Ground reference data for sugarcane biomass estimation in São Paulo state, Brazil

In order to make effective decisions on sustainable development, it is essential for sugarcane-producing countries to take into account sugarcane acreage and sugarcane production dynamics. The availability of sugarcane biophysical data along the growth season is key to an effective mapping of such dynamics, especially to tune agronomic models and to cross-validate indirect satellite measurements. Here, we introduce a dataset comprising 3,500 sugarcane observations collected from October 2014 until October 2015 at four fields in the São Paulo state (Brazil). The campaign included both non-destructive measurements of plant biometrics and destructive biomass weighing procedures. The acquisition plan was designed to maximize cost-effectiveness and minimize field-invasiveness, hence the non-destructive measurements outnumber the destructive ones. To compensate for such imbalance, a method to convert the measured biometrics into biomass estimates, based on the empirical adjustment of allometric models, is proposed. In addition, the paper addresses the precisions associated to the ground measurements and derived metrics. The presented growth dynamics and associated precisions can be adopted when designing new sugarcane measurement campaigns.5FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2013/50942-

Vegetation characterization through the use of precipitation-affected SAR signals

Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.1010FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2013/50943-

Sensoriamento remoto espacial com dados de sensores radar e óptico para mapeamento e monitoramento da produtividade de cana-de-açúcar

Orientadores: Jansle Vieira Rocha, Ramon HanssenTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia AgrícolaResumo: Este estudo apresenta uma pesquisa sobre produtividade e monitoramento da cobertura de uso do solo, com foco na cana-de-açúcar, baseado em observações de sensoriamento remoto, coletadas por Synthetic Aperture Radar (SAR) e sensores ópticos. O estudo tem como objetivo fornecer novas discussões sobre técnicas e metodologias que permitem um monitoramento eficaz da produtividade da cana-de-açúcar e de sua expansão em larga escala ao longo do tempo. Este estudo faz parte de uma estrutura mais ampla de projetos de pesquisa, que possuem um objetivo geral de contribuir para uma sociedade de baseada na bioeconomia. O estado de São Paulo, Brasil, foi selecionado como área de estudo por ser uma das regiões mais importantes do mundo na produção de cana-de-açúcar, uma das culturas mais importantes para a produção de bioenergia. Como parte da pesquisa de monitoramento de produtividade, descrevemos detalhadamente os trabalhos de campo para a coleta de dados de cana de açúcar realizado em quatro áreas no estado de São de Paulo ao longo de um ano. Esses resultados são acompanhados de análises de qualidade das amostras, ilustrando sua confiabilidade das medições realizadas. Posteriormente, os dados de sensoriamento remoto de vários sensores ópticos e SAR (banda C e banda L) foram comparados com as medições realizadas em campo para desenvolver técnicas de monitoramento espacial da produtividade da cana de açúcar. Como parte da pesquisa de mapeamento, aplicamos uma adaptação do Modelo oculto de Markov para combinar dados SAR e observações ópticas. Uma atenção especial foi dada ao efeito da precipitação nas observações SAR e em certas condições da vegetação no desempenho do modelo. A pesquisa também discorre sobre as vantagens de se combinar dados SAR e observações ópticas. Posteriormente, foi demonstrada uma técnica que explora as flutuações do sinal SAR causadas por diferentes condições de umidade da superfície, a fim de melhorar a caracterização da vegetação. Finalmente, mostramos alguns exemplos de aplicações de nosso modelo no estado de São Paulo, desde os efeitos da seca em um reservatório de hidrelétrica, até mapas de ganhos e perdas de cobertura da terra ao longo de 15 anos no EstadoAbstract: In this study, research on productivity and land cover monitoring is presented, with a focus on sugarcane, based on space-based remote sensing observations that were collected by Synthetic Aperture Radar (SAR) and optical sensors. The study aims to provide new insights into techniques and methodologies that allow for cost-efficient monitoring of sugarcane productivity and the wide-scale expansion of sugarcane over long time series. It is part of a wider framework of research projects, sharing the overarching goal to contribute to a biobased society. São Paulo state, Brazil, was selected as study area, primarily because it is one of the most prominent regions worldwide that hosts sugarcane, one of the most prominent crops for bio-energy production. As part of the productivity monitoring research we describe into detail the year-long sugarcane ground measurement campaign we conducted on four sugarcane fields in São Paulo state. These results are accompanied by detailed quality assessments, illustrating their reliability when collecting such measurements. Subsequently, remote sensing signals from various (C-band and L-band) SAR and optical satellites are compared to the ground reference measurements to develop space-based sugarcane productivity monitoring techniques. As part of the mapping research, we apply an adaptation of the Hidden Markov to combine SAR and optical observations. Special attention is paid to the effect of precipitation during SAR observations on and to certain vegetation conditions on the model¿s performance. The research also provides insights into the advantages when combining SAR and optical observations. Subsequently, a technique is demonstrated exploiting SAR signal fluctuations caused by varying surface wetness conditions in order to improve the characterization of vegetation. Finally, we show some examples of applications of our model in São Paulo state, from the effects of drought on a hydroelectric water reservoir to land cover gain and loss maps over 15 years in the stateDoutoradoGestão de Sistemas na Agricultura e Desenvolvimento RuralDoutor em Engenharia Agrícol

Sugarcane Productivity Mapping through C-Band and L-Band SAR and Optical Satellite Imagery

Space-based remote sensing imagery can provide a valuable and cost-effective set of observations for mapping crop-productivity differences. The effectiveness of such signals is dependent on several conditions that are related to crop and sensor characteristics. In this paper, we present the dynamic behavior of signals from five Synthetic Aperture Radar (SAR) sensors and optical sensors with growing sugarcane, focusing on saturation effects and the influence of precipitation events. In addition, we analyzed the level of agreement within and between these spaceborne datasets over space and time. As a result, we produced a list of conditions during which the acquisition of satellite imagery is most effective for sugarcane productivity monitoring. For this, we analyzed remote sensing data from two C-band SAR (Sentinel-1 and Radarsat-2), one L-band SAR (ALOS-2), and two optical sensors (Landsat-8 and WorldView-2), in conjunction with detailed ground-reference data acquired over several sugarcane fields in the state of São Paulo, Brazil. We conclude that satellite imagery from L-band SAR and optical sensors is preferred for monitoring sugarcane biomass growth in time and space. Additionally, C-band SAR imagery offers the potential for mapping spatial variations during specific time windows and may be further exploited for its precipitation sensitivity

Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account

Erratum: Ramses A.M. et al. Vegetation Characterization through the Use of Precipitation-Affected SAR Signals. Remote Sens. 2018, <em>10</em>, 1647

After publication of the research paper [...

Erratum: Ramses A.M. et al. Vegetation characterization through the use of precipitation-affected SAR signals. Remote sens. 2018, 10, 1647

After publication of the research paper [1], the authors wish to make the following correction. The link to the affiliation of Ramon F. Hanssen should have been (1). Hence, the affiliation of Ramon F. Hanssen is Geoscience and Remote Sensing at Delft University of Technology. The authors would like to apologize for any inconvenience caused. The change does not affect the scientific results. The manuscript will be updated and the original will remain online on the article webpage, with a reference to this correction.101