Soil moisture (SM) is a key variable for assessing plant water availability, especially in rain-fed systems where imbalances strongly affect crop development. Satellite missions such as SMAP provide global SM estimates, though representing vertical SM variability remains challenging. This study evaluates the performance of SMAP Level 4 Global 3-hourly 9 km grid EASE-Grid Surface and Root-Zone Soil Moisture Geophysical Data (SPL4SMGP, version 7 and the new and scarcely evaluated version 8) using field observations from the Argentine Pampas, a region dominated by Typic Argiudolls soils (~16 million ha). The analysis covered normal-wet and dry conditions across several crop seasons. Surface (SSM, ~5 cm) and root zone (RZSM, 0–100 cm) soil moisture were compared against field data using Pearson’s correlation (r), bias, and unbiased root mean square deviation (ubRMSD). Both SSM and RZSM achieved ubRMSD values close to the SMAP accuracy target (≈0.04 m3/m3). SSM correlated moderately with observations (r = 0.57–0.72) and showed a consistent negative bias (−0.08 ± 0.05 m3/m3). In contrast, RZSM exhibited low sensitivity to soil profile variability and a narrow dynamic range. Version 8 showed similar performance to version 7, with a tendency toward overestimation, mainly during dry periods. Overall, SPL4SMGP products effectively capture SSM dynamics but show limited skill in representing root zone variability in Typic Argiudolls.

Evapotranspiration (ET) is a crucial component of the hydrological cycle, influencing water and energy exchanges between the Earth's surface and the atmosphere. This study evaluates the performance of four satellite-derived Actual ET (ETa) products-SebOp-VIIRS, SebOp-MODIS, TerraClimate, and MOD16A2-by comparing them to ETa estimates obtained from a superconducting gravimeter (SG) at the Argentine-German Geodetic Observatory (AGGO) over a period of five years and ten months. Results show that SebOpVIIRS provides the best overall performance, with the highest coefficient of determination $\left(R^{2}=0.61\right)$, a slope close to unity (a $=1.01$), and a moderate positive bias ($3.70 \text{mm} /$ month). SebOpMODIS also performed well, though with a slightly lower $\mathbf{R}^{2}$ (0.52) and a small negative bias ($-1.55 \text{mm} /$ month). In contrast, TerraClimate and MOD16A2 exhibited significant limitations, with TerraClimate showing the lowest slope $(a=0.61)$ and $a$ large negative bias ($-17.09 \text{mm} /$ month). A common issue across all products was the attenuation of extreme ETa values, where low ETa was overestimated and high ETa was underestimated. These findings underscore the importance of validating satellitebased ETa products with ground-based observations, as the SG provides valuable, continuous measurements of water storage changes, offering a reliable benchmark for hydrological studies and water management.

The synergy between spectral indices and land surface temperature (LST) has been used as an indicator of crop status and soil moisture (SM). However, studies of the response times of these to changes in SM in the soil profile arescarce. The aim of this study was to analyze the sensitivity and response times of LST and the Normalized Difference Water Index (NDWI) to changes in SM as a determining factor of barley and wheat water status in the southeasternArgentine Pampas. Daily SM and LST were provided by stations installed over the crops, and NDWI from Sentinel 2 (S2). Results showed instantaneous correlations between SM and LST (r > -0.65) for soil depths explored by croproots (20-50 cm). No consistent relationship between NDWI and SM was observed, suggesting NDWI limitation for short-time changes in SM in humid and sub-humid conditions. During the critical period of crops, the relationship between NDWI and SM showed high correlation (r > 0.92) only at 10 cmwhen considering 1-day time lag. This study contributes to the understanding of spectral behavior of crops in the optical and thermal spectrum in field conditions, which is key to evaluating the usefulness of these data for monitoring agricultural systems.

Wet rainfall pulses control vegetation growth through evapotranspiration in most dryland areas. This topic has not been extensively analyzed with respect to the vast semi-arid ecosystems of Central Australia. In this study, we investigated vegetation water responses to in situ root zone soil moisture (SM) variations in savanna woodlands (Mulga) in Central Australia using satellite-based optical and thermal data. Specifically, we used the Land Surface Water Index (LSWI) derived from the Advanced Himawari Imager on board the Himawari 8 (AHI) satellite, alongside Land Surface Temperature (LST) from MODIS Terra and Aqua (MOD/MYD11A1), as indicators of vegetation water status and surface energy balance, respectively. The analysis covered the period from 2016 to 2021. The LSWI increased with the magnitude of wet pulses and showed significant lags in the temporal response to SM, with behavior similar to that of the Enhanced Vegetation Index (EVI). By contrast, LST temporal responses were quicker and correlated with daily in situ SM at different depths. These results were consistent with in situ relationships between LST and SM, with the decreases in LST being coherent with wet pulse magnitude. Daily LSWI and EVI scores were best related to subsurface SM through quadratic relationships that accounted for the lag in vegetation response. Tower flux measures of gross primary production (GPP) were also related to the magnitude of wet pulses, being more correlated with the LSWI and EVI than LST. The results indicated that the vegetation response varied with SM depths. We propose a conceptual model for the relationship between LST and SM in the soil profile, which is useful for the monitoring/forecasting of wet pulse impacts on vegetation. Understanding the temporal changes in rainfall-driven vegetation in the thermal/optical spectra associated with increases in SM can allow us to predict the spatial impact of wet pulses on vegetation dynamics in extensive drylands.

The influence of the Water Table (WT) and the capillary fringe plays a critical role in soil water dynamics, affecting plant‑available water, soil moisture, evapotranspiration, and Land Surface Temperature (LST). This study examined the functioning of the aquifer–soil–plant–atmosphere system such as transpiration, evaporation, plant root water uptake and capillarity to assess how the WT and the capillary fringe affect LST. Field measurements were integrated with satellite data, including WT depth, precipitation records, and satellite‑derived products such as LST, Normalized Difference Vegetation Index (NDVI), and potential evapotranspiration from reanalysis data (ERA5‑Ag). The research was conducted in a shallow aquifer within the Salado River watershed, Buenos Aires Province, Argentina, over the period 2007–2023. Results revealed a strong inverse relationship (R² = 0.74) between the WT and LST. This relationship was modeled using an equation valid during the summer months, when atmospheric demand is high and soils are dry. The approach was validated using measurements from nearby piezometers, yielding a bias of −0.17 m and a root mean square deviation (RMSD) of 0.44 m. Satellite‑derived LST was shown to effectively reflect the influence of the WT on plant transpiration under water‑stressed conditions. By isolating the effect of evaporation, this method offers a novel means of indirectly assessing the hydrogeological status of shallow aquifers.

An exploratory work was carried out on the sensitivity of L-band radar waves from satellite platforms on the production of grapes per area of the plantation (yield). SAOCOM images over vine plots located in Estación Experimental Agropecuaria INTA in Luján de Cuyo, Mendoza, Argentina were used. Sixteen yield data distributed in the 2021-2022 and 2022-2023 campaigns were compared against a radar index involving the cross-polarized backscatter coefficient HV from radar images acquired before and after harvesting, within a time span of 8 days. The results indicate a sensitivity to yield subjected to the orientation of the crop rows, which requires an input reference map with the orientation of the rows in order to decouple the effect of these on the radar wave, for near-future development of a satellite product of grapevine yield.

The spatial monitoring of crop water status is crucial for agricultural purposes. The aim of this study was to analyze the sensitivity and response times of land surface temperature (LST) and the Normalized Difference Water Index (NDWI) to changes in soil moisture (VWC) in barley and wheat crops at plot scale in southeastern Argentine Pampas. VWC and LST were recorded daily by stations installed over the crops, and NDWI from Sentinel 2 (S2). Results showed instantaneous correlations between VWC and LST (r>-0.65) for soil depths explored by crop roots (20-50 cm). Correlations with NDWI were negative, which lacks biophysical meaning from a soil-plant system perspective. However, this index showed lower maximum values during the dry campaign. During the critical period of crops, the relationship between NDWI and VWC at 10 cm was positive, with a high correlation (r≈0.92) when considering responses with a 1-day time lag. This study contributes to the understanding of crops spectral behavior in the optical and thermal spectrum according to their water status, which is key to evaluating the usefulness of these data for monitoring agricultural systems.

Vertical flows, within the hydrological cycle, are one of the most relevant variables in the plains, since slopes vary between 0 and 5%, and horizontal flows are not significant. In this sense, evapotranspiration plays a fundamental role in water management since about 85% of the water leaving the system does so through this process, requiring precise quantification. The main objective is to calculate potential and actual evapotranspiration (ETp and ETa) with satellite and reanalysis data using Google Earth Engine platform. For its calculation, the Priestley-Taylor (PT) equation combined with soil moisture information was used, with a spatio-temporal resolution of 250 x 250 m every eight days, in the Argentine Pampas region (APR). The product was valued in seven stations of the APR, whose results showed, for ETp, an R-RMSE (Robust Root Mean Square Error) of 0.5 mm d(-1), a systematic error (Median) of 0.3 mm d(-1), and the random error (RSD-Robust Standard Deviation) of 0.5 mm d(-1); while, for ETa, these values are 0.6, -0.2 and 0.5 mm d(-1), respectively. The overall results show that the method used is a valid tool to characterize ET in the APR and that it can be used to analyze its spatio-temporal variability under different extreme conditions and to carry out applied environmental studies.

Las observaciones de microondas en las frecuencias entre los 1-2 GHz (banda L) son sensibles a la humedad de suelo superficial (SM) y al contenido de agua en la vegetación, el cual puede parametrizarse por medio de la profundidad óptica de la vegetación (VOD). El objetivo de este trabajo fue presentar los principios físicos de las mediciones de microondas pasivas y un análisis preliminar sobre las dinámicas hídricas de la vegetación en el sudeste de la Región Pampeana por medio de la observación de las series temporales de VOD y SM de la misión Soil Moisture Active Passive mission (SMAP). Para complementar a estos análisis, se incorporaron observaciones del índice de vegetación de diferencia normalizada (NDVI) y de la temperatura de superficie (LST) obtenidos en campo y derivados de medidas registradas por sensor Moderate-Resolution Imaging Spectroradiometer (MODIS). Los resultados mostraron que la VOD presenta capacidad para determinar variaciones subsemanales relacionadas al contenido de agua en la vegetación mediante comportamientos acoplados con la SM y la LST bajo condiciones homogéneas de superficie. Particularmente, durante las temporadas de verano se observaron que aumentos localizados de LST coincidieron con bajas de VOD. Estas observaciones demostraron el potencial de VOD tanto para el seguimiento de la dinámica hídrica de la vegetación a escala subsemanal como para la detección de eventos puntuales de estrés hídrico.

Soil moisture (SM) plays a vital role in the water and energy cycles, affecting evapotranspiration, infiltration, and runoff processes. The SM available for evapotranspiration is crucial for food security, especially given the significant in-terannual variability in the yield of rainfed crops in large agricultural regions. Due to the limited availability of field data, satellite-derived values are essential for studying SM. Therefore, evaluating this information across different surfaces, climates, and soil types is paramount. This study analyzed SMAP data (36 km, 9 km, and rescaled to 1 km) and the SMAP/Sentinel-1 product (1 km and 3 km) concerning field data to understand the behavior of SM products with different spatial resolutions. The study areas include short grass and rainfed crop zones in the Pampean region of Argentina and in Northern Italy, ranging from semiarid to subhumid/humid climates. In both regions, results showed that the 9 km product exhibits errors between 5% and 15% in the Pampean region and 7% in Italy. While the 36 km data behaves similarly, higher spatial resolution data shows increased errors, ranging from 5% to around 20%. Therefore, it is recommended to consider validations for the 1 km and 3 km products and avoid using them directly without prior evaluation.

Argentina is one of the main producers and exporters of grains and oilseeds, ranking third in soybean exports and fourth in barley ones. The 90% of this production occurs within the Argentine Pampas region (APR) under rainfed conditions, but its water consumption and pollution has not been studied in depth. Likewise, the link between soil moisture (SM) and Water Footprint (WF) generation is poorly studied at the global level. And yet, SM is a critical factor for the development of rainfed crops. This study aims to evaluate, at plot scale, the role of SM in the generation of the green (WFgreen) and grey (WFgrey) (WF). Additionally, it estimates the WF for rainfed barley and soybean crops in the Southeast of APR, where there are no reference values. Yields, water consumption and nitrogen (N) pollution load were estimated for different campaigns. Field data (weather, crop and production management) recorded in the study plots were used. Results indicated an average WFgreen of 1236 m3/t for soybeans and a WFgreen of 349 m3/t and WFgrey of 547 m3/t for barley. The study highlights the critical role of SM in both WF sub-indicators. Soil water availability, based on the evaporative fraction during critical growth stages, influenced yields and final WFgreen volumes. In addition, there was an effect on N uptake by crops. In the driest barley campaign, WFgrey increased by 234%. Insufficient SM restricted nutrient uptake, reducing yields and increasing N with the potential to leach or runoff. Consequently, it is suggested to adjust the WFgrey methodology incorporating SM fluctuations and unaccounted N losses. The study contributes to understand the WF drivers and highlights the need to assess them accurately. In particular, it aims to reduce the gaps surrounding the water consumption of rainfed crops, thereby supporting resource conservation and grain provisioning efforts.

This study presents an initial investigation into crop (barley and soybean) monitoring using vegetation optical depth (VOD) retrieved from the NASA Soil Moisture Active Passive mission (SMAP). VOD was consistent with Normalized Difference Vegetation Index (NDVI) and latent heat flux (LE) from Moderate-Resolution Imaging Spectroradiometer (MODIS) within the southeastern area of the Argentine Pampas. Pre-liminary results reveal the potential of L-band VOD as a tool for monitoring water crops condition, demonstrating relevant synergies with traditional vegetation indicators.

El manejo hidrológico de un país depende, en gran medida, del conocimiento de las cuencas existentes, del potencial de estas y de la manera de gestionar adecuadamente los excedentes hídricos. En este sentido, es de vital importancia el estudio y análisis de la evapotranspiración (ET) de referencia (ET0), real y potencial (ETp). Por lo que, se torna indispensable evaluar el comportamiento de los diferentes productos de ET que se encuentran disponibles para su uso de manera libre. En este sentido, el objetivo principal de este trabajo es analizar los datos de los modelos existentes en la plataforma Climate Engine (TerraClimate, ERA 5, MERRA-2 y MOD16A2), que posee datos a diferentes escalas temporales y espaciales. Además, evaluar el algoritmo Support Vector Machine Regression (SVR) de inteligencia artificial, aplicado con parámetros obtenidos de NASA Power y con datos locales registrados en la región Pampeana argentina (RPA). En general, se obtuvieron errores entre 0.5 y 1.2 mm d-1 y valores del índice de eficiencia de Nash-Sutcliffe (NSE) entre 0.6 y 0.9 (para ET0); entre 0.4 y 0.7 para ET real y entre 0.6 y 0,9 para ETp. Asimismo, queda demostrado que el modelo más propicio para el cálculo de ET0 como ET real, es el SVR, mientras que para ETp es ERA 5.

Los flujos verticales, dentro del ciclo hidrológico, son una de las variables de mayor relevancia en zona de llanura, dado que las pendientes varían entre 0% y 5%, y los flujos horizontales no son significativos. En este sentido, la evapotranspiración juega un rol fundamental en el manejo hídrico ya que alrededor del 85% del agua que sale del sistema lo hace mediante este proceso, requiriendo una cuantificación precisa. El objetivo principal de este trabajo es calcular la evapotranspiración potencial y real (ETp y ETr) con datos de satélite y reanálisis mediante el uso de la plataforma Google Earth Engine. Para su cálculo se tomó la ecuación de Priestley-Taylor (PT) combinado con información de humedad de suelo, con una resolución espacio-temporal de 250 m por 250 m cada 8 días, en la región Pampeana Argentina (RPA). El producto se valoró en siete estaciones de la RPA, cuyos resultados mostraron, para la ETp, un R-RMSE (error cuadrático medio robusto) de 0.5 mm d-1, un error sistemático (Mediana) de 0.3 mm d-1, y el error aleatorio (RSD- desviación estándar robusta) de 0.5 mm d-1 mientras que, para la ETr, estos valores son de 0.6, -0.2 y 0.5 mm d-1 respectivamente. Los resultados globales muestran que el método utilizado es una herramienta válida para caracterizar la ET en la RPA y que puede ser utilizado para analizar su variabilidad espacio-temporal diferentes condiciones extremas y realizar estudios ambientales aplicados.

