A model to estimate daily albedo from remote sensing data : accuracy assessment of MODIS MCD43 product

L’albedo superficial és un paràmetre físic que afecta al clima de la Terra i, a més, suposa una de les majors incerteses radiatives en l’actual modelització climàtica. Aquest paràmetre és molt variable tant a nivell espacial com temporal degut als canvis en les propietats de les superfícies i als canvis en les condicions d’il•luminació. En conseqüència, es requereix una resolució temporal diària de l’albedo per a realitzar estudis climàtics. L’augment de la resolució espacial dels models climàtics fa necessari l’estudi de les seues característiques espacials a nivell global. La teledetecció proporciona l’única opció pràctica de proporcionar dades d’albedo a nivell global amb alta qualitat i alta resolució tant espacial com temporal. El sensor MODerate Resolution Imaging Spectroradiometer (MODIS) a bord dels satèl•lits Terra i Aqua presenta unes característiques adequades per a l’estimació d’aquest paràmetre. En el present treball realitzem diversos estudis buscant les possibles fonts d’incertesa del producte oficial d’albedo de MODIS (MCD43). A més, presentem un model que millora la resolució temporal d’aquest paràmetre.Surface albedo is a critical land physical parameter affecting the earth’s climate and is among the main radiative uncertainties in current climate modelling. This parameter is highly variable in space and time, both as a result of changes in surface properties and as a function of changes in the illumination conditions. Consequently, an albedo daily temporal resolution is required for climate studies. The increasing spatial resolution of modern climate models makes it necessary to examine its spatial features. Satellite remote sensing provides the only practical way of producing high-quality global albedo data sets with high spatial and temporal resolutions. The MODerate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Terra and Aqua satellites presents the required sampling characteristics in order to derive the this parameter. In this PhD we develop several studies looking for the improvement of the official MODIS albedo product (MCD43) accuracy. Moreover, we present a model that improves the temporal resolution of this parameter

Rotifer adaptation to environmental unpredictability

Environmental unpredictability is a phenomenon that occurs in any habitat, and is an important selection pressure for the evolution of adaptive responses in the organisms. Interestingly, the ponds located in the Mediterranean region are characterized by having a high degree of seasonality and uncertainty at various temporal scales. Unpredictability in natural populations can act on several organism traits, especially in those species with complex life histories. The rotifer Brachionus plicatilis is a zooplankton species that frequently inhabits the salt ponds of the Mediterranean region. It has a type of reproduction in which proliferation by parthenogenesis (asexual phase) combines with occasional bouts of male production and sexual reproduction; the latter resulting in diapausing egg production (sexual phase). This makes B. plicatilis populations inhabiting Mediterranean ponds a good model system to study the adaptation to environmental unpredictability. In this thesis the first objective was to quantify the degree of environmental unpredictability of the studied Mediterranean ponds. In order to do that the variation in water-surface area during 27 years of a group of twenty saline water bodies was obtained from the scenes from the satellites Landsat 5 and 7. Different models for predictability estimation were developed here by considering how the variation in water-surface area could be relevant for the focus organism. The group of Mediterranean ponds studied were found to have a wide range of predictability values. A posteriori classification of the models for predictability estimation showed that some assumptions had negligible effects, while others can be associated with the species assemblages for which predictability needs to be assessed. The second objective was to study variation in diapause-related life-history traits in B. plicatilis. To do this, nine populations from a set of Mediterranean saline ponds were studied and showed significant levels of within-population genetic variation for the following life-history traits: propensity for sex and for hatching fraction of diapausing eggs. The propensity for sex in rotifer populations, and hence the early investment in diapause, decreased with environmental predictability. This suggests a conservative, bet-hedging strategy. Diapausing egg hatching fractions showed intermediate values (from 44 to 88%) in all the studied populations, but this trait was not related to the level of environmental predictability. Rotifer populations are able to locally diverge in diapause-related traits within a small geographical range (240 km2) despite their potential for widespread genetic exchange through the passive dispersal of diapausing eggs. The third objective was to unveil, at a genome wide scale, genotypes correlated to adaptation to local environmental unpredictability. The B. plicatilis genome was assembled in this thesis and its structural annotation yielded 54,725 predicted genes. Functions were tentatively assigned to 30% of them. Genotyping by sequencing (GBS), and the subsequent bioinformatics analyses in the 30 clones for each of the nine saline ponds studied provided a large number (4,543) of high quality single nucleotide polymorphisms (SNPs). A number of SNPs —most of them located within genes– showed higher between-population differentiation than expected by chance and were correlated with life-history traits and environmental parameters, so that they are candidates for diversifying selection for local adaptation. Unexpectedly, a large set of SNPs, more than half of them located within genes were found to present signatures of balancing/purifying selection in B. plicatilis. A number of genes were identified as strong candidates to be part of the genomic basis of local adaptation to fluctuating environments and constitute a database for future studies. Overall this thesis supports the expectation that wild populations of B. plicatilis can develop evolutionary responses to face environmental unpredictability and contribute to the empirical evidence of bet hedging strategies

Empirical evidence for fast temperature-dependent body size evolution in rotifers

Organisms tend to decrease in size with increasing temperature by phenotypic plasticity (the temperature-size rule; ectotherms) and/or genetically (Bergmann's rule; all organisms). In this study, the evolutionary response of body size to temperature was examined in the cyclically parthenogenetic rotifer Brachionus plicatilis. Our aim was to investigate whether this species, already known to decrease in size with increasing temperature by phenotypic plasticity, presents a similar pattern at the genetic level. We exposed a multiclonal mixture of B. plicatilis to experimental evolution at low and high temperature and monitored body size weekly. Within a month, we observed a smaller size at higher temperature, as compared to body size at lower temperature. The pattern was consistent for the size of both mature females and eggs; rotifers kept at high temperature evolved to be on average 14% (after 2 weeks) and 3% (after 3 weeks) smaller than the ones kept at low temperature (10 and 5% in the case of eggs, respectively). We therefore found that B. plicatilis is genetically programmed to adjust its body size-toenvironmental temperature

Genomic signatures of local adaptation to the degree of environmental predictability in rotifers

Environmental fuctuations are ubiquitous and thus essential for the study of adaptation. Despitethis, genome evolution in response to environmental fuctuations —and more specifcally to thedegree of environmental predictability– is still unknown. Saline lakes in the Mediterranean regionare remarkably diverse in their ecological conditions, which can lead to divergent local adaptationpatterns in the inhabiting aquatic organisms. The facultatively sexual rotifer Brachionus plicatilis showsdiverging local adaptation in its life-history traits in relation to estimated environmental predictabilityin its habitats. Here, we used an integrative approach —combining environmental, phenotypic andgenomic data for the same populations– to understand the genomic basis of this diverging adaptation.Firstly, a novel draft genome for B. plicatilis was assembled. Then, genome-wide polymorphisms werestudied using genotyping by sequencing on 270 clones from nine populations in eastern Spain. As aresult, 4,543 high-quality SNPs were identifed and genotyped. More than 90 SNPs were found tobe putatively under selection with signatures of diversifying and balancing selection. Over 140 SNPswere correlated with environmental or phenotypic variables revealing signatures of local adaptation,including environmental predictability. Putative functions were associated to most of these SNPs, sincethey were located within annotated genes. Our results reveal associations between genomic variationand the degree of environmental predictability, providing genomic evidence of adaptation to localconditions in natural rotifer populations

Ecological genomics of adaptation to unpredictability in experimental rotifer populations

Elucidating the genetic basis of phenotypic variation in response to different environments is key to understanding how populations evolve. Facultatively sexual rotifers can develop adaptive responses to fluctuating environments. In a previous evolution experiment, diapause-related traits changed rapidly in response to two selective regimes (predictable vs unpredictable) in laboratory populations of the rotifer Brachionus plicatilis. Here, we investigate the genomic basis of adaptation to environmental unpredictability in these experimental populations. We identified and genotyped genome-wide polymorphisms in 169 clones from both selective regimes after seven cycles of selection using genotyping by sequencing (GBS). Additionally, we used GBS data from the 270 field clones from which the laboratory populations were established. This GBS dataset was used to identify candidate SNPs under selection. A total of 76 SNPs showed divergent selection, three of which are candidates for being under selection in the particular unpredictable fluctuation pattern studied. Most of the remaining SNPs showed strong signals of adaptation to laboratory conditions. Furthermore, a genotype-phenotype association approach revealed five SNPs associated with two key life-history traits in the adaptation to unpredictability. Our results contribute to elucidating the genomic basis for adaptation to unpredictable environments and lay the groundwork for future evolution studies in rotifers

Evaluation of Near-Surface Air Temperature from Reanalysis over the United States and Ukraine: Application to Winter Wheat Yield Forecasting

In this work we evaluate the near-surface air temperature datasets from the ERA-Interim, JRA55, MERRA2, NCEP1, and NCEP2 reanalysis projects. Reanalysis data were first compared to observations from weather stations located on wheat areas of the United States and Ukraine, and then evaluated in the context of a winter wheat yield forecast model. Results from the comparison with weather station data showed that all datasets performed well (r2>0.95) and that more modern reanalysis such as ERAI had lower errors (RMSD ~ 0.9) than the older, lower resolution datasets like NCEP1 (RMSD ~ 2.4). We also analyze the impact of using surface air temperature data from different reanalysis products on the estimations made by a winter wheat yield forecast model. The forecast model uses information of the accumulated Growing Degree Day (GDD) during the growing season to estimate the peak NDVI signal. When the temperature data from the different reanalysis projects were used in the yield model to compute the accumulated GDD and forecast the winter wheat yield, the results showed smaller variations between obtained values, with differences in yield forecast error of around 2% in the most extreme case. These results suggest that the impact of temperature discrepancies between datasets in the yield forecast model get diminished as the values are accumulated through the growing season

Strategy for Monitoring the Blast Incidence in Crops of Bomba Rice Variety Using Remote Sensing Data

[EN] In this paper, we investigated the monitoring and characterization of the pest Magnaporthe oryzae, known as rice blast, in the Bomba rice variety at the Albufera Natural Park, located in Valencia, Spain during the 2022 and 2023 seasons. Using reflectance data from different Sentinel-2 satellite bands, various vegetative indices were calculated for each year. Significant differences in reflectance in the visible (B4), infrared (B8), red-edge (B6 and B7), and SWIR (B11) bands were detected between healthy and unhealthy fields. Additionally, variations were observed in the vegetation indices, with RVI and IRECI standing out for their higher accuracy in identifying blast-affected plots compared to NDVI and NDRE. Early differences in band values, vegetative indices, and spectral signatures were observed between the unhealthy and healthy plots, allowing for the anticipation of control treatments, whose effectiveness relies on timely intervention.This research has been funded by the DETECTORYZA project INNEST/2022/227, INNEST/2022/319 and INNEST/2022/361 Regional Operational Programme, FEDER Comunitat Valenciana de la Innovacio, Generalitat Valenciana.Agenjos-Moreno, A.; Rubio Michavila, C.; Uris Martínez, A.; Simeon-Brocal, R.; Franch-Gras, B.; Domingo Carrasco, C.; San Bautista Primo, A. (2024). Strategy for Monitoring the Blast Incidence in Crops of Bomba Rice Variety Using Remote Sensing Data. Agriculture. 14(8). https://doi.org/10.3390/agriculture1408138514

Within-Field Rice Yield Estimation Based on Sentinel-2 Satellite Data

[EN] Rice is considered one of the most important crops in the world. According to the Food and Agriculture Organization of the United Nations (FAO), rice production has increased significantly (156%) during the last 50 years, with a limited increase in cultivated area (24%). With the recent advances in remote sensing technologies, it is now possible to monitor rice crop production for a better understanding of its management at field scale to ultimately improve rice yields. In this work, we monitor within-field rice production of the two main rice varieties grown in Valencia (Spain) JSendra and Bomba during the 2020 season. The sowing date of both varieties was May 22-25, while the harvesting date was September 15-17 for Bomba and October 5-8 for JSendra. Rice yield data was collected over 66.03 ha (52 fields) by harvesting machines equipped with onboard sensors that determine the dry grain yield within irregular polygons of 3-7 m width. This dataset was split in two, selecting 70% of fields for training and 30% for validation purposes. Sentinel-2 surface reflectance spectral data acquired from May until September 2020 was considered over the test area at the two different spatial resolutions of 10 and 20 m. These two datasets were combined assessing the best combination of spectral reflectance bands (SR) or vegetation indices (VIs) as well as the best timing to infer final within-field yields. The results show that SR improves the performance of models with VIs. Furthermore, the correlation of each spectral band and VIs with the final yield changes with the dates and varieties. Considering the training data, the best correlation with the yields is obtained on July 4, with R-2 for JSendra of 0.72 at 10 m and 0.76 at 20 m resolution, while the R-2 for Bomba is 0.87 at 10 m and 0.92 at 20 m resolution. Based on the validation dataset, the proposed models provide within-field yield modelling Mean Absolute Error (MAE) of 0.254 t.ha(-1) (Mean Absolute Percentage Error, MAPE, of 3.73%) for JSendra at 10 m (0.240 t.ha(-1); 3.48% at 20 m) and 0.218 t.ha(-1) (MAPE 5.82%) for Bomba (0.223 t.ha(-1); 5.78% at 20 m) on July 4, that is three months before harvest. At parcel level the model's MAE is 0.176 t.ha(-1) (MAPE 2.61%) for JSendra and 0.142 t.ha(-1) (MAPE 4.51%) for Bomba. These results confirm the close correlation between the rice yield and the spectral information from satellite imagery. Additionally, these models provide a timeliness overview of underperforming areas within the field three months before the harvest where farmers can improve their management practices. Furthermore, it highlights the importance of optimum agronomic management of rice plants during the first weeks of rice cultivation (40-50 days after sowing) to achieve high yields.This research was partially funded by the program Generacio Talent of Generalitat Valenciana (CIDEGENT/2018/009).Franch-Gras, B.; San Bautista Primo, A.; Fita-Silvestre, D.; Rubio Michavila, C.; Tarrazó-Serrano, D.; Sánchez, A.; Skakun, S.... (2021). Within-Field Rice Yield Estimation Based on Sentinel-2 Satellite Data. Remote Sensing. 13(20). https://doi.org/10.3390/rs13204095132

Founder effects drive the genetic structure of passively dispersed aquatic invertebrates

Populations of passively dispersed organisms in continental aquatic habitats typically show high levels of neutral genetic differentiation despite their high dispersal capabilities. Several evolutionary factors, including founder events, local adaptation, and life cycle features such as high population growth rates and the presence of propagule banks, have been proposed to be responsible for this paradox. Here, we have modeled the colonization process to assess the impact of migration rate, population growth rate, population size, local adaptation and life-cycle features on the population genetic structure in these organisms. Our simulations show that the strongest effect on population structure are persistent founder effects, resulting from the interaction of a few population founders, high population growth rates, large population sizes and the presence of diapausing egg banks. In contrast, the role of local adaptation, genetic hitchhiking and migration is limited to small populations in these organisms. Our results indicate that local adaptation could have different impact on genetic structure in different groups of zooplankters

Quantifying unpredictability: A multiple-model approach based on satellite imagery data from Mediterranean ponds.

Fluctuations in environmental parameters are increasingly being recognized as essential features of any habitat. The quantification of whether environmental fluctuations are prevalently predictable or unpredictable is remarkably relevant to understanding the evolutionary responses of organisms. However, when characterizing the relevant features of natural habitats, ecologists typically face two problems: (1) gathering long-term data and (2) handling the hard-won data. This paper takes advantage of the free access to long-term recordings of remote sensing data (27 years, Landsat TM/ETM+) to assess a set of environmental models for estimating environmental predictability. The case study included 20 Mediterranean saline ponds and lakes, and the focal variable was the water-surface area. This study first aimed to produce a method for accurately estimating the water-surface area from satellite images. Saline ponds can develop salt-crusted areas that make it difficult to distinguish between soil and water. This challenge was addressed using a novel pipeline that combines band ratio water indices and the short near-infrared band as a salt filter. The study then extracted the predictable and unpredictable components of variation in the water-surface area. Two different approaches, each showing variations in the parameters, were used to obtain the stochastic variation around a regular pattern with the objective of dissecting the effect of assumptions on predictability estimations. The first approach, which is based on Colwell's predictability metrics, transforms the focal variable into a nominal one. The resulting discrete categories define the relevant variations in the water-surface area. In the second approach, we introduced General Additive Model (GAM) fitting as a new metric for quantifying predictability. Both approaches produced a wide range of predictability for the studied ponds. Some model assumptions-which are considered very different a priori-had minor effects, whereas others produced predictability estimations that showed some degree of divergence. We hypothesize that these diverging estimations of predictability reflect the effect of fluctuations on different types of organisms. The fluctuation analysis described in this manuscript is applicable to a wide variety of systems, including both aquatic and nonaquatic systems, and will be valuable for quantifying and characterizing predictability, which is essential within the expected global increase in the unpredictability of environmental fluctuations. We advocate that a priori information for organisms of interest should be used to select the most suitable metrics estimating predictability, and we provide some guidelines for this approach