Mapping soybean planting area in midwest Brazil with remotely sensed images and phenology-based algorithm using the Google Earth Engine platform

Silva, Carlos Antônio da; Leonel-Junior, Antonio Hérbete Sousa; Rossi, Fernando Saragosa; Filho, Washington Luiz Félix Correia; Santiago, Dimas de Barros; Oliveira‐Júnior, José Francisco de; Teodoro, Paulo Eduardo; Lima, Mendelson; Capristo-Silva, Guilherme Fernando

doi:10.1016/j.compag.2019.105194

Cited by 38 publications

(13 citation statements)

References 24 publications

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“…A large amount of orbital data was used to perform the calculations of the PCEI index (Perpendicular Crop Enhancement Index—Eq. 1 ), developed by Silva Junior et al 20 , 21 . The PCEI index uses reflectance factor values centered on the red and near-infrared wavelengths, ranging from -1 to 1, which allows for significant positive differences between the maximum and minimum observed in soybean crop phenology.…”

Section: Methodsmentioning

confidence: 99%

“…The Perpendicular Crop Enhancement Index (PCEI), recently used in mapping soybean areas 20 , 21 , has been positively effective in monitoring soybean farming and has become an essential tool in monitoring the expansion of the crop in the face of deforestation in the Amazonia 17 , 22 – 24 . Monitoring anthropic impacts using the SR technique has been effective in environmental studies 20 , 25 .…”

Section: Introductionmentioning

confidence: 99%

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Advance of soy commodity in the southern Amazonia with deforestation via PRODES and ImazonGeo: a moratorium-based approach

Lourençoni

Silva

Lima

et al. 2021

Sci Rep

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The guidance on decision-making regarding deforestation in Amazonia has been efficient as a result of monitoring programs using remote sensing techniques. Thus, the objective of this study was to identify the expansion of soybean farming in disagreement with the Soy Moratorium (SoyM) in the Amazonia biome of Mato Grosso from 2008 to 2019. Deforestation data provided by two Amazonia monitoring programs were used: PRODES (Program for Calculating Deforestation in Amazonia) and ImazonGeo (Geoinformation Program on Amazonia). For the identification of soybean areas, the Perpendicular Crop Enhancement Index (PCEI) spectral model was calculated using a cloud platform. To verify areas (polygons) of largest converted forest-soybean occurrences, the Kernel Density (KD) estimator was applied. Mann–Kendall and Pettitt tests were used to identify trends over the time series. Our findings reveal that 1,387,288 ha were deforested from August 2008 to October 2019 according to PRODES data, of which 108,411 ha (7.81%) were converted into soybean. The ImazonGeo data showed 729,204 hectares deforested and 46,182 hectares (6.33%) converted into soybean areas. Based on the deforestation polygons of the two databases, the KD estimator indicated that the municipalities of Feliz Natal, Tabaporã, Nova Ubiratã, and União do Sul presented higher occurrences of soybean fields in disagreement with the SoyM. The results indicate that the PRODES system presents higher data variability and means statistically superior to ImazonGeo.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advance of soy commodity in the southern Amazonia with deforestation via PRODES and ImazonGeo: a moratorium-based approach

Lourençoni

Silva

Lima

et al. 2021

Sci Rep

Self Cite

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“…Silva et al [61] developed an algorithm (phenology-based) for soybean crop mapping by spectral indices and Landsat and Sentinel-2 images. The study season was 2016-2017.…”

Section: Related Workmentioning

confidence: 99%

Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine

Pech-May

Aquino-Santos

Ríos-Toledo

et al. 2022

Sensors

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Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: water bodies, land in recovery, urban areas, sandy areas, and tropical rainforest. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops.

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“…More complex algorithms combined with significant practical research should be tried with ScienceGeoSpark in future experiments. For example, large scale vegetation indices calculation is as suitable for cloud-based cluster framework as Google Earth Engine or ScienceEarth [47].…”

Section: Performance Of Sciencegeosparkmentioning

confidence: 99%

ScienceEarth: A Big Data Platform for Remote Sensing Data Processing

Yan

et al. 2020

Remote Sensing

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Mass remote sensing data management and processing is currently one of the most important topics. In this study, we introduce ScienceEarth, a cluster-based data processing framework. The aim of ScienceEarth is to store, manage, and process large-scale remote sensing data in a cloud-based cluster-computing environment. The platform consists of the following three main parts: ScienceGeoData, ScienceGeoIndex, and ScienceGeoSpark. ScienceGeoData stores and manages remote sensing data. ScienceGeoIndex is an index and query system, a spatial index based on quad-tree and Hilbert curve which is combined for heterogeneous tiled remote sensing data that makes efficient data retrieval in ScienceGeoData. ScienceGeoSpark is an easy-to-use computing framework in which we use Apache Spark as the analytics engine for big remote sensing data processing. The result of tests proves that ScienceEarth can efficiently store, retrieve, and process remote sensing data. The results reveal ScienceEarth has the potential and capabilities of efficient big remote sensing data processing.

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Mapping soybean planting area in midwest Brazil with remotely sensed images and phenology-based algorithm using the Google Earth Engine platform

Cited by 38 publications

References 24 publications

Advance of soy commodity in the southern Amazonia with deforestation via PRODES and ImazonGeo: a moratorium-based approach

Advance of soy commodity in the southern Amazonia with deforestation via PRODES and ImazonGeo: a moratorium-based approach

Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine

ScienceEarth: A Big Data Platform for Remote Sensing Data Processing

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