The use of ALOS/PALSAR data for estimating sugarcane productivity

Picoli, Michelle Cristina Araújo; Lamparelli, Rubens Augusto Camargo; Sano, Edson Eyji; Rocha, Jansle Vieira

doi:10.1590/s0100-69162014000600019

Cited by 8 publications

(5 citation statements)

References 16 publications

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“…The potential of sugarcane crop growth monitoring and biomass estimation using Synthetic Aperture Radar (SAR) signals were explored by several studies [26][27][28][29][30]. Molijn et al [27], analysed 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 the state of São Paulo, Brazil and reported that the satellite imagery from L-band SAR and optical sensors is preferred for monitoring sugarcane biomass growth in time and space.…”

Section: Introductionmentioning

confidence: 99%

Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level

Rahman

Robson

2020

Remote Sensing

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Early prediction of sugarcane crop yield at the commercial block level (unit area of a single crop of the same variety, ratoon or planting date) offers significant benefit to growers, consultants, millers, policy makers, crop insurance companies and researchers. This current study explored a remote sensing based approach for predicting sugarcane yield at the block level by further developing a regionally specific Landsat time series model and including individual crop sowing (or previous seasons' harvest) date. For the Bundaberg growing region of Australia this extends over a five months period, from July to November. For this analysis, the sugarcane blocks were clustered into 10 groups based on their specific planting or ratoon commencement date within the specified five months period. These clustered or groups of blocks were named 'bins'. Cloud free (<20%) satellite data from the polar-orbiting Landsat-8 (launched 2013), Sentinel-2A (launched 2015) and Sentinel-2B (launched 2017) sensors were acquired over the cane growing region in Bundaberg (area of 32,983 ha), from the growing season starting in July 2014, with the average green normalised difference vegetation index (GNDVI) derived for each block. The number of images acquired for each season was defined by the number of cloud free acquisitions. Using the Simple Linear Machine Learning (ML) algorithm, the extracted Landsat derived GNDVI values for each of the blocks were converted to Sentinel GNDVI. The average GNDVI of each 'bin' was plotted and a quadratic model was fitted through the time series to identify the peak growth stage defined as the maximum GNDVI value. The model derived maximum GNDVI values for each of the bins were then regressed against the average actual yield (t·ha -1 ) achieved for the respective bin over the five growing years, producing strong correlations (R 2 = 0.92 to 0.99). The quadratic curves developed for the different bins were shifted according to the specific planting or ratoon date of an individual block allowing for the peak GNDVI value of the block to be calculated, regressed against the actual block yield (t·ha -1 ) and the prediction of yield to be made. To validate the accuracies of the 10 time series algorithms representing each of the 10 bins, 592 individual blocks were selected from the Bundaberg region during the 2019 harvest season. The crops were clustered into the appropriate bins with the respective algorithm applied. From a Sentinel image acquired on the 5 May 2019, the prediction accuracies were encouraging (R 2 = 0.87 and RMSE = 11.33 (t·ha -1 )) when compared to actual harvested yield, as reported by the mill. The results presented in this paper demonstrate significant progress in the accurate prediction of sugarcane yield at the individual sugarcane block level using a remote sensing, time-series based approach.

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Section: Introductionmentioning

confidence: 99%

Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level

Rahman

Robson

2020

Remote Sensing

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“…The next development phase of the sugarcane occurs from November to April, when the growth of the sugarcane starts to reduce due to the weather conditions characterized by a lack of rain and lower average temperatures. Sugarcane planted from May to August is called winter sugarcane, where irrigation is needed and the harvest also takes place 12 months after it has been planted (Picoli et al 2014;Rudorff et al 2010). In general, the period (in months) for harvesting (t 1 ) is calculated by t 1 = t 0 + t * ± d, where t 0 is the month in which the sugarcane was planted, t * is the number of periods (months) required for the sugarcane to mature (which is dependent on t 0 ) and d is a deviation between the ideal and the actual harvesting points.…”

Section: Factors In the Timing Of The Sugarcane Planting And Harvestimentioning

confidence: 99%

A multiple objective methodology for sugarcane harvest management with varying maturation periods

et al. 2017

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This paper addresses the management of a sugarcane harvest over a multi-year planning period. A methodology to assist the harvest planning of the sugarcane is proposed in order to improve the production of POL (a measure of the amount of sucrose contained in a sugar solution) and the quality of the raw material, considering the constraints imposed by the mill such as the demand per period. An extended goal programming model is proposed for optimizing the harvest plan of the sugarcane so the harvesting point is as close as possible to the ideal, considering the constrained nature of the problem. A genetic algorithm (GA) is developed to tackle the problem in order to solve realistically large problems within an appropriate computational time. A comparative analysis between the GA and an exact method for small instances is also given in order to validate the performance of the developed model and methods. Computational results for medium and large farm instances using GA are also presented in order to demonstrate the capability of the developed method. The computational results illustrate the trade-off between satisfying the conflicting goals of harvesting as closely as possible to the ideal and making optimum use of harvesting equipment with a minimum of movement between farms. They also demonstrate that, whilst harvesting plans for small scale farms can be generated by the exact method, a meta-heuristic GA method is currently required in order to devise plans for medium and large farms.

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“…During the last phase, the sucrose content in the stem accumulates and senescence of the leaves occurs [5][6][7]. The sensitivity of remote observations to crop conditions was largely investigated for crop-monitoring uses [9][10][11] as well as to improve the understanding of the plant-microwave interaction [6,12]. Review works by McNairn and Brisco, Steele-Dunne et al [13,14], and a study by Moran et al [12] give a general introduction on the factors that affect Synthetic Aperture Radar (SAR) signals over the course of crop growth.…”

Section: Introductionmentioning

confidence: 99%

“…Other works, led by Unicamp in Brazil, focused on the behavior of ALOS signals with changing sugarcane conditions [11,22,23]. It was found that signals can be used to discriminate between the first phases of sugarcane growth until the grand growth phase commences, after which the signals saturate.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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The use of ALOS/PALSAR data for estimating sugarcane productivity

Cited by 8 publications

References 16 publications

Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level

Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level

A multiple objective methodology for sugarcane harvest management with varying maturation periods

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

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