Vegetation indices time series analysis is increasingly improved for characterizing agricultural land processes. However, this is challenging because of the multeity of factors affecting vegetation growth. In semiarid regions the rainfall, the soil properties and climate are strongly correlated with crop growth. These relationships are commonly analyzed using the normalized difference vegetation index (NDVI). NDVI series from two sites, belonging to different agroclimatic zones, were examined, decomposing them into the overall average pattern, residuals, and anomalies series. All of them were studied by applying the concept of the generalized Hurst exponent. This is derived from the generalized structure function, which characterizes the series’ scaling properties. The cycle pattern of NDVI series from both zones presented differences that could be explained by the differences in the climatic precipitation pattern and soil characteristics. The significant differences found in the soil reflectance bands confirm the differences in both sites. The scaling properties of NDVI original series were confirmed with Hurst exponents higher than 0.5 showing a persistent structure. The opposite was found when analyzing the residual and the anomaly series with a stronger anti-persistent character. These findings reveal the influences of soil–climate interactions in the dynamic of NDVI series of rainfed cereals in the semiarid.

Rangeland ecosystems comprise more than a third of the global land surface, sustaining essential ecosystem services and livelihoods. In Spain, Southeast Spain includes some of the driest regions; accordingly, rangelands from Murcia and Almeria provinces were selected for this study. We used time series metrics and the Hurst Exponent from rescale range and detrended fluctuation analysis to cluster different rangeland dynamics to classify temporally and spatially diverse rangelands. The metrics were only calculated for three time periods that showed significant NDVI changes: March to April, April to July, and September to December. Detrended fluctuation analysis was not previously employed to cluster vegetation. This study used it to improve rangeland classification. K-means and unsupervised random forest were used to cluster the pixels using time series metrics and Hurst exponents. The best clustering results were obtained when unsupervised random forest was used with the Hurst exponent calculated with detrended fluctuation analysis. We used the Silhouette Index to evaluate the clustering results and a spatial comparison with topographical data. Our results show that adding the Hurst exponent, calculated with detrended fluctuation analysis, provided a better classification when clustering NDVI time series, while classifications without the Hurst exponent or with the Hurst exponent calculated with the rescale range method showed lower silhouette values. Overall, this shows the importance of using detrending when calculating the Hurst exponent on vegetation time series, and its usefulness in studying rangeland dynamics for management and research.

The dynamic of rangelands results from complex interactions between vegetation, soil, climate, and human activity. This scenario makes rangeland’s condition challenging to monitor, and degradation assessment should be carefully considered when studying grazing pressures. In the present work, we study the interaction of vegetation and soil moisture in semiarid rangelands using vegetation and soil moisture indices. We aim to study the feasibility of using soil moisture negative anomalies as a warning index for vegetation or agricultural drought. Two semiarid agricultural regions were selected in Spain for this study: Los Vélez (Almería) and Bajo Aragón (Teruel). MODIS images, with 250 m and 500 m spatial resolution, from 2002 to 2019, were acquired to calculate the Vegetation Condition Index (VCI) and the Water Condition Index (WCI) based on the Normalised Difference Vegetation Index (NDVI) and soil moisture component (W), respectively. The Optical Trapezoid Model (OPTRAM) estimated this latter W index. From them, the anomaly (Z-score) for each index was calculated, being $Z_{VCI}$ and $Z_{WCI}$, respectively. The probability of coincidence of their negative anomalies was calculated every 10 days (10-day periods). The results show that for specific months, the $Z_{WCI}$ had a strong probability of informing in advance, where the negative $Z_{VCI}$ will decrease. Soil moisture content and vegetation indices show more similar dynamics in the months with lower temperatures (from autumn to spring). In these months, given the low temperatures, precipitation leads to vegetation growth. In the following months, water availability depends on evapotranspiration and vegetation type as the temperature rises and the precipitation falls. The stronger relationship between vegetation and precipitation from autumn to the beginning of spring is reflected in the feasibility of $Z_{WCI}$ to aid the prediction of $Z_{VCI}$. During these months, using $Z_{WCI}$ as a warning index is possible for both areas studied. Notably, November to the beginning of February showed an average increase of 20–30% in the predictability of vegetation anomalies, knowing moisture soil anomalies four lags in advance. We found other periods of relevant increment in the predictability, such as March and April for Los Vélez, and from July to September for Bajo Aragón.

Deforestation by human activities is a common issue in Amazonian countries. This occurs at different spatial and temporal scales causing primary forest loss and land fragmentation issues. During the deforestation process as the forest loses connectivity, the deforested patches create new intricate connections, which in turn create complex networks. In this study, we analyzed the local connected fractal dimension (LCFD) of the deforestation process in the Sumaco Biosphere Reserve (SBR) with two segmentation methods, —CA-wavelet and K-means—to categorize the complexity of deforested patches’ connections and then relate these with the spatial processes. The results showed an agreement with both methods, in which LCFD values below 1 corresponded to isolated patches with simple shapes and those above 1 signified more complex and connected patches. From CA-wavelet a threshold of 1.57 was detected allowing us to identify and discern low and high land transformation, while the threshold for K-means was 1.61. Both values represent the region from which deforestation performs local aggressive expansion networks. The thresholds were used to map the LCFD in which all spatial processes were visually detected. However, the threshold of 1.6 ± 0.03 was more effective in discerning high land transformation. such as shrinkage and attrition, in the deforestation process in the SBR.

Water, is in Spain a scarce commodity and although we have an age-old water culture, with an emblematic Court, such as the “Tribunal de las Aguas de Valencia”, hydraulic infrastructure, hydrological basin plans, legislation and hydraulic administration since the 20th Century, there are problems of scarcity, water quality and extreme events that often lead to conflicts between users and also among the responsible administrations for their management. Within this framework, it is of a great interest the training of technicians in matters related to planning, quality and integrated water resources management for sustainable development.In Argentina (especially in the NOA) and until a few years ago, water has not been considered as a scarce commodity. In addition to this, they do not have the history and culture of Spain on issues related to their management, planning and governance. Now, they have begun to establish laws and regulations, as well as, an Association of Consortia of Public Water Users, needing external advice. Therefore, it is necessary, to train technicians in water resources capable of working in areas related to their planning and sustainable management, with knowledge related to the quality required by users. These technicians could be integrated, both in the responsible water administrations’, as well as, in private companies.The project that is the object of this paper is based on preparing a double master's degree, in which the training needs of the students graduated of Spanish and Argentina Engineering Schools are taken into account.

In most Mediterranean climate regions drought events are of great importance and their effects on rainfed crops are evident. Crop yields of rainfed cereal are highly dependent of the soil-plant-atmosphere system, especially referred to the weather conditions and soil properties. However, very few studies are found on the importance of both factors on crop condition. Several plots were localized in the midlands of Eresma-Adaja watershed. Combining remote sensing data and agricultural survey work those with monocrop cereal sequences were identify. These plots were clustered based on which soil class were allocated based on a Self-Organizing Map and precipitation regimen elaborated in the area (Rivas-Tabares et al., 2019). Within this area, two contrasting soil properties sites were selected to assess plots with at least 20 years of rainfed monocropping sequences but under similar weather regime. This allows us to analyze the effect and relationships of soil type and rainfall with Normalized Difference Vegetation Index (NDVI) in time. The NDVI average from both areas are statistically different in the growing season suggesting that soils and weather conditions are motivating the spectral variability of sites. The influence of soil texture and rainfall regimen related to NDVI values and interannual variability during the crop growth are discussed. References Rivas-Tabares, D., AM Tarquis, B Willaarts, Á De Miguel. 2019. An accurate evaluation of water availability in sub-arid Mediterranean watersheds through SWAT: Cega-Eresma-Adaja. Agricultural Water Management 212, 211-225.   ACKNOWLEDGEMENTS Finding for this work was partially provided by Boosting agricultural Insurance based 465 on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020. The authors also acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain. The data provided by ITACyL and AEMET is greatly appreciated.