피복이 복잡한 공간에서 탄소 순환 모니터링시 필수적이다. 그러나 Sentinel-2, Landsat 및 MODIS와 같이 태양 동조 궤도에 있는 위성은 공간 해상도가 높거나 시간 해상도 높은 위성영상만 제공할 수 있다. 최근 발사된 초소형위성군은 이러한 해상도 한계을 극복할 수 있다. 특히 Planet Fusion은 초소형위성 자료의 시공간 해상도로 지표면을 관측할 수 있다. 4장에서, Planet Fusion 지표반사도를 이용하여 식생에서 반사된 근적외선 복사(NIRvP)를 3m 해상도 지도를 일간격으로 생성했다. 그런 다음 미국 캘리포니아주 새크라멘토-샌 호아킨 델타의 플럭스 타워 네트워크 데이터와 비교하여 식물 광합성을 추정하기 위한 NIRvP 지도의 성능을 평가하였다. 전체적으로 NIRvP 지도는 습지의 잦은 수위 변화에도 불구하고 개별 대상지의 식물 광합성의 시간적 변화를 포착하였다. 그러나 대상지 전체에 대한 NIRvP 지도와 식물 광합성 사이의 관계는 NIRvP 지도를 플럭스 타워 관측범위와 일치시킬 때만 높은 상관관계를 보였다. 관측범위를 일치시킬 경우, NIRvP 지도는 식물 광합성을 추정하는 데 있어 현장 NIRvP보다 우수한 성능을 보였다. 이러한 성능 차이는 플럭스 타워 관측범위를 일치시킬 때, 연구 대상지 간의 NIRvP-식물 광합성 관계의 기울기가 일관성을 보였기 때문이다. 본 연구 결과는 위성 관측을 플럭스 타워 관측범위와 일치시키는 것의 중요성을 보여주고 높은 시공간 해상도로 식물 광합성을 원격으로 모니터링하는 초소형위성군 자료의 잠재력을 보여준다.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 −3.2% to 1.2% and −12.4% to −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 202박

A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery

(1) Background: Information rich hyperspectral sensing, together with robust image analysis, is providing new research pathways in plant phenotyping. This combination facilitates the acquisition of spectral signatures of individual plant organs as well as providing detailed information about the physiological status of plants. Despite the advances in hyperspectral technology in field-based plant phenotyping, little is known about the characteristic spectral signatures of shaded and sunlit components in wheat canopies. Non-imaging hyperspectral sensors cannot provide spatial information; thus, they are not able to distinguish the spectral reflectance differences between canopy components. On the other hand, the rapid development of high-resolution imaging spectroscopy sensors opens new opportunities to investigate the reflectance spectra of individual plant organs which lead to the understanding of canopy biophysical and chemical characteristics. (2) Method: This study reports the development of a computer vision pipeline to analyze ground-acquired imaging spectrometry with high spatial and spectral resolutions for plant phenotyping. The work focuses on the critical steps in the image analysis pipeline from pre-processing to the classification of hyperspectral images. In this paper, two convolutional neural networks (CNN) are employed to automatically map wheat canopy components in shaded and sunlit regions and to determine their specific spectral signatures. The first method uses pixel vectors of the full spectral features as inputs to the CNN model and the second method integrates the dimension reduction technique known as linear discriminate analysis (LDA) along with the CNN to increase the feature discrimination and improves computational efficiency. (3) Results: The proposed technique alleviates the limitations and lack of separability inherent in existing pre-defined hyperspectral classification methods. It optimizes the use of hyperspectral imaging and ensures that the data provide information about the spectral characteristics of the targeted plant organs, rather than the background. We demonstrated that high-resolution hyperspectral imagery along with the proposed CNN model can be powerful tools for characterizing sunlit and shaded components of wheat canopies in the field. The presented method will provide significant advances in the determination and relevance of spectral properties of shaded and sunlit canopy components under natural light conditions

Diversity of 3D APAR and LAI dynamics in broadleaf and coniferous forests: Implications for the interpretation of remote sensing-based products

Forests substantially mediate the water and carbon dioxide exchanges between terrestrial ecosystems and the atmosphere. The rate of this exchange, including evapotranspiration (ET) and gross primary production (GPP), depends mainly on the underlying vegetation type, health state, and the composition of abiotic environmental drivers. However, the complex 3D structure of forest canopies and the inherent top-view perspective of optical and thermal remote sensing complicate remote sensing-based retrievals of biotic and abiotic factors that eventually determine ET and GPP. This study investigates the sensitivity of remote sensing approaches to 3D variation of abiotic and biotic environmental drivers. We use 3D virtual scenes of two structurally different Swiss forests and the radiative transfer model DART to simulate the 3D distribution of solar irradiance and reflected radiance in the forest canopy. These simulations, in combination with LiDAR data, are used to derive the absorbed photosynthetic active radiation (APAR) and the leaf area index (LAI) in 3D space. The 3D variation of both parameters was quantified and analyzed. We then simulated images of the top-of-canopy bi-directional reflectance factor (BRF) and compared them with the hemispheric-conical reflectance factor (HCRF) data derived from HyPlant airborne imaging spectrometer measurements. The simulated BRF data was used to derive APAR and LAI, and the results were compared to their respective 3D representations. We unravel considerable spatial differences between both representations. We discuss possible reasons for the disagreement, including a potential insensitivity of the inherent top-of-canopy view for the real 3D product dynamics and limitations of the processing of remote sensing data, especially the approximation of effective surface irradiance. Our results can help understanding sources of uncertainties in remote sensing based gas exchange products and defining mitigation strategies

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF) : A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ

Solar-induced chlorophyll a Fluorescence (SIF), which is distributed over a relatively broad (similar to 200 nm) spectral range, is a signal intricately connected to the efficiency of photosynthesis and is now observable from space. Variants of the Fraunhofer Line Depth/Discriminator (FLD) method are used as the basis of retrieval algorithms for estimating SIF from space. Although typically unobserved directly, recent advances in FLD-based algorithms now facilitate the prediction (by model inversion) of the canopy emitted fluorescence spectrum from the discrete-feature FLD retrievals. Here we present first canopy scale measurements of chlorophyll a fluorescence spectra emitted from Scots pine at two times of year, and also from a lingonberry dominated understory. We used a high power mul-tispectral Light Emitting Diode (LED) array to illuminate the respective canopies at night and measured under standardised conditions using a field spectrometer mounted in the nadir position above the canopy. We refer to the technique, which facilitates the in situ upscaling of a commonly measured leaf scale quantity to the canopy, as nocturnal LED-Induced chlorophyll a Fluorescence (LEDIF). The shape of the LEDIF spectra was dependant on the colour of the excitation light and also on the dominant species. Because we measured pine at two different times of year we were also able to show an increase in the canopy scale apparent quantum yield of fluorescence which was consistent with leaf-level increase in fluorescence yield recorded with a monitoring PAM fluorometer. The automation of the LEDIF technique could be used to estimate seasonal changes in canopy fluorescence spectra and yield from fixed or mobile platforms and provide a window into functional traits across species and architectures. LEDIF could also be used to evaluate FLD and inversion-based retrievals of canopy spectra, as well as different irradiance normalisation schemes typically applied to SIF data to account for the dependence of SIF on ambient light conditions.Peer reviewe