Remote sensing applications to hydrologic modeling

An energy balance snowmelt model for rugged terrain was devised and coupled to a flow model. A literature review of remote sensing applications to hydrologic modeling was included along with a software development outline

Debris Thickness of Glaciers in the Everest Area (Nepal Himalaya) Derived from Satellite Imagery Using a Nonlinear Energy Balance Model

Debris thickness is an important characteristic of debris-covered glaciers in the Everest region of the Himalayas. The debris thickness controls the melt rates of the glaciers, which has large implications for hydrologic models, the glaciers' response to climate change, and the development of glacial lakes. Despite its importance, there is little knowledge of how the debris thickness varies over these glaciers. This paper uses an energy balance model in conjunction with Landsat7 Enhanced Thematic Mapper Plus (ETM+) satellite imagery to derive thermal resistances, which are the debris thickness divided by the thermal conductivity. Model results are reported in terms of debris thickness using an effective thermal conductivity derived from field data. The developed model accounts for the nonlinear temperature gradient in the debris cover to derive reasonable debris thicknesses. Fieldwork performed on Imja-Lhotse Shar Glacier in September 2013 was used to compare to the modeled debris thicknesses. Results indicate that accounting for the nonlinear temperature gradient is crucial. Furthermore, correcting the incoming shortwave radiation term for the effects of topography and resampling to the resolution of the thermal band's pixel is imperative to deriving reasonable debris thicknesses. Since the topographic correction is important, the model will improve with the quality of the digital elevation model (DEM). The main limitation of this work is the poor resolution (60m) of the satellite's thermal band. The derived debris thicknesses are reasonable at this resolution, but trends related to slope and aspect are unable to be modeled on a finer scale. Nonetheless, the study finds this model derives reasonable debris thicknesses on this scale and was applied to other debris-covered glaciers in the Everest region.USAID Climate Change Resilient Development (CCRD) projectCenter for Research in Water Resource

Mapping evapotranspiration variability over a complex oasis-desert ecosystem based on automated calibration of Landsat 7 ETM+ data in SEBAL

Fragmented ecosystems of the desiccated Aral Sea seek answers to the profound local hydrologically- and water-related problems. Particularly, in the Small Aral Sea Basin (SASB), these problems are associated with low precipitation, increased temperature, land use and evapotranspiration (ET) changes. Here, the utility of high-resolution satellite dataset is employed to model the growing season dynamic of near-surface fluxes controlled by the advective effects of desert and oasis ecosystems in the SASB. This study adapted and applied the sensible heat flux calibration mechanism of Surface Energy Balance Algorithm for Land (SEBAL) to 16 clear-sky Landsat 7 ETM+ dataset, following a guided automatic pixels search from surface temperature T-s and Normalized Difference Vegetation Index NDVI (). Results were comprehensively validated with flux components and actual ET (ETa) outputs of Eddy Covariance (EC) and Meteorological Station (KZL) observations located in the desert and oasis, respectively. Compared with the original SEBAL, a noteworthy enhancement of flux estimations was achieved as follows: - desert ecosystem ETa R-2 = 0.94; oasis ecosystem ETa R-2 = 0.98 (P < 0.05). The improvement uncovered the exact land use contributions to ETa variability, with average estimates ranging from 1.24 mm to 6.98 mm . Additionally, instantaneous ET to NDVI (ETins-NDVI) ratio indicated that desert and oasis consumptive water use vary significantly with time of the season. This study indicates the possibility of continuous daily ET monitoring with considerable implications for improving water resources decision support over complex data-scarce drylands

Sea ice-atmospheric interaction: Application of multispectral satellite data in polar surface energy flux estimates

This is the third annual report on: Sea Ice-Atmosphere Interaction - Application of Multispectral Satellite Data in Polar Surface Energy Flux Estimates. The main emphasis during the past year was on: radiative flux estimates from satellite data; intercomparison of satellite and ground-based cloud amounts; radiative cloud forcing; calibration of the Advanced Very High Resolution Radiometer (AVHRR) visible channels and comparison of two satellite derived albedo data sets; and on flux modeling for leads. Major topics covered are arctic clouds and radiation; snow and ice albedo, and leads and modeling

Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX’08 field campaign

Robust spatial information about environmental water use at field scales and daily to seasonal timesteps will benefit many applications in agriculture and water resource management. This information is particularly critical in arid climates where freshwater resources are limited or expensive, and groundwater supplies are being depleted at unsustainable rates to support irrigated agriculture as well as municipal and industrial uses. Gridded evapotranspiration (ET) information at field scales can be obtained periodically using land–surface temperature-based surface energy balance algorithms applied to moderate resolution satellite data from systems like Landsat, which collects thermal-band imagery every 16 days at a resolution of approximately 100 m. The challenge is in finding methods for interpolating between ET snapshots developed at the time of a clear-sky Landsat overpass to provide complete daily time-series over a growing season. This study examines the efficacy of a simple gap-filling algorithm designed for applications in data-sparse regions, which does not require local ground measurements of weather or rainfall, or estimates of soil texture. The algorithm relies on general conservation of the ratio between actual ET and a reference ET, generated from satellite insolation data and standard meteorological fields from a mesoscale model. The algorithm was tested with ET retrievals from the Atmosphere–Land Exchange Inverse (ALEXI) surface energy balance model and associated DisALEXI flux disaggregation technique, which uses Landsat-scale thermal imagery to reduce regional ALEXI maps to a finer spatial resolution. Daily ET at the Landsat scale was compared with lysimeter and eddy covariance flux measurements collected during the Bushland Evapotranspiration and Agricultural Remote sensing EXperiment of 2008 (BEAREX08), conducted in an irrigated agricultural area in the Texas Panhandle under highly advective conditions. The simple gap-filling algorithm performed reasonably at most sites, reproducing observed cumulative ET to within 5–10% over the growing period from emergence to peak biomass in both rainfed and irrigated fields

Greenland Ice Sheet surface melt amplified by snowline migration and bare ice exposure

Greenland Ice Sheet mass loss has recently increased because of enhanced surface melt and runoff. Since melt is critically modulated by surface albedo, understanding the processes and feedbacks that alter albedo is a prerequisite for accurately forecasting mass loss. Using satellite imagery, we demonstrate the importance of Greenland’s seasonally fluctuating snowline, which reduces ice sheet albedo and enhances melt by exposing dark bare ice. From 2001 to 2017, this process drove 53% of net shortwave radiation variability in the ablation zone and amplified ice sheet melt five times more than hydrological and biological processes that darken bare ice itself. In a warmer climate, snowline fluctuations will exert an even greater control on melt due to flatter ice sheet topography at higher elevations. Current climate models, however, inaccurately predict snowline elevations during high melt years, portending an unforeseen uncertainty in forecasts of Greenland’s runoff contribution to global sea level ris

구글 스트릿뷰를 이용한 도시 협곡 내 평균복사온도 추정

학위논문 (석사) -- 서울대학교 대학원 : 농업생명과학대학 생태조경학과, 2021. 2. 이동근.도시개발로 인해 보행자의 에너지 균형을 변화시키며 도시공간의 열 쾌적성이 악화되는 등 열 환경문제가 발생하고 있다. 선행연구에서는 도시 공간 내 열 쾌적성을 정량적으로 평가하기 위해 인간의 가장 중요한 생체 기상 변수 중 하나인 평균복사온도를 산정하는 연구가 진행되고 있다. 하지만 산정식이 복잡하거나, 넓은 범위에서의 공간 데이터 취득이 어렵기 때문에, 커뮤니티 단위에서 고해상도의 평균복사온도를 추정하는 것은 어렵다. 따라서 본 연구에서는 구글스트릿뷰 이미지를 사용하여 도시 거리 협곡내 평균복사 온도를 추정하는 방법을 제시하고, 도시 스케일에서 도시열섬 분석을 위해 많은 연구가 진행된 지표면 온도와 평균복사온도간 관계를 공간패턴 측면에서 분석하였다. 우선 평균복사온도 추정식에 큰 영향을 미치는 천공률은 파노라마 이미지를 바탕으로 딥러닝을 활용하여 도시 요인별(건물, 나무, 하늘 등)분류하고, 어안렌즈 이미지로 변환하여 도출하였다. 또한 어안렌즈 이미지를 중심으로 태양경로 알고리즘을 활용하여 시간별 그림자의 유무를 판단하였다. 마지막으로 기후요인, 시간, 위치 등 데이터를 활용하여 장파, 단파 복사를 도출하여 평균복사온도를 산정하였다. 제안된 평균복사온도 추정 방법과 실측간 비교(7 곳) 결과 단파, 장파 값의 R^2값이 각각 0.97, 0.77로 나타났다. 다른 모델과 비교한 결과, 높은 정확도를 확인할 수 있으며 복잡한 도시 환경에서의 활용가능성을 확인할 수 있다. 도시규모에서 지표면온도, 평균복사온도를 공간패턴 측면에서 비교한 결과 천공률, 빌딩 뷰팩터가 각각 0.6~1.0, 0.35-0.5인 오픈스페이스 혹은 저층 밀집지역에서 높은 평균복사온도(>59.4°C)를 보였다. 반면 높은 빌딩이 밀집된 지역의 경우(빌딩 뷰팩터 :0.4-0.6, 나무 뷰팩터 0.6-0.9) 낮은 평균복사온도(<47.6°C)를 보였다. 특히 거리의 방향이 동-서 인 경우에는 천공률이 0.3-0.55 일지라도 높은 평균복사온도를 확인할 수 있었다. 추가적으로 평균복사온도와 지표면 온도간 비교결과 전반적으로 높은 온도 값을 가진 공간이 유사하였으나, 저층 고밀도 건물 지역 혹은 초지 지역에서 상반된 결과를 확인할 수 있었다. 본 연구에서는 도시스케일에서 높은 해상도로 평균복사온도를 추정하는 방법을 딥러닝을 활용하여 제시하였으며, 지표면 온도와 공간패턴별 분석을 통해 실제 보행자가 체감하는 열 환경을 개선하기 위한 방안을 제시할 수 있는 기초자료를 제공하였다. 이는 도시 열 환경을 고려한 지속가능한 도시 공간 설계 및 환경 계획 측면에서 활용 될 수 있으며, 특히 공간데이터 취득이 어려운 곳에서의 높은 활용성을 기대해 볼 수 있다.This paper presents a method for estimating Mean Radiant Temperature (MRT) of street canyons using Google Street View (GSV) images and investigates its spatial patterns in street-level on large scale. We used image segmentation using deep learning, project panorama to fisheye image and sun path algorithms to estimate MRT using GSV. Verification of proposed method can be explained by total of 7 field measurements in clear-sky of street-level, since the estimated shortwave and longwave radiation of value is 0.97, 0.77 respectively. The method proposed in this study is suitable for actual complex urban environment consisting of buildings, tree and streets. Additionally, we compared calculated MRT and LST (Land Surface Temperature) from Landsat 8 in a city scale. As a result of investigating spatial patterns of MRT in Seoul, We found that Higher MRT of street canyons ( >59.4℃) is mainly distributed in open space areas and compact low-rise density building where SVF (Sky View Factor) is 0.6–1.0 and BVF(Building View Factor) is 0.35–0.5, or West-East orientation street canyons with SVF(0.3–0.55). On the other hand, high density building (BVF is 0.4–0.6) or high density tree areas (TVF (Tree View Factor) is 0.6–0.99) showed Low MRT ( < 47.6). The mapped MRT results had similar spatial distribution with LST, but the MRT(?) lower (?) than LST in low tree density or low-rise high-density building areas. And it will help decision makers how to improve thermal comfort at the street-level.Chapter 1. Introduction １ 1.1. Study Background １ 1.2. Literature review ４ 1.2.1 Mean radiant temperature formula ４ 1.2.2 Surface temperature simulation model ５ Chapter 2. Study area and data １０ 2.1. Study area １０ 2.2. Data collection １１ Chapter 3. Method １３ 3.1. Research flow １３ 3.2. MRT simulation １４ 3.2.1. Schematic flow for MRT simulation １４ 3.2.2. Urban canyon geometry calculation using GSV images (Phase I: built geometry data) １６ 3.2.3. Street canyon solar radiation calculation (Phase II:radiation transfer calculation.) １７ 3.2.3.1 Calculation of street-level shortwave radiation １７ 3.2.3.2 Calculation of street-level long-wave radiation １９ 3.2.4. Phase III mean radiation temperature calculation ２１ Chapter 4. Result and Discussion ２２ 4.1. verification of solar radiation estimated in street-level ２２ 4.2. Validation of Long-wave radiation ２４ 4.3. Comparison between LST and MRT estimated using GSV ２６ 4.4. Comparison of GSV_MRT with other models ２９ 4.5. limitations and future development ３２ Chapter 5. Conclusion ３４ Bibliography ３６ Abstract in Korean ４３ Appendix ４５Maste

Aqua: AIRS, AMSU, HSB, AMSR-E, CERES, MODIS

This brochure provides an overview of the Aqua spacecraft, instruments, science, and data products Aqua, Latin for water, is a NASA Earth Science satellite mission named for the large amount of information that the mission is collecting about the Earth's water cycle, including evaporation from the oceans, water vapor in the atmosphere, clouds, precipitation, soil moisture, sea ice, land ice, and snow cover on the land and ice. Additional variables also measured by Aqua include radiative energy fluxes, aerosols, vegetation cover on the land, phytoplankton and dissolved organic matter in the oceans, and air, land, and water temperatures. Note: this guide was produced before Aqua was launched; for the most recent information on Aqua, go to http://aqua.nasa.gov. Educational levels: Undergraduate lower division, Undergraduate upper division, Graduate or professional, Informal education