Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI

Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on time series of green Leaf Area Index (LAI) generated from NASA’s Harmonized Landsat 8 (L8) and Sentinel-2 (S2) surface reflectance dataset from 2016 to 2019. LAI time series were processed for each satellite dataset, which were used separately and combined to identify seasonal dynamics for a selection of crop types (wheat, clover, maize and rice). For the combination of L8 with S2 LAI products, we proposed two time series smoothing and fitting methods: (1) the Savitzky–Golay (SG) filter and (2) the Gaussian Processes Regression (GPR) fitting function. Single-sensor and L8-S2 combined LAI time series were used for the calculation of key crop Land Surface Phenology (LSP) metrics (start of season, end of season, length of season), whereby the detection of cropland growing seasons was based on two established threshold methods, i.e., a seasonal or a relative amplitude value. Overall, the developed phenology extraction scheme enabled identifying up to two successive crop cycles within a year, with a superior performance observed for the seasonal than for the relative threshold method, in terms of consistency and cropland season detection capability. Differences between the time series collections were analyzed by comparing the phenology metrics per crop type and year. Results suggest that L8-S2 combined LAI data streams with GPR led to a more precise detection of the start and end of growing seasons for most crop types, reaching an overall detection of 74% over the total planted crops versus 69% with S2 and 63% with L8 alone. Finally, the phenology mapping allowed us to evaluate the spatial and temporal evolution of the croplands over the agroecosystem in the Nile Delta.E.A. was supported by the predoctoral scholarship, grant number ACIF/2019/187, funded by the Generalitat Valenciana and co-funded by the European Social Fund. J.V. and S.B. were supported by the European Research Council (ERC) under the ERC-2017-STG SENTIFLEX project, grant number 755617. J.V. was additionally supported by a Ramón y Cajal Contract (Spanish Ministry of Science, Innovation and Universities). S.B. was additionally supported by the Generalitat Valenciana SEJIGENT program (SEJIGENT/2021/001) and European Union—NextGenerationEU (ZAMBRANO 21-04)

Triennial Report: 2012-2014

Triennial Report Purpose [Page] 3 Geographical Information Science Center of Excellence [Page] 5 SDSU Faculty [Page] 6 EROS Faculty [Page] 13 Research Professors [Page] 19 Postdoctoral Fellows [Page] 24 GSE Ph.D Program [Page] 36 Ph.D. Fellowships [Page] 37 Ph.D. Students [Page] 38 Recent Ph.D. Graduates [Page] 46 Masters Students [Page] 56 Previous Ph.D. Students [Page] 58 Center Scholars Program [Page] 59 Research Staff [Page] 60 Administrative and Information Technology Staff [Page] 62 Computer Resources [Page] 66 Research Funding [Page] 67 Glancing Back, Looking Forward [Page] 68 Appendix I Alumni Faculty and Staff Appendix II Cool Faculty Research and Locations Appendix III Non-Academic Fun Things To Do Appendix IV Publications 2012-2014 Appendix V Directory Appendix VI GIScCE Birthplace Map Appendix VII How To Get To The GIScC

Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA

The Evaporative Stress Index (ESI) quantifies temporal anomalies in a normalized evapotranspiration (ET) metric describing the ratio of actual-to-reference ET (fRET) as derived from satellite remote sensing. At regional scales (3–10 km pixel resolution), the ESI has demonstrated the capacity to capture developing crop stress and impacts on regional yield variability in water-limited agricultural regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to spatial and temporal limitations in the standard ESI products. In this study, we investigated potential improvements to ESI by generating maps of ET, fRET, and fRET anomalies at high spatiotemporal resolution (30-m pixels, daily time steps) using a multi-sensor data fusion method, enabling separation of landcover types with different phenologies and resilience to drought. The study was conducted for the period 2010–2014 covering a region around Mead, Nebraska that includes both rainfed and irrigated crops. Correlations between ESI and measurements of maize yield were investigated at both the field and county level to assess the potential of ESI as a yield forecasting tool. To examine the role of crop phenology in yield-ESI correlations, annual input fRET time series were aligned by both calendar day and by biophysically relevant dates (e.g. days since planting or emergence). At the resolution of the operational U.S. ESI product (4 km), adjusting fRET alignment to a regionally reported emergence date prior to anomaly computation improves r2 correlations with county-level yield estimates from 0.28 to 0.80. At 30-m resolution, where pure maize pixels can be isolated from other crops and landcover types, county-level yield correlations improved from 0.47 to 0.93 when aligning fRET by emergence date rather than calendar date. Peak correlations occurred 68 days after emergence, corresponding to the silking stage for maize when grain development is particularly sensitive to soil moisture deficiencies. The results of this study demonstrate the utility of remotely sensed ET in conveying spatially and temporally explicit water stress information to yield prediction and crop simulation models

Developing Earth Observations Requirements for Global Agricultural Monitoring: Toward a Multi-Mission Data Acquisition Strategy

Global food supply and our understanding of it have never been more important than in today's changing world. For several decades, Earth observations (EO) have been employed to monitor agriculture, including crop area, type, condition, and yield forecasting processes, at multiple scales. However, the EO data requirements to consistently derive these informational products had not been well defined. Responding to this dearth, I have articulated spatially explicit EO requirements with a focus on moderate resolution (10-70m) active and passive remote sensors, and evaluate current and near-term missions' capabilities to meet these EO requirements. To accomplish this, periods requiring monitoring have been identified through the development of agricultural growing season calendars (GSCs) at 0.5 degrees from MODIS surface reflectance. Second, a global analysis of cloud presence probability and extent using MOD09 daily cloud flags over 2000-2012 has shown that the early-to-mid agricultural growing season (AGS) - an important period for monitoring - is more persistently and pervasively occluded by clouds than is the late and non-AGS. Third, spectral, spatial, and temporal resolution data requirements have been developed through collaboration with international agricultural monitoring experts. These requirements have been spatialized through the incorporation of the GSCs and cloud cover information, establishing the revisit frequency required to yield reasonably clear views within 8 or 16 days. A comparison of these requirements with hypothetical constellations formed from current/planned moderate resolution optical EO missions shows that to yield a scene at least 70% clear within 8 or 16 days, 46-55% or 10-32% of areas, respectively, need a revisit more frequent than Landsat 7 & 8 combined can deliver. Supplementing Landsat 7 & 8 with missions from different space agencies leads to an improved capacity to meet requirements, with Resourcesat-2 providing the largest incremental improvement in requirements met. No single mission/observatory can consistently meet requirements throughout the year, and the only way to meet a majority (77-94% for ≥70% clear; 47-73% for 100% clear) of 8 day requirements is through coordination of multiple missions. Still, gaps exist in persistently cloudy regions and periods, highlighting the need for data coordination and for consideration of active EO for agricultural monitoring

Impact of Satellite Geometric Distortions on Landscape Analysis: Effects on Albedo

Data from wide field-of-view sensors have been providing information about the Earth's surface since the early 1980's. This manuscript is the result of investigations designed to determine the effective resolution and geometric variability of the NASA Earth Observing System MODerate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imager Radiometer Suite (VIIRS) gridded data. Although the wide field-of-view and high temporal frequency of MODIS provide near-daily global coverage, inconsistent observation assignment in geolocated MODIS pixels measurably demonstrates how spatial accuracy is affected by pixel-size growth (up to 4.8x) along-scan. For studying the effective resolution, the point spread function of nominal 250m MODIS gridded surface reflectance products (L2G) was estimated from [man-made] large size targets. The findings indicate that in near-optimal locations the resolution of (sinusoidal grid) gridded products varies between 344m-835m along-scan for a range of viewing angles, but also indicate location-dependent variability with along-scan and along-track ranges of 314m-1363m and 284m-501m respectively. Albedo was identified as a well-known physical metric to study the effects of geometric variability, thus a broadband albedo using MODIS-like geometry was simulated for five EOS validation sites. Results of each site simulation exhibit compounded uncertainty attributable to the geometric distortion in ranges sufficient to influence climate models (i.e. ranges from 0.01-0.045 albedo). A second series of broadband albedo simulations was developed for the same five EOS validation sites using VIIRS-like geometries and aggregation zones. Spatially heterogeneous land cover demonstrated a marginally significant difference in the mean albedo between aggregation zones (< 0.015). Results from data simulating temporal compositing, demonstrate the influence of geometric artifacts through differing levels of uncertainty between periods (i.e. ranges from 0.01-0.05 albedo). The variability in both MODIS and VIIRS L2G questions the standard application of a global fixed grid, and indicates that regional projections combined with a representative grid cell 4x the nominal detector size (i.e. 1000m and 1500m for MODIS and VIIRS, respectively) are potentially useful for products using off-nadir views. This work ultimately resolves the surface-feature representation of temporo-spatial wide field-of-view instrument observations and quantifies the results of associating inherently-variable observations into an artificially-fixed and geometrically-regular space

Multi-scale actual evapotranspiration mapping in South America with remote sensing data and the geeSEBAL model

O monitoramento preciso da evapotranspiração (ET) é crucial para gerenciar os recursos hídricos, garantir a segurança alimentar e avaliar os impactos das mudanças climáticas. Modelos de Balanço de Energia da Superfície (SEB) que usam dados de sensoriamento remoto são os mais confiáveis para estimar a ET, mas muitas vezes são difíceis de aplicar em grande escala devido ao longo tempo de processamento, necessidade de calibração local, entre outros obstáculos. Esta tese tem como foco a melhoria do geeSEBAL, uma implementação do modelo Surface Energy Balance Algorithm for Land (SEBAL) na plataforma Google Earth Engine (GEE), adaptando-o para modelagem em escala continental, usando imagens do Moderate Resolution Imaging Spectroradiometer (MODIS). O novo modelo, chamado geeSEBALMODIS, foi usado para gerar uma série temporal de ET a cada 8 dias para a América do Sul com pixels de 500 m. Estudos de validação mostram que o geeSEBAL-MODIS é mais preciso do que outros produtos globais de ET, com uma redução do erro de 13% na escala de campo e 30% na escala de bacia hidrográfica. O conjunto de dados está disponível para o público e pode ser usado para monitorar tanto mudanças climáticas em grande escala quanto as variações locais de ET relacionadas às atividades humanas. A análise de tendências mostra um aumento de 8,4% na ET sobre a América do Sul, associado ao aumento da demanda atmosférica, e à redução da precipitação e disponibilidade de água. Esses resultados destacam a importância de informações precisas sobre os processos do ciclo hidrológico para auxiliar no planejamento e gerenciamento dos recursos hídricos em um cenário de maior escassez. Nesse contexto, projetos como o OpenET, que fornece dados confiáveis e de alta resolução espacial de ET nos Estados Unidos, são cruciais para monitorar o consumo de água e auxiliar no desenvolvimento sustentável. Este trabalho também apresenta uma reprodução parcial do processo do OpenET para a intercomparação de modelos de sensoriamento remoto com dados de torres de fluxo, usando torres micrometeorológicas na América do Sul. Os resultados são promissores e abrem caminho para a expansão do OpenET além dos Estados Unidos e em direção a uma aplicação global.Accurately monitoring evapotranspiration (ET) is crucial for managing water resources, ensuring food security, and assessing the impacts of climate change. Surface Energy Balance (SEB) models that use remote sensing data are the most reliable for estimating ET, but they are often challenging to apply on a large scale due to long processing times, and local calibration requirements, among other obstacles. This dissertation focuses on improving geeSEBAL, an implementation of the Surface Energy Balance Algorithm for Land (SEBAL) model on the Google Earth Engine (GEE) platform, by adapting it for continental-scale modeling using Moderate Resolution Imaging Spectroradiometer (MODIS) images. The new model, called geeSEBAL-MODIS, was used to generate a temporal series of ET every 8 days for South America with pixels of 500 m. Validation studies show that geeSEBAL-MODIS is more accurate than other global ET products, with a reduction in error of 13% at the field scale and 30% at the basin scale. The dataset is publicly available and can be used to monitor both largescale climate change and local ET variations related to human activities. Trend analysis shows an 8.4% increase in ET over South America, associated with increased atmospheric demand, and reductions in precipitation and water availability. These findings underscore the importance of accurate information on hydrological cycle processes to assist in planning and managing water resources in a scenario of greater scarcity. In this context, projects like OpenET, which provides reliable and high spatial-resolution ET data in the United States, are crucial for monitoring water consumption and aiding in sustainable development. This work also presents a partial reproduction of the OpenET process for intercomparing remote sensing models with flux tower data, using micrometeorological towers in South America. The results are promising and pave the way for expanding OpenET beyond the United States and toward global application

Triennial Report: 2006-2008

Triennial Report Purpose [Page] 2 The Geographic Information Science Center of Excellence [Page] 4 Three Years in Review [Page] 5 SDSU Faculty [Page] 6-11 EROS Faculty [Page] 12-16 Post-Doctoral Researchers [Page] 17-26 GSE Ph.D. program [Page] 27 Ph.D. Students [Page] 28-39 Center Scholars Program [Page] 40 Masters Students [Page] 41 Geospatial Analysts [Page] 42 Administrative Staff [Page] 43 Center Alumni [Page] 44 Research Funding [Page] 45-46 Ph.D. Student Scholarship Grants [Page] 47 Computing Resources [Page] 48 Looking Forward [Page] 49 Appendix I Faculty publications 2006-2008 [Page] 50-58 Appendix II Cool faculty research and locations [Page] 60-65 Appendix III GIScCE birthplace map [Page] 66 Appendix IV Telephone and email contact information [Page] 67-68 Appendix V How to get to the GIScCE [Page] 6