A Qualitative Study on Microwave Remote Sensing and Challenges Faced in India

Over the past few decades remote sensing has expanded its limits with exponential rise in technology that facilitates accurate data fetching in real time. In view of some of the major problems faced by developing nations, particularly India with its recent advancement in space technology, remote sensing has a vital role to play in resolving many such problems. In the light of recent Global Space Programs where several satellites have been launched for large area mapping using microwave sensors, microwave remote sensing can play a vital role as India experiences a large number of disasters every year. Also, majority of Indian population relies on farming for their livelihood. Microwave remote sensing can have significant effects in both these two scenarios as opposed to its conventional counterpart, optical remote sensing under diverse conditions and facilitate better results in terms of disaster management, prediction and increasing crop yield. The current paper brings out the various details on the work done by using active microwave remote sensing, with specific illustrative examples, for disaster management support, crop management techniques and the challenges associated on carrying out such researches in a diverse terrain like India

Estimating Global Ecosystem Isohydry/Anisohydry Using Active and Passive Microwave Satellite Data

The concept of isohydry/anisohydry describes the degree to which plants regulate their water status, operating from isohydric with strict regulation to anisohydric with less regulation. Though some species level measures of isohydry/anisohydry exist at a few locations, ecosystem-scale information is still largely unavailable. In this study, we use diurnal observations from active (Ku-Band backscatter from QuikSCAT) and passive (X-band vegetation optical depth (VOD) from Advanced Microwave Scanning Radiometer on EOS Aqua) microwave satellite data to estimate global ecosystem isohydry/anisohydry. Here diurnal observations from both satellites approximate predawn and midday plant canopy water contents, which are used to estimate isohydry/anisohydry. The two independent estimates from radar backscatter and VOD show reasonable agreement at low and middle latitudes but diverge at high latitudes. Grasslands, croplands, wetlands, and open shrublands are more anisohydric, whereas evergreen broadleaf and deciduous broadleaf forests are more isohydric. The direct validation with upscaled in situ species isohydry/anisohydry estimates indicates that the VOD-based estimates have much better agreement than the backscatter-based estimates. The indirect validation with prior knowledge suggests that both estimates are generally consistent in that vegetation water status of anisohydric ecosystems more closely tracks environmental fluctuations of water availability and demand than their isohydric counterparts. However, uncertainties still exist in the isohydry/anisohydry estimate, primarily arising from the remote sensing data and, to a lesser extent, from the methodology. The comprehensive assessment in this study can help us better understand the robustness, limitation, and uncertainties of the satellite-derived isohydry/anisohydry estimates. The ecosystem isohydry/anisohydry has the potential to reveal new insights into spatiotemporal ecosystem response to droughts

Estimation of surface canopy water in Pacific Northwest forests by fusing radar, lidar, and climatic data

Surface Canopy Water (SCW) is the intercepted rain water that resides within the tree canopy and plays a significant role in the hydrological cycle. Challenges arise in measuring SCW in remote areas using traditional ground based techniques. Remote sensing in the radio spectrum has the potential to overcome the challenges where traditional modelling approaches face difficulties. In this study we investigated the capability of the most recent SAR platform, the Sentinel-1 constellation to estimate SCW. We measured the backscatter of six forest stands in the H J Andrews experimental forest in central Oregon (as well as four clear cut areas and one golf course) over three summers to describe how the backscatter signal changes with moisture. We found significant results when we executed the analysis on radar images on which individual trees crowns were delineated from lidar, as opposing to SCW estimated from individual pixels backscatter. Significant differences occur in the mean backscatter between radar images taken during rain vs. during dry periods (no rain for > 1h). A lack in sufficient data prevented the formulation of a robust predictive model, however our results suggest the posibilty of mapping canopy moisture using SAR in the Pacific Northwest

Detecting forest response to droughts with global observations of vegetation water content

Droughts in a warming climate have become more common and more extreme, making understanding forest responses to water stress increasingly pressing. Analysis of water stress in trees has long focused on water potential in xylem and leaves, which influences stomatal closure and water flow through the soil-plant-atmosphere continuum. At the same time, changes of vegetation water content (VWC) are linked to a range of tree responses, including fluxes of water and carbon, mortality, flammability, and more. Unlike water potential, which requires demanding in situ measurements, VWC can be retrieved from remote sensing measurements, particularly at microwave frequencies using radar and radiometry. Here, we highlight key frontiers through which VWC has the potential to significantly increase our understanding of forest responses to water stress. To validate remote sensing observations of VWC at landscape scale and to better relate them to data assimilation model parameters, we introduce an ecosystem-scale analog of the pressure–volume curve, the non-linear relationship between average leaf or branch water potential and water content commonly used in plant hydraulics. The sources of variability in these ecosystem-scale pressure-volume curves and their relationship to forest response to water stress are discussed. We further show to what extent diel, seasonal, and decadal dynamics of VWC reflect variations in different processes relating the tree response to water stress. VWC can also be used for inferring belowground conditions—which are difficult to impossible to observe directly. Lastly, we discuss how a dedicated geostationary spaceborne observational system for VWC, when combined with existing datasets, can capture diel and seasonal water dynamics to advance the science and applications of global forest vulnerability to future droughts

Detecting forest response to droughts with global observations of vegetation water content

Droughts in a warming climate have become more common and more extreme, making understanding forest responses to water stress increasingly pressing. Analysis of water stress in trees has long focused on water potential in xylem and leaves, which influences stomatal closure and water flow through the soil-plant-atmosphere continuum. At the same time, changes of vegetation water content (VWC) are linked to a range of tree responses, including fluxes of water and carbon, mortality, flammability, and more. Unlike water potential, which requires demanding in situ measurements, VWC can be retrieved from remote sensing measurements, particularly at microwave frequencies using radar and radiometry. Here, we highlight key frontiers through which VWC has the potential to significantly increase our understanding of forest responses to water stress. To validate remote sensing observations of VWC at landscape scale and to better relate them to data assimilation model parameters, we introduce an ecosystem-scale analog of the pressure-volume curve, the non-linear relationship between average leaf or branch water potential and water content commonly used in plant hydraulics. The sources of variability in these ecosystem-scale pressure-volume curves and their relationship to forest response to water stress are discussed. We further show to what extent diel, seasonal, and decadal dynamics of VWC reflect variations in different processes relating the tree response to water stress. VWC can also be used for inferring belowground conditions-which are difficult to impossible to observe directly. Lastly, we discuss how a dedicated geostationary spaceborne observational system for VWC, when combined with existing datasets, can capture diel and seasonal water dynamics to advance the science and applications of global forest vulnerability to future droughts

Desarrollo de una herramienta software para el monitoreo de cultivos a partir de la integración de imágenes ópticas y SAR COSMO-SkyMed

Tesis (Magister en Aplicaciones Espaciales de Alerta y Respuesta Temprana a Emergencias)--Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, 2019.Maestría conjunta entre FAMAF y el Instituto de Altos Estudios Espaciales "Mario Gulich" CONAE/UNC.En este trabajo de tesis se desarrolló una herramienta software que permite al usuario cargar, pre-procesar y analizar los resultados derivados de un conjunto heterogéneo de datos satelitales y archivos vectoriales con el fin de evaluar la capacidad de monitoreo del crecimiento de las especies agrícolas utilizando tanto imágenes ópticas como de radar (SAR), en particular de la constelación COSMO-SkyMed®. Para la implementación, se utilizó el ambiente de programación Matlab®, y para facilitar su uso, se desarrolló la herramienta en forma de una interfaz gráfica con su manual de usuario. La herramienta desarrollada fue evaluada comparando las salidas que presenta al usuario la misma herramienta con los resultados obtenidos a través de otros softwares comerciales sobre los mismos datos, a fin de corroborar la validez de los algoritmos implementados en la aplicación. Ambas pruebas de validación comprobaron la confiabilidad de la interfaz. Además, se presentaron los resultados que arroja la herramienta para una caso de uso particular. Basados en los resultados obtenidos, podemos concluir que la herramienta desarrollada resulta fiable y útil para realizar estudios que impliquen la integración de datos ópticos y de radar para el monitoreo de las especies agrícolas. Tanto la aplicación desarrollada como el correspondiente código son de libre acceso para cualquier usuario que quiera utilizarlos en sus estudios.In this thesis work, it has been developed a software tool that allows a user to upload, pre-process and analyze the results derived form an heterogeneous set of satellite data and vector files in order to evaluate the growth monitoring capability of the agricultural species using both optical and radar (SAR) images. In particular, the tool was used to evaluate the crop monitoring capability of the COSMO-SkyMed® constellation SAR data. For the implementation, the Matlab® programming environment was used, and to facilitate its use, a graphic interface and its user manual were also developed. The developed tool was evaluated by comparing the outputs presented to the user by the developed interface with the results obtained through other commercial software on the same data, in order to corroborate the validity of the algorithms implemented in the application. Both validation tests verified the reliability of the interface. Once the validity of the application was developed and tested, the results of the tool for a particular use case were analyzed. Based on the results obtained, we can conclude that the tool developed is reliable and useful for studies that involve the integration of optical and radar data for the monitoring of agricultural species. Both the developed application and the corresponding code are available so that any user can use it in their studies.Fil: Mastronardi, Giovanni. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Fil: Mastronardi, Giovanni. Universidad Nacional de Córdoba. Instituto de Altos Estudios Espaciales Mario Gulich; Argentina.Fil: Mastronardi, Giovanni. Comisión Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales Mario Gulich; Argentina

