Spatio-temporal soil drying in southeastern South America: the importance of effective sampling frequency and observational errors on drydown time scale estimates

The study of the spatio-temporal dynamics of surface soil moisture (SSM) drydowns integrates the soil response to climatic conditions, drainage and land cover and is key to advances in our knowledg...

Physics-Aware Machine Learning For Geosciences And Remote Sensing

Machine learning models alone are excellent approximators, but very often do not respect the most elementary laws of physics, like mass or energy conservation, so consistency and confidence are compromised. In this paper we describe the main challenges ahead in the field, and introduce several ways to live in the Physics and machine learning interplay: encoding differential equations from data, constraining data-driven models with physics-priors and dependence constraints, improving parameterizations, emulating physical models, and blending data-driven and process-based models. This is a collective long-term AI agenda towards developing and applying algorithms capable of discovering knowled…

L-Band vegetation optical depth for crop phenology monitoring and crop yield assessment

Vegetation Optical Depth (VOD) at L-band is highly sensitive to the water content and above-ground biomass of vegetation. Hence, it has great potential for monitoring crop phenology and for providing crop yield forecasts. Recently, the Multi-Temporal Dual Channel Algorithm (MT -DCA) has been proposed to retrieve L-band VOD from Soil Moisture Active Passive (SMAP) measurements. In previous research, SMAP VOD has been compared to crop phenology and has been used to derive crop yield estimates. Here, we review and expand these initial research studies. In particular, we quantify the capability of VOD to detect different crop stages, and test different VOD metrics (i.e., maximum, range and inte…

The Added-Value of Remotely-Sensed Soil Moisture Data for Agricultural Drought Detection in Argentina

In countries where the economy relies mostly on agricultural-livestock activities, such as Argentina, droughts cause significant economic losses. Currently, the most-used drought indices by the Argentinian National Meteorological and Hydrological Services are based on field precipitation data, such as the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). In this article, we explored the performance of the satellite-based soil moisture agricultural drought index (SMADI) for agricultural drought detection in Argentina during 2010-2015, and compared it with the one from the standardized soil moisture anomalies (SSMA), SPI and SPEI (at on…

Nonlinear Distribution Regression for Remote Sensing Applications

In many remote sensing applications, one wants to estimate variables or parameters of interest from observations. When the target variable is available at a resolution that matches the remote sensing observations, standard algorithms, such as neural networks, random forests, or the Gaussian processes, are readily available to relate the two. However, we often encounter situations where the target variable is only available at the group level, i.e., collectively associated with a number of remotely sensed observations. This problem setting is known in statistics and machine learning as multiple instance learning (MIL) or distribution regression (DR). This article introduces a nonlinear (kern…

Evaluation Of The Soil Moisture Agricultural Drought Index (SMADI) And Precipitation-Based Drought Indices In Argentina

Abstract. Agricultural drought is one of the most critical hazards with regard to intensity, severity, frequency, spatial extension and impact on livelihoods. This is especially true for Argentina, where agricultural exports can represent up to 10% of gross domestic product (GDP), and where drought events for 2018 led to a decrease of nearly 0.5% of GDP. In this work, we investigate the applicability of the Soil Moisture Agricultural Drought Index (SMADI) for detection of droughts in Argentina, and compare its performance with the use of two well-known precipitation-based indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation- Evaporation Index (SPEI). SMADI …

Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality

Global warming is leading to unprecedented changes in our planet, with great societal, economical and environmental implications, especially with the growing demand of biofuels and food. Assessing the impact of climate on vegetation is of pressing need. We approached the attribution problem with a novel nonlinear Granger causal (GC) methodology and used a large data archive of remote sensing satellite products, environmental and climatic variables spatio-temporally gridded over more than 30 years. We generalize kernel Granger causality by considering the variables cross-relations explicitly in Hilbert spaces, and use the covariance in Gaussian processes. The method generalizes the linear an…

Estimation of volume fraction and gravimetric moisture of winter wheat based on microwave attenuation: a field scale study

A considerable amount of water can be stored in vegetation, especially in regions experiencing large quantities of precipitation (mid-latitudes). In this context, an accurate estimate of the actual water status of the vegetation could lead to an improved understanding of the effect of plant water on the water budget. In this study, we developed and validated a novel approach to retrieve the vegetation volume fraction (δ) (i.e., volume percentage of solid plant material of a canopy in air) and the gravimetric vegetation water content (m g ) (i.e., amount of water per wet biomass) for winter wheat. The estimation was based on the attenuation of L-band microwave measurements through vegetation…

Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysis

Information theory is an excellent framework for analyzing Earth system data because it allows us to characterize uncertainty and redundancy, and is universally interpretable. However, accurately estimating information content is challenging because spatio-temporal data is high-dimensional, heterogeneous and has non-linear characteristics. In this paper, we apply multivariate Gaussianization for probability density estimation which is robust to dimensionality, comes with statistical guarantees, and is easy to apply. In addition, this methodology allows us to estimate information-theoretic measures to characterize multivariate densities: information, entropy, total correlation, and mutual in…

