A Cross Comparison of Spatiotemporally Enhanced Springtime Phenological Measurements From Satellites and Ground in a Northern U.S. Mixed Forest

Cross comparison of satellite-derived land surface phenology (LSP) and ground measurements is useful to ensure the relevance of detected seasonal vegetation change to the underlying biophysical processes. While standard 16-day and 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index (VI)-based springtime LSP has been evaluated in previous studies, it remains unclear whether LSP with enhanced temporal and spatial resolutions can capture additional details of ground phenology. In this paper, we compared LSP derived from 500-m daily MODIS and 30-m MODIS-Landsat fused VI data with landscape phenology (LP) in a northern U.S. mixed forest. LP was previously developed from intensively observed deciduous and coniferous tree phenology using an upscaling approach. Results showed that daily MODIS-based LSP consistently estimated greenup onset dates at the study area (625 m × 625 m) level with 4.48 days of mean absolute error (MAE), slightly better than that of using 16-day standard VI (4.63 days MAE). For the observed study areas, the time series with increased number of observations confirmed that post-bud burst deciduous tree phenology contributes the most to vegetation reflectance change. Moreover, fused VI time series demonstrated closer correspondences with LP at the community level (0.1-20 ha) than using MODIS alone at the study area level (390 ha). The fused LSP captured greenup onset dates for respective forest communities of varied sizes and compositions with four days of the overall MAE. This study supports further use of spatiotemporally enhanced LSP for more precise phenological monitoring

Potential of VIIRS Time Series Data for Aiding the USDA Forest Service Early Warning System for Forest Health Threats: A Gypsy Moth Defoliation Case Study

This report details one of three experiments performed during FY 2007 for the NASA RPC (Rapid Prototyping Capability) at Stennis Space Center. This RPC experiment assesses the potential of VIIRS (Visible/Infrared Imager/Radiometer Suite) and MODIS (Moderate Resolution Imaging Spectroradiometer) data for detecting and monitoring forest defoliation from the non-native Eurasian gypsy moth (Lymantria dispar). The intent of the RPC experiment was to assess the degree to which VIIRS data can provide forest disturbance monitoring information as an input to a forest threat EWS (Early Warning System) as compared to the level of information that can be obtained from MODIS data. The USDA Forest Service (USFS) plans to use MODIS products for generating broad-scaled, regional monitoring products as input to an EWS for forest health threat assessment. NASA SSC is helping the USFS to evaluate and integrate currently available satellite remote sensing technologies and data products for the EWS, including the use of MODIS products for regional monitoring of forest disturbance. Gypsy moth defoliation of the mid-Appalachian highland region was selected as a case study. Gypsy moth is one of eight major forest insect threats listed in the Healthy Forest Restoration Act (HFRA) of 2003; the gypsy moth threatens eastern U.S. hardwood forests, which are also a concern highlighted in the HFRA of 2003. This region was selected for the project because extensive gypsy moth defoliation occurred there over multiple years during the MODIS operational period. This RPC experiment is relevant to several nationally important mapping applications, including agricultural efficiency, coastal management, ecological forecasting, disaster management, and carbon management. In this experiment, MODIS data and VIIRS data simulated from MODIS were assessed for their ability to contribute broad, regional geospatial information on gypsy moth defoliation. Landsat and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data were used to assess the quality of gypsy moth defoliation mapping products derived from MODIS data and from simulated VIIRS data. The project focused on use of data from MODIS Terra as opposed to MODIS Aqua mainly because only MODIS Terra data was collected during 2000 and 2001-years with comparatively high amounts of gypsy moth defoliation within the study area. The project assessed the quality of VIIRS data simulation products. Hyperion data was employed to assess the quality of MODIS-based VIIRS simulation datasets using image correlation analysis techniques. The ART (Application Research Toolbox) software was used for data simulation. Correlation analysis between MODIS-simulated VIIRS data and Hyperion-simulated VIIRS data for red, NIR (near-infrared), and NDVI (Normalized Difference Vegetation Index) image data products collectively indicate that useful, effective VIIRS simulations can be produced using Hyperion and MODIS data sources. The r(exp 2) for red, NIR, and NDVI products were 0.56, 0.63, and 0.62, respectively, indicating a moderately high correlation between the 2 data sources. Temporal decorrelation from different data acquisition times and image misregistration may have lowered correlation results. The RPC experiment also generated MODIS-based time series data products using the TSPT (Time Series Product Tool) software. Time series of simulated VIIRS NDVI products were produced at approximately 400-meter resolution GSD (Ground Sampling Distance) at nadir for comparison to MODIS NDVI products at either 250- or 500-meter GSD. The project also computed MODIS (MOD02) NDMI (Normalized Difference Moisture Index) products at 500-meter GSD for comparison to NDVI-based products. For each year during 2000-2006, MODIS and VIIRS (simulated from MOD02) time series were computed during the peak gypsy moth defoliation time frame in the study area (approximately June 10 through July 27). Gypsy moth defoliation mapping products from simated VIIRS and MOD02 time series were produced using multiple methods, including image classification and change detection via image differencing. The latter enabled an automated defoliation detection product computed using percent change in maximum NDVI for a peak defoliation period during 2001 compared to maximum NDVI across the entire 2000-2006 time frame. Final gypsy moth defoliation mapping products were assessed for accuracy using randomly sampled locations found on available geospatial reference data (Landsat and ASTER data in conjunction with defoliation map data from the USFS). Extensive gypsy moth defoliation patches were evident on screen displays of multitemporal color composites derived from MODIS data and from simulated VIIRS vegetation index data. Such defoliation was particularly evident for 2001, although widespread denuded forests were also seen for 2000 and 2003. These visualizations were validated using aforementioned reference data. Defoliation patches were visible on displays of MODIS-based NDVI and NDMI data. The viewing of apparent defoliation patches on all of these products necessitated adoption of a specialized temporal data processing method (e.g., maximum NDVI during the peak defoliation time frame). The frequency of cloud cover necessitated this approach. Multitemporal simulated VIIRS and MODIS Terra data both produced effective general classifications of defoliated forest versus other land cover. For 2001, the MOD02-simulated VIIRS 400-meter NDVI classification produced a similar yet slightly lower overall accuracy (87.28 percent with 0.72 Kappa) than the MOD02 250-meter NDVI classification (88.44 percent with 0.75 Kappa). The MOD13 250-meter NDVI classification had a lower overall accuracy (79.13 percent) and a much lower Kappa (0.46). The report discusses accuracy assessment results in much more detail, comparing overall classification and individual class accuracy statistics for simulated VIIRS 400-meter NDVI, MOD02 250-meter NDVI, MOD02-500 meter NDVI, MOD13 250-meter NDVI, and MOD02 500-meter NDMI classifications. Automated defoliation detection products from simulated VIIRS and MOD02 data for 2001 also yielded similar, relatively high overall classification accuracy (85.55 percent for the VIIRS 400-meter NDVI versus 87.28 percent for the MOD02 250-meter NDVI). In contrast, the USFS aerial sketch map of gypsy moth defoliation showed a lower overall classification accuracy at 73.64 percent. The overall classification Kappa values were also similar for the VIIRS (approximately 0.67 Kappa) versus the MOD02 (approximately 0.72 Kappa) automated defoliation detection product, which were much higher than the values exhibited by the USFS sketch map product (overall Kappa of approximately 0.47). The report provides additional details on the accuracy of automated gypsy moth defoliation detection products compared with USFS sketch maps. The results suggest that VIIRS data can be effectively simulated from MODIS data and that VIIRS data will produce gypsy moth defoliation mapping products that are similar to MODIS-based products. The results of the RPC experiment indicate that VIIRS and MODIS data products have good potential for integration into the forest threat EWS. The accuracy assessment was performed only for 2001 because of time constraints and a relative scarcity of cloud-free Landsat and ASTER data for the peak defoliation period of the other years in the 2000-2006 time series. Additional work should be performed to assess the accuracy of gypsy moth defoliation detection products for additional years.The study area (mid-Appalachian highlands) and application (gypsy moth forest defoliation) are not necessarily representative of all forested regions and of all forest threat disturbance agents. Additional work should be performed on other inland and coastal regions as well as for other major forest threats

드론을 활용한 위성 지표반사도 산출물 공간 패턴 분석

학위논문(석사) -- 서울대학교대학원 : 농업생명과학대학 생태조경·지역시스템공학부(생태조경학), 2021.8. 조대솔.High-resolution satellites are assigned to monitor land surface in detail. The reliable surface reflectance (SR) is the fundamental in terrestrial ecosystem modeling so the temporal and spatial validation is essential. Usually based on multiple ground control points (GCPs), field spectroscopy guarantees the temporal continuity. Due to limited sampling, however, it hardly illustrates the spatial pattern. As a map, the pixelwise spatial variability of SR products is not well-documented. In this study, we introduced drone-based hyperspectral image (HSI) as a reference and compared the map with Sentinel 2 and Landsat 8 SR products on a heterogeneous rice paddy landscape. First, HSI was validated by field spectroscopy and swath overlapping, which assured qualitative radiometric accuracy within the viewing geometry. Second, HSI was matched to the satellite SRs. It involves spectral and spatial aggregation, co-registration and nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance (NBAR) conversion. Then, we 1) quantified the spatial variability of the satellite SRs and the vegetation indices (VIs) including NDVI and NIRv by APU matrix, 2) qualified them pixelwise by theoretical error budget and 3) examined the improvement by BRDF normalization. Sentinel 2 SR exhibits overall good agreement with drone HSI while the two NIRs are biased up to 10%. Despite the bias in NIR, the NDVI shows a good match on vegetated areas and the NIRv only displays the discrepancy on built-in areas. Landsat 8 SR was biased over the VIS bands (-9 ~ -7.6%). BRDF normalization just contributed to a minor improvement. Our results demonstrate the potential of drone HSI to replace in-situ observation and evaluate SR or atmospheric correction algorithms over the flat terrain. Future researches should replicate the results over the complex terrain and canopy structure (i.e. forest).원격탐사에서 지표 반사도(SR)는 지표정보를 비파괴적이고 즉각적인 방법으로 전달해주는 매개체 역할을 한다. 신뢰할 수 있는 SR은 육상 생태계 모델링의 기본이고, 이에 따라 SR의 시공간적 검증이 요구된다. 일반적으로 SR은 여러 지상 기준점(GCP)을 기반으로 하는 현장 분광법을 통해서 시간적 연속성이 보장된다. 그러나 현장 분광법은 제한적인 샘플링으로 공간 패턴을 거의 보여주지 않아, 위성 SR의 픽셀 별 공간 변동성은 잘 분석되지 않았다. 본 연구에서는 드론 기반의 초분광 영상(HSI)을 참고자료로 도입하여, 이를 이질적인 논 경관에서 Sentinel 2 및 Landsat 8 SR과 비교하였다. 우선, 드론 HSI는 현장 분광법 및 경로 중첩을 통해서 관측각도 범위 내에서 정성적인 방사 측정을 보장한다고 검증되었다. 이후, 드론 HSI는 위성 SR의 분광반응특성, 공간해상도 및 좌표계를 기준으로 맞춰졌고, 관측 기하를 통일하기 위해서 드론 HIS와 위성 SR은 각각 양방향반사율분포함수 (BRDF) 정규화 반사도 (NBAR)로 변환되었다. 마지막으로, 1) APU 행렬으로 위성 SR과 NDVI, NIRv를 포함하는 식생지수(VI)의 공간변동성을 정량화 했고, 2) 대기보정의 이론적 오차를 기준으로 SR과 VI를 픽셀별로 평가했고, 3) BRDF 정규화를 통한 개선 사항을 검토했다. Sentinel 2 SR은 드론 HSI와 전반적으로 좋은 일치를 보이나, 두 NIR 채널은 최대 10% 편향되었다. NIR의 편향은 식생지수에서 토지 피복에 따라 다른 영향을 미쳤다. NDVI는 식생에서는 낮은 편향을 보여줬고, NIRv는 도시시설물 영역에서만 높은 편향을 보였다. Landsat 8 SR은 VIS 채널에 대해 편향되었다 (-9 ~ -7.6%). BRDF 정규화는 위성 SR의 품질을 개선했지만, 그 영향은 부수적이었다. 본 연구에서는 평탄한 지형에서 드론 HSI가 현장 관측을 대체할 수 있고, 따라서 위성 SR이나 대기보정 알고리즘을 평가하는데 활용될 수 있다는 것을 보였다. 향후 연구에서는 산림으로 대상지를 확대하여, 지형과 캐노피 구조가 드론 HSI 및 위성 SR에 미치는 영향을 분석할 필요가 있다.Chapter 1. Introduction 1 1.1 Background 1 Chapter 2. Method 3 2.1 Study Site 3 2.2 Drone campaign 4 2.3 Data processing 4 2.3.1 Sensor calibration 5 2.3.2 Bidirectional reflectance factor (BRF) calculation 7 2.3.3 BRDF correction 7 2.3.4 Orthorectification 8 2.3.5 Spatial Aggregation 9 2.3.6 Co-registration 10 2.4 Satellite dataset 10 2.4.2 Landsat 8 12 Chapter 3. Result and Discussion 12 3.1 Drone BRF map quality assessment 12 3.1.1 Radiometric accuracy 12 3.1.2 BRDF effect 15 3.2 Spatial variability in satellite surface reflectance product 16 3.2.1 Sentinel 2B (10m) 17 3.2.2 Sentinel 2B (20m) 22 3.2.3 Landsat 8 26 Chapter 4. Conclusion 28 Supplemental Materials 30 Bibliography 34 Abstract in Korean 43석

Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)

Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions Sentinel 2 (ESA) and Landsat 7/8 (NASA) provides this unprecedented opportunity at a global scale; however, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a robust stream processing for the harmonization of Landsat 7, Landsat 8 and Sentinel 2 in the Google Earth Engine cloud platform, connecting the benefit of coherent data structure, built-in functions and computational power in the Google Cloud. The harmonized surface reflectance images were generated for two agricultural schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during 2018–2019. We evaluated the performance of several pre-processing steps needed for the harmonization including the image co-registration, Bidirectional Reflectance Distribution Functions correction, topographic correction, and band adjustment. We found that the misregistration between Landsat 8 and Sentinel 2 images varied from 10 m in Ninh Thuan (Vietnam) to 32 m in Bekaa (Lebanon), and posed a great impact on the quality of the final harmonized data set if not treated. Analysis of a pair of overlapped L8-S2 images over the Bekaa region showed that, after the harmonization, all band-to-band spatial correlations were greatly improved. Finally, we demonstrated an application of the dense harmonized data set for crop mapping and monitoring. An harmonic (Fourier) analysis was applied to fit the detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns during one crop year in Ninh Thuan province. The derived phase and amplitude values of the crop cycles were combined with max-NDVI as an R-G-B false composite image. The final image was able to highlight croplands in bright colors (high phase and amplitude), while the non-crop areas were shown with grey/dark (low phase and amplitude). The harmonized data sets (with 30 m spatial resolution) along with the Google Earth Engine scripts used are provided for public use

Simulation of multiangular remote sensing products using small satellite formations

To completely capture the multiangular reflectance of an opaque surface, one must estimate the bidirectional reflectance distribution function (BRDF), which seeks to represent variations in surface reflectance as a function of measurement and illumination angles at any time instant. The gap in angular sampling abilities of existing single satellites in Earth observation missions can be complemented by small satellites in formation flight. The formation would have intercalibrated spectrometer payloads making reflectance measurements, at many zenith and azimuthal angles simultaneously. We use a systems engineering tool coupled with a science evaluation tool to demonstrate the performance impact and mission feasibility. Formation designs are generated and compared to each other and multisensor single spacecraft, in terms of estimation error of BRDF and its dependent products such as albedo, light use efficiency (LUE), and normalized difference vegetation index (NDVI). Performance is benchmarked with respect to data from previous airborne campaigns (NASA's Cloud Absorption Radiometer), and tower measurements (AMSPEC II), and assuming known BRDF models. Simulations show that a formation of six small satellites produces lesser average error (21.82%) than larger single spacecraft (23.2%), purely in terms of angular sampling benefits. The average monolithic albedo error of 3.6% is outperformed by a formation of three satellites (1.86%), when arranged optimally and by a formation of seven to eight satellites when arranged in any way. An eight-satellite formation reduces albedo errors to 0.67% and LUE errors from 89.77% (monolithic) to 78.69%. The average NDVI for an eight satellite, nominally maintained formation is better than the monolithic 0.038

Landsat 5 Thematic Mapper Reflectance and NDVI 27-year Time Series Inconsistencies Due to Satellite Orbit Change

The Landsat 5 Thematic Mapper (TM) sensor provided the longest single mission terrestrial remote sensing data record but temporally sparse station keeping maneuvers meant that the Landsat 5 orbit changed over the 27 year mission life. Long-term Landsat 5 TM reflectance inconsistencies may be introduced by orbit change induced solar zenith variations combined with surface reflectance anisotropy, commonly described by the Bi-directional Reflectance Distribution Function (BRDF). This study quantifies the local overpass time and observed solar zenith angle changes for all the Landsat 5 TM images available at two latitudinally separated locations along the same north-south Landsat path (27) in Minnesota (row 26) and Texas (row 42). Over the 27 years the Landsat 5 orbit changed by nearly 1 h and resulted in changes in the Landsat 5 observed solar zenith angle of N10°. The Landsat 5 orbit was relatively stable from 1984 to 1994 and from 2007 to 2011, but changed rapidly from 1995 to 2000, and from 2003 to 2007. Rather than directly examine Landsat 5 TM reflectance time series a modelling approach was used. This was necessary because unambiguous separation of orbit change induced Landsat reflectance variations from other temporal variations is non-trivial. The impact of Landsat 5 orbit induced observed solar zenith angle variations on the red and near-infrared reflectance and derived normalized difference vegetation index (NDVI) values were modelled with respect to different Moderate-Resolution Imaging Spectroradiometer (MODIS) BRDF land cover types. Synthetic nadir BRDF-adjusted reflectance (NBAR) for the Landsat 5 TM observed and a modelled reference year 2011 solar zenith were compared over the 27 years of acquisitions. Ordinary least squares linear regression fits of the NBAR difference values as a function of the acquisition date indicated an increasing trend in red and near-infrared NBAR and a decreasing trend in NDVI NBAR due to orbit changes. The trends are statistically significant but small, no more than 0.0006 NDVI/year. Comparison of certain years of Landsat 5 data may result in significant reflectance and NDVI differences due only to Landsat 5 orbit changes and cause spurious detection of “browning” vegetation events and underestimation of greening trends. The greatest differences will occur when 1995 Landsat 5 TM data are compared with 2007 to 2011 data; NDVI values could be up to 0.11 greater in 1995 than in 2011 for anisotropic land cover types and up to 0.05 greater for average CONUS land cover types. A smaller number of Landsat 5 TM images were also examined and provide support for the modelled based findings. The paper concludes with a discussion of the implications of the research findings for Landsat 5 TM time series analyses

A model to estimate daily albedo from remote sensing data : accuracy assessment of MODIS MCD43 product

L’albedo superficial és un paràmetre físic que afecta al clima de la Terra i, a més, suposa una de les majors incerteses radiatives en l’actual modelització climàtica. Aquest paràmetre és molt variable tant a nivell espacial com temporal degut als canvis en les propietats de les superfícies i als canvis en les condicions d’il•luminació. En conseqüència, es requereix una resolució temporal diària de l’albedo per a realitzar estudis climàtics. L’augment de la resolució espacial dels models climàtics fa necessari l’estudi de les seues característiques espacials a nivell global. La teledetecció proporciona l’única opció pràctica de proporcionar dades d’albedo a nivell global amb alta qualitat i alta resolució tant espacial com temporal. El sensor MODerate Resolution Imaging Spectroradiometer (MODIS) a bord dels satèl•lits Terra i Aqua presenta unes característiques adequades per a l’estimació d’aquest paràmetre. En el present treball realitzem diversos estudis buscant les possibles fonts d’incertesa del producte oficial d’albedo de MODIS (MCD43). A més, presentem un model que millora la resolució temporal d’aquest paràmetre.Surface albedo is a critical land physical parameter affecting the earth’s climate and is among the main radiative uncertainties in current climate modelling. This parameter is highly variable in space and time, both as a result of changes in surface properties and as a function of changes in the illumination conditions. Consequently, an albedo daily temporal resolution is required for climate studies. The increasing spatial resolution of modern climate models makes it necessary to examine its spatial features. Satellite remote sensing provides the only practical way of producing high-quality global albedo data sets with high spatial and temporal resolutions. The MODerate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Terra and Aqua satellites presents the required sampling characteristics in order to derive the this parameter. In this PhD we develop several studies looking for the improvement of the official MODIS albedo product (MCD43) accuracy. Moreover, we present a model that improves the temporal resolution of this parameter

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

Characterising vegetation structure using MODIS multi-angular data

Bidirectional Reflectance Distribution Functions (BRDF) seek to represent variations in surface reflectance resulting from changes in a satellite sensor’s view and solar illumination angles. NASA’s Moderate Resolution Image Spectroradiometer (MODIS) is a wide field of view Earth orbiting sensor that generates observations over a large range of view angles. Based on MODIS observations, a BRDF product and several sub-products have been developed by MODIS science teams, i.e. the MCD43 product suite. With the aim of using BRDF information from the MCD43 product to assist with the characterisation of the vertical structure of vegetation, a simple geometric optical model has been developed within this thesis for interpreting the MCD43 BRDF product in terms of an alternative set of parameters. The model developed within the thesis, and its application to single species cropped fields, a transect between Melbourne – Darwin and a semi-arid area in central Australia. The thesis identified that reflectance variations associated with enlargement of pixels’ ground instantaneous field of view is the principal source of variation in the MODIS BRDF product; rather than directional scatter effects that the product is intended to measure. Variations in pixels’ ground instantaneous field of view is a well known effect associated with wide field of view sensors such as MODIS, but is not explicitly considered in the MODIS BRDF algorithm. The presence of this artefact within the MODIS BRDF product has implications for the validity, use and interpretation of all land surface products based directly or indirectly on MODIS BRDF modelling