Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario

Optical satellite data have been proven as an efficient source to extract crop information and monitor crop growth conditions over large areas. In local- to subfield-scale crop monitoring studies, both high spatial resolution and high temporal resolution of the image data are important. However, the acquisition of optical data is limited by the constant contamination of clouds in cloudy areas. This thesis explores the potential of polarimetric Synthetic Aperture Radar (SAR) satellite data and the spatio-temporal data fusion approach in crop monitoring and yield estimation applications in southwestern Ontario. Firstly, the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) was investigated. The results show that the SAR backscatters are affected by many factors unrelated to the crop canopy such as the incidence angle and the soil background and the degree of sensitivity varies with the crop types, growing stages, and the polarimetric SAR parameters. Secondly, the Minimum Noise Fraction (MNF) transformation, for the first time, was applied to multitemporal Radarsat-2 polarimetric SAR data in cropland area mapping based on the random forest classifier. An overall classification accuracy of 95.89% was achieved using the MNF transformation of the multi-temporal coherency matrix acquired from July to November. Then, a spatio-temporal data fusion method was developed to generate Normalized Difference Vegetation Index (NDVI) time series with both high spatial and high temporal resolution in heterogeneous regions using Landsat and MODIS imagery. The proposed method outperforms two other widely used methods. Finally, an improved crop phenology detection method was proposed, and the phenology information was then forced into the Simple Algorithm for Yield Estimation (SAFY) model to estimate crop biomass and yield. Compared with the SAFY model without forcing the remotely sensed phenology and a simple light use efficiency (LUE) model, the SAFY incorporating the remotely sensed phenology can improve the accuracy of biomass estimation by about 4% in relative Root Mean Square Error (RRMSE). The studies in this thesis improve the ability to monitor crop growth status and production at subfield scale

Remote Sensing of the Aquatic Environments

The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet

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

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

Analyse der Wasserfarbe von Seen mithilfe räumlich hoch und mittel auflösender Satelliten

Remote sensing techniques can assist traditional lake monitoring approaches by supplying spatial information on optically active lake ecology indicators, i.e. chlorophyll-a (CHL), total suspended matter (TSM), coloured dissolved organic matter (CDOM), and, especially in optically shallow waters, water depth and substrate composition. The present thesis provides an overview on the current research status concerning lake remote sensing and the benefit of time series analyses for lake ecology. To investigate the suitability of Sentinel-2 and Landsat 8 for lake monitoring and their combination with other sensors this thesis focused on two study areas with highly different optical characteristics, i.e. the oligotrophic Lake Starnberg (southern Germany) and the mesotrophic-eutrophic Lake Kummerow (northern Germany). Using the bio-optical model WASI-2D, Sentinel-2A turned out to be suited for retrieving low TSM and CDOM values. The high spatial resolution enabled the differentiation between bare ground and areas covered by submerged aquatic vegetation. Water depth estimations performed well until half Secchi disk depth. Cross-sensor comparisons demonstrated high correlation of CHL among timely acquired, spatially high and medium resolved sensors. Evaluations with in situ data showed that most of the sensor-in situ match-ups were within an uncertainty range of in situ measurements. Analysing a 9-year MERIS time series with FUB/WeW revealed unprecedented information on temporal trends and seasonal behaviour of CHL, TSM and CDOM at the study area Lake Kummerow. Combining CHL, retrieved with the Modular Inversion and Processing System, from different satellite sensors (MODIS, Landsat 7/ 8, Sentinel-2A) enabled detailed observations of phytoplankton development. Such combinations are a step forward to future lake analyses which may integrate remote sensing data, in situ measurements and environmental modelling.Fernerkundungstechniken können das Seemonitoring mit räumlichen Informationen über optisch aktive Indikatoren der Gewässerökologie liefern, z.B. Chlorophyll-a (CHL), suspendierte Schwebstoffe (TSM), Gelbstoffe (CDOM) und insbesondere in optisch flachen Gewässern, Wassertiefe und Substratbedeckung. Die vorliegende Arbeit gibt einen Überblick über den aktuellen Forschungsstand zur Seefernerkundung und den Nutzen von Zeitreihenanalysen für die Seeökologie. Um die Eignung von Sentinel-2 und Landsat 8 für ein Seenmonitoring und deren Kombination mit anderen Sensoren zu untersuchen, konzentrierte sich diese Arbeit auf zwei Untersuchungsgebiete mit sehr unterschiedlichen optischen Eigenschaften: den oligotrophen Starnberger See (Süddeutschland) und den mesotroph-eutrophen Kummerower See (Norddeutschland). Mit dem bio-optischen Modell WASI-2D erwies sich Sentinel-2A als geeignet, um niedrige TSM- und CDOM-Werte zu bestimmen. Die hohe räumliche Auflösung ermöglichte eine Unterscheidung zwischen unbewachsenem und mit Makrophyten bewachsenem Untergrund. Die Wassertiefenbestimmung verlief bis zur halben Sichttiefe gut. Sensorübergreifende Vergleiche zeigten eine hohe Korrelation von CHL zwischen zeitnah erfassten, räumlich mittel und hoch aufgelösten Sensoren. Auswertungen mit in-situ-Daten zeigten, dass die meisten Sensor-in-situ-Match-ups innerhalb eines Unsicherheitsbereichs von in-situ-Messungen lagen. Die Analyse einer 9-jährigen MERIS-Zeitreihe mit FUB/WeW ergab neue Informationen über zeitliche Trends und saisonales Verhalten von CHL, TSM und CDOM im Untersuchungsgebiet Kummerow See. Die Kombination von CHL aus verschiedenen Satellitensensoren (MODIS, Landsat 7/ 8, Sentinel-2A) mit dem Modular Inversion and Processing System ermöglichte detaillierte Beobachtungen der Phytoplanktonentwicklung. Solche Kombinationen sind ein Schritt für zukünftigen Gewässeranalysen, die Fernerkundungsdaten, in-situ-Messungen und Umweltmodellierung integrieren sollten

The global tree carrying capacity (keynote)

editorial reviewe

Land Degradation Assessment with Earth Observation

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools