A physics-constrained machine learning method for mapping gapless land surface temperature

More accurate, spatio-temporally, and physically consistent LST estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-driven machine learning (ML) models are two major paradigms for gapless LST estimation, which have their respective advantages and disadvantages. In this paper, a physics-constrained ML model, which combines the strengths in the mechanism model and ML model, is proposed to generate gapless LST with physical meanings and high accuracy. The hybrid model employs ML as the primary architecture, under which the input variable physical constraints are incorporated to enhance the interpretability and extrapolation ability of the model. Specifically, the light gradient-boosting machine (LGBM) model, which uses only remote sensing data as input, serves as the pure ML model. Physical constraints (PCs) are coupled by further incorporating key Community Land Model (CLM) forcing data (cause) and CLM simulation data (effect) as inputs into the LGBM model. This integration forms the PC-LGBM model, which incorporates surface energy balance (SEB) constraints underlying the data in CLM-LST modeling within a biophysical framework. Compared with a pure physical method and pure ML methods, the PC-LGBM model improves the prediction accuracy and physical interpretability of LST. It also demonstrates a good extrapolation ability for the responses to extreme weather cases, suggesting that the PC-LGBM model enables not only empirical learning from data but also rationally derived from theory. The proposed method represents an innovative way to map accurate and physically interpretable gapless LST, and could provide insights to accelerate knowledge discovery in land surface processes and data mining in geographical parameter estimation

Thermal Inertia-Based Method for Estimating Soil Moisture

Thermal inertia is a parameter that characterizes a property of soil that is defined as the square root of the product of the volumetric heat capacity and thermal conductivity. Both properties increase as soil moisture increases. Therefore, soil moisture can be inversely determined using thermal inertia if a relationship between the parameters is obtained in advance. In this chapter, methods for estimating surface soil moisture using thermal inertia are comprehensively reviewed, with emphases on the followings: How thermal inertia is retrieved accurately from a surface heat balance model, and how it is accurately converted to surface soil moisture. In addition, the advantages and disadvantages of the thermal inertia methods are discussed and compared to microwave-based methods, such as spatial resolution and the sky conditions. Precise and accurate data from earth observing satellites are indispensable for estimating the spatial distribution of thermal inertia at a high resolution. On the other hand, data assimilation methods are rapidly developing, which may be competitive with thermal inertia methods. Finally, applications of thermal inertia methods are described and discussed for future explorations, such as dust emission in relation to soil moisture, and estimating regional water budgets by combining other satellite data

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

AN INVESTIGATION OF REMOTELY SENSED URBAN HEAT ISLAND CLIMATOLOGY

Satellite remotely sensed temperatures are widely used for urban heat island (UHI) studies. However, the abilities of satellite surface and atmospheric data to assess the climatology of UHI face many unknowns and challenges. This research addresses the problems and potential for satellite remotely sensed UHI climatology by examining three different issues. The first issue is related to the temporal aggregation of land surface temperature (LST) and the potential biases that are induced on the surface UHI (SUHI) intensity. Composite LST data usually are preferred to avoid the missing values due to clouds for long-term UHI monitoring. The impact of temporal aggregation shows that SUHI intensities are more notably enhanced in the daytime than nighttime with an increasing trend for larger composite periods. The cause of the biases is highly related to the amount and distribution of clouds. The second issue is related to model validation and the appropriate use of LST for comparison to modeled radiometric temperatures in the urban environment. Sensor view angle, cloud distribution, and cloud contaminated pixels can confound comparisons between satellite LST and modeled surface radiometric temperature. Three practical sampling methods to minimize the confounding factors are proposed and evaluated for validating different aspects of model performance. The third issue investigated is to assess to what extent remotely sensed atmospheric profiles collected over the urban environment can be used to examine the UHI. The remotely sensed air and dew-point temperatures are compared with the ground observations, showing an ability to capture the temporal and spatial dynamics of atmospheric UHI at a fine scale. Finally, a new metric for quantifying the urban heat island is proposed. The urban heat island curve (UHIC), is developed to represent UHI intensity by integrating the urban surface heterogeneity in a curve. UHIC illustrates the relationship between the air temperature and the urban fractions, and emphasizes the temperature gradients, consequently decreasing the impact of the data biases. This research illustrates the potential for satellite data to monitor and increase our understanding of UHI climatology

