Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing

Laurila, Heikki; Karjalainen, Mika; Kleemola, Jouko; Hyyppä, Juha

doi:10.3390/rs2092185

Cited by 19 publications

(9 citation statements)

References 48 publications

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“…Although the correlation results were smaller than those found in studies with annual crops [11][12][13][14][15] and errors were high, the trends suggest a leaf area dependence in relation to coffee yield in the same year. Coffee is a perennial crop and takes two years to complete its phenological cycle, unlike most other crops, which complete their reproductive cycle in one year.…”

Section: Discussioncontrasting

confidence: 49%

Monitoring Biennial Bearing Effect on Coffee Yield Using MODIS Remote Sensing Imagery

et al. 2012

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Abstract:Coffee is the second most valuable traded commodity worldwide. Brazil is the world's largest coffee producer, responsible for one third of the world production. A coffee plot exhibits high and low production in alternated years, a characteristic so called biennial yield. High yield is generally a result of suitable conditions of foliar biomass. Moreover, in high production years one plot tends to lose more leaves than it does in low production years. In both cases some correlation between coffee yield and leaf biomass can be deduced which can be monitored through time series of vegetation indices derived from satellite imagery. In Brazil, a comprehensive, spatially distributed study assessing this relationship has not yet been done. The objective of this study was to assess possible correlations between coffee yield and MODIS derived vegetation indices in the Brazilian largest coffee-exporting province. We assessed EVI and NDVI MODIS products over the period between 2002 and 2009 in the south of Minas Gerais State whose production accounts for about one third of the Brazilian coffee production. Landsat images were used to obtain a reference map of coffee areas and to identify MODIS 250 m pure pixels overlapping homogeneous coffee crops. Only MODIS pixels with 100% coffee were included in the analysis. A wavelet-based filter was used to smooth EVI and NDVI time profiles. Correlations were observed between variations on yield of coffee plots and variations on vegetation indices for pixels overlapping the same coffee plots. The vegetation index metrics best correlated to yield were the amplitude and the minimum values over the growing season. The best correlations were obtained between variation on OPEN ACCESSRemote Sens. 2012, 4 2493 yield and variation on vegetation indices the previous year (R = 0.74 for minEVI metric and R = 0.68 for minNDVI metric). Although correlations were not enough to estimate coffee yield exclusively from vegetation indices, trends properly reflect the biennial bearing effect on coffee yield.

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Section: Discussioncontrasting

confidence: 49%

Monitoring Biennial Bearing Effect on Coffee Yield Using MODIS Remote Sensing Imagery

et al. 2012

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“…These data have been widely used for mapping and monitoring ecosystems and land resources. Accurate mapping of the land use and land cover (LULC) classes provides the basis for many applications and research subjects, such as environmental analysis and modeling (Laurila et al 2010), global and regional climate change (Renzullo et al 2008), and multitemporal analysis (Mcnairn et al 2009), among others. Therefore, as the result of several efforts, progress has been made in improving the methods for extracting information from different data types to increase the discriminability of the classes and, consequently, the accuracy of the LULC mapping (Dutra et al 2002;Lu, Batistella, and Moran 2007;Santos and Messina 2008;Ban, Hu, and Rangel 2010;Walker et al 2010), leading to better classification results.…”

Section: Introductionmentioning

confidence: 99%

Optical and radar data integration for land use and land cover mapping in the Brazilian Amazon

Pereira

Freitas

Sant´Ánna

et al. 2013

GIScience & Remote Sensing

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This study aims to evaluate different methods of integrating optical and multipolarized radar data for land use and land cover (LULC) mapping in an agricultural frontier region in the Central Brazilian Amazon, which requires continuous monitoring due to the increasing human intervention. The evaluation is performed using different sets of fused and combined data. This article also proposes to apply the principal component (PC) technique to the multipolarized synthetic aperture radar (SAR), prior to the optical and radar data PC fusion process, aiming at the use of all available polarized information in the fusion process. Although the fused images improve the visual interpretation of the land use classes, the best results are achieved with the simple combination of the Advanced Land Observing Satellite (ALOS)/phased array L-Band SAR (PALSAR) with the LANDSAT5/Thematic Mapper (TM) images. Radar information is found to be particularly useful for improving the user accuracies (UAs) of Soybean with 40 days after seeding (an increase of about 55%), Dirty Pasture (22%), Degraded Forest and Regeneration (5%), and the producer accuracies (PAs) of Clean Pasture (39%), Fallow Agriculture (16%), Degraded Forest and Regeneration (3%), and Primary Forest (2%). Information from the HH (horizontal transmit and horizontal receive) polarization contributes more than that from HV (horizontal transmit and vertical receive) polarization to discriminate the classes, although the use of both polarizations produces results that are statistically better than those obtained with a single polarization.

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“…This index is sensitive to the presence of green vegetation [18]. It has been used for several regional and global applications, in studies concerning the distribution and potential photosynthetic activity of vegetation [19][20][21][22][23][24]. Due to its formulation, it robustly describes green vegetation in spite of varying atmospheric conditions in the red and NIR bands [25,26].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Vegetation Behavior in a North African Semi-Arid Region, Using SPOT-VEGETATION NDVI Data

et al. 2011

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Abstract:The analysis of vegetation dynamics is essential in semi-arid regions, in particular because of the frequent occurrence of long periods of drought. In this paper, multi-temporal series of the Normalized Difference of Vegetation Index (NDVI), derived from SPOT-VEGETATION satellite data between September 1998 and June 2010, were used to analyze the vegetation dynamics over the semi-arid central region of Tunisia. A study of the persistence of three types of vegetation (pastures, annual agriculture and olive trees) is proposed using fractal analysis, in order to gain insight into the stability/instability of vegetation dynamics. In order to estimate the state of vegetation cover stress, we propose evaluating the properties of an index referred to as the Vegetation Anomaly Index (VAI). A positive VAI indicates high vegetation dynamics, whereas a negative VAI indicates the presence of vegetation stress. The VAI is tested for the above three types of vegetation, during the study period from 1998 to 2010, and is compared with other drought indices. The VAI is found to be strongly correlated with precipitation.

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Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing

Cited by 19 publications

References 48 publications

Monitoring Biennial Bearing Effect on Coffee Yield Using MODIS Remote Sensing Imagery

Monitoring Biennial Bearing Effect on Coffee Yield Using MