Sensitivity to Soil Moisture and Observation Geometry of Spaceborne GNSS-R Delay-Doppler Maps

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Thanks to the successful operations of the UK TDS-1 and NASA CYGNSS GNSS-R missions, a wealth of Delay-Doppler Maps (DDM) are being measured from the ocean, but also from land reflections. Using the land reflected DDM, several studies are being conducted to retrieve the land geophysical parameters, such as soil moisture, vegetation depth, and biomass. Although they have shown the dependence of the land geophysical parameters on the DDM, it is also shown that many other parameters impact the DDM. This work presents the impacts of some parameters on the DDM. For the systematical and efficient study, an E2E simulator is used. The simulator generates the synthesized DDM reflected over land varying the input parameters, which are the specular point position on the Earth, the elevation angle at the specular points, soil moisture, etc. From the simulation results, the relation between the input parameters and the DDM is individually analyzed, providing the clue to the retrieval algorithm of the geophysical parameters.Peer ReviewedPostprint (author's final draft

Tools for communicating agricultural drought over the Brazilian Semiarid using the soil moisture index

Soil moisture over the Brazilian semiarid region is presented in different visualizations that highlight spatial, temporal and short-term agricultural risk. The analysis used the Soil Moisture Index (SMI), which is based on a normalization of soil moisture by field capacity and wilting point. The index was used to characterize the actual soil moisture conditions into categories from severe drought to very wet. In addition, the temporal evolution of SMI was implemented to visualize recent trends in short-term drought and response to rainfall events at daily time steps, as new data are available. Finally, a visualization of drought risk was developed by considering a critical value of SMI (assumed as 0.4), below which water stress is expected to be triggered in plants. A novel index based on continuous exposure to critical SMI was developed to help bring awareness of real time risk of water stress over the region: the Index of Stress in Agriculture (ISA). The index was tested during a drought over the region and successfully identified locations under water stress for periods of three days or more. The monitoring tools presented here help to describe the real time conditions of drought over the region using daily observations. The information from those tools support decisions on agricultural management such as planting dates, triggering of irrigation, or harvesting.Peer ReviewedPostprint (published version

Remote Sensing as a Tool for Agricultural Drought Alert Over the South Region of Brazil

In this study the estimative of the Combined Drought Index (CDI) to identify agricultural drought over Southern Brazil is introduced. This combined drought index is based on a combination of three indicators: Standardized Precipitation Index (SPI), Soil Moisture Anomalies (SMA) and Vegetation Health Index (VHI). The proposed CDI has four levels, watch, warning, alert I and alert II, thus benefiting an increasing degrees of severity. This CDI was applied during the first 6 months of 2020 to different study sites over Southern Brasil, representative of the crop areas. The performance of the CDI levels was assessed by comparison with risk areas. Observations show a good match between these areas and the CDI. Important crop drought events in 2020 were correctly predicted by the proposed CDI in all areas

Assessment of SM2RAIN-Derived and State-of-the-Art Satellite Rainfall Products over Northeastern Brazil

Microwave-based satellite rainfall products offer an opportunity to assess rainfall-related events for regions where rain-gauge stations are sparse, such as in Northeast Brazil (NEB). Accurate measurement of rainfall is vital for water resource managers in this semiarid region. In this work, the SM2RAIN-CCI rainfall data obtained from the inversion of the microwave-based satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (CCI), and ones from three state-of-the-art rainfall products (Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), Climate Prediction Center Morphing Technique (CMORPH), and Multi-SourceWeighted-Ensemble Precipitation (MSWEP)) were evaluated against in situ rainfall observations under different bioclimatic conditions at the NEB (e.g., AMZ, Amazônia; CER, Cerrado; MAT, Mata Atlântica; and CAAT, Caatinga). Comparisons were made at daily, 5-day, and 0.25° scales, during the time-span of 1998 to 2015. It was found that 5-day SM2RAIN-CCI has a reasonably good performance in terms of the correlation coefficient over the CER biome (R median: 0.75). In terms of the root mean square error (RMSE), it exhibits better performance in the CAAT biome (RMSE median: 12.57 mm). In terms of bias (B), the MSWEP, SM2RAIN-CCI, and CHIRPS datasets show the best performance in MAT (B median: −8.50%), AMZ (B median: −0.65%), and CER (B median: 0.30%), respectively. Conversely, CMORPH poorly represents the rainfall variability in all biomes, particularly in the MAT biome (R median: 0.43; B median: −67.50%). In terms of detection of rainfall events, all products show good performance (Probability of detection (POD) median > 0.90). The performance of SM2RAIN-CCI suggests that the SM2RAIN algorithm fails to estimate the amount of rainfall under very dry or very wet conditions. Overall, results highlight the feasibility of SM2RAIN-CCI in those poorly gauged regions in the semiarid region of NEB

Tools for communicating agricultural drought over the Brazilian Semiarid using the soil moisture index

Soil moisture over the Brazilian semiarid region is presented in different visualizations that highlight spatial, temporal and short-term agricultural risk. The analysis used the Soil Moisture Index (SMI), which is based on a normalization of soil moisture by field capacity and wilting point. The index was used to characterize the actual soil moisture conditions into categories from severe drought to very wet. In addition, the temporal evolution of SMI was implemented to visualize recent trends in short-term drought and response to rainfall events at daily time steps, as new data are available. Finally, a visualization of drought risk was developed by considering a critical value of SMI (assumed as 0.4), below which water stress is expected to be triggered in plants. A novel index based on continuous exposure to critical SMI was developed to help bring awareness of real time risk of water stress over the region: the Index of Stress in Agriculture (ISA). The index was tested during a drought over the region and successfully identified locations under water stress for periods of three days or more. The monitoring tools presented here help to describe the real time conditions of drought over the region using daily observations. The information from those tools support decisions on agricultural management such as planting dates, triggering of irrigation, or harvesting.Peer Reviewe

Evaluating the soil moisture retrievals for agricultural drought monitoring over Brazil

A model for monitoring agricultural drought (SIMAGRI) has been developed in Brazil. This model is based on gridded precipitation product, real evapotranspiration calculated from vegetation index data (as proposed by [1]), and soil water storage. The soil water storage is derived from the estimation of field capacity and wilting point using pedo-transfer functions (PTFs). The SIMAGRI model suggest that the soil moisture influence is unquestionably a quantitative indicator of drought. In addition, using this model, it is possible to monitor drought episodes in agricultural regions of Brazil, especially over the Northeast, where vulnerability to drought is the highest in the country due to the prevalence of rain fed agricultural practice and frequent droughts

Assessment of different SMOS Level 3 soil moisture products

European Space Agency’s 2019 Living Planet Symposium, 13-17 May 2019, Milan, ItalyThe European Space Agency (ESA)’s Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite specifically dedicated to measuring soil moisture (SM) [1]. SMOS was launched in November 2009 and is still in orbit, providing an unprecedented record of L-band brightness temperature (TB) observations at tens of km of spatial resolution (~40 km) with a 3-day revisit time. Since its launch, SMOS has exhibited a successful performance, fulfilling the scientific requirements in terms of both SM and ocean salinity [2]. SMOS as well as the Soil Moisture Active Passive (SMAP) mission have allowed obtaining the most ever accurate SM measurements at global scale [3]. The operational continuity of L-band observations after SMOS and SMAP is now being proposed via the Copernicus Microwave Imaging Radiometer (CIMR) high priority candidate mission [4]. The latest release (v650) of the ESA’s SMOS Level 2 (L2) Soil Moisture User Data Product (SMUDP) has several improvements on the L2 processor [5]. SMOS L2 SMUDP is generated for each orbit at a 15 km Icosahedral Snyder Equal Area (ISEA) 4H9 grid. Due to that, SMOS Level 3 (L3) SM products, which are a composite map of all orbits within the day at a 25 km Equal Area Scalable Earth (EASE)-2 grid, are preferred by research community in some cases to avoid the manipulation difficulties of ISEA. Nevertheless, all SMOS L2 and L3 SM products, and even a higher spatial resolution Level 4 (L4) SM product [6], have demonstrated to be useful for a wide range of scientific and operational applications up to date, being used in predictive hydrological and atmospheric models [7, 8], to monitor flood and drought events [9, 10], to predict wildfire risks [11] and to estimate root zone SM [12]. Nowadays, there are three different available daily SMOS L3 SM products. The first one is a composite of binned data generated by the Centre Aval de Traitement des Données SMOS (CATDS). The SMOS-CATDS L3 SM product is retrieved using a multi-orbit algorithm developed by the Centre d’Etudes Spatiales de la Biosphere (CESBIO) [13]. The second L3 SM product is generated by the Barcelona Expert Centre (BEC). The SMOS-BEC L3 SM product is obtained directly from L2 SMUDP, after applying a filtering based on the Data Quality Index (DQX) parameter and a weighted binning [14]. However, this filtering criterion may be questioned by a general increase of DQX in L2 v650 compared to the previous version (v620) [5]. As an alternative, a filtering based on the retrieval fit quality index, called Chi-Squared (Χi2) parameter, is currently being evaluated. The third L3 SM product is generated by the Institut National de la Recherche Agronomique (INRA) and CESBIO. The SMOS-IC L3 SM product is retrieved with an algorithm that has some simplifications with respect to the official L2 processor, mainly related to the pixel heterogeneity, angle geometry, and vegetation scattering albedo and soil roughness parameters [15]. This study assesses the SMOS-CATDS, SMOS-BEC and SMOS-IC L3 SM products from January 2015 to December 2016. The alternative SMOS-BEC L3 SM filtered by Χi2 (instead of DQX) has also been tested for several thresholds. Different in situ SM networks have been used to validate all possible L3 SM products over several climate types and land covers. Many present and upcoming applications could get benefit of the improvement and refinement of these global SM products. The obtained results and the inferred conclusions will be presented at the conference.References [1] Kerr, Y.K.; Waldteufel, P.; Wigneron, J.P.; Delwart, S.; Cabot, F.; Boutin, J.; Escorihuela, M.J.; Font, J.; Reul, N.; Gruhier, C.; et al. (2010) “The SMOS Mission: New Tool for Monitoring Key Elements of the Global Water Cycle”, Proceedings of IEEE, 98: 666-687. [2] Mecklenburg, S.; Drusch, M.; Kaleschke, L.; Rodriguez-Fernandez, N.; Reul, N.; Kerr, Y.H.; Font, J.; Martin-Neira, M.; Oliva, R.; Daganzo-Eusebio, E.; et al. (2016) “ESA's Soil Moisture and Ocean Salinity mission: From science to operational applications”, Remote Sensing of Environment, 180: 3-18. [3] Kerr, Y.H.; Al-Yaari, A.; Rodríguez-Fernández, N.; Parrens, M.; Molero, B.; Leroux, D.; Bircher, S.; Mahmoodi, A.; Mialon, A.; Richaume, P.; et al. (2016) “Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation”, Remote Sensing of Environment, 180: 40-63. [4] Donlon, C.J. (2018) “Copernicus Imaging Microwave Radiometer (CIMR). Mission Requirements Document”, Technical report ESA-EOPSM-CIMR-MRD-3236, revision 1.5, Mission Science Division, European Space Agency (ESA), Noordwijk, Netherlands. [5] ESA (2017) “Read-me-first note for the release of the SMOS Level 2 Soil Moisture data products”, Technical report, Expert Support Laboratory (ESL) Level 2 Soil Moisture and Array Systems Computing Inc. [6] Portal, G.; Vall-llossera, M.; Piles, M.; Camps, A.; Chaparro, D.; Pablos, M.; Rossato, L. (2018) “A spatially consistent downscaling approach for SMOS using an adaptive moving window”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(6): 1883-1894.. [7] Ridler, M.E.; Madsen, H.; Stisen, S.; Bircher, S.; Fensholt, R. (2014) “Assimilation of SMOS‐derived soil moisture in a fully integrated hydrological and soil‐vegetation‐atmosphere transfer model in Western Denmark”, Water Resources Research, 50: 8962-8981. [8] Leroux, D.J.; Pellarin, T.; Vischel, T.; Cohard, J.M.; Gascon, T.; Gibon, F.; Mialon, A.; Galle, S.; Peugeot, C.; Seguis, L. (2016) “Assimilation of SMOS soil moisture into a distributed hydrological model and impacts on the water cycle variables over the Ouémé catchment in Benin”, Hydrological Earth System Sciences, 20: 2827-2840. [9] Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M.F.P. (2014) “The suitability of remotely sensed soil moisture for improving operational flood forecasting”, Hydrological Earth System Sciences, 18: 2343-2357. [10] Pablos, M.; Martínez-Fernández, J.; Sánchez, N.; González-Zamora, Á. (2017) “Temporal and Spatial Comparison of Agricultural Drought Indices from Moderate Resolution Satellite Soil Moisture Data over Northwest Spain”, Remote Sensing, 9:1168. [11] Chaparro, D.; Vall-llossera, M.; Piles, M.; Camps, A.; Rüdiger, C.; Riera-Tatché, R. (2016) "Predicting the Extent of Wildfires Using Remotely Sensed Soil Moisture and Temperature Trends," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6): 2818-2829. [12] Pablos, M.; González-Zamora, Á.; Sánchez, N.; Martínez-Fernández, J. (2018) “Assessment of Root Zone Soil Moisture Estimations from SMAP, SMOS and MODIS Observations”, Remote Sensing, 10: 981. [13] Al Bitar, A.; Mialon, A.; Kerr, Y.H.; Cabot, F.; Richaume, P.; Jacquette, E.; Quesney, A.; Mahmoodi, A.; Tarot, S.; Parrens, M.; et al. (2017) “The global SMOS Level 3 daily soil moisture and brightness temperature maps”, Earth System Science Data, 9, 293–315. [14] González-Zamora, Á.; Sánchez, N.; Martínez-Fernández, J.; Gumuzzio, Á.; Piles, M.; Olmedo, E. (2015) “Long-term SMOS soil moisture products: A comprehensive evaluation across scales and methods in the Duero Basin (Spain)”, Physics and Chemistry of the Earth, Parts A/B/C, 83-84: 123-136. [15] Fernández-Morán, R.; Al-Yaari, A.; Mialon, A.; Mahmoodi, A.; Al Bitar, A.; De Lannoy, G.; Rodríguez-Fernández, N.; López-Baeza, E.; Kerr, Y.H.; Wigneron, J.P. (2017) "SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product", Remote Sensing, 9: 45

Tools for Communicating Agricultural Drought over the Brazilian Semiarid Using the Soil Moisture Index

Soil moisture over the Brazilian semiarid region is presented in different visualizations that highlight spatial, temporal and short-term agricultural risk. The analysis used the Soil Moisture Index (SMI), which is based on a normalization of soil moisture by field capacity and wilting point. The index was used to characterize the actual soil moisture conditions into categories from severe drought to very wet. In addition, the temporal evolution of SMI was implemented to visualize recent trends in short-term drought and response to rainfall events at daily time steps, as new data are available. Finally, a visualization of drought risk was developed by considering a critical value of SMI (assumed as 0.4), below which water stress is expected to be triggered in plants. A novel index based on continuous exposure to critical SMI was developed to help bring awareness of real time risk of water stress over the region: the Index of Stress in Agriculture (ISA). The index was tested during a drought over the region and successfully identified locations under water stress for periods of three days or more. The monitoring tools presented here help to describe the real time conditions of drought over the region using daily observations. The information from those tools support decisions on agricultural management such as planting dates, triggering of irrigation, or harvesting