A key aspect in agricultural zones, such as the Pampean Plain of Argentina, is to accurately estimate evapotranspiration rates to optimize crops and irrigation requirements and the floods and droughts prediction. In this sense, we evaluate six machine learning approaches to estimate the reference and actual evapotranspiration (ET0 and ETa) through CERES satellite products data. The results obtained applying machine learning techniques were compared with values obtained from ground-based information. After training and validating the algorithms, we observed that Support Vector machine-based Regressor (SVR) showed the best accuracy. Then, with an independent dataset, the calibrated SVR were tested. For predicting the reference evapotranspiration, we observed statistical errors of MAE = 0.437 mm d−1, and RMSE = 0.616 mm d−1, with a determination coefficient, R2, of 0.893. Regarding actual evapotranspiration modelling, we observed statistical errors of MAE = 0.422 mm d−1, and RMSE =0.599 mm d−1, with a R2 of 0.614. Comparing the results obtained with the machine learning models developed another studies in the same field, we understand that the results are promising and represent a baseline for future studies. Combining CERES data with information from other sources may generate more specific evapotranspiration products, considering the different land covers.

This paper evaluates the ability of Sentinel-3 satellite data to qualitatively estimate chlorophyll fluorescence, a direct indicator of the physiological state of vegetation. Estimates are compared in absorption zones, O2-B and O2-A, using data collected in three different ecosystems. The proposed method calculates the simple ratio of radiance outside and inside the oxygen absorption bands. Results show that the method, using the O2-A absorption band, can effectively predict field-derived chlorophyll fluorescence measurements. The observed differences between the variants are discussed and the importance of considering errors and biases in the measurements is pointed out. This qualitative approach based on satellite data offers potential for improving spatial and temporal resolution in monitoring chlorophyll fluorescence at global or regional scales.

The practical utility of remote sensing techniques depends on their validation with ground-truth data. Validation requires similar spatial-temporal scales for ground measurements and remote sensing resolution. Evapotranspiration (ET) estimates are commonly compared to weighing lysimeter data, which provide accurate but localized measurements. To address this limitation, we propose the use of superconducting gravimeters (SGs) to obtain ground-truth ET data at larger spatial scales. SGs measure gravity acceleration with high resolution (tenths of nm s−2) within a few hundred meters. Similar to lysimeters, gravimeters provide direct estimates of water mass changes to determine ET without disturbing the soil. To demonstrate the practical applicability of SG data, we conducted a case study in Buenos Aires Province, Argentina (Lat: −34.87, Lon: −58.14). We estimated cumulative ET values for 8-day and monthly intervals using gravity and precipitation data from the study site. Comparing these values with Moderate Resolution Imaging Spectroradiometer (MODIS)-based ET products (MOD16A2), we found a very good agreement at the monthly scale, with an RMSE of 32.6 mm month−1 (1.1 mm day−1). This study represents a step forward in the use of SGs for hydrogeological applications. The future development of lighter and smaller gravimeters is expected to further expand their use.

An important issue for agricultural planning is to estimate evapotranspiration accurately due to its fundamental role in sustainable use of water resources. It is essential to have reliable and precise evapotranspiration (ET) measurements to improve models or products. This work aims to evaluate a generalized linear model (GLM) in order to estimate actual evapotranspiration of barley crop with satellite (Landsat, Sentinel, and CERES) and reanalysis (MERRA-2) data. The results obtained were compared with water balance values from an agrometeorological station. The GLM with the combination of MERRA-2/CERES/Sentinel 2 as input was the best performance (R2 = 0.59). The results show the feasibility of applying machine learning algorithms for obtaining actual evapotranspiration values in agricultural plains without ground agro-meteorological data.

In this paper we present several hydrological time series from Argentina that include, evapotranspiration, precipitation, and stream flow. We survey previous results and apply the 0-1 test for chaos to classify the sequences as regular or chaotic. Previous studies have shown evidence of chaos in several observables from hydrology using the traditional phase space reconstruction method and the computation of Lyapunov exponents. The 0-1 test for chaos can be used as a first step to identify the type of time series, that later can be subjected to the more detailed analysis of the phase space reconstruction. Assuming that the systems that generated these time series are deterministic, the 0-1 test for chaos classifies all of them as chaotic

Evapotranspiration is a key variable of the water cycle. Its calculation requires several ground data that frequently are not available. This study contains a detailed method and measurements of meteorological and energy balance variables that can be used to estimate the daily actual evapotranspiration (ETa). A linear generalized model is obtained to calculate the ETa from common variables measured in meteorological stations. The method showed a good performance over a barley crop of easthern Argentine Pampas and can be applied and tested in other great plains. Measurements of soil-plant-atmosphere are included The routines to reproduce the method are included The generalized method allows the calculation of daily ETa over crops and was tested over barley crops.