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Due to increasing algae bloom occurrence and water degradation on a global scale, there is a demand for water quality monitoring systems based on remote sensing imagery. This paper describes the scientific, theoretical, and methodological background for creating a cloud-computing interface on Google Earth Engine (GEE) which allows end-users to access algae bloom related products with high spatial (30 m) and temporal (~5 day) resolution. The proposed methodology uses Sentinel-2 images corrected for atmospheric and sun-glint effects to generate an image collection of the Normalized Difference Chlorophyll-a Index (NDCI) for the entire time-series. NDCI is used to estimate both Chl-a concentration, based on a non-linear fitting model, and Trophic State Index (TSI), based on a tree-decision model classification into five classes. Once the Chl-a and TSI algorithms had been calibrated and validated they were implemented in GEE as an Earth Engine App, entitled Algae Bloom Monitoring Application (AlgaeMAp). AlgaeMAp is the first online platform built within the GEE platform that offers high spatial resolution of water quality parameters. The App benefits from the huge processing capability of GEE that allows any user with internet access to easily extract detailed spatial (30 m) and long temporal Chl-a and TSI information (from August 2015 and with images every 5 days) throughout the most important reservoirs in the State of São Paulo/Brazil. The application will be adapted to extend to other relevant areas in Latin America. [ABSTRACT FROM AUTHOR]

Intensive cropland expansion for an increasing population has driven soil degradation worldwide. Modeling how agroecosystems respond to variations in soil attributes, relief and crop management dynamics can guide soil conservation. This research presents a new approach to evaluate soil loss by water erosion in cropland using the RUSLE model and Synthetic Soil Image (spectroscopy technique), which uses time series remotely sensed environmental, agricultural and anthropic variables, in the southeast region of São Paulo State, Brazil. The availability of the open-access satellite images of Tropical Rainfall Measuring Mission (TRMM) and Landsat satellite images provided ten years of rainfall data and 35 years of exposed soil surface. The bare soil surface and agricultural land use were extracted, and the multi-temporal rainfall erosivity was assessed. We predict soil maps' attributes (texture and organic matter) through innovative soil spectroscopy techniques to assess the soil erodibility and soil loss tolerance. The erosivity, erodibility, and topography obtained by the Earth observations were adopted to estimate soil erosion in four scenarios of sugarcane (Saccharum spp.) residue coverage (0%, 50%, 75%, and 100%) in five years of the sugarcane cycle: the first year of sugarcane harvest and four subsequent harvesting years from 2013 to 2017. Soil loss tolerance means 4.3 Mg ha−1 exceeds the minimum rate in 40% of the region, resulting in a total soil loss of ~6 million Mg yr−1 under total coverage management (7 Mg ha−1). Our findings suggest that sugarcane straw production has not been sufficient to protect the soil loss against water erosion. Thus, straw removal is unfeasible unless alternative conservation practices are adopted, such as minimum soil tillage, contour lines, terracing and other techniques that favor increases in organic matter content and soil flocculating cations. This research also identifies a spatiotemporal erosion-prone area that requests an immediately sustainable land development guide to restore and rehabilitate the vulnerable ecosystem service. The high-resolution spatially distribution method provided can identify soil degradation-prone areas and the cropland expansion frequency. This information may guide farms and the policymakers for a better request of conservation practices according to site-specific management variation. [ABSTRACT FROM AUTHOR]

The determination of the orthometric height from geometric leveling has practical difficulties that, despite a number of scientific and technological advances, passed a century without substantial modifications or advances. Currently, the Global Navigation Satellite System (GNSS) has been used with reasonable success for orthometric height determination. With a sufficient number of benchmarks with known horizontal and vertical coordinates, it is often possible to adjust using the least squares method mathematical expressions that allow interpolation of geoid heights. The objective of this study is to present an alternative method to interpolate geoid heights based on the technique of Artificial Neural Networks (ANNs). The study area is the Brazilian state of São Paulo, and for training the ANN the authors have used geoid height information from the EGM08 gravity model with a grid spacing of 10 minutes of arc. The efficiency of the model was tested at 157 points with known geoid heights distributed across the study area. The results were also compared with the Brazilian Geoid Model (MAPGEO2004). Based on those 157 benchmarks it was possible to verify that the model generated by ANNs provided a mean absolute error of 0.24 m in obtaining a geoid height value. Statistical tests have shown that there was no difference between the means from known geoid heights and geoid heights provided by the neural model for a significance level of 5%. It was also found that ANNs provided an improvement of 2.7 times in geoid height estimates when compared with the MAPGEO2004 geoid model. [ABSTRACT FROM AUTHOR]

The objectives of this research were to: (i) develop hyperspectral narrow-band models to determine soil variables such as organic matter content (OM), sum of cations (SC = Ca + Mg + K), aluminum saturation (m%), cations saturation (V%), cations exchangeable capacity (CEC), silt, sand and clay content using visible-near infrared (Vis-NIR) diffuse reflectance spectra; (ii) compare the variations of the chemical and the spectroradiometric soil analysis (Vis-NIR). The study area is located in São Paulo State, Brazil. The soils were sampled over an area of 473 ha divided into grids (100 × 100 m) with a total of 948 soil samples georeferenced. The laboratory RS data were obtained using an IRIS (Infrared Intelligent Spectroradiometer) sensor (400-2,500 nm) with a 2-nm spectral resolution between 450 and 1,000 nm and 4-nm between 1,000 and 2,500 nm. Satellite reflectance values were sampled from corrected Landsat Thematic Mapper (TM) images. Each pixel in the image was evaluated as its vegetation index, color compositions and soil line concepts regarding certain locations of the field in the image. Chemical and physical analysis (organic matter content, sand, silt, clay, sum of cations, cations saturation, aluminum saturation and cations exchange capacity) were performed in the laboratory. Statistical analysis and multiple regression equations for soil attribute predictions using radiometric data were developed. Laboratory data used 22 bands and 13 ?Reflectance Inflexion Differences, RID? from different wavelength intervals of the optical spectrum. However, for TM-Landsat six bands were used in analysis (1, 2, 3, 4, 5, and 7). Estimations of some tropical soil attributes were possible using laboratory spectral analysis. Laboratory spectral reflectance (SR) presented high correlations with traditional laboratory analyses for the soil attributes such as clay (R2 = 0.84, RMSE = 3.75) and sand (R2 = 0.85, RMSE = 3.74). The most sensitive narrow-bands in modeling (using 474 observations) these attributes were B8 (1,350-1,417 nm), B10 (1,417- 1,449 nm), B11 (1,449-1,793 nm), B15 (1,927-2,102 nm), B16 (2,101-2,139 nm), and B17 (2,139-2,206 nm); B7 (975-1,350 nm), B10, B11, B16, B19 (2,206-2,258 nm) and B21 (2,258-2,389 nm) for clay and sand, respectively. The bands selected to model sand and clay, by orbital data, were 3, 5 and 7 of TM-Landsat-5 and 2, 5 and 7 sand and clay, respectively. The use of soil analysis methodology by ground remote sensing constitutes an alternative to traditional routine laboratory analysis. [ABSTRACT FROM AUTHOR]