A simplified approach to satellite-based monitoring system of sugarcane plantation to manage yield decline at Wonji-Shoa Sugar Estate, central Ethiopia.

Drastic and continuous decline in cane yields has become a major threat to sustainable sugarcane production in Ethiopia. Among the causes for the decline are the inefficient and ineffective system of monitoring sugarcane plantations. Adopting satellite-based crop monitoring through the Landviewer platform may circumvent this problem. However, the reliability of vegetation indexes calculated by the platform is unknown and thus requires evaluation. Accordingly, we tested the accuracy of selected Landviewer Calculated Vegetation Indexes (LCVIs) on three major sugarcane varieties and two cropping types. The goodness-of-fit of the sigmoid curve to the LCVIs profile of sugarcane was evaluated. The correlations between LCVIs and yield components, LCVIs and fractional green canopy cover (FGCC), as well as the time-serious Normalized Difference Vegetation Index (NDVI) and yields, were also analysed. We found that the goodness-of-fit of the sigmoid curve was significant (p < 0.001), with 84%-95% accuracy in all the indexes. The majority of LCVIs showed significant (p < 0.05) relationships with yield components and FGCC. The time-series NDVI also demonstrated a significant relationship with cane yield (R2 = 0.73-0.85) at the age of 10 months and above. The accuracy level of LCVIs varies with varieties and crop types, but the Normalized Difference Phenology Index (NDPI), Soil Adjusted Vegetation Index (SAVI), and NDVI were identified as the most consistent and effective LCVIs for sugarcane monitoring. Therefore, the accuracy of LCVIs was dependable and can be used effectively in monitoring sugarcane plantations to tackle the problem of continuous decline in the yield of the crop.

Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region.

Studying changes in temperature is fundamental for understanding its interactions with the environment and biodiversity. However, studies in mountainous areas are few, due to their complex formation and the difficulty of obtaining local data. We analysed changes in temperature over time in Montesinho Natural Park (MNP) (Bragança, Portugal), an important conservation area due to its high level of biodiversity. Specifically, we aimed to analyse: i) whether temperature increased in MNP over time, ii) what environmental factors influence the Land Surface Temperature (LST), and iii) whether vegetation is related to changes in temperature. We used annual summer and winter mean data acquired from the Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets/products (e.g. LST, gathered at four different times: 11am, 1pm, 10pm and 2am, Enhance vegetation index - EVI, and Evapotranspiration - ET), available on the cloud-based platform Google Earth Engine between 2003 and 2021). We analysed the dynamics of the temporal trend patterns between the LST and local thermal data (from a weather station) by correlations; the trends in LST over time with the Mann-Kendall trend test; and the stability of hot spots and cold spots of LST with Local Statistics of Spatial Association (LISA) tests. The temporal trend patterns between LST and Air Temperature (Tair) data were very similar (ρ > 0.7). The temperature in the MNP remained stable over time during summer but increased during winter nights. The biophysical indices were strongly correlated with the summer LST at 11am and 1pm. The LISA results identified hot and cold zones that remained stable over time. The remote-sensed data proved to be efficient in measuring changes in temperature over time.

A method to produce a flexible and customized fuel models dataset.

Simulation of vegetation fires very often resorts to fire-behavior models that need fuel models as input. The lack of fuel models is a common problem for researchers and fire managers because its quality depends on the quality/availability of data. In this study we present a method that combines expert- and research-based knowledge with several sources of data (e.g. satellite and fieldwork) to produce customized fuel models maps. Fuel model classes are assigned to land cover types to produce a basemap, which is then updated using empirical and user-defined rules. This method produces a map of surface fuel models as detailed as possible. It is reproducible, and its flexibility relies on juxtaposing independent spatial datasets, depending on their quality or availability. This method is developed in a ModelBuilder/ArcGis toolbox named FUMOD that integrates ten sub-models. FUMOD has been used to map the Portuguese annual fuel models grids since 2019, supporting regional fire risk assessments and suppression decisions. Datasets, models and supplementary files are available in a repository (https://github.com/anasa30/PT_FuelModels). •FUMOD is a flexible toolbox with ten sub-models included that maps updated Portuguese fuel models.

Biophysical, Biochemical, and Photochemical Analyses Using Reflectance Hyperspectroscopy and Chlorophyll a Fluorescence Kinetics in Variegated Leaves.

The adjustments that occur during photosynthesis are correlated with morphological, biochemical, and photochemical changes during leaf development. Therefore, monitoring leaves, especially when pigment accumulation occurs, is crucial for monitoring organelles, cells, tissue, and whole-plant levels. However, accurately measuring these changes can be challenging. Thus, this study tests three hypotheses, whereby reflectance hyperspectroscopy and chlorophyll a fluorescence kinetics analyses can improve our understanding of the photosynthetic process in Codiaeum variegatum (L.) A. Juss, a plant with variegated leaves and different pigments. The analyses include morphological and pigment profiling, hyperspectral data, chlorophyll a fluorescence curves, and multivariate analyses using 23 JIP test parameters and 34 different vegetation indexes. The results show that photochemical reflectance index (PRI) is a useful vegetation index (VI) for monitoring biochemical and photochemical changes in leaves, as it strongly correlates with chlorophyll and nonphotochemical dissipation (Kn) parameters in chloroplasts. In addition, some vegetation indexes, such as the pigment-specific simple ratio (PSSRc), anthocyanin reflectance index (ARI1), ratio analysis of reflectance spectra (RARS), and structurally insensitive pigment index (SIPI), are highly correlated with morphological parameters and pigment levels, while PRI, moisture stress index (MSI), normalized difference photosynthetic (PVR), fluorescence ratio (FR), and normalized difference vegetation index (NDVI) are associated with photochemical components of photosynthesis. Combined with the JIP test analysis, our results showed that decreased damage to energy transfer in the electron transport chain is correlated with the accumulation of carotenoids, anthocyanins, flavonoids, and phenolic compounds in the leaves. Phenomenological energy flux modelling shows the highest changes in the photosynthetic apparatus based on PRI and SIPI when analyzed with Pearson's correlation, the hyperspectral vegetation index (HVI) algorithm, and the partial least squares (PLS) to select the most responsive wavelengths. These findings are significant for monitoring nonuniform leaves, particularly when leaves display high variation in pigment profiling in variegated and colorful leaves. This is the first study on the rapid and precise detection of morphological, biochemical, and photochemical changes combined with vegetation indexes for different optical spectroscopy techniques.

Correlation between vegetation indexes generated at Vitis Vinifera L. and soil, plant and production parameters for emergency application in decision making / Correlação entre indices de vegetação gerados em Vitis Vinifera L. e parâmetros de solo, planta e produção para aplicação emergencial na tomada de decisão

Correlation between proximal sensing techniques and laboratory results of qualitative variables plus agronomic attributes was evaluated of a 3,0 ha vineyard in the county of Muitos Capões, Northeast of Rio Grande do Sul State, Brazil, in Vitis vinifera L. at 2017/2018 harvest, aiming to evaluate the replacement of conventional laboratory analysis in viticulture by Vegetation Indexes, at situations were laboratory access are unavailable. Based on bibliographic research, looking for vegetative indexes developed or used for canopy reflectance analysis on grapevines and whose working bands were within the spectral range provided by the equipment used, a total of 17 viable candidates were obtained. These chosen vegetation indices were correlated, through Pearson (5%), with agronomic soil attributes (apparent electrical conductivity, clay, pH in H2O, phosphorus, potassium, organic matter, aluminum, calcium, magnesium, effective CTC, CTC at pH 7.0, zinc, copper, sulfur and boron) for depths 0 -20 cm and 20-40 cm, and plant tissue (Nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, copper, zinc, iron, manganese and boron) , in addition to some key oenological and phytotechnical parameters for the quantification of wine production and quality. One hundred and thirty ninesignificant correlations were obtained from this cross, with 36 moderate coefficients between 19 parameter variables versus 12 of the indexes. We concluded that in cases where access or availability of laboratory analyzes is difficult or impracticable, the use of vegetation indices is possible if the correlation coefficients reach, at least, the moderate magnitude, serving as a support to decision making until the lack analytical structure to be remedied.

Avaliou-se a correlação entre as técnicas de sensoriamento proximal e os resultados laboratoriais de variáveis qualitativas, mais os atributos agronômicos do solo de um vinhedo de 3,0 ha no município de Muitos Capões, região nordeste do estado do Rio Grande do Sul, Brasil, na safra 2017/2018. Objetivou avaliar a substituição das análises laboratoriais convencionais em viticultura por Índices de Vegetação, em situações de indisponibilidade de acesso ao laboratório. Com base em pesquisa bibliográfica, buscaram-se índices vegetativos desenvolvidos ou utilizados para análise de refletância de dossel em videiras e cujas bandas de trabalho estavam dentro do intervalo espectral fornecido pelo equipamento utilizado, obtendo-se um total de 17 candidatos viáveis. Esses índices de vegetação escolhidos foram correlacionados, por meio de Pearson (5%), com atributos agronômicos do solo (condutividade elétrica aparente, argila, pH em H2O, fósforo, potássio, matéria orgânica, alumínio, cálcio, magnésio, CTC efetivo, CTC em pH 7,0, zinco, cobre, enxofre e boro) para profundidades de 0 - 20 cm e 20 - 40 cm, e tecido vegetal (nitrogênio, fósforo, potássio, cálcio, magnésio, enxofre, cobre, zinco, ferro, manganês e boro), além de alguns parâmetros enológicos e fitotécnicos essenciais para a quantificação da produção e qualidade do vinho. Deste cruzamento foram obtidas 139 correlações significativas, resultando 36 coeficientes moderados entre 19 variáveis de parâmetros versus 12 dos índices. Concluímos que nos casos em que o acesso ou disponibilidade de análises laboratoriais é difícil ou impraticável, a utilização de índices de vegetação é possível, desde que os coeficientes de correlação atinjam, pelo menos, a magnitude moderada, servindo como suporte para a tomada de decisão até a falta de estrutura analítica ser remediada.

Evaluating the Effects of Environmental Conditions on Sensed Parameters for Green Areas Monitoring and Smart Irrigation Systems.

The irrigation of green areas in cities should be managed appropriately to ensure its sustainability. In large cities, not all green areas might be monitored simultaneously, and the data acquisition time can skew the gathered value. Our purpose is to evaluate which parameter has a lower hourly variation. We included soil parameters (soil temperature and moisture) and plant parameters (canopy temperature and vegetation indexes). Data were gathered at 5 different hours in 11 different experimental plots with variable irrigation and with different grass composition. The results indicate that soil moisture and Normalized Difference Vegetation Index are the sole parameters not affected by the data acquisition time. For soil moisture, the maximum difference was in experimental plot 4, with values of 21% at 10:45 AM and 27% at 8:45 AM. On the other hand, canopy temperature is the most affected parameter with a mean variation of 15 °C in the morning. The maximum variation was in experimental plot 8 with a 19 °C at 8:45 AM and 39 °C at 12:45 PM. Data acquisition time affected the correlation between soil moisture and canopy temperature. We can affirm that data acquisition time has to be included as a variability source. Finally, our conclusion indicates that it is vital to consider data acquisition time to ensure water distribution for irrigation in cities.

Relationships between soil respiration and hyperspectral vegetation indexes and crop characteristics under different warming and straw application modes.

Examining the relationship between seasonal variations in soil respiration and abiotic factors and vegetation indexes is crucial for modeling soil respiration using upscaled remote sensing satellite data. A field experiment including control (CK), warming (WA), straw application (SA), and warming and straw application (WASA) treatments was performed in a winter wheat-soybean rotation cropland on the north shore of the lower reaches of the Yangtze River. Soil respiration, abiotic factors, crop hyperspectral vegetation indexes, leaf area index (LAI), and chlorophyll content (represented as the SPAD value) were measured during the 2018-2020 rotation growing seasons. The results indicated that the mean annual soil respiration was 2.27 ± 0.04, 3.08 ± 0.06, 3.64 ± 0.08, and 3.95 ± 0.20 µmol m-2 s-1 in the CK, WA, SA, and WASA plots, respectively, during the 2-year experimental period. Soil respiration was significantly (P < 0.05) correlated with soil temperature, soil moisture, hyperspectral vegetation indexes, LAI, and SPAD value in all plots. Models that included temperature, moisture, hyperspectral vegetation indexes, LAI, and SPAD value explained 50.5-74.7% of the seasonal variation in soil respiration in the CK, WA, SA, and WASA plots during the 2-year experimental period. A model including the seasonal mean NDVI, DVI, EVI, PRI, and LAI explained 72.4% of the interseasonal and intertreatment variations in seasonal mean soil respiration in the different plots across the four different crop-growing seasons. Our study indicated the potential applicability of hyperspectral vegetation indexes, LAI, and SPAD value to the estimation of soil respiration at a regional scale.

Leaf development stages and ontogenetic changes in passionfruit (Passiflora edulis Sims.) are detected by narrowband spectral signal.

During shoot development, leaves undergo various ontogenetic changes, including variation in size, shape, and geometry. Passiflora edulis (passionfruit) is a heteroblastic species, which means that it experiences conspicuous changes throughout development, enabling a morphological distinction between the juvenile and adult vegetative phases. Quantification of heteroblasty requires a practical, inexpensive, reliable, and non-destructive method, such as remote sensing. Moreover, relationships among ontogenetic changes and spectral signal at leaf level can be scaled up to support precision agriculture in passion fruit crops. In the present study, we used laboratory spectroscopic measurements (400-2500 nm) and narrowband vegetation indexes (or hyperspectral vegetation indexes - HVIs) to evaluate ontogenetic changes related to development and aging in P. edulis leaves. We also assessed leaf pigment concentration to further support the application of biochemical-related narrowband indexes. We report that 30-d-old leaves can be discriminated into developmental stages through their spectral signals. MSI (Moisture Stress Index) and NDVI750 (Normalized Difference Vegetation Index ρ750) contribute most to the variation of age (15 to 30-d-old leaves) and developmental stage (phytomer positions along the plant axis) in passionfruit leaves. PRI (Photochemical Reflectance Index) played an important role in detecting age and development alterations, including heteroblasty. A biochemical and spectral comparison of pigments revealed that spectroscopy offered potential for diagnosing phenology in P. edulis, as some narrowband indexes correlated strongly with chlorophylls and carotenoids content. Narrowband vegetation indexes are found to be a suitable tool for monitoring passionfruit crops.

A New Remote Sensing-Based System for the Monitoring and Analysis of Growth and Gas Exchange Rates of Photosynthetic Microorganisms Under Simulated Non-Terrestrial Conditions.

Oxygenic photosynthetic microorganisms are a focal point of research in the context of human space exploration. As part of the bioregenerative life-support systems, they could have a key role in the production of breathable O2, edible biomasses and in the regeneration of CO2 rich-atmospheres and wastewaters produced by astronauts. The test of the organism's response to simulated physico-chemical parameters of planetary bodies could also provide important information about their habitability potential. It is believed that the success of future planetary and space missions will require innovative technologies, developed on the base of preliminary experiments in custom-made laboratory facilities. In this context, simulation chambers will play a pivotal role by allowing the growth of the microorganisms under controlled conditions and the evaluation in real-time of their biomass productivity and impact on atmosphere composition. We here present a system capable of addressing these requirements with high replicability and low costs. The setup is composed by three main parts: 1) a Star Light Simulator, able to generate different light intensities and spectra, including those of non-solar stars; 2) an Atmosphere Simulator Chamber where cultures of photosynthetic microorganisms can be exposed to different gas compositions; 3) a reflectivity detection system to measure from remote the Normalized Difference Vegetation Indexes (NDVI). Such a setup allows us to monitor photosynthetic microorganism's growth and gas exchange performances under selected conditions of light quality and intensity, temperature, pressure, and atmospheres simulating non-terrestrial environments. All parameters are detected by remote sensing techniques, thus without interfering with the experiments and altering the environmental conditions set. We validated the setup by growing cyanobacteria liquid cultures under different light intensities of solar illumination, collecting data on their growth rate, photosynthetic activity, and gas exchange capacity. We utilized the reflectivity detection system to measure the reflection spectra of the growing cultures, obtaining their relative NDVI that was shown to correlate with optical density, chlorophyll content, and dry weight, demonstrating the potential application of this index as a proxy of growth.

Anthropogenic and climatic influences in the swamp environment of the Pandeiros River basin, Minas Gerais-Brazil.

Several environmental impacts are resulting from the process of anthropization and climate variability that have caused degradation of biomes and humid environments. Thus, the objective of this study was to evaluate the effect of the anthropic process and the variation of climatic conditions on the dynamics of the marsh vegetation in the Pandeiros River preservation area in the north of Minas Gerais, Brazil. The Enhanced Vegetation Index (EVI) of product MOD13Q1 and the gross primary productivity (GPP) of product MOD17A2 of the Moderate Resolution Imaging Spectroradiometer (MODIS) were used for the period from 2001 to 2017 were used in this process. Rain and air temperature data were obtained from the Conventional Weather Station of Januária-MG. The time series were submitted to the nonparametric statistical test of Mann-Kendall. The process of anthropization of the swamp area in the environmental protection area of the Pandeiros River/MG (EPA) showed a pattern of expansion of vegetation cover associated with the reduction of the water table, a phenomenon resulting from the silting process and reduction of the incidence of rain in this region, which contributes to the reduction of ecosystem services. Thus, understanding the influence of climatic variables on the dynamics of vegetation in humid environments, such as the EPA swamp area on the Pandeiros River, is essential for the preservation and recovery of these ecosystems and for the implementation of public policies for preservation and conservation.

Estimación de la calidad y cantidad de pasto kikuyo (Cenchrus clandestinum (Hochst. ex Chiov.) Morrone) usando imágenes multiespectrales / Estimation of the quality and quantity of Kikuyo grass (Cenchrus clandestinum (Hochst. Ex Chiov.) Morrone) using multispectral images

RESUMEN La evaluación de las praderas destinadas a ganadería es esencial para la productividad de los animales. Los datos de sensores multiespectrales remotos aerotransportados (SM) permiten construir índices de vegetación (VI, por sus siglas en idioma inglés) y relacionarlos con características fisiológicas y biofísicas de las pasturas. El objetivo fue evaluar VI para la estimación de la cantidad y calidad de pasto kikuyo en sistemas lecheros, del norte de Antioquia, Colombia. Se calcularon 10 diferentes VI, con 168 muestras de pasto kikuyo. Las muestras fueron pesadas, para estimar la biomasa verde (BV) y analizadas por espectroscopia del infrarrojo cercano, para los contenidos de proteína bruta (PB), fibra en detergente neutro (FDN) y fibra en detergente ácido (FDA). Los datos, se analizaron usando componentes principales (CP) y modelos aditivos generalizados suavizados. Las variables que más contribuyeron a la formación de la primera componente principal (CP1) fueron el índice de vegetación de diferencia normalizada (NDVI), el índice de vegetación simple (RVI), el índice de vegetación de diferencia normalizada verde (GNDVI), el índice clorofílico verde (Clg) y la BV del pasto kikuyo. Para la segunda componente principal (CP2) fueron el índice de vegetación de diferencia normalizada borde del rojo (RNDVI), el índice borde del rojo de clorofila (Clrg) y PB, FDN y FDA del pasto kikuyo. La BV fue explicada por el NDVI y PB por el RNDVI. La estimación obtenida para FDN y FDA del pasto kikuyo no fueron precisas.

ABSTRACT The evaluation of grazing lands is essential to improve livestock productivity. Data from multispectral airborne sensors allow calculating vegetation indexes (VI) and relating them to physiological and biophysical characteristics of the pastures. The objective of this study was to evaluate the usefulness of VI to estimate the quantity and quality of Kikuyu grass in dairy farms of northern Antioquia, Colombia. We calculated 10 different VI using 168 samples of Kikuyu grass. The samples were weighted to estimate green biomass (BV) and analyzed by near infrared spectroscopy for the contents of crude protein (PB), neutral detergent fiber (FDN) and acid detergent fiber (ADF). Data were analyzed using principal components (CP) and smoothed generalized additive models. The variables that contributed most to the formation of the first principal component (CP1) were the Normalized Difference Vegetation Index (NDVI), the Simple Vegetation Index (RVI), the Normalized Difference Vegetation Green Index (GNDVI), the Green Chlorophyll Index (Clg) and the BV of Kikuyu grass. The mayor contributors to the second principal component (CP2) were the Normalized Red-Edge Vegetation Index (RNDVI), the Red-Edge Chlorophyll Index (Clrg), and the PB, NDF and FDA of Kikuyu. The NDVI explained the BV, and the RNDVI explained the PB. The FDN and FDA estimations in Kikuyu were not precise.

Seasonal patterns of canopy photosynthesis captured by remotely sensed sun-induced fluorescence and vegetation indexes in mid-to-high latitude forests: A cross-platform comparison.

Characterized by the noticeable seasonal patterns of canopy photosynthesis, mid-to-high latitude forests are sensitive to climate change and crucial for understanding the global carbon cycle. To monitor the seasonal cycle of the canopy photosynthesis from space, several remotely sensed indexes, such as normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI) have been implemented within the past decades. Recently, satellite-derived sun-induced fluorescence (SIF) has shown great potential of providing retrievals that are more related to photosynthesis process. However, the potentials of different canopy measurements have not been thoroughly assessed in the context of recent advances of new satellites and proposals of improved indexes. At 15 forested sites, we present a cross-platform intercomparison of one emerging remote sensing based index of phenology index (PI) and two SIF datasets against the conventional indexes such as NDVI, EVI, and LAI to capture the seasonal cycles of canopy photosynthesis. NDVI, EVI, LAI, and PI were calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements, while SIF were evaluated from Global Ozone Monitoring Experiment-2 (GOME-2) and Orbiting Carbon Observatory-2 (OCO-2) observations. Results indicated that GOME-2 SIF was highly correlated with gross primary production (GPP) and absorbed photosynthetically active radiation during the growing seasons. The SIF-GPP relationship can generally be considered linear at the 16-day scale. Key phenological metrics such as start of the seasons and end of the seasons captured by SIF from GOME-2 and OCO-2 matched closely with photosynthesis phenology as inferred by GPP. However, the applications of OCO-2 SIF for phenological studies may be limited only for a small range of sites (at site-level) due to a limited spatial sampling. Among the MODIS estimations, PI and NDVI provided most reliable predictions of start of growing seasons, while no indexes accurately captured the end of growing seasons.

Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data.

BACKGROUND: Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g., green normalized difference vegetation index or GNDVI, simple ratio or SRa, etc.) which are used for the prediction of primary traits (e.g., biomass). However, these indices only use some bands and are cultivar-specific; therefore they lose considerable information and are not robust for all cultivars. RESULTS: This study proposes models that use all available bands as predictors to increase prediction accuracy; we compared these approaches with eight conventional vegetation indexes (VIs) constructed using only some bands. The data set we used comes from CIMMYT's global wheat program and comprises 1170 genotypes evaluated for grain yield (ton/ha) in five environments (Drought, Irrigated, EarlyHeat, Melgas and Reduced Irrigated); the reflectance data were measured in 250 discrete narrow bands ranging between 392 and 851 nm. The proposed models for the simultaneous analysis of all the bands were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square. The results of these models were compared with the OLS performed using as predictors each of the eight VIs individually and combined. CONCLUSIONS: We found that using all bands simultaneously increased prediction accuracy more than using VI alone. The Splines and Fourier models had the best prediction accuracy for each of the nine time-points under study. Combining image data collected at different time-points led to a small increase in prediction accuracy relative to models that use data from a single time-point. Also, using bands with heritabilities larger than 0.5 only in Drought as predictor variables showed improvements in prediction accuracy.

Temporal dynamics of spectral bioindicators evidence biological and ecological differences among functional types in a cork oak open woodland.

The application of spectral vegetation indices for the purpose of vegetation monitoring and modeling increased largely in recent years. Nonetheless, the interpretation of biophysical properties of vegetation through their spectral signature is still a challenging task. This is particularly true in Mediterranean oak forest characterized by a high spatial and temporal heterogeneity. In this study, the temporal dynamics of vegetation indices expected to be related with green biomass and photosynthetic efficiency were compared for the canopy of trees, the herbaceous layer, and two shrub species: cistus (Cistus salviifolius) and ulex (Ulex airensis). coexisting in a cork oak woodland. All indices were calculated from in situ measurements with a FieldSpec3 spectroradiometer (ASD Inc., Boulder, USA). Large differences emerged in the temporal trends and in the correlation between climate and vegetation indices. The relationship between spectral indices and temperature, radiation, and vapor pressure deficit for cork oak was opposite to that observed for the herbaceous layer and cistus. No correlation was observed between rainfall and vegetation indices in cork oak and ulex, but in the herbaceous layer and in the cistus, significant correlations were found. The analysis of spectral vegetation indices with fraction of absorbed PAR (fPAR) and quantum yield of chlorophyll fluorescence (ΔF/Fm') evidenced strongest relationships with the indices Normalized Difference Water Index (NDWI) and Photochemical Reflectance Index (PRI)512, respectively. Our results, while confirms the ability of spectral vegetation indices to represent temporal dynamics of biophysical properties of vegetation, evidence the importance to consider ecosystem composition for a correct ecological interpretation of results when the spatial resolution of observations includes different plant functional types.