A spatially consistent downscaling approach for SMOS using an adaptive window

The European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) is the first spaceborne mission using L-band radiometry to monitor the Earth's global surface soil moisture (SM). After more than 7 years in orbit, many studies have contributed to improve the quality and applicability of SMOS-derived SM maps. In this research, a novel downscaling algorithm for SMOS is proposed to obtain high-resolution (HR) SM maps at 1 km (L4), from the ∼40 km native resolution of the instrument. This algorithm introduces the concept of a shape adaptive moving window as an improvement of the current semi-empirical downscaling approach at SMOS Barcelona Expert Center, based on the “universal triangle…

Living in the Physics and Machine Learning Interplay for Earth Observation

Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem. Inferences mean understanding variables relations, deriving models that are physically interpretable, that are simple parsimonious, and mathematically tractable. Machine learning models alone are excellent approximators, but very often do not respect the most elementary laws of physics, like mass or energy conservation, so consistency and confidence are compromised. In this paper, we describe the main challenges ahead in the field, and introduce several ways to live in the Physics and machine learning interplay: to encode differential equations from da…

Integrated remote sensing approach to global agricultural drought monitoring

Abstract This study explores the use of the Soil Moisture Agricultural Drought Index (SMADI) as a global estimator of agricultural drought. Previous research presented SMADI as a novel index based on the joint use of remotely sensed datasets of land surface temperature (LST) and normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) together with the surface soil moisture (SSM) from the Soil Moisture and Ocean Salinity (SMOS) mission. This study presents the results of applying SMADI at the global scale with a spatial resolution of 0.05° every 15 days. The period of the study spanned from 2010 to 2015. Three spatial scales (local, region…

Comparison of downscaling techniques for high resolution soil moisture mapping

Soil moisture impacts exchanges of water, energy and carbon fluxes between the land surface and the atmosphere. Passive microwave remote sensing at L-band can capture spatial and temporal patterns of soil moisture in the landscape. Both ESA and NASA have launched L-band radiometers, in the form of the SMOS and SMAP satellites respectively, to monitor soil moisture globally, every 3-day at about 40 km resolution. However, their coarse scale restricts the range of applications. While SMAP included an L-band radar to downscale the radiometer soil moisture to 9 km, the radar failed after 3 months and this initial approach is not applicable to developing a consistent long term soil moisture prod…

Towards Estimation of Seasonal Water Dynamics of Winter Wheat from Ground-Based L-Band Radiometry 

The vegetation optical depth (VOD) parameter contains information on plant water content and biomass, and can be estimated alongside soil moisture from currently operating satellite radiometer missions, such as SMOS (ESA) and SMAP (NASA). The estimation of water fluxes, such as plant water uptake (PWU) and transpiration rate (TR), from these Earth system parameters (VOD, soil moisture) requires assessing potential (suction tension) gradients of water and flow resistances in the soil, the vegetation and the atmosphere, yet it remains an elusive challenge especially on global scale. Here, we used a field-scale experiment to test mechanistic models for the estimation of seasonal water fluxes (…

Remote sensing of vegetation dynamics in agro-ecosystems using smap vegetation optical depth and optical vegetation indices

The ESA's SMOS and the NASA's SMAP missions, launched in 2009 and 2015, respectively, are the first two missions having on-board L-band microwave sensors, which are very sensitive to the water content in soils and vegetation. Focusing on the vegetation signal at L-band, we have implemented an inversion approach for SMAP that allows deriving vegetation optical depth (VOD, a microwave parameter related to biomass and plant water content) alongside soil moisture, without reliance on ancillary optical information on vegetation. This work aims at using this new observational data to monitor the phenology of crops in major global agro-ecosystems and enhance present agricultural monitoring and pre…

Multi-Frequency Estimation of Canopy Penetration Depths from SMAP/AMSR2 Radiometer and IceSAT Lidar Data

In this study, the $\tau-\omega$ model framework is used to derive extinction coefficient and canopy penetration depths from multi-frequency SMAP and AMSR2 retrievals of vegetation optical depth together with ICESat LiDAR vegetation heights. The vegetation extinction coefficient serves as an indicator of how strong absorption and scattering processes within the canopy attenuate microwaves at $\mathrm{L}$ and C-band. Through inversion of the extinction coefficient, the penetration depth into the canopy can be obtained, which is analyzed on local (Sahel, Illinois) and continental scale (Africa, parts of North America) as well as for a one year time series (04/2015-04/2016). First analyses of …

Nonlinear PCA for Spatio-Temporal Analysis of Earth Observation Data

Remote sensing observations, products, and simulations are fundamental sources of information to monitor our planet and its climate variability. Uncovering the main modes of spatial and temporal variability in Earth data is essential to analyze and understand the underlying physical dynamics and processes driving the Earth System. Dimensionality reduction methods can work with spatio-temporal data sets and decompose the information efficiently. Principal component analysis (PCA), also known as empirical orthogonal functions (EOFs) in geophysics, has been traditionally used to analyze climatic data. However, when nonlinear feature relations are present, PCA/EOF fails. In this article, we pro…

Relationship between vegetation microwave optical depth and cross-polarized backscatter from multiyear Aquarius observations

Soil moisture retrieval algorithms based on passive microwave remote sensing observations need to account for vegetation attenuation and emission, which is generally parameterized as vegetation optical depth (VOD). This multisensor study tests a new method to retrieve VOD from cross-polarized radar backscattering coefficients. Three years of Aquarius/SAC-D data were used to establish a relationship between the cross-polarized backscattering coefficient σ HV and VOD derived from a multitemporal passive dual-channel algorithm (VODMT). The dependence of the correspondence is analyzed for different land use classes. There are no systematic differences in the slope for woody versus nonwoody vege…

Global Estimation of Soil Moisture Persistence with L and C-Band Microwave Sensors

© 2018 IEEE Measurements of soil moisture are needed for a better global understanding of the land surface-climate feedbacks at both the local and the global scale. Satellite sensors operating in the low frequency microwave spectrum (from 1 to 10 GHz) have proven to be suitable for soil moisture retrievals. These sensors now cover nearly 4 decades thus allowing for global multi-mission climate data records. In this paper, we assess the possibility of using L-band (SMOS) and C-band (AMSR2, ASCAT) remotely sensed soil moisture time series for the global estimation of soil moisture persistence. A multi-output Gaussian process regression model is applied to ensure spatio-temporal coverage of th…

Activities of the IEEE GRSS Spain Chapter [Chapters]

Simultaneous retrieval of global scale Vegetation Optical Depth, surface roughness, and soil moisture using X-band AMSR-E observations

Abstract The radiative transfer scheme implemented for the retrieval of soil moisture from passive microwaves is a function of scattering, polarization mixing and attenuation effects of soil and vegetation. Theses factors are usually represented by Vegetation Optical Depth (VOD), vegetation scattering albedo, and surface roughness parameter, along with soil moisture. The VOD is the degree to which vegetation attenuates the microwave radiation. It has generally the dominant effect from vegetation, whereas scattering is negligible and close to zero. The surface roughness (which varies in space but not much in time) is until recently, often assumed to be a global constant. In this work, we att…

Incidence Angle Diversity on L-Band Microwave Radiometry and Its Impact on Consistent Soil Moisture Retrievals

Incidence angle diversity of space-borne L-band radiometers needs to be taken into account for a consistent estimation of surface soil moisture (SM). In this study, the Land Parameter Retrieval Model (LPRM) is applied to SMOS brightness temperatures to calibrate the effective scattering albedo (w) and the soil roughness (h 1 ) parameter against ERA5-land SM. The analysis is carried out for SMOS data at three different incidence angles ( 32.5±5∘, 42.5±5∘ and 52.5±5∘ ) focusing in 2016 on the three main land cover types of the Iberian Peninsula according to the Climate Change Initiative (agricultural, forest and grassland). The parameterization shows an increasing trend of w and h 1 with rise…

The reduction of tris-dithiolene complexes of molybdenum(vi) and tungsten(vi) by hydroxide ion: kinetics and mechanism

The kinetic study of the spontaneous reduction of some neutral tris-dithiolene complexes [ML3] of molybdenum(VI) and tungsten(VI), (L = S2C6H42−, S2C6H3CH32− and S2C2(CH3)22−; M = Mo or W) by tetrabutylammonium hydroxide in tetrahydrofuran-water solutions demonstrates that OH− is an effective reductant. Their reduction is fast, clean and quantitative. Depending upon both the molar ratio in which the reagents are mixed and the amount of water present, one- or two-electron reductions of these tris-dithiolene complexes were observed. If Bu4NOH is present in low concentration or/and at high concentrations of water, the total transformation of the neutral M(VI) complex into the monoanionic M(V) …

Nonlinear Complex PCA for spatio-temporal analysis of global soil moisture

Soil moisture (SM) is a key state variable of the hydrological cycle, needed to monitor the effects of a changing climate on natural resources. Soil moisture is highly variable in space and time, presenting seasonalities, anomalies and long-term trends, but also, and important nonlinear behaviours. Here, we introduce a novel fast and nonlinear complex PCA method to analyze the spatio-temporal patterns of the Earth's surface SM. We use global SM estimates acquired during the period 2010-2017 by ESA's SMOS mission. Our approach unveils both time and space modes, trends and periodicities unlike standard PCA decompositions. Results show the distribution of the total SM variance among its differ…

A global Canopy Water Content product from AVHRR/Metop

Abstract Spatially and temporally explicit canopy water content (CWC) data are important for monitoring vegetation status, and constitute essential information for studying ecosystem-climate interactions. Despite many efforts there is currently no operational CWC product available to users. In the context of the Satellite Application Facility for Land Surface Analysis (LSA-SAF), we have developed an algorithm to produce a global dataset of CWC based on data from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board Meteorological–Operational (MetOp) satellites forming the EUMETSAT Polar System (EPS). CWC reflects the water conditions at the leaf level and information related …

Sensitivity of L-band vegetation optical depth to carbon stocks in tropical forests: a comparison to higher frequencies and optical indices

Supplementary data to this article can be found online at https://doi.org/10.1016/j.rse.2019.111303. Monitoring vegetation carbon in tropical regions is essential to the global carbon assessment and to evaluate the actions oriented to the reduction of forest degradation. Mainly, satellite optical vegetation indices and LiDAR data have been used to this purpose. These two techniques are limited by cloud cover and are sensitive only to the top of vegetation. In addition, the vegetation attenuation to the soil microwave emission, represented by the vegetation optical depth (VOD), has been applied for biomass estimation using frequencies ranging from 4 to 30¿GHz (C- to K-bands). Atmosphere is t…

Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe

Abstract Soil moisture (SM) is a key variable that plays an important role in land-atmosphere interactions. Monitoring SM is crucial for many applications and can help to determine the impact of climate change. Therefore, it is essential to have continuous and long-term databases for this variable. Satellite missions have contributed to this; however, the continuity of the series is compromised due to the data gaps derived by different factors, including revisit time, presence of seasonal ice or Radio Frequency Interference (RFI) contamination. In this work, the applicability of different gap-filling techniques is evaluated on the ESA Climate Change Initiative (CCI) SM combined product, whi…

Influence of Quality Filtering Approaches in BEC SMOS L3 Soil Moisture Products

2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), 28 July - 2 August 2019, Yokohama, Japan

Seamless downscaling of the ESA CCI soil moisture data at the daily scale with MODIS land products

Abstract Spatial downscaling has recently become a crucial process in the regional application of coarse-resolution passive microwave surface soil moisture (SSM) products. Extensive gaps in auxiliary optical/thermal infrared observation data (mainly caused by cloud cover) and gaps in coarse-resolution passive microwave SSM data lead to spatiotemporal discontinuity in downscaled SSM maps, thereby limiting their applications. An improved downscaling method for the 25-km European Space Agency (ESA) Climate Change Initiative (CCI) SSM product was proposed to obtain daily seamless downscaled SSM series at a 1-km scale. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daily land su…

Explicit Granger causality in kernel Hilbert spaces

Granger causality (GC) is undoubtedly the most widely used method to infer cause-effect relations from observational time series. Several nonlinear alternatives to GC have been proposed based on kernel methods. We generalize kernel Granger causality by considering the variables cross-relations explicitly in Hilbert spaces. The framework is shown to generalize the linear and kernel GC methods, and comes with tighter bounds of performance based on Rademacher complexity. We successfully evaluate its performance in standard dynamical systems, as well as to identify the arrow of time in coupled R\"ossler systems, and is exploited to disclose the El Ni\~no-Southern Oscillation (ENSO) phenomenon f…

Revisiting impacts of MJO on soil moisture: a causality perspective

L-Band Vegetation optical depth and effective scattering albedo estimation from SMAP

Abstract Over land the vegetation canopy affects the microwave brightness temperature by emission, scattering and attenuation of surface soil emission. Attenuation, as represented by vegetation optical depth (VOD), is a potentially useful ecological indicator. The NASA Soil Moisture Active Passive (SMAP) mission carries significant potential for VOD estimates because of its radio frequency interference mitigation efforts and because the L-band signal penetrates deeper into the vegetation canopy than the higher frequency bands used for many previous VOD retrievals. In this study, we apply the multi-temporal dual-channel retrieval algorithm (MT-DCA) to derive global VOD, soil moisture, and ef…

Mapping Carbon Stocks In Central And South America With Smap Vegetation Optical Depth

Mapping carbon stocks in the tropics is essential for climate change mitigation. Passive microwave remote sensing allows estimating carbon from deep canopy layers through the Vegetation Optical Depth (VOD) parameter. Although their spatial resolution is coarser than that of optical vegetation indices or airborne Lidar data, microwaves present a higher penetration capacity at low frequencies (L-band) and avoid cloud masking. This work compares the relationships of airborne carbon maps in Central and South America with both (i) SMAP L-band VOD at 9 km gridding and (ii) MODIS Enhanced Vegetation Index (EVI). Models to estimate carbon stocks are built from these two satellite-derived variables.…

Estimation of vegetation loss coefficients and canopy penetration depths from SMAP radiometer and IceSAT lidar data

In this study the framework of the τ — ω model is used to derive vegetation loss coefficients and canopy penetration depths from SMAP multi-temporal retrievals of vegetation optical depth, single scattering albedo and ICESat lidar vegetation heights. The vegetation loss coefficients serve as a global indicator of how strong absorption and scattering processes attenuate L-band microwave radiation. By inverting the vegetation loss coefficients, penetration depths into the canopy can be obtained, which are displayed for the global forest reservoirs. A simple penetration index is formed combining vegetation heights and penetration depth estimates. The distribution and level of this index reveal…

Reactivity of Neutral Mo(S2C6H4)3 in Aqueous Media: an Alternative Functional Model of Sulfite Oxidase.

