Global irrigated area mapping: Overview and recommendations

Mapping / Data collection / Data storage and retrieval / Water harvesting / Irrigated sites / Climate / Satellite surveys / Evaporation / Food production / Sustainability / Soil water / Models

Lem benchmark database for tropical agricultural remote sensing application.

Abstract: The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic?s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data

LEM BENCHMARK DATABASE FOR TROPICAL AGRICULTURAL REMOTE SENSING APPLICATION

The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic’s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data

Teledetección. Nuevas plataformas y sensores aplicados a la gestión del agua, la agricultura y el medio ambiente

Este libro recoge las comunicaciones presentadas al XVII Congreso de la Asociación Española de Teledetección (AET), celebrado del 3 al 7 de octubre de 2017 en el auditorio y palacio de congresos de Murcia y organizado por el Grupo de Sistemas de Información Geográfica y Teledetección del Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario (IMIDA),con el soporte de la AET,el Instituto Geográfico Nacional (IGN), las universidades politécnicas de Cartagena y Valencia, la Confederación Hidrográfica del Segura, el ayuntamiento de Murcia,las empresas Gade Eventos y Geodim y la Universidad Católica de San Antonio El lema elegido para el Congreso ha sido "Nuevas plataformas y sensores de teledetección" aplicados a la gestión del agua,la agricultura y el medio ambiente, con la intención de promover el encuentro entre las comunidades académicas, científicas e industriales en el área de la teledetección, destacando las nuevas plataformas de bajo coste y los logros conseguidos en la generación y difusión de productos útiles para la sociedadRuiz Fernández, LÁ.; Estornell Cremades, J.; Erena Arrabal, M. (2017). Teledetección. Nuevas plataformas y sensores aplicados a la gestión del agua, la agricultura y el medio ambiente. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/90688EDITORIA

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

Burning with Potential: Understanding the Relationship between Biochar and Agriculture of the Northern Glaciated Plains Ecoregion

Achieving global sustainable agriculture is one of the most incredible challenges of this century, yet many continue to try to solve this problem through the development of precision technologies. Biotechnologies, such as biochar, can perform like a precision technology while protecting agricultural land from soil erosion and fertility loss. The Northern Glaciated Plains ecoregion of the United States is little researched in the benefits from the use of biochar through improved soil nutrient capture and water retention, crop health improvements, and yield increases. The study plot has four sections of corn stover biochar and eight sections of control sections. This project assessed soil chemical properties by testing topsoil samples, resulting in increased soil pH and electrical conductivity in biochar-amended soils. Remotely sensed normalized difference vegetation index images created from a spectral camera measured soybean phenology through reproductive growth stages and showed the positive effect biochar has on health and associated greenness of soybean plants. Destructive, dry weight soybean biomass measurements taken at soybean maturity showed increased soybean biomass in biochar amended plot sections. The goal was to determine how biochar reacts with a haploboroll soil in Brookings County, South Dakota and if biochar application is an appropriate management strategy for this soil and soils of the greater Northern Glaciated Plains ecoregion of the United States. In this study, results conclude that biochar application may not have the significant productivity increases necessary to make biochar a highly recommended amendment for this region through this study’s soil and soybean reactions to biochar, but biochar has the potential to reduce soil productivity loss through other aspects of soil fertility improvement

DIAGNOSTIC ANALYSIS OF TERRESTRIAL GROSS PRIMARY PRODUCTIVITY USING REMOTE SENSING AND IN SITU OBSERVATIONS

Vegetation play a critical role in the interactions between atmosphere and biosphere. CO2 fixed by plants through photosynthesis process at ecosystem scale is termed as gross primary production (GPP). It is also the first step CO2 entering the biosphere from the atmosphere. It not only fuels the ecosystem functioning, but also drives the global carbon cycle. Accurate estimation of the ecosystem photosynthetic carbon uptake at a global scale can help us better understand the global carbon budget, and the ecosystem sensitivity to the global climate change. Satellite observations have the advantage of global coverage and high revisit cycle, hence, are ideal for global GPP estimation. The simple production efficiency model that utilize the remote sensing imagery and climate data can provide reasonably well estimates of GPP at a global scale. With the solar induced chlorophyll fluorescence (SIF) being retrieved from satellite observations, new opportunities emerge in directly estimating photosynthesis from the energy absorption and partitioning perspective. In this thesis, by combining observations from both in situ and remotely acquired, I tried to (1) investigate the GPP SIF relationship using data from observations and model simulations; (2) improve a production efficiency model (vegetation photosynthesis model, VPM) and apply it to the regional and global scale; (3) investigate the GPP and SIF sensitivity to drought at different ecosystems; (4) explore the global interannual variation of GPP and its contributing factors. Chapter 2 uses site level observations of both SIF and GPP to explore their linkage at both leaf and canopy/ecosystem scale throughout a growing season. Two drought events happened during this growing season also highlight the advantage of SIF in early drought warning and its close linkage to photosynthetic activity. Chapter 3 compares the GPP and SIF relationships using both instantaneous and daily integrated observations, the daily GPP and satellite retrieved SIF are latitudinal dependent and time-of-overpass dependent. Daily integrated SIF estimation shows better correlation with daily GPP observations. Chapter 4 compares different vegetation indices with SIF to get an empirical estimation of fraction of photosynthetically active radiation by chlorophyll (fPARchl). By comparing this fPARchl estimation with ecosystem light use efficiency retrieved from eddy covariance flux towers, the light use efficiency based on light absorption by chlorophyll shows narrower range of variation that can be used for improving production efficiency models. Chapter 5 investigates the drought impact on GPP through the change of vegetation canopy optical properties and physiological processes. Forest and non-forest ecosystems shows very different responses in terms of these two limitation and need to be treated differently in GPP modelling. Chapter 6 applies the improved VPM to North America and compared with SIF retrieval from GOME-2 instrument. The comparison shows good consistency between GPP and SIF in both spatial and seasonal variation. Chapter 7 uses an ensemble of GPP product to explore the cause of hot spots of GPP interannual variability. GPP in semiarid regions are strongly coupled with evapotranspiration and show high sensitivity to interannual variation of precipitation. The results demonstrate the importance of precipitation in regional carbon flux variability

Contaminant biotransport by Pacific salmon to Lake Michigan tributaries

The Great Lakes are ideal systems for evaluating the synergistic components of environmental change, such as exotic species introductions and legacy pollutants. Introduced Pacific Salmon (Oncorhynchus spp.) represent an intersection of these drivers because they are non-native species of economic importance that bioaccumulate contaminants during the open water phase of their life cycle. Furthermore, Pacific salmon can deliver a significant pulse of contaminated tissue to tributaries during spawning and subsequent death. Thus, salmon represent a key pathway by which contaminants accumulated in Lake Michigan are transported inland to tributaries that otherwise lack point source pollution. Our research has revealed that salmon exhibit basin-specific persistent organic pollutant (POP) and mercury (Hg) concentrations reflecting pollutant inputs from both current and historic sources. Overall, Lake Michigan salmon were more contaminated with POPs and Hg than conspecifics from Lakes Huron or Superior. Consequently, Lake Michigan salmon pose a higher risk and magnitude of contaminant biotransport and transfer. Resident stream fish (e.g., brook trout) sampled from salmon spawning reaches had higher pollutant concentrations than fish sampled from upstream reaches lacking salmon, but the extent of fish contamination varied among lake basins and streams. In general, Lake Michigan tributaries were the most impacted, suggesting a direct relationship between the extent of salmon-derived contaminant inputs and resident fish contaminant levels. Within and among lake basins, contaminant biotransport by salmon is context dependent and likely reflects a suite of ecological characteristics such as species identity and trophic position, dynamics of the salmon run, watershed land-use, and instream geomorphology such as sediment size. We suggest that future management of salmon-mediated contaminant biotransport to stream communities in the Great Lakes basin should consider biological, chemical, and physical factors that constitute the environmental context

A spatially explicit methodology for assessing and monitoring land degradation neutrality at a national scale

Land degradation is occurring in all parts of the terrestrial world, and is negatively impacting the well-being of billions of people. In recognition of the need for sustained global action on land degradation, the Sustainable Development Goals, adopted by the global community in 2015, include a specific goal aimed at halting the decline of land resources and achieving land degradation-neutrality (LDN) by 2030. The primary objective of this doctoral research was to operationalise the LDN target at the national level, using Kenya as the case study. The main research questions addressed in this dissertation have been positioned within a social-ecological systems framework in which ecosystems are integrated with human society. The first task of this research focused on determining the extent of land degradation and regeneration, and in establishing the LDN national baseline using the three LDN indicators (land cover, land productivity, and carbon stocks). This was then followed by identifying the key drivers that affect land degradation (browning) and land regeneration (greening) trends within the 4 main land cover types (agriculture, forest, grassland and shrubland), and within an area characterised by land cover change. The third task involved an assessment of the effectiveness of the current land-use policy framework, and associated institutions, to facilitate the implementation of LDN. Finally, in the last part of this dissertation, a climate-smart landscape approach at the water catchment level was proposed as a possible mechanism through which LDN can be operationalised at the sub-national level; Metodologia espacialmente explicita para a avaliação e monitorização da neutralidade da degradação do solo à escala nacional RESUMO: A degradação do solo é um fenómeno que está a acontecer em todas as partes do mundo terrestre, com impactos negativos no bem estar de milhares de milhões de pessoas. Reconhecendo a necessidade de uma ação global contra a degradação do solo, os Objetivos de Desenvolvimento Sustentável, adotados pela comunidade global em 2015, incluem um objetivo específico para travar o declínio de recursos terrestres e atingir a neutralidade de degradação do solo (NDS) até 2030. A presente tese de doutoramento teve como grande objetivo a operacionalização da NDS a nível nacional, usando o Quénia como caso de estudo. As principais perguntas de investigação consideradas nesta dissertação foram colocadas num enquadramento socio-ecológico, em que ecossistemas estão integrados com a sociedade. A primeira tarefa desta investigação consistiu em determinar valores de degradação e de regeneração do solo para estabelecer a base de referência de NDS nacional usando três indicadores de NDS (cobertura de solo, produtividade do solo e reservas de carbono). Seguidamente foram identificados os principais fatores que influenciam a degradação do solo (browning) e a regeneração do solo (greening) nas 4 principais coberturas de solo (agricultura, floresta, pastos e matos), bem como numa área marcada por alterações da cobertura de solo. Para a terceira tarefa foi avaliada a eficácia do atual quadro político sobre o uso de solo, bem como das instituições associadas, na viabilização da implementação da NDS. Na última parte da dissertação é adotada uma escala a nível da bacia hidrográfica, como uma abordagem “climate smart” adequada para a operacionalização da NDS a um nível sub-nacional