THE BIG PICTURE - SATELLITE REMOTE SENSING APPLICATIONS IN RANGELAND ASSESSMENT AND CROP INSURANCE

Livestock Production/Industries, Risk and Uncertainty,

Climate Change Impacts on Agriculture in Europe

COST Action 734 was launched thanks to the coordinated activity of 29 EU countries. The main objective of the Action was the evaluation of impacts from climate change and variability on agriculture for various European areas. Secondary objectives were: collection and review of existing agroclimatic indices and simulation models, to assess hazard impacts on European agricultural areas; to apply climate scenarios for the next few decades; the definition of harmonised criteria to evaluate the impacts of climate change and variability on agriculture; the definition of warning systems guidelines. Based on the result, possible actions (specific recommendations, suggestions, warning systems) were elaborated and proposed to the end-users, depending on their needs

Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI); 21-year drought monitoring in Iran using satellite imagery within Google Earth Engine

Remote Sensing (RS) offers efficient tools for drought monitoring, especially in countries with a lack of reliable and consistent in-situ multi-temporal datasets. In this study, a novel RS- based Drought Index (RSDI) named Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI) was proposed. To the best of our knowledge, TVMPDI is the first RSDI using four different drought indicators in its formulation. TVMPDI was then validated and compared with six conventional RSDIs including VCI, TCI, VHI, TVDI, MPDI and TVMDI. To this end, precipitation and soil temperature in-situ data have been used. Different time scales of meteorological Standardized Precipitation Index (SPI) index have also been used for the validation 2 of the RSDIs. TVMPDI was highly correlated with the monthly precipitation and soil temperature in-situ data at 0.76 and 0.81 values respectively. The correlation coefficients between the RSDIs and 3-month SPI ranged from 0.07 to 0.28, identifying the TVMPDI as the most suitable index for subsequent analyses. Since the proposed TVMPDI could considerably outperform the other selected RSDIs, all spatiotemporal drought monitoring analyses in Iran were conducted by TVMPDI over the past 21 years. In this study, different products of the Moderate Resolution Imaging Spectrometer (MODIS), Tropical Rainfall Measuring Mission (TRMM), and Global Precipitation Measurement (GPM) datasets containing 15206 images were used on the Google Earth Engine (GEE) cloud computing platform. According to the results, Iran experienced the most severe drought in 2000 with a 0.715 TVMPDI value lasting for almost two years. Conversely, the TVMPDI showed a minimum value equal to 0.6781 in 2019 as the lowest annual drought level. The drought severity and trend in the 31 provinces of Iran have also been mapped. Consequently, various levels of decrease over the 21 years were found for different provinces, while Isfahan and Gilan were the only provinces showing an ascending drought trend (with a 0.004% and 0.002% trendline slope respectively). Khuzestan also faced a worrying drought prevalence that occurred in several years. In summary, this study provides updated information about drought trends in Iran using an advanced and efficient RSDI implemented in the cloud computing GEE platform. These results are beneficial for decision-makers and officials responsible for environmental sustainability, agriculture and the effects of climate change.Peer ReviewedPostprint (author's final draft

Integrated approach for monitoring the vulnerability of Mediterranean forests affected by drought-induced dieback

Rising aridity, mostly driven by higher temperatures and reduced precipitation, will likely undermine the health status of forest ecosystems. Experiments and observations point to the likelihood that if climate changes proceed at its current rate, the resilience of many forests will be threated by altering their structure and functions and reducing their capability to provide ecosystem services. Such increasing drought conditions, coupled to other biotic and abiotic drivers, are synergistically leading to responses in tree morphology, physiology, growth, reproduction, and forest mortality in different areas of the Mediterranean Basin. However, our understanding of vegetation dynamics in response to climate changes is still lacking, as a robust comprehension relies on the need to obtain insights at multiple temporal and spatial scales. In this context, we sought to forecasting vegetation response to climate stressors, particularly during dieback episodes when tree vulnerability is exacerbated. The first section of this study focused on tree and shrub populations exhibiting recent dieback phenomena in Italy (Quercus pubescens, Quercus frainetto) and Spain (Pinus sylvestris, Juniperus phoenicea). The general aim was to investigate how remotely sensed measures of vegetation activity and radial growth (BAI, basal area increment) responded to climate extreme events. To this purpose, we compared trees and nearby stands showing different vigor, i.e., dieback vs non-dieback, assessed as growth decline, elevated canopy defoliation and rising tree mortality rate. To disentangle growth and NDVI responses to drought, we accounted for two components of drought, namely elevated vapor pressure deficit (VPD) and low soil moisture. As a whole, the response of the investigated species to VPD increase was characterized by growth reduction. In Scots pine, high VPD was linked to a loss of growth in declining individuals which did not respond to changes in soil moisture. Oaks responded mostly to soil moisture, whereas the juniper was the most negatively affected by higher VPD. Indeed, the different hydraulic strategies (anisohydric vs. isohydric species) could partially explain the contrasting growth responses to drought proxies. We also found that dieback stands exhibited lower NDVI values than non-dieback stands. In most cases, NDVI and BAI was positively correlated and such relation likely relied on specific time windows. In the second part of the thesis, the phenological behavior of Mediterranean oak forest stands (Quercus cerris, Quercus pubescens, and Quercus frainetto), showing evident decline symptoms, are investigated by using a satellite-based approach. We explored how a phenological (PPI, Plant Phenology Index) index would be capable to reflect the seasonal vegetative dynamics of forests affected by dieback phenomena. We found that dieback forest stands - characterized by a higher ratio of crown-defoliated trees - showed distinct phenological performance as compared to non-dieback stands. In detail, our results revealed that dieback stands lengthened the growing season by delaying autumn leaf senescence. Nevertheless, both seasonal amplitude and productivity were found to have higher values for non-dieback stands as compared to dieback stands. Furthermore, it was highlighted that non-dieback stands experienced either greening up or senescence periods more rapidly than dieback ones. Overall, our framework demonstrated that the effects of climate extremes on vegetation can be detected either in terms of canopy greenness or radial growth reductions, thus hinting at the opportunity to both employ remotely sensed data as a stand-level indicator of vegetation stress and to scaling up informations from tree to stand levels by using the maximum growing season NDVI and tree-ring width data taken at the individual scales. Our findings also highlighted how patterns of vegetation response to climate extremes may depend on both the water use strategies of trees and shrubs and site-specific climatic conditions. Hence, coupling proxies of forest productivity (NDVI, BAI) may be employed for retrospective modeling of the impact of drought stress on forest productivity and tree growth, enhancing our knowledge and forecast of drought-induced dieback phenomena in woody plant communities. Furthermore, the second part of the work revealed the phenological behaviour of Mediterranean forest populations showing clear symptoms of decline. We speculated that the lengthened growing season may be related to the dieback trees' effort to compensate for the reduction in whole-plant photosynthesis, associated to canopy decline. Increased photosynthesis during the season under higher temperatures and increased light availability, due to global warming, provided a possible explanation for the greater seasonal amplitude and productivity of healthier stands. Our findings may provide new insights on phenological response to climate change in semi-arid regions, highlighting how trees, showing clear symptoms of decline, may keep their vital activities by changing their phenological performance. What described above leads to a crucial question concerning the potential implications of observed phenological shifts on the global carbon and water balance of terrestrial ecosystems under future climate change. Hence, in the coming years, this study could provide a more comprehensive overview on climate-vegetation interactions, mainly in the Mediterranean Basin, where intensified global warming and aridification trends are expected. Nonetheless, more investigations on the interactive effects among different environmental factors, are needed to improve our understanding of the underlying mechanisms affecting vegetation response

Quantifying Spatial Heterogeneity of Wild Blueberries and Crop Water Stress Monitoring Using Remote Sensing Technologies

The wild blueberry is one of the major crops of Maine, with significant economic value and potential health benefits. Due to global climate change, drought impacts have been increasing significantly in recent years in the northeast region of the USA, causing significant economic losses in the agricultural sectors. It has been predicted to increase further in the future. Changing patterns of the elevated atmospheric temperatures, increased rainfall variabilities, and more frequent drought events have made the wild blueberry industry of Maine vulnerable, suggesting the adoption of novel approaches to mitigate the negative impacts of global climate changes. Also, wild blueberry fields show high spatial heterogeneity, making precise and effective management difficult. Our research focuses on quantifying the spatial heterogeneity in functional traits of wild blueberries, analyzing the impact of historical drought on wild blueberry production, and testing the use of drone-based thermal sensors to quantify spatial heterogeneity in water stress across wild blueberry fields. In chapter two, we aimed to quantify the inter-genotype variation in several structural, functional, and yield-related traits and to establish the relationship between functional traits and yield-related traits. We conducted a study during the 2019 harvest season measuring several structural, functional, and yield traits from two wild blueberry farms. We found high variations in structural, functional, and yield-related traits among genotypes but not between the two fields, confirming the spatially heterogeneous nature within wild blueberry fields. We also found negative associations of the leaf mass per unit area and midday leaf temperature with the yield, whereas the leaf chlorophyll concentration was positively associated with the yield. Additionally, we found quadratic relationships between some yield-related traits and stem length, with the optimum stem length for yield at 25 cm. Our results suggest that some leaf and stem functional traits can be used to predict wild blueberry yields. In chapter three, we analyzed historical drought patterns using a drought index Standardised Precipitation-Evapotranspiration Index (SPEI). We assessed drought impacts on production (yield) and remotely sensed vegetation indices; Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) of the wild blueberry fields in Maine, USA. Despite a significant warming pattern, we found no significant changes in SPEI in the past 71 years. We also analyzed the impact of short and long-term water conditions (SPEI) during the growing season on the wild blueberry vegetation condition and production. We found that drought has had a significant impact on vegetation status and production historically. Further, the relationship between the relatively long-term SPEI and vegetation indices EVI and NDVI was significantly more substantial than short-term SPEI, suggesting water conditions in a relatively long-term probably determine crop health. We also compared an irrigated and non-irrigated wild blueberry field at the same location (Deblois, Maine). We found that irrigation decoupled the relationship between SPEI and vegetation indices and yield, suggesting the need for effective irrigation strategies to mitigate drought impacts. In chapter four, we tested the use of remotely sensed canopy temperature-based crop water stress index (CWSI) to remotely and non-destructively detect the water status of wild blueberries. By detecting crop water status using the CWSI, irrigation can be intelligently controlled in the highly spatially heterogeneous wild blueberry fields to increase efficiency and profitability. A drone-based thermal sensor was used to acquire the canopy temperature data remotely and then calculate CWSI. CWSI calculated from bio-indicator based Twet and Tdry reference was found to be effective (R² = 0.78: p \u3c 0.05) in detecting leaf water potential (LWP), which is superior compared to the statistical Twet and empirical Tdry reference-based CWSI. The CWSI-LWP model-based crop water status (LWP) maps showed high variability in crop water stress within irrigated and non-irrigated fields, suggesting the need for precise water stress monitoring and management in wild blueberry fields

The role of remote sensing in assessing the impact of climate variability on vegetation dynamics in Europe

Tese de doutoramento em Ciências Geofísicas e da Geoinformação (Detecção Remota), apresentada à Universidade de Lisboa através da Faculdade de Ciências, 2008The study aims at investigating the relationship between climate variability and vegetation dynamics by combining meteorological and remote-sensed information. The vegetation response to both precipitation and temperature in two contrasting areas (Northeastern Europe and the Iberian Peninsula) of the European continent is analysed and special attention is devoted to the impact of the North Atlantic Oscillation (NAO) on the vegetative cycle in the two regions which is assessed taking into account the different land cover types and the respective responses to climate variability. An analysis is performed of the impact of climate variability on wheat yield in Portugal and. the role of NAO and of relevant meteorological variables (net solar radiation, temperature and precipitation) is investigated. Using spring NDVI and NAO in June as predictors, a simple regression model of wheat yield is built up that shows a general good agreement between observed and modelled wheat yield values. The severity of a given drought episode in Portugal is assessed by evaluating the cumulative impact over time of negative anomalies of NDVI. Special attention is devoted to the drought episodes of 1999, 2002 and 2005. While in the case of the drought episode of 1999 the scarcity of water in the soil persisted until spring, the deficit in greenness in 2005 was already apparent at the end of summer. Although the impact of dry periods on vegetation is clearly noticeable in both arable land and forest, the latter vegetation type shows a higher sensitivity to drought conditions. Persistence of negative anomalies of NDVI was also used to develop a procedure aiming to identify burned scars in Portugal and then assess vegetation recovery over areas stricken by large wildfires. The vulnerability of land cover to wildfire is assessed and a marked contrast is found between forest and shrubland vs. arable land and crops. Vegetation recovery reveals to strongly depend on meteorological conditions of the year following the fire event, being especially affected in case of a drought event.Fundação para a Ciência e Tecnologia (FCT), (SFRH/BD/32829/2006

Examining environmental drivers of spatial variability in aflatoxin accumulation in Kenyan maize: potential utility in risk prediction models

Maize, a staple food in most African countries, is prone to contamination by aflatoxins, toxic secondary metabolites mainly produced by Aspergillus flavus and A. parasiticus. Aflatoxins are known to cause liver cancer, and chronic exposure has been linked to other adverse health outcomes including growth faltering in children. To mitigate exposure in maize-dependent populations, there is need to identify the factors associated with aflatoxin contamination. This is difficult, however, because of high sampling cost and lack of affordable and accurate analytical methods. Publicly available, remotely-sensed data on vegetation, precipitation, and soil properties could be useful in predicting locations at risk for aflatoxin contamination in maize. This study investigates the utility of publicly available remotely-sensed data on rainfall, vegetation cover (indicated by normalized difference vegetation index or NDVI), and soil characteristics as potential predictors of aflatoxin contamination in Kenyan maize. Aflatoxin was analyzed in maize samples (n=2466) that were collected in 2009 and 2010 at 243 local hammer mills in eastern and western Kenya. Overall, 60% of maize samples had detectable aflatoxin. Global positioning system coordinates of each mill location were linked to remotely-sensed, spatially explicit indicators of average monthly NDVI, total monthly rainfall, and soil properties. Higher rainfall and vegetation cover during the maize pre-flowering period were significantly associated with higher prevalence of aflatoxin contamination. Conversely, higher rainfall and vegetation cover during the maize flowering and post-flowering periods (not including harvest) were associated with lower prevalence of aflatoxin contamination. Water stress throughout the growing season may cause increased plant susceptibility to fungal colonization and aflatoxin accumulation. Soil organic carbon content, pH, total exchangeable bases, salinity, texture, and soil type were significantly associated with aflatoxin. In conclusion, this study shows that remotely-sensed data can be regressed on available aflatoxin data highlighting important potential predictors that could reduce the cost of data collection and the cost of aflatoxin risk forecasting models.Keywords: Aflatoxin, GIS, NDVI, soil characteristics, rainfall, mycotoxins, East Africa, Keny

Measuring and modeling near-surface reflected and emitted radiation fluxes at the FIFE site

Information is presented pertaining to the measurement and estimation of reflected and emitted components of the radiation balance. Information is included about reflectance and transmittance of solar radiation from and through the leaves of some grass and forb prairie species, bidirectional reflectance from a prairie canopy is discussed and measured and estimated fluxes are described of incoming and outgoing longwave and shortwave radiation. Results of the study showed only very small differences in reflectances and transmittances for the adaxial and abaxial surfaces of grass species in the visible and infrared wavebands, but some differences in the infrared wavebands were noted for the forbs. Reflectance from the prairie canopy changed as a function of solar and view zenith angles in the solar principal plane with definite asymmetry about nadir. The surface temperature of prairie canopies was found to vary by as much as 5 C depending on view zenith and azimuth position and on the solar azimuth. Aerodynamic temperature calculated from measured sensible heat fluxes ranged from 0 to 3 C higher than nadir-viewed temperatures. Models were developed to estimate incoming and reflected shortwave radiation from data collected with a Barnes Modular Multiband Radiometer. Several algorithms for estimating incoming longwave radiation were evaluated and compared to actual measures of that parameter. Net radiation was calculated using the estimated components of the shortwave radiation streams, determined from the algorithms developed, and from the longwave radiation streams provided by the Brunt, modified Deacon, and the Stefan-Boltzmann models. Estimates of net radiation were compared to measured values and found to be within the measurement error of the net radiometers used in the study

Applications of ISES for vegetation and land use

Remote sensing relative to applications involving vegetation cover and land use is reviewed to consider the potential benefits to the Earth Observing System (Eos) of a proposed Information Sciences Experiment System (ISES). The ISES concept has been proposed as an onboard experiment and computational resource to support advanced experiments and demonstrations in the information and earth sciences. Embedded in the concept is potential for relieving the data glut problem, enhancing capabilities to meet real-time needs of data users and in-situ researchers, and introducing emerging technology to Eos as the technology matures. These potential benefits are examined in the context of state-of-the-art research activities in image/data processing and management

Earth Observations and Integrative Models in Support of Food and Water Security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries