Luciana Rossato

Evaluation of soil moisture from satellite observations over South America

A study was performed to evaluate the surface soil moisture derived from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) sensor observations over South America. Other soil moisture and rainfall datasets were also used for the analysis. The information for the soil data came from the Eta regional climate model, and for the rainfall data from the Tropical Rainfall Microwave Mission (TRMM) satellite. Statistical analysis was used to evaluate the quality of the soil moisture and rainfall products, with estimates of the correlation coefficient (R), χ2 and Cramers phi (ϕc). The results show high correlations (R > 0.8) of the AMSR-E soil moisture products with the Eta model for different regions of South America. Comparison of soil moisture products with rainfall datasets showed that the AMSR-E C-band soil moisture product was highly correlated with the TRMM satellite rainfall datasets, with the highest values of χ2 and ϕ. The results show that the AMSR-E C-band soil moisture products contain important information that can be used for various purposes, such as monitoring floods or droughts in arid areas or as input within the framework of an assimilation scheme of numerical weather prediction models.

Evapotranspiration estimation in the Brazil using NDVI data

The purpose of this study is to analyze the monthly mean spatial and temporal distribution of evapotranspiration (ET) during the 1981-2000 period in Brazil using remotely sensed data. The methodology involves the use of a relationship between ET and Normalized Difference Vegetation Index (NDVI). ET was estimated for the main Brazilian biomes through the Penman-Monteith method using climatological data from 194 Brazilian meteorological stations during the 1961-1990 period. NDVI data for the July 1981 to July 2000 period was obtained from Advanced Very High Resolution Radiometer (AVHRR) sensor on board the NOAA satellite. A relatively high correlation coefficient between ET and NDVI (r=0.81) was found, showing a near linear relationship involving these variables. Also, the monthly mean ET over Brazil was estimated using NDVI data. The results showed that the ET rate in the Amazon region is not well defined because the maximum values occur after the rainy season, while for the Northeast Brazil, the highest ET values occur in according to period of rainy season. The annual cycle of ET is most defined in the Central region, with maximum values occurring in January to May period and minimum in September. In the South and Southeast regions, the annual cycle ET does not change very much. Finally, this study suggests that NDVI is an important variable for indirectly monitoring ET over large areas, thus with great potential for agronomical and climatic applications.

Impact of Soil Moisture on Crop Yields over Brazilian Semiarid

The objective of this work was to investigate the relationship between soil water content and rainfall with rice, beans, cassava and corn yields of in the semiarid region of Northeast Brazil. Precipitation and modeled soil water content were compared to yields recorded at the county levels in this region. The results were also integrated over the area of the nine States that lie within the officially recognized region of semiarid climate in Brazil. The influence of water balance components was quantified by calculating their correlation coefficient with yields of the different crop species over the municipalities of the region. It was found that rainfall had higher correlation to crop yields over most of the region, while soil water content had lower values of correlation. This result is consistent with the fact that average root depth is 40 cm, lower than the layer of soil used in the model used to estimate soil water content (100 cm). Plants respond better to the precipitation in the top layers of soil, while the water storage in the deep layer of soil might be important only in other temporal and spatial scales of the hydrological cycle. It is concluded that the average crop yield is directly associated with practices that increase soil moisture at the depth of the root system in order to reduce the effects caused by drought.

A spatially consistent downscaling approach for SMOS using an adaptive moving window

The ESAs Soil Moisture and Ocean Salinity (SMOS, 2009–2017) is the first mission using L-band radiometry to monitor the Earths global surface soil moisture (SM). After more than 7 years in orbit, many studies have contributed to improving the quality and applicability of SMOS-derived SM maps. In this research, a novel downscaling algorithm is proposed for retrieving high resolution (1 km) SM. This model is an extension of the “universal triangle” technique, and also introduces the concept of adaptive moving window. Its inputs are the low resolution SMOS BEC L3 SM and the brightness temperatures at vertical and horizontal polarizations (SMOS L1C), and the high resolution NDVI and LST from optically-based sensors. The proposed method allows obtaining high resolution SM maps worldwide, with no limitation in extension.

Impact of soil moisture over Palmer Drought Severity Index and its future projections in Brazil

A umidade do solo constitui-se num dos fatores principais para o estudo da seca, do clima e da vegetacao. No caso da seca, esta e um fenomeno regional e afeta a seguranca alimentar mais do que qualquer outro desastre natural. Atualmente, o monitoramento dos diversos tipos de seca e feito com base em indices que os padronizam em escala temporal e regional permitindo, com isso, a comparacao das condicoes hidricas de diferentes areas. Assim sendo, a fim de avaliar o impacto da umidade do solo durante os periodos de seca, o Indice de Severidade de Seca de Palmer foi estimado para toda a regiao do territorio brasileiro durante o periodo de 2000 a 2015, os quais incluem periodos com ocorrencia de seca. Para isto foram utilizadas informacoes meteorologicas e pedologicas extraidas do modelo de balanco hidrico. A fim de avaliar as projecoes de secas futuras, considerando o conjunto de dados de precipitacao e de umidade do solo do Coupled Model Intercomparison Project Phase 5 (CMIP5) para o periodo de 2071-2100. Os resultados mostraram que o Indice de Seca de Palmer esta diretamente associado aos padroes climatologicos de precipitacao e de umidade do solo, em qualquer escala espacial e temporal (incluindo as projecoes futuras). Assim sendo, conclui-se que este indice constitui de uma ferramenta importante para avaliar a umidade do solo em diferentes condicoes hidricas, bem como para a associacao com informacoes economicas e sociais para gerar mapas de riscos para subsidios aos tomadores de decisao.Soil moisture is a main factor for the study of drought impacts on vegetation. Drought is a regional phenomenon and affects the food security more than any other natural disaster. Currently, the monitoring of different types of drought is based on indexes that standardize in temporal and regional level allowing, thus, comparison of water conditions in different areas. Therefore, in order to assess the impact of soil moisture during periods of drought, drought Palmer Severity Index was estimated for the entire region of the territory. For this were used meteorological data (rainfall and evapotranspiration) and soil (field capacity, permanent wilting point and water storage in the soil). The data field capacity and wilting point were obtained from the physical properties of soil; while the water storage in soil was calculated considering the water balance model. The results of the PSDI were evaluated during the years 2000 to 2015, which correspond to periods with and without occurrence of drought. In order to assess the future drought projections, considering the set of the Coupled Model Intercomparison rainfall data Project Phase 5 (CMIP5). Climate projections precipitation in CMIP5 for the period 2071-2100 was extracted generating entitled forcing scenarios Representative Concentration Pathways - RCPs, and referred to as RCOP 8.5, corresponding to an approximate radiative forcing the end the twenty-first century of 8.5 Wm-2. The results showed that the PDSI is directly associated with climatological patterns of precipitation and soil moisture in any spatial and temporal scale (including future projections). Therefore, it is concluded that the PDSI is an important index to assess soil moisture different water conditions, as well as the association with economic and social information to create risk maps for subsidies to decision makers.

Analyses of Shallow Convection over the Amazon Coastal Region Using Satellite Images, Data Observations and Modeling

The Belem region of the state of Para, which is located in northern of Brazil and part of the Amazon biome is characterized by high temperatures, strong convection, unstable air conditions and high humidity favoring the formation of convective clouds. Shallow convection and deep convection are among the main components of the local energy balance. Typically a deep convection over the continents is preceded by a shallow convection. An analysis of the performance of the Jet Propulsion Laboratory / National Aeronautics and Space Administration (JPL/NASA) model of shallow convection parameterization in a framework of the single column model (SCM), in relation to the cluster of cumulus clouds formed in the coastal region of the Amazon forest due to squall lines, is provided. To achieve this purpose enhanced satellite images and infrared images from channels 2 and 4 from the GOES-12 satellite, and data obtained by the “Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement)” CHUVA campaign, during the month of June of 2011, were used. During that period, clusters of cumulus clouds penetrated the interior of the Amazon, causing heavy rains. Results demonstrated that the parameterizations performed well in the case where only a core of clouds was observed, such as at 18:00h on 14 June. This period of the day also presents the smallest bias and root mean square error (rmse) values for the relative humidity. For the potential temperature the smallest value of bias is at 12:00h on June 7 (0.18 K), the largest one is on June 11 (-2.32 K) and the rmse ranges from 0.59 to 2.99 K.

Use of SMOS L3 Soil Moisture Data: Validation and Drought Assessment for Pernambuco State, Northeast Brazil

The goal of this study was to validate soil moisture data from Soil Moisture Ocean Salinity (SMOS) using two in situ databases for Pernambuco State, located in Northeast Brazil. The validation process involved two approaches, pixel-station comparison and areal average, for three regions in Pernambuco with different climatic characteristics. After validation, the SMOS data were used for drought assessment by calculating soil moisture anomalies for the available period of data. Four statistical criteria were used to verify the quality of the satellite data: Pearson correlation coefficient, Willmott index of agreement, BIAS, and root mean squared difference (RMSD). The average RMSD calculated from the daily time series in the pixel and the areal assessment were 0.071 m3m-3 and 0.04 m3m-3, respectively. Those values are near to the expected 0.04 m3m-3 accuracy of the SMOS mission. The analysis of soil moisture anomalies enabled the assessment of the dry period between 2012 and 2017 and the identification of regions most impacted by the drought. The driest year for all regions was 2012, when the anomaly values achieved -50% in some regions. The use of SMOS data provided additional information that was used in conjunction with the precipitation data to assess drought periods. This may be particularly relevant for planning in agriculture and supporting decision makers and farmers.

Impacto das Características da Superfície Terrestre no Algoritmo de Inferência da Umidade do Solo no Brasil, Utilizando Observações do Sensor AMSR-E/Aqua

Studies must be performed using the LPRM (Land Parameter Retrieval Model ) algorithm to estimate the surface moisture of soil throughout the South American continent in order to improve control of seasonal patterns of soil moisture in different regions of South America, since the model was developed for specific North American conditions. Thus, the purpose of this study was to improve the LPRM algorithm and the results of Rossato et al. (2011), considering the land surface characteristic of Brazil and using temperature data from the surface measured in situ and estimated by satellite (at the frequency of 37 GHz). Data on the physical properties of the soil extracted from the Survey and Reconnaissance of Soils in Brazil were used to determine the dielectric constant of the soil which is the function of soil moisture. Statistical analyses, such as correlation coefficient, bias and mean quadratic error (REMQ) were used to validate the surface temperature and soil moisture derived by the new version of the algorithm adjusted for the surface conditions of the Brazilian territory (LPRM/BR), obtained from the information about the sensor AMSR-E/Aqua (6.9 GHz band C). The results indicated a significant improvement of the LPRM/BR for experimental sites BA-06 and BA-10 of SMEX03, whose correlations were equal to 0.94 and 0.84, respectively. As to the results of BIAS and REMQ, for the original version of LPRM, the bias was up to 0.23 for site BA-06. However, for LPRM/BR significant differences were observed, presenting a value of 0.01 of bias for site BA-06 and REMQ equal to 0 for site BA-11. Due to the absence of data on soil moisture measured “in situ”, data on re-analysis of soil moisture (from the ETA model) and of precipitation were also utilized in the evaluation of LPRM/BR. Thus, the underestimation of the surface temperature and the overestimation of the soi moisture presented by LPRM were solved using LPRM/BR. Besides, a increase in the areas with high correlations was also observed (r > 0.8) obtained between LPRM/BR and the different databases (model Eta and precipitation observed by CPTEC/INPE). It was thus concluded that the LPRM/BR allows estimating soil moisture from the observations in microwaves of sensor AMSR# (band C0, with greater accuracy than the original version of the algorithm.