Impact of diurnal variation in vegetation water content on radar backscatter from maize during water stress

Microwave backscatter from vegetated surfaces is influenced by vegetation structure and vegetation water content (VWC), which varies with meteorological conditions and moisture in the root zone. Radar backscatter observations are used for many vegetation and soil moisture monitoring applications under the assumption that VWC is constant on short timescales. This research aims to understand how backscatter over agricultural canopies changes in response to diurnal differences in VWC due to water stress. A standard water-cloud model and a two-layer water-cloud model for maize were used to simulate the influence of the observed variations in bulk/leaf/stalk VWC and soil moisture on the various contributions to total backscatter at a range of frequencies, polarizations, and incidence angles. The bulk VWC and leaf VWC were found to change up to 30% and 40%, respectively, on a diurnal basis during water stress and may have a significant effect on radar backscatter. Total backscatter time series are presented to illustrate the simulated diurnal difference in backscatter for different radar frequencies, polarizations, and incidence angles. Results show that backscatter is very sensitive to variations in VWC during water stress, particularly at large incidence angles and higher frequencies. The diurnal variation in total backscatter was dominated by variations in leaf water content, with simulated diurnal differences of up to 4 dB in X- through Ku-bands (8.6–35 GHz). This study highlights a potential source of error in current vegetation and soil monitoring applications and provides insights into the potential use for radar to detect variations in VWC due to water stress.Water ManagementCivil Engineering and Geoscience

Seguimiento y clasificación de parámetros biofísicos de superficies agrícolas a partir de sensores remotos radar

[ES] El seguimiento y la clasificación de los cultivos agrícolas tienen una gran importancia en la gestión socio-económica de las sociedades y son esenciales para la gestión sostenible de las actividades agrícolas. Con esta información, autoridades locales, nacionales o internacionales, cooperativas agrícolas o agricultores, pueden tener acceso a información precisa y actualizada para poder llevar a cabo una mejor gestión de los cultivos, además de obtener información sobre el crecimiento de los cultivos o la estimación de su rendimiento. El empleo de la teledetección, al ser una forma no destructiva de monitorear la vegetación, es una herramienta ideal para ayudar a logar la información necesaria. Y su cobertura temporal ininterrumpida permite seguir los ciclos fenológicos de las plantas. Aunque la teledetección óptica se ha utilizado con éxito para el seguimiento y clasificación de cultivos agrícolas, estos sistemas se limitan a los datos adquiridos en condiciones de cielo despejado. En este contexto, los datos adquiridos por sensores radar de apertura sintética (SAR) son de gran interés para aplicaciones agrícolas debido a la capacidad de estos sistemas para monitorear los cultivos en todas las condiciones climáticas y la sensibilidad de la señal de microondas a las propiedades dieléctricas y geométricas del objetivo. Dependiendo de la configuración del sistema, los sensores SAR pueden adquirir datos en diferentes modos. La adquisición de datos en diferentes modos ha establecido técnicas de procesamiento como la polarimetría (PolSAR), interferometría (InSAR) e interferometría diferencial (DInSAR). Para el desarrollo de esta tesis se ha empleado la polarimetría, ya que en el ámbito de la agricultura el empleo de esta técnica se basa en la bien conocida sensibilidad de las microondas a la estructura del cultivo, las propiedades dieléctricas del dosel y las propiedades físicas del suelo subyacente. Los objetivos de esta tesis han sido varios. Por una parte, ampliar el conocimiento de los observables SAR (más allá de los coeficientes de retrodispersión) para el seguimiento/monitoreo de cultivos; investigar el efecto del ángulo de incidencia en la relación entre los observables polarimétricos y diferentes variables biofísicas; y finalmente, estudiar la viabilidad de los observables SAR para clasificar y distinguir cultivos agrícolas. Para llevar a cabo el primer y segundo objetivo se empleó una serie temporal de 20 imágenes RADARSAT-2 adquiridas a diferentes ángulos de incidencia (25°, 31° y 36°) durante la temporada de crecimiento de cultivos de secano. A partir de las imágenes se extrajeron 10 observables polarimétricos, mientras que 6 variables biofísicas se estimaron a partir de mediciones in situ. Posteriormente, se realizó un análisis descriptivo y de correlación estadística entre ambos conjuntos de datos. Los resultados expuestos en esta tesis muestran correlaciones significativas entre varios observables polarimétricos (HH/VV, HV/VV, γHHVV, α1, γP1P2) con varias variables biofísicas como la biomasa, la altura y el índice de área foliar para ángulos de incidencia de 31° y 36°. Para cumplir con el último objetivo, se realizó una clasificación de cultivos aplicando un algoritmo de aprendizaje automático y usando como datos de entrada para el clasificador los 10 observables polarimétricos de la serie temporal de RADARSAT-2 junto con 3 observables más extraídos de una serie temporal de imágenes Sentinel-1. Debido a la gran cantidad de datos, se crearon 7 escenarios distintos para evaluar la clasificación. El empleo de todos los observables e imágenes RADARSAT-2 demostró tener claros beneficios en términos de precisión general a la hora de clasificar. El análisis individual para cubierta mostró la buena separación de los cereales de primavera, típicamente difícil debido a su estructura y fenología similares; mientras que los cultivos de verano mostraron resultados no tan buenos de exactitud debido a la falta de imágenes en esas fechas. En cuanto a las capacidades polarimétricas de RADARSAT-2 (full) y Sentinel-1 (dual) son bastante diferentes, el enfoque multitemporal reforzó el proceso de clasificación y proporcionó resultados satisfactorios similares para los diferentes escenarios de clasificación propuestos

Deriving crop productivity indicators from satellite synthetic aperture radar to assess wheat production at field-scale.

Richter, G. M. Industrial supervisor ( Rothamsted Research) Burgess, Paul J. and Meersmans, Jeroen Associate supervisorsThe deployment of high-revisit satellite-based radar sensors raises the question of whether the data collected can provide quantitative information to improve agricultural productivity. This thesis aims to develop and test mathematical algorithms to describe the dynamic backscatter of high-resolution Synthetic Aperture Radar (Sentinel-1) in order to describe the development and productivity of wheat at field-scale. A time series of the backscatter ratio (VH/VV), collected over a cropping season, could be characterised by a growth and a senescence logistic curve and related to critical phases of crop development. The curve parameters, referred to as Crop Productivity Indicators (CPIs), compared well with the crop production for three years at the Rothamsted experimental farm. The combination of different parameters (e.g. midpoints of the two curves) helped to define CPIs, such as duration, that significantly (r = 0.61, p = 0.05) correlated with measured yields. Field observations were used to understand the wheat evolution by sampling canopy characteristics across the seasons. The correlation between the samples and the CPIs showed that structural changes, like biomass increase, influence the CPIs during the growth phase, and that declining plant water content was correlated with VH/VV values during maturation. The methodology was upscaled to other farms in Hertfordshire and Norfolk. The ANOVA identified significant effects (p<0.001) of farm management, year (weather conditions) and the interaction between soil type and year on the selected CPIs. Multilinear regression models between yields and selected CPIs displayed promising predictive power (R²= 0.5) across different farms in the same year. However, these models could not explain yield differences within high-yielding farms across seasons because of the dominant effect of weather patterns on the CPIs in each year. The potential impact of the research includes estimation of yield across the landscape, phenology monitoring and indication biophysical parameters. Future work on SAR-derived CPIs should focus on improving the correlations with biophysical properties, applying of the methodology in other crops, with different soils and climates.PhD in Environment and Agrifoo