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

A global long-term (1981–2000) land surface temperature product for NOAA AVHRR

Land surface temperature (LST) plays an important role in the research of climate change and various land surface processes. Before 2000, global LST products with relatively high temporal and spatial resolutions are scarce, despite a variety of operational satellite LST products. In this study, a global 0.05∘×0.05∘ historical LST product is generated from NOAA advanced very-high-resolution radiometer (AVHRR) data (1981–2000), which includes three data layers: (1) instantaneous LST, a product generated by integrating several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST; and (3) monthly averages of ODC LST. For an assumed maximum uncertainty in emissivity and column water vapor content of 0.04 and 1.0 g cm−2, respectively, evaluated against the simulation dataset, the RF-SWA method has a mean bias error (MBE) of less than 0.10 K and a standard deviation (SD) of 1.10 K. To compensate for the influence of orbital drift on LST, the retrieved RF-SWA LST was normalized with an improved ODC method. The RF-SWA LST were validated with in situ LST from Surface Radiation Budget (SURFRAD) sites and water temperatures obtained from the National Data Buoy Center (NDBC). Against the in situ LST, the RF-SWA LST has a MBE of 0.03 K with a range of −1.59–2.71 K, and SD is 1.18 K with a range of 0.84–2.76 K. Since water temperature only changes slowly, the validation of ODC LST was limited to SURFRAD sites, for which the MBE is 0.54 K with a range of −1.05 to 3.01 K and SD is 3.57 K with a range of 2.34 to 3.69 K, indicating good product accuracy. As global historical datasets, the new AVHRR LST products are useful for filling the gaps in long-term LST data. Furthermore, the new LST products can be used as input to related land surface models and environmental applications. Furthermore, in support of the scientific research community, the datasets are freely available at https://doi.org/10.5281/zenodo.3934354 for RF-SWA LST (Ma et al., 2020a), https://doi.org/10.5281/zenodo.3936627 for ODC LST (Ma et al., 2020c), and https://doi.org/10.5281/zenodo.3936641 for monthly averaged LST (Ma et al., 2020b)

The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields

Soil moisture measurements are needed in a large number of applications such as hydro-climate approaches, watershed water balance management and irrigation scheduling. Nowadays, different kinds of methodologies exist for measuring soil moisture. Direct methods based on gravimetric sampling or time domain reflectometry (TDR) techniques measure soil moisture in a small volume of soil at few particular locations. This typically gives a poor description of the spatial distribution of soil moisture in relatively large agriculture fields. Remote sensing of soil moisture provides widespread coverage and can overcome this problem but suffers from other problems stemming from its low spatial resolution. In this context, the DISaggregation based on Physical And Theoretical scale CHange (DISPATCH) algorithm has been proposed in the literature to downscale soil moisture satellite data from 40 to 1¿km resolution by combining the low-resolution Soil Moisture Ocean Salinity (SMOS) satellite soil moisture data with the high-resolution Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) datasets obtained from a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. In this work, DISPATCH estimations are compared with soil moisture sensors and gravimetric measurements to validate the DISPATCH algorithm in an agricultural field during two different hydrologic scenarios: wet conditions driven by rainfall events and wet conditions driven by local sprinkler irrigation. Results show that the DISPATCH algorithm provides appropriate soil moisture estimates during general rainfall events but not when sprinkler irrigation generates occasional heterogeneity. In order to explain these differences, we have examined the spatial variability scales of NDVI and LST data, which are the input variables involved in the downscaling process. Sample variograms show that the spatial scales associated with the NDVI and LST properties are too large to represent the variations of the average soil moisture at the site, and this could be a reason why the DISPATCH algorithm does not work properly in this field site.Peer ReviewedPostprint (published version

The effect of land use on land surface temperature in the Netherlands

The Netherlands has experienced a rapid rate of land use change from 2000 to 2008. Land use change is especially urban expansion and open agriculture reduction which is due to enhanced economic growth. This thesis reports an investigation into the application of remote sensing, geographic information systems (GIS) and statistical methods to provide quantitative information on the effect of land use on land surface temperature. Remote sensing techniques were used to retrieve the land surface temperature by using MODIS Terra (MOD11A2) product. As land use change alters the thermal environment, LST could be a proper change indicator to show thermal changes in relation to land use changes. GIS was further applied to extract the coverage ratio of each land use in the context of LST pixels. Using correlation and linear regression this interrelationship was then quantified. Night land surface temperature correlates positively with the coverage percentage of open agriculture, forest and greenhouse farming. This association is negative for buildup are and inland water and offshore land use types. The results also show that inland water and offshore area has the highest night LST and the lowest day LST. Build up is the warmest land use during the days and the second warm land use during the night time. The result of this research will be helpful for urban planners and environmental scientists