Sharpening ECOSTRESS and VIIRS Land Surface Temperature Using Harmonized Landsat-Sentinel Surface Reflectances

Land surface temperature (LST) is a key diagnostic indicator of agricultural water use and crop stress. LST data retrieved from thermal infrared (TIR) band imagery, however, tend to have a coarser spatial resolution (e.g., 100 m for Landsat 8) than surface reflectance (SR) data collected from shortwave bands on the same instrument (e.g., 30 m for Landsat). Spatial sharpening of LST data using the higher resolution multi-band SR data provides an important path for improved agricultural monitoring at sub-field scales. A previously developed Data Mining Sharpener (DMS) approach has shown great potential in the sharpening of Landsat LST using Landsat SR data co-collected over various landscapes. This work evaluates DMS performance for sharpening ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST (~70 m native resolution) and Visible Infrared Imaging Radiometer Suite (VIIRS) LST (375 m) data using Harmonized Landsat and Sentinel-2 (HLS) SR data, providing the basis for generating 30-m LST data at a higher temporal frequency than afforded by Landsat alone. To account for the misalignment between ECOSTRESS/VIIRS and Landsat/HLS caused by errors in registration and orthorectification, we propose a modified version of the DMS approach that employs a relaxed box size for energy conservation (EC). Sharpening experiments were conducted over three study sites in California, and results were evaluated visually and quantitatively against LST data from unmanned aerial vehicles (UAV) flights and from Landsat 8. Over the three sites, the modified DMS technique showed improved sharpening accuracy over the standard DMS for both ECOSTRESS and VIIRS, suggesting the effectiveness of relaxing EC box in relieving misalignment-induced errors. To achieve reasonable accuracy while minimizing loss of spatial detail due to the EC box size increase, an optimal EC box size of 180–270 m was identified for ECOSTRESS and about 780 m for VIIRS data based on experiments from the three sites. Results from this work will facilitate the development of a prototype system that generates high spatiotemporal resolution LST products for improved agricultural water use monitoring by synthesizing multi-source remote sensing data

Evaluating the impact of road infrastructure on household income in Papua New Guinea:Spatial data compilation and analysis

As part of the ADB sub‐project ‘Developing Impact Evaluation Methodologies, Approaches, and Capacities in Selected Developing Member Countries’ two closely related research projects were carried out by VU to evaluate the impact of road infrastructure on household income in Papua New Guinea. One project focused on econometric analysis (contract no. 117629‐S85196), while the other developed the spatial database needed for this analysis (contract no. 117642‐S84962). The current report documents the data collection process and provides a concise description of the data that were collected for the project and the analyses that were performed to enrich the available data sources to create meaningful variables for the statistical analysis. It serves as a background document with the scientific paper‐that concisely describes the findings of the two joint research projects

Satellite thermographies as an essential tool for the identification of cold air pools: an example from SE Spain

The processes involved in the formation of nocturnal temperature inversions (NTIs) are of great relevance throughout the year, notably influencing the surface distribution of minimum temperatures during nights of atmospheric stability. The low density of surface meteorological stations in the study area motivated the use of thermographies for the mapping and identification of cold air pools CAPs. Thermal distribution during stable nights leads to the formation of CAPs in valley areas and depressed areas, and in areas with warmer air (WAM) in orographically complex areas. The thermographies carried out with satellite products from AQUA and SUOMI-NPP (MODIS and VIIRS LST) represent the only tool capable of fully radiographing the territory under study, thus overcoming the limitations in the interpolation of minimum surface temperatures. The main objective of the research was, therefore, to value thermography as an important tool in the identification of CAPs. The products used were subjected to statistical validation with the surface temperatures recorded in meteorological observatories (R2 0.87/0.88 and Bias −1.2/-1.3) with a new objective of making thermal distribution maps in nocturnal stability processes

Phenological corrections to a field-scale, ET-based crop stress indicator: An application to yield forecasting across the U.S. Corn Belt

Soil moisture deficiency is a major factor in determining crop yields in water-limited agricultural production regions. Evapotranspiration (ET), which consists of crop water use through transpiration and water loss through direct soil evaporation, is a good indicator of soil moisture availability and vegetation health. ET therefore has been an integral part of many yield estimation efforts. The Evaporative Stress Index (ESI) is an ET-based crop stress indicator that describes temporal anomalies in a normalized evapotranspiration metric as derived from satellite remote sensing. ESI has demonstrated the capacity to explain regional yield variability in water-limited regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to interannual phenological variability. This investigation selected three study sites across the U.S. Corn Belt – Mead, NE, Ames, IA and Champaign, IL – to investigate the potential operational value of 30-m resolution, phenologically corrected ESI datasets for yield prediction. The analysis was conducted over an 8-year period from 2010 to 2017, which included both drought and pluvial conditions as well as a broad range in yield values. Detrended yield anomalies for corn and soybean were correlated with ESI computed using annual ET curves temporally aligned based on (1) calendar date, (2) crop emergence date, and (3) a growing degree day (GDD) scaled time axis. Results showed that ESI has good correlations with yield anomalies at the county scale and that phenological corrections to the annual temporal alignment of the ET timeseries improve the correlation, especially when the time axis is defined by GDD rather than the calendar date. Peak correlations occur in the silking stage for corn and the reproductive stage for soybean – phases when these crops are particularly sensitive to soil moisture deficiencies. Regression equations derived at the time of peak correlation were used to estimate yields at county scale using a leave-one-out cross-validation strategy. The ESI-based yield estimates agree well with the USDA National Agricultural Statistics Service (NASS) county-level crop yield data, with correlation coefficients ranging from 0.79 to 0.93 and percent root-mean-square errors of 5–8%. These results demonstrate that remotely sensed ET at high spatiotemporal resolution can convey valuable water stress information for forecasting crop yields across the Corn Belt if interannual phenological variability is considered

Utility of thermal sharpening over Texas high plains irrigated agricultural fields

Irrigated crop production in the Texas high plains (THP) is dependent on water extracted from the Ogallala Aquifer, an area suffering from sever water shortage. Water management in this area is therefore highly important. Thermal satellite imagery at high temporal (~daily) and high spatial (~100 m) resolutions could provide important surface boundary conditions for vegetation stress and water use monitoring, mainly through energy balance models such as DisALEXI. At present, however, no satellite platform collects such high spatiotemporal resolution data. The objective of this study is to examine the utility of an image sharpening technique (TsHARP) for retrieving land surface temperature at high spatial resolution (down to 60 m) from moderate spatial resolution (1 km) imagery, which is typically available at higher (~daily) temporal frequency. A simulated sharpening experiment was applied to Landsat 7 imagery collected over the THP in September 2002 to examine its utility over both agricultural and natural vegetation cover. The Landsat thermal image was aggregated to 960 m resolution and then sharpened to its native resolution of 60 m and to various intermediate resolutions. The algorithm did not provide any measurable improvement in estimating high-resolution temperature distributions over natural land cover. In contrast, TsHARP was shown to retrieve high-resolution temperature information with good accuracy over much of the agricultural area within the scene. However, in recently irrigated fields, TsHARP could not reproduce the temperature patterns. Therefore we conclude that TsHARP is not an adequate substitute for 100-m-scale observations afforded by the current Landsat platforms. Should the thermal imager be removed from follow-on Landsat platforms, we will lose valuable capacity to monitor water use at the field scale, particularly in many agricultural regions where the typical field size is ~100 X 100 m. In this scenario, sharpened thermal imagery from instruments like MODIS or VIIRS would be the suboptimal alternative

Utility of thermal sharpening over Texas high plains irrigated agricultural fields

Irrigated crop production in the Texas high plains (THP) is dependent on water extracted from the Ogallala Aquifer, an area suffering from sever water shortage. Water management in this area is therefore highly important. Thermal satellite imagery at high temporal (~daily) and high spatial (~100 m) resolutions could provide important surface boundary conditions for vegetation stress and water use monitoring, mainly through energy balance models such as DisALEXI. At present, however, no satellite platform collects such high spatiotemporal resolution data. The objective of this study is to examine the utility of an image sharpening technique (TsHARP) for retrieving land surface temperature at high spatial resolution (down to 60 m) from moderate spatial resolution (1 km) imagery, which is typically available at higher (~daily) temporal frequency. A simulated sharpening experiment was applied to Landsat 7 imagery collected over the THP in September 2002 to examine its utility over both agricultural and natural vegetation cover. The Landsat thermal image was aggregated to 960 m resolution and then sharpened to its native resolution of 60 m and to various intermediate resolutions. The algorithm did not provide any measurable improvement in estimating high-resolution temperature distributions over natural land cover. In contrast, TsHARP was shown to retrieve high-resolution temperature information with good accuracy over much of the agricultural area within the scene. However, in recently irrigated fields, TsHARP could not reproduce the temperature patterns. Therefore we conclude that TsHARP is not an adequate substitute for 100-m-scale observations afforded by the current Landsat platforms. Should the thermal imager be removed from follow-on Landsat platforms, we will lose valuable capacity to monitor water use at the field scale, particularly in many agricultural regions where the typical field size is ~100 X 100 m. In this scenario, sharpened thermal imagery from instruments like MODIS or VIIRS would be the suboptimal alternative

Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring

Fusing satellite imagery acquired with different sensors has been a long-standing challenge of Earth observation, particularly across different modalities such as optical and Synthetic Aperture Radar (SAR) images. Here, we explore the joint analysis of imagery from different sensors in the light of representation learning: we propose to learn a joint embedding of multiple satellite sensors within a deep neural network. Our application problem is the monitoring of lake ice on Alpine lakes. To reach the temporal resolution requirement of the Swiss Global Climate Observing System (GCOS) office, we combine three image sources: Sentinel-1 SAR (S1-SAR), Terra MODIS, and Suomi-NPP VIIRS. The large gaps between the optical and SAR domains and between the sensor resolutions make this a challenging instance of the sensor fusion problem. Our approach can be classified as a late fusion that is learned in a data-driven manner. The proposed network architecture has separate encoding branches for each image sensor, which feed into a single latent embedding. I.e., a common feature representation shared by all inputs, such that subsequent processing steps deliver comparable output irrespective of which sort of input image was used. By fusing satellite data, we map lake ice at a temporal resolution of < 1.5 days. The network produces spatially explicit lake ice maps with pixel-wise accuracies > 91% (respectively, mIoU scores > 60%) and generalises well across different lakes and winters. Moreover, it sets a new state-of-the-art for determining the important ice-on and ice-off dates for the target lakes, in many cases meeting the GCOS requirement

Satellite based methane emission estimation for flaring activities in oil and gas industry: A data-driven approach(SMEEF-OGI)

Klimaendringer, delvis utløst av klimagassutslipp, utgjør en kritisk global utfordring. Metan, en svært potent drivhusgass med et globalt oppvarmings potensial på 80 ganger karbondioksid, er en betydelig bidragsyter til denne krisen. Kilder til metanutslipp inkluderer olje- og gassindustrien, landbruket og avfallshåndteringen, med fakling i olje- og gassindustrien som en betydelig utslippskilde. Fakling, en standard prosess i olje- og gassindustrien, antas ofte å være 98 % effektiv ved omdannelse av metan til mindre skadelig karbondioksid. Nyere forskning fra University of Michigan, Stanford, Environmental Defense Fund og Scientific Aviation indikerer imidlertid at den allment aksepterte effektiviteten på 98 % av fakling ved konvertering av metan til karbondioksid, en mindre skadelig klimagass, kan være unøyaktig. Denne undersøkelsen revurderer fakkelprosessens effektivitet og dens rolle i metankonvertering. Dette arbeidet fokuserer på å lage en metode for uavhengig å beregne metanutslipp fra olje- og gassvirksomhet for å løse dette problemet. Satellittdata, som er et nyttig verktøy for å beregne klimagassutslipp fra ulike kilder, er inkludert i den foreslåtte metodikken. I tillegg til standard overvåkingsteknikker, tilbyr satellittdata en uavhengig, ikke-påtrengende, rimelig og kontinuerlig overvåkingstilnærming. På bakgrunn av dette er problemstillingen for dette arbeidet følgende "Hvordan kan en datadrevet tilnærming utvikles for å forbedre nøyaktigheten og kvaliteten på estimering av metanutslipp fra faklingsaktiviteter i olje- og gassindustrien, ved å bruke satellittdata fra utvalgte plattformer for å oppdage og kvantifisere fremtidige utslipp basert på maskinlæring mer effektivt?" For å oppnå dette ble følgende mål og aktiviteter utført. * Teoretisk rammeverk og sentrale begreper * Teknisk gjennomgang av dagens toppmoderne satellittplattformer og eksisterende litteratur. * Utvikling av et Proof of Concept * Foreslå en evaluering av metoden * Anbefalinger og videre arbeid Dette arbeidet har tatt i bruk en systematisk tilnærming, som starter med et omfattende teoretisk rammeverk for å forstå bruken av fakling, de miljømessige implikasjonene av metan, den nåværende «state-of-the-art» av forskning, og «state-of-the-art» i felt for fjernmåling via satellitter. Basert på rammeverket utviklet i de innledende fasene av dette arbeidet, ble det formulert en datadrevet metodikk, som benytter VIIRS-datasettet for å få geografiske områder av interesse. Hyperspektrale data og metandata ble samlet fra Sentinel-2 og Sentinel-5P satellittdatasettet. Denne informasjonen ble behandlet via en foreslått rørledning, med innledende justering og forbedring. I dette arbeidet ble bildene forbedret ved å beregne den normaliserte brennindeksen. Resultatet var et datasett som inneholdt plasseringen av kjente fakkelsteder, med data fra både Sentinel-2 og Sentinel-5P-satellitten. Resultatene understreker forskjellene i dekningen mellom Sentinel-2- og Sentinel-5P-data, en faktor som potensielt kan påvirke nøyaktigheten av metanutslippsestimater. De anvendte forbehandlingsteknikkene forbedret dataklarheten og brukervennligheten markant, men deres effektivitet kan avhenge av fakkelstedenes spesifikke egenskaper og rådatakvaliteten. Dessuten, til tross for visse begrensninger, ga kombinasjonen av Sentinel-2 og Sentinel-5P-data effektivt et omfattende datasett egnet for videre analyse. Avslutningsvis introduserer dette prosjektet en oppmuntrende metodikk for å estimere metanutslipp fra fakling i olje- og gassindustrien. Den legger et grunnleggende springbrett for fremtidig forskning, og forbedrer kontinuerlig presisjonen og kvaliteten på data for å bekjempe klimaendringer. Denne metodikken kan sees i flytskjemaet nedenfor. Basert på arbeidet som er gjort i dette prosjektet, kan fremtidig arbeid fokusere på å innlemme alternative kilder til metan data, utvide interesseområdene gjennom industrisamarbeid og forsøke å trekke ut ytterligere detaljer gjennom bildesegmenteringsmetoder. Dette prosjektet legger et grunnlag, og baner vei for påfølgende utforskninger å bygge videre på.Climate change, precipitated in part by greenhouse gas emissions, presents a critical global challenge. Methane, a highly potent greenhouse gas with a global warming potential of 80 times that of carbon dioxide, is a significant contributor to this crisis. Sources of methane emissions include the oil and gas industry, agriculture, and waste management, with flaring in the oil and gas industry constituting a significant emission source. Flaring, a standard process in the Oil and gas industry is often assumed to be 98% efficient when converting methane to less harmful carbon dioxide. However, recent research from the University of Michigan, Stanford, the Environmental Defense Fund, and Scientific Aviation indicates that the widely accepted 98% efficiency of flaring in converting methane to carbon dioxide, a less harmful greenhouse gas, may be inaccurate. This investigation reevaluates the flaring process's efficiency and its role in methane conversion. This work focuses on creating a method to independently calculate methane emissions from oil and gas activities to solve this issue. Satellite data, which is a helpful tool for calculating greenhouse gas emissions from various sources, is included in the suggested methodology. In addition to standard monitoring techniques, satellite data offers an independent, non-intrusive, affordable, and continuous monitoring approach. Based on this, the problem statement for this work is the following “How can a data-driven approach be developed to enhance the accuracy and quality of methane emission estimation from flaring activities in the Oil and Gas industry, using satellite data from selected platforms to detect and quantify future emissions based on Machine learning more effectively?" To achieve this, the following objectives and activities were performed. * Theoretical Framework and key concepts * Technical review of the current state-of-the-art satellite platforms and existing literature. * Development of a Proof of Concept * Proposing an evaluation of the method * Recommendations and further work This work has adopted a systematic approach, starting with a comprehensive theoretical framework to understand the utilization of flaring, the environmental implications of methane, the current state-of-the-art of research, and the state-of-the-art in the field of remote sensing via satellites. Based upon the framework developed during the initial phases of this work, a data-driven methodology was formulated, utilizing the VIIRS dataset to get geographical areas of interest. Hyperspectral and methane data were aggregated from the Sentinel-2 and Sentinel-5P satellite dataset. This information was processed via a proposed pipeline, with initial alignment and enhancement. In this work, the images were enhanced by calculating the Normalized Burn Index. The result was a dataset containing the location of known flare sites, with data from both the Sentinel-2, and the Sentinel-5P satellite. The results underscore the disparities in coverage between Sentinel-2 and Sentinel-5P data, a factor that could potentially influence the precision of methane emission estimates. The applied preprocessing techniques markedly enhanced data clarity and usability, but their efficacy may hinge on the flaring sites' specific characteristics and the raw data quality. Moreover, despite certain limitations, the combination of Sentinel-2 and Sentinel-5P data effectively yielded a comprehensive dataset suitable for further analysis. In conclusion, this project introduces an encouraging methodology for estimating methane emissions from flaring activities within the oil and gas industry. It lays a foundational steppingstone for future research, continually enhancing the precision and quality of data in combating climate change. This methodology can be seen in the flow chart below. Based on the work done in this project, future work could focus on incorporating alternative sources of methane data, broadening the areas of interest through industry collaboration, and attempting to extract further features through image segmentation methods. This project signifies a start, paving the way for subsequent explorations to build upon. Climate change, precipitated in part by greenhouse gas emissions, presents a critical global challenge. Methane, a highly potent greenhouse gas with a global warming potential of 80 times that of carbon dioxide, is a significant contributor to this crisis. Sources of methane emissions include the oil and gas industry, agriculture, and waste management, with flaring in the oil and gas industry constituting a significant emission source. Flaring, a standard process in the Oil and gas industry is often assumed to be 98% efficient when converting methane to less harmful carbon dioxide. However, recent research from the University of Michigan, Stanford, the Environmental Defense Fund, and Scientific Aviation indicates that the widely accepted 98% efficiency of flaring in converting methane to carbon dioxide, a less harmful greenhouse gas, may be inaccurate. This investigation reevaluates the flaring process's efficiency and its role in methane conversion. This work focuses on creating a method to independently calculate methane emissions from oil and gas activities to solve this issue. Satellite data, which is a helpful tool for calculating greenhouse gas emissions from various sources, is included in the suggested methodology. In addition to standard monitoring techniques, satellite data offers an independent, non-intrusive, affordable, and continuous monitoring approach. Based on this, the problem statement for this work is the following “How can a data-driven approach be developed to enhance the accuracy and quality of methane emission estimation from flaring activities in the Oil and Gas industry, using satellite data from selected platforms to detect and quantify future emissions based on Machine learning more effectively?" To achieve this, the following objectives and activities were performed. * Theoretical Framework and key concepts * Technical review of the current state-of-the-art satellite platforms and existing literature. * Development of a Proof of Concept * Proposing an evaluation of the method * Recommendations and further work This work has adopted a systematic approach, starting with a comprehensive theoretical framework to understand the utilization of flaring, the environmental implications of methane, the current state-of-the-art of research, and the state-of-the-art in the field of remote sensing via satellites. Based upon the framework developed during the initial phases of this work, a data-driven methodology was formulated, utilizing the VIIRS dataset to get geographical areas of interest. Hyperspectral and methane data were aggregated from the Sentinel-2 and Sentinel-5P satellite dataset. This information was processed via a proposed pipeline, with initial alignment and enhancement. In this work, the images were enhanced by calculating the Normalized Burn Index. The result was a dataset containing the location of known flare sites, with data from both the Sentinel-2, and the Sentinel-5P satellite. The results underscore the disparities in coverage between Sentinel-2 and Sentinel-5P data, a factor that could potentially influence the precision of methane emission estimates. The applied preprocessing techniques markedly enhanced data clarity and usability, but their efficacy may hinge on the flaring sites' specific characteristics and the raw data quality. Moreover, despite certain limitations, the combination of Sentinel-2 and Sentinel-5P data effectively yielded a comprehensive dataset suitable for further analysis. In conclusion, this project introduces an encouraging methodology for estimating methane emissions from flaring activities within the oil and gas industry. It lays a foundational steppingstone for future research, continually enhancing the precision and quality of data in combating climate change. This methodology can be seen in the flow chart below. Based on the work done in this project, future work could focus on incorporating alternative sources of methane data, broadening the areas of interest through industry collaboration, and attempting to extract further features through image segmentation methods. This project signifies a start, paving the way for subsequent explorations to build upon

Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring

Fusing satellite imagery acquired with different sensors has been a long-standing challenge of Earth observation, particularly across different modalities such as optical and synthetic aperture radar (SAR) images. Here, we explore the joint analysis of imagery from different sensors in the light of representation learning: we propose to learn a joint embedding of multiple satellite sensors within a deep neural network. Our application problem is the monitoring of lake ice on Alpine lakes. To reach the temporal resolution requirement of the Swiss Global Climate Observing System (GCOS) office, we combine three image sources: Sentinel-1 SAR (S1-SAR), Terra moderate resolution imaging spectroradiometer (MODIS), and Suomi-NPP visible infrared imaging radiometer suite (VIIRS). The large gaps between the optical and SAR domains and between the sensor resolutions make this a challenging instance of the sensor fusion problem. Our approach can be classified as a late fusion that is learned in a data-driven manner. The proposed network architecture has separate encoding branches for each image sensor, which feed into a single latent embedding, i.e., a common feature representation shared by all inputs, such that subsequent processing steps deliver comparable output irrespective of which sort of input image was used. By fusing satellite data, we map lake ice at a temporal resolution of 91% [respectively, mean per-class Intersection-over-Union (mIoU) scores >60%] and generalizes well across different lakes and winters. Moreover, it sets a new state-of-the-art for determining the important ice-on and ice-off dates for the target lakes, in many cases meeting the GCOS requirement