The kinetics of the reaction of neutral [Mo(S2C6H4)3] with hydrogen sulfite to produce the anionic Mo(V) complex, [Mo(S2C6H4)3]-, and sulfate have been investigated. It has been shown that [Mo(S2C6H4)3] acts as the electron-proton sink in the oxygenation reaction of HSO3(-) by water. Reaction rates, monitored by UV/vis stopped-flow spectrometry, were studied in THF/water media as a function of the concentration of HSO3(-) and molybdenum complex, pH, ionic strength, and temperature. The reaction exhibits pH-dependent HSO3(-) saturation kinetics, and it is first-order in complex concentration. The kinetic data and MS-ESI spectra are consistent with the formation of [Mo O(S2C6H4)2(S2C6H5)]- (1…

Microwave and optical data fusion for global mapping of soil moisture at high resolution

After more than 8 years in orbit the Soil Moisture and Ocean Salinity (SMOS) satellite is still in good health and several algorithms for improving its spatial resolution have been proposed and validated in a variety of catchments. However, none of them has yet been applied at the global scale. In this article we present: i) a review of the latest SMOS-BEC downscaling algorithm, which allows for its global application using an adaptive moving window and ii) a thorough validation of the resulting maps over two in-situ networks: REMEDHUS in Spain and OzNet in Australia. The proposed algorithm combines SMOS brightness temperatures (at ~40 km spatial resolution), and MODIS-derived Land Surface …

Preliminary assessment of an integrated SMOS and MODIS application for global agricultural drought monitoring

An application of the Soil Moisture Agricultural Drought Index (SMADI) for global agricultural drought monitoring is presented. The index integrates surface soil moisture from the Soil Moisture and Ocean Salinity (SMOS) mission with the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) and allows for global drought monitoring at medium spatial scales (0.05°). Biweekly maps of SMADI were obtained from year 2010 to 2015 over all agricultural areas on Earth. The SMADI time-series were compared with state-of-the-art drought indices over the Iberian Peninsula. Results show a good agreement between SMADI and the …

Long-term persistence, invariant time scales and on-off intermittency of fog events

Abstract In this work we study different characteristics of fog long-term persistence, in events with different physical formation mechanisms. Specifically, we focus on the characterization of fog long-term persistence from observational data, by means of a Detrended Fluctuation Analysis (DFA) of its associated low-visibility time series. We analyze fog events with radiation and orographic underlying physical formation mechanisms, and identify a two-range pattern of long-term persistence. Our analysis leads to the emergence of a characteristic time, τ∗, at the crossover point between different scaling exponents in the DFA, independent of the time scale at which the fog event is studied. We …

Learning main drivers of crop progress and failure in Europe with interpretable machine learning

Abstract A wide variety of methods exist nowadays to address the important problem of estimating crop yields from available remote sensing and climate data. Among the different approaches, machine learning (ML) techniques are being increasingly adopted, since they allow exploiting all the information on crop progress and environmental conditions and their relations with crop yield, achieving reliable and accurate estimations. However, interpreting the relationships learned by the ML models, and hence getting insights about the problem, remains a complex and usually unexplored task. Without accountability, confidence and trust in the ML models can be compromised. Here, we develop interpretab…

Interpretability of Recurrent Neural Networks in Remote Sensing

In this work we propose the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for multivariate time series of satellite data for crop yield estimation. Recurrent nets allow exploiting the temporal dimension efficiently, but interpretability is hampered by the typically overparameterized models. The focus of the study is to understand LSTM models by looking at the hidden units distribution, the impact of increasing network complexity, and the relative importance of the input covariates. We extracted time series of three variables describing the soil-vegetation status in agroe-cosystems -soil moisture, VOD and EVI- from optical and microwave satellites, as well as available in si…

Global L-band vegetation volume fraction estimates for modeling vegetation optical depth

The attenuation of microwave emissions through the canopy is quantified by the vegetation optical depth (VOD), which is related to the amount of water, the biomass and the structure of vegetation. To provide microwave-derived plant water estimates, one must account for biomass/structure contributions in order to extract the water component from the VOD. This study uses Aquarius scatterometer data to build an L-band global seasonality of vegetation volume fraction (d), representative of biomass/structure dynamics. The dynamic range of d is adapted for its application in a gravimetric moisture (Mg) retrieval model. Results show that d ranging from 0 to 3.35.10- 4 is needed for modelling physi…

Analysis of the radar vegetation index and assessment of potential for improvement

The Radar Vegetation Index (RVI) is widely applied to indicate vegetation cover. The index includes the backscattering intensities of co- and cross-polarization that do not only contain information coming from vegetation scattering at longer wavelength (L-band), but also from the soil underneath. A forward modelling approach using active and passive microwave-derived parameters to obtain the scattering contribution of the soil is pursued. The idea of this research study is a subtraction of the attenuated soil scattering contribution from the measured backscattering intensities, to provide a clean vegetation-based solution, called improved RVI (RVII). For latter analysis, the vegetation volu…

Synergistic integration of optical and microwave satellite data for crop yield estimation

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index (EVI) derived from MODIS and Vegetation Optical Depth (VOD) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or des…

Modelling forest decline using SMOS soil moisture and vegetation optical depth

Global change is increasing the risk of forest decline worldwide, impacting carbon and water cycles. Hence, there is an urgent need for predicting forest decline occurrence. To that purpose, this study links forest decline events in Catalonia, detected by the DEBOSCAT forest monitoring program, with information from the Soil Moisture and Ocean Salinity (SMOS) satellite. Firstly, this study reviews the role of the SMOS soil moisture in a previous forest decline episode occurred in 2012, where the authors concluded that dry soils increased the probability of observing decline in broadleaved forests. Secondly, the present study detects that forest decline in 2012 and 2016 was linked to very dr…

Seasonal Analysis of Surface Soil Moisture Dry-Downs in a Land-Atmosphere Hotspot as Seen by LSM and Satellite Products

The soil drying process is a challenging framework to assess climatic, hydrologic and ecosystem processes. This work develops a seasonal analysis of temporal e-folding decay ( $\tau$ ) of surface soil moisture dry-downs using ORCHIDEE land surface model and SMOS observations over South Eastern South America (SESA). Results show that the soil drying process depends on both location and season, and that the modeled drying velocity is faster than the observed one, even when modeled data is sampled at the same frequency as the observations. Differences between observed and modeled data have been found both in the analysis of the regional overall $\tau$ and in the spatial patterns of $\tau$ esti…

Learning drivers of climate-induced human migrations with Gaussian processes

In the current context of climate change, extreme heatwaves, droughts, and floods are not only impacting the biosphere and atmosphere but the anthroposphere too. Human populations are forcibly displaced, which are now referred to as climate-induced migrants. In this work, we investigate which climate and structural factors forced major human displacements in the presence of floods and storms during the years 2017-2019. We built, curated, and harmonized a database of meteorological and remote sensing indicators along with structural factors of 27 developing countries worldwide. We show how we can use Gaussian Processes to learn what variables can explain the impact of floods and storms in th…

L-band vegetation optical depth seasonal metrics for crop yield assessment

Attenuation of surface microwave emission due to the overlying vegetation is proportional to the density of the canopy and to its water content. The vegetation optical depth (VOD) parameter measures this attenuation. VOD could be a valuable source of information on agroecosystems, especially at lower frequencies for which greater portion of the vegetation canopy contributes to the observed brightness temperature. In the past, visible-infrared indices have been used to provide yield estimates based on measuring the photosynthetic activity from the surface canopy layer. These indices are affected by clouds and apply only in the presence of solar illumination. In this study we instead use the …

Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

19 pges, 10 figures, 6 tables

SMAP Multi-Temporal vegetation optical depth retrieval as an indicator of crop yield trends and crop composition

Vegetation Optical Depth (VOD) is related to Vegetation Water Content (VWC). This provides new and highly valuable information for ecological and agricultural studies. In this work, VOD from the Soil Moisture Active-Passive (SMAP) satellite has been retrieved with the new Multi-Temporal Dual-Channel Algorithm (MT-DCA). Then, it has been applied to the study of crop yield trends and crop composition. The increase on VOD (¿VOD) during crop development has been compared to yield data in two selected regions located in the United States. The first region presents a heterogeneous crop composition and weak ¿VOD-yield relationship (r2=0.21). The second region presents a highly homogenous cover and…

Spatio-temporal soil drying in southeastern South America: the importance of effective sampling frequency and observational errors on drydown time scale estimates

The study of the spatio-temporal dynamics of surface soil moisture (SSM) drydowns integrates the soil response to climatic conditions, drainage and land cover and is key to advances in our knowledge of the soil–atmosphere water exchange. SSM drydowns have also been employed to compare soil moisture spatio-temporal behaviour between different data sources such as satellite-derived data and land–surface models, difficult to compare with standard methodologies. However, the errors introduced by satellite effective sampling frequencies (SF) and by different methodologies employed to define a drydown period have until now not been properly addressed in the literature. Here, SSM from microwave re…

ERA5-Land: A state-of-the-art global reanalysis dataset for land applications

Framed within the Copernicus Climate Change Service (C3S) of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies. This is achieved through global high-resolution numerical integrat…

Crop Yield Estimation and Interpretability With Gaussian Processes

This work introduces the use of Gaussian processes (GPs) for the estimation and understanding of crop development and yield using multisensor satellite observations and meteo- rological data. The proposed methodology combines synergistic information on canopy greenness, biomass, soil, and plant water content from optical and microwave sensors with the atmospheric variables typically measured at meteorological stations. A com- posite covariance is used in the GP model to account for varying scales, nonstationary, and nonlinear processes. The GP model reports noticeable gains in terms of accuracy with respect to other machine learning approaches for the estimation of corn, wheat, and soybean …

Esa's SMOS Mission – Supporting Agricultural Applications

The European Space Agency's (ESA) SMOS mission, in orbit since more than 8 years, carries a passive microwave interferometric radiometer measuring in L-Band and provides accurate global observations of emitted radiation originating from the Earth's surfaces since the atmosphere is almost transparent in this spectral range. In addition, over land the effect of vegetation on the measurements is smaller than for shorter wavelengths. The scientific objectives of the SMOS mission directly respond to the need for global observations of soil moisture and ocean salinity, two key variables used in predictive hydrological, oceanographic and atmospheric models. SMOS observations also provide informati…

Estimating Gravimetric Moisture of Vegetation Using an Attenuation-Based Multi-Sensor Approach

Estimating parameters for global climate models via combined active and passive microwave remote sensing data has been a subject of intensive research in recent years. A variety of retrieval algorithms has been proposed for the estimation of soil moisture, vegetation optical depth and other parameters. A novel attenuation-based retrieval approach is proposed here to globally estimate the gravimetric moisture of vegetation (m g ) and retrieve information about the amount of water [kg] per amount of wet vegetation [kg]. The parameter m g is particularly interesting for agro-ecosystems, to assess the status of growing vegetation. The key feature of the proposed approach is that it relies on mu…

Integrating Domain Knowledge in Data-Driven Earth Observation With Process Convolutions

The modelling of Earth observation data is a challenging problem, typically approached by either purely mechanistic or purely data-driven methods. Mechanistic models encode the domain knowledge and physical rules governing the system. Such models, however, need the correct specification of all interactions between variables in the problem and the appropriate parameterization is a challenge in itself. On the other hand, machine learning approaches are flexible data-driven tools, able to approximate arbitrarily complex functions, but lack interpretability and struggle when data is scarce or in extrapolation regimes. In this paper, we argue that hybrid learning schemes that combine both approa…

Smap-based retrieval of vegetation opacity and albedo

Over land the vegetation canopy affects the microwave brightness temperature by emission, scattering and attenuation of surface soil emission. The questions addressed in this study are: 1) what is the transparency of the vegetation canopy for different biomes around the Globe at the low-frequency L-band?, 2) what is the seasonal amplitude of vegetation microwave optical depth for different biomes?, 3) what is the effective scattering at this frequency for different vegetation types?, 4) what is the impact of imprecise characterization of vegetation microwave properties on retrieval of soil surface conditions? These questions are addressed based on the recently completed one full annual cycl…

Estrategia de enseñanza y aprendizaje de programación basada en la idea de ’hackathon’

[EN] The acquisition of programming and data analysis skills in higher education is increa-singly necessary in all areas of Science and Engineering. In this paper we present a methodology for the motivation of programming learning, mainly focused on the deve-lopment of machine learning algorithms. This methodology is based on the hackathon idea and will have different levels. On the one hand the basic level where a competition is proposed in an improvised way during the development of the class. A second level where a programmed hackathon is proposed but within the classroom environment and using learning management systems such as Moodle. The last level consists of parti-cipation in an exte…

Time-variations of zeroth-order vegetation absorption and scattering at L-band

Abstract Surface soil moisture and vegetation optical depth (VOD), as an indicator of vegetation wet biomass, from passive microwave remote sensing have been increasingly applied in global ecology and climate research. Both soil moisture and VOD are retrieved from satellite brightness temperature measurements assuming a zeroth order radiative transfer model, commonly known as the tau-omega model. In this model the emission of a vegetated surface is dependent on soil moisture, vegetation absorption and vegetation scattering. Vegetation scattering is normally represented by the single scattering albedo, ω, and is commonly assumed to be a time-invariant calibration parameter to achieve high ac…

Estimation of Vegetation Structure Parameters From SMAP Radar Intensity Observations

In this article, we present a multipolarimetric estimation approach for two model-based vegetation structure parameters (shape A and orientation distribution ψ of the main canopy elements). The approach is based on a reduced observation set of three incoherent (no phase information) polarimetric backscatter intensities (|S HH | 2 , |S HV | 2 , and |S VV | 2 ) combined with a two-parameter (A P and ψ) discrete scatterer model of vegetation. The objective is to understand whether this confined set of observations contains enough information to estimate the two vegetation structure parameters from the L-band radar signals. In order to disentangle soil and vegetation scattering influences on th…

PHYSICS-based retrieval of scattering albedo and vegetation optical depth using multi-sensor data integration

Vegetation optical depth and scattering albedo are crucial parameters within the widely used τ-ω model for passive microwave remote sensing of vegetation and soil. A multi-sensor data integration approach using ICESat lidar vegetation heights and SMAP radar as well as radiometer data enables a direct retrieval of the two parameters on a physics-derived basis. The crucial step within the retrieval methodology is the calculus of the vegetation scattering coefficient KS, where one exact and three approximated solutions are provided. It is shown that, when using the assumption of a randomly oriented volume, the backscatter measurements of the radar provide a sufficient first order estimate and …

Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources

This paper reviews the most important information fusion data-driven algorithms based on Machine Learning (ML) techniques for problems in Earth observation. Nowadays we observe and model the Earth with a wealth of observations, from a plethora of different sensors, measuring states, fluxes, processes and variables, at unprecedented spatial and temporal resolutions. Earth observation is well equipped with remote sensing systems, mounted on satellites and airborne platforms, but it also involves in-situ observations, numerical models and social media data streams, among other data sources. Data-driven approaches, and ML techniques in particular, are the natural choice to extract significant i…

Quantifying water stress effect on daily light use efficiency in Mediterranean ecosystems using satellite data

16 pages, 2 figures, 6 tables, supplemental material https://dx.doi.org/10.1080/17538947.2016.1247301

The different kinetic and mechanistic behaviors of molybdenum and tungsten in the reduction of tris(benzene-1,2-dithiolato)Mo(VI) and W(VI) complexes by ascorbic acid in aqueous media

The mono-electronic reduction of tris(benzene-1,2-dithiolato)Mo(VI) and W(VI) complexes (ML3: M = Mo, W; L = S2C6H2−4, S2C6H3CH2−3) to their anionic forms ML−3 by L(+)-ascorbic acid (H2A) has been studied in tetrahydrofurane (THF):water and THF:methanol by means of diode-array, stopped-flow, and mass spectrometry–electrospray ionization (MS-ESI) spectroscopy. The kinetic study in methanol demonstrates that the reaction is first order in each reactant, the electron transfer being rate limiting. This fact was assessed by the absence of a primary saline effect and by the correlation observed between the activation free enthalpy (ΔG≠) and the reduction potentials measured by cyclic voltamperome…

Spatio-temporal soil drying in southeastern South America: the importance of effective sampling frequency and observational errors on drydown time scale estimates

The study of the spatio-temporal dynamics of surface soil moisture (SSM) drydowns integrates the soil response to climatic conditions, drainage and land cover and is key to advances in our knowledge of the soil–atmosphere water exchange. SSM drydowns have also been employed to compare soil moisture spatio-temporal behaviour between different data sources such as satellite-derived data and land–surface models, difficult to compare with standard methodologies. However, the errors introduced by satellite effective sampling frequencies (SF) and by different methodologies employed to define a drydown period have until now not been properly addressed in the literature. Here, SSM from microwave re…

Machine Learning Methods for Spatial and Temporal Parameter Estimation

Monitoring vegetation with satellite remote sensing is of paramount relevance to understand the status and health of our planet. Accurate and constant monitoring of the biosphere has large societal, economical, and environmental implications, given the increasing demand of biofuels and food by the world population. The current democratization of machine learning, big data, and high processing capabilities allow us to take such endeavor in a decisive manner. This chapter proposes three novel machine learning approaches to exploit spatial, temporal, multi-sensor, and large-scale data characteristics. We show (1) the application of multi-output Gaussian processes for gap-filling time series of…

Retrieval of Forest Water Potential from L-Band Vegetation Optical Depth

A retrieval methodology for forest water potential from ground-based L-band radiometry is proposed. It contains the estimation of the gravimetric and the relative water content of a forest stand and tests in situ- and model-based functions to transform these estimates into forest water potential. The retrieval is based on vegetation optical depth data from a tower-based experiment of the SMAPVEX 19–21 campaign for the period from April to October 2019 at Harvard Forest, MA, USA. In addition, comparison and validation with in situ measurements on leaf and xylem water potential as well as on leaf wetness and complex permittivity are foreseen to understand limitations and potentials of the pro…

Global Cropland Yield Monitoring with Gaussian Processes

Agriculture monitoring, and in particular food security, requires near real-time information on crop growing conditions for early detection of possible production deficits. In this work, we propose the use of Gaussian processes (GPs). together with in-situ, EO and ERA-Interim climate reanalysis data for crop yield forecasting. Country-level agricultural survey data from FAOSTAT are used for quantitative assessment. The study is conducted in the framework of the ASAP (Anomaly hot Spots of Agricultural Production) early warning decision support system of the European Commission, which aims at providing timely information about possible crop production anomalies worldwide. After grouping count…

Remote sensing data for crop yield in CONUS

I) SUMMARY This database contains harmonized time series for the study of crop yields using remote sensing data and meteorological data. We collected information on soybean, corn, and wheat yields (t/ha) over the CONUS (continuous US) from USDA-NASS for years 2015–2018 at a county level, and collocated time series for the following variables: Enhanced Vegetation Index (EVI) from MODIS satellite (MOD13C1 v6 product) Soil Moisture (SM) from SMAP satellite through MT-DCA algorithm Vegetation Optical Depth (VOD) from SMAP satellite through MT-DCA algorithm Maximum temperature (TMAX) from Daymet v3 Precipitation (PRCP) from Daymet v3 II) CONTACT For questions, please email Laura Mart&iacut…

Revisiting impacts of MJO on soil moisture: a causality perspective