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Geotechnologies allow natural resources to be surveyed more quickly and cheaply than traditional methods. This paper aimed to produce a digital soil map (DSM) based on Landsat time series data. The study area, located in the eastern part of the Brazilian Federal District (Rio Preto hydrographic basin), comprises a representative basin of the Central Brazil plateau in terms of pedodiversity. A spectral library was produced based on the soil spectroscopy (from the visible to shortwave infrared spectral range) of 42 soil samples from 0–15 cm depth using the Fieldspec Pro equipment in a laboratory. Pearson’s correlation and principal component analysis of the soil attributes revealed that the dataset could be grouped based on the texture content. Hierarchical clustering analysis allowed for the extraction of 13 reference spectra. We interpreted the spectra morphologically and resampled them to the Landsat 5 Thematic Mapper satellite bands. Afterward, we elaborated a synthetic soil/rock image (SySI) and a soil frequency image (number of times the bare soil was captured) from the Landsat time series (1984–2020) in the Google Earth Engine platform. Multiple Endmember Spectral Mixture Analysis (MESMA) was used to model the SySI, using the endmembers as the input and generating a DSM, which was validated by the Kappa index and the confusion matrix. MESMA successfully modeled 9 of the 13 endmembers: Dystric Rhodic Ferralsol (clayic); Dystric Rhodic Ferralsol (very clayic); Dystric Haplic Ferralsol (loam-clayic); Dystric Haplic Ferralsol (clayic); Dystric Petric Plinthosol (clayic); Dystric Petric Plinthosol (very clayic); Dystric Regosol (clayic); Dystric Regosol (very clayic); and Dystric, Haplic Cambisol (clayic). The root mean squared error (RMSE) varied from 0 to 1.3%. The accuracy of DSM achieved a Kappa index of 0.74, describing the methodology’s effectiveness to differentiate the studied soils.

Global-scale forest/non-forest (FNF) maps are of crucial importance for applications like biomass estimation and deforestation monitoring. Global FNF maps based on optical remote sensing data have been produced by the wall-to-wall satellite image analyses or sampling strategies. The German Aerospace Center (DLR) and the Japan Aerospace Exploration Agency (JAXA) also made available their global FNF maps based on synthetic aperture radar (SAR) data. This paper attempted to answer the following scientific question: how comparable are the FNF products derived from optical and SAR data? As test sites we selected the Amazon (tropical rainforest) and Cerrado (tropical savanna) biomes, the two largest Brazilian biomes. Forest estimations from 2015 derived from TanDEM-X (X band; HH polarization) and ALOS-2 (L band; HV polarization) SAR data, as well as forest cover information derived from Landsat 8 optical data were compared with each other at the municipality and image sampling levels. The optical-based forest estimations considered in this study were derived from the MapBiomas project, a Brazilian multi-institutional project to map land use and land cover (LULC) classes of an entire country based on historical time series of Landsat data. In addition to the existing forest maps, a set of 1619 Landsat 8 RGB color composites was used to generate new independent comparison data composed of circular areas with 5-km diameter, which were visually interpreted after image segmentation. The Spearman rank correlation estimated the correlation among the data sets and the paired Mann–Whitney–Wilcoxon tested the hypothesis that the data sets are statistically equal. Results showed that forest maps derived from SAR and optical satellites are statistically different regardless of biome or scale of study (municipality or image sampling), except for the Cerrado´s forest estimations derived from TanDEM-X and ALOS-2. Nevertheless, the percentage of pixels classified as forest or non-forest by both SAR sensors were 90% and 80% for the Amazon and Cerrado biome, respectively, indicating an overall good agreement.

We discriminated different successional forest stages, forest degradation, and land use classes in the Tapajós National Forest (TNF), located in the Central Brazilian Amazon. We used full polarimetric images from ALOS/PALSAR-2 that have not yet been tested for land use and land cover (LULC) classification, neither for forest degradation classification in the TNF. Our specific objectives were: (1) to test the potential of ALOS/PALSAR-2 full polarimetric images to discriminate LULC classes and forest degradation; (2) to determine the optimum subset of attributes to be used in LULC classification and forest degradation studies; and (3) to evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) supervised classifications to discriminate LULC classes and forest degradation. PALSAR-2 images from 2015 and 2016 were processed to generate Radar Vegetation Index, Canopy Structure Index, Volume Scattering Index, Biomass Index, and Cloude–Pottier, van Zyl, Freeman–Durden, and Yamaguchi polarimetric decompositions. To determine the optimum subset, we used principal component analysis in order to select the best attributes to discriminate the LULC classes and forest degradation, which were classified by RF. Based on the variable importance score, we selected the four first attributes for 2015, alpha, anisotropy, volumetric scattering, and double-bounce, and for 2016, entropy, anisotropy, surface scattering, and biomass index, subsequently classified by SVM. Individual backscattering indexes and polarimetric decompositions were also considered in both RF and SVM classifiers. Yamaguchi decomposition performed by RF presented the best results, with an overall accuracy (OA) of 76.9% and 83.3%, and Kappa index of 0.70 and 0.80 for 2015 and 2016, respectively. The optimum subset classified by RF showed an OA of 75.4% and 79.9%, and Kappa index of 0.68 and 0.76 for 2015 and 2016, respectively. RF exhibited superior performance in relation to SVM in both years. Polarimetric attributes exhibited an adequate capability to discriminate forest degradation and classes of different ecological succession from the ones with less vegetation cover.

Some models have been developed using agrometeorological and remote sensing data to estimate agriculture production. However, it is expected that the use of SAR images can improve their performance. The main objective of this study was to estimate the sugarcane production using a multiple linear regression model which considers agronomic data and ALOS/PALSAR images obtained from 2007/08, 2008/09 and 2009/10 cropping seasons. The performance of models was evaluated by coefficient of determination, t-test, Willmott agreement index (d), random error and standard error. The model was able to explain 79%, 12% and 74% of the variation in the observed productions of the 2007/08, 2008/09 and 2009/10 cropping seasons, respectively. Performance of the model for the 2008/09 cropping season was poor because of the occurrence of a long period of drought in that season. When the three seasons were considered all together, the model explained 66% of the variation. Results showed that SAR-based yield prediction models can contribute and assist sugar mill technicians to improve such estimates.

This study proposes a workflow for land use and land cover (LULC) classification of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) images of the Brazilian tropical savanna (Cerrado) biome. The following LULC classes were considered: forestlands; shrublands; grasslands; reforestations; croplands; pasturelands; bare soils/straws; urban areas; and water reservoirs. The proposed approach combines polarimetric attributes, image segmentation, and machine-learning procedures. A set of 125 attributes was generated using polarimetric ALOS-2/PALSAR-2 images, including the van Zyl, Freeman−Durden, Yamaguchi, and Cloude−Pottier target decomposition components, incoherent polarimetric parameters (biomass indices and polarization ratios), and HH-, HV-, VH-, and VV-polarized amplitude images. These attributes were classified using the Naive Bayes (NB), DT J48 (DT = decision tree), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) algorithms. The RF, MLP, and SVM classifiers presented the most accurate performances. NB and DT J48 classifiers showed a lower performance in relation to the RF, MLP, and SVM. The DT J48 classifier was the most suitable algorithm for discriminating urban areas and natural vegetation cover. The proposed workflow can be replicated for other SAR images with different acquisition modes or for other types of vegetation domains.

Brazil has the largest commercial beef cattle herd in the world, with cattle ranching being particularly prominent in the 200-million ha, Brazilian neotropical moist savanna biome, known as Cerrado, one of the world’s hotspots for biodiversity conservation. As decreasing productivity is a major concern affecting the Cerrado pasturelands, evaluation of pasture conditions through the determination of biophysical parameters is instrumental for more effective management practices and herd occupation strategies. Within this context, the primary goal of this study was the regional assessment of pasture biophysical properties, through the scaling of wet- and dry-season ground truth data (total biomass, green biomass, and % green cover) via the combined use of high (Landsat-TM) and moderate (MODIS) spatial resolution vegetation index images. Based on the high correlation found between NDVI (normalized difference vegetation index) and % green cover (r = 0.95), monthly MODIS-based % green cover images were derived for the 2009–2010 hydrological cycle, which were able to capture major regional patterns and differences in pasture biophysical responses, including the increasing greenness values towards the southern portions of the biome, due to both local conditions (e.g., more fertile soils) and management practices. These results corroborate the development of biophysically-based landscape degradation indices, in support of improved land use governance and natural area conservation in the Cerrado.

The objective of this work was to develop and validate a mathematical model to estimate the duration of cotton (Gossypium hirsutum L. r. latifolium hutch) cycle in the State of Goiás, Brazil, by applying the method of growing degree-days (GD), and considering, simultaneously, its time-space variation. The model was developed as a linear combination of elevation, latitude, longitude, and Fourier series of time variation. The model parameters were adjusted by using multiple-linear regression to the observed GD accumulated with air temperature in the range of 15°C to 40°C. The minimum and maximum temperature records used to calculate the GD were obtained from 21 meteorological stations, considering data varying from 8 to 20 years of observation. The coefficient of determination, resulting from the comparison between the estimated and calculated GD along the year was 0.84. Model validation was done by comparing estimated and measured crop cycle in the period from cotton germination to the stage when 90 percent of bolls were opened in commercial crop fields. Comparative results showed that the model performed very well, as indicated by the Pearson correlation coefficient of 0.90 and Willmott agreement index of 0.94, resulting in a performance index of 0.85.O objetivo deste trabalho foi desenvolver e validar um modelo matemático para estimar a duração do ciclo do algodoeiro (Gossypium hirsutum L. r. latifolium hutch) no Estado de Goiás, aplicando o método de graus-dia (GD) e considerando sua variação espaçotemporal. O modelo foi desenvolvido por meio de combinação linear entre altitude, latitude, longitude e a variação temporal, representada pela série trigonométrica incompleta de Fourier. Os parâmetros do modelo foram ajustados, por meio de regressão linear múltipla, aos dados de GD para o algodoeiro, acumulados entre 15°C e 40°C. Os dados utilizados de temperaturas máxima e mínima foram obtidos de séries históricas de 21 estações meteorológicas, com períodos de observações variando entre 8 e 24 anos. A validação do modelo foi feita comparando-se a duração da fase entre a emergência e 90% de capulhos abertos de cultivares de algodoeiros plantados em lavouras comerciais. O coeficiente de determinação resultante do ajuste do modelo aos dados observados foi de 0,84. Os resultados dessa comparação mostraram que o modelo proposto teve um desempenho muito bom, conforme indicado pelo coeficiente de correlação de Pearson de 0,90 e o índice de concordância de Willmott de 0,94, que resultaram em um índice de desempenho de 0,85.

O aumento da demanda de água para a irrigação tem sido objeto de constante preocupação dos órgãos de gestão dos recursos hídricos e da população do Distrito Federal. Os objetivos deste trabalho foram determinar a variação da área irrigada por pivôs-centrais no Distrito Federal entre os anos de 1992 e 2002, estimar a demanda hídrica desses sistemas, bem como verificar sua importância em relação ao consumo humano. Utilizando imagens de satélite e ferramentas de geoprocessamento, foram determinadas as áreas irrigadas por pivô-central. O consumo de água dessas áreas irrigadas foi estimado com base em dados da literatura referentes às principais culturas semeadas na região. Entre 1992 e 2002, o número de pivôs-centrais no Distrito Federal passou de 55 para 104, a área irrigada de 3.894 ha para 6.823 ha e o consumo, de 23,36 para 40,94 milhões de m³ ano-1. O consumo de água para irrigação por pivô-central no Distrito Federal é, ainda, menos representativo do que o consumo de água para o abastecimento humano; entretanto, a demanda para a irrigação tem crescido de forma mais acelerada, indicando tendência de alteração desse quadro.The increase in the water demand for irrigation is a matter of concern of water resources management institutions and of the population from the Distrito Federal in Brazil. The objectives of this work were to determine the variation of the irrigated area by center pivot in the Brazilian Distrito Federal in the period between 1992 and 2002, in order to estimate the water demand by these systems of irrigation, as well as to verify its importance in relation to human consumption. Using satellite images and GIS tools, the center pivot irrigated areas were determined and the water consumption by the main irrigated crops in this region was estimated using reference data obtained from the literature. In the period between 1992 and 2002, the number of center pivots in the Distrito Federal increased from 55 to 104 and the corresponding irrigated area changed from 3.894 to 6.823 ha; consequently, the consumption of water increased from 23.36 to 40.94 million m³ year-1. Although the demand of water for irrigation by center pivot in the Federal District is less representative than by human water consumption, the demand for irrigation has been growing in a higher rate, indicating a significant change in this trend.

Este artigo se propõe a apresentar exemplos de questões científicas que puderam ser respondidas no contexto do Projeto LBA (Large Sale Biosphere-Atmosphere Experiment in Amazonia) graças à contribuição de informações derivadas de sensoriamento remoto. Os métodos de sensoriamento remoto permitem integrar informações sobre os vários processos físicos e biológicos em diferentes escalas de tempo e espaço. Nesse artigo, são enfatizados aqueles avanços de conhecimento que jamais seriam alcançados sem a concorrência da informação derivada de sensoriamento.This paper aims to assess the contribution of remote sensing technology in addressing key questions raised by the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA). The answers to these questions foster the knowledge on the climatic, biogechemical and hydrologic functioning of the Amazon, as well as on the impact of human activities at regional and global scales. Remote sensing methods allow integrating information on several processes at different temporal and spatial scales. By doing so, it is possible to perceive hidden relations among processes and structures, enhancing their teleconnections. Key advances in the remote sensing science are summarized in this article, which is particularly focused on information that would not be possible to be retrieved without the concurrence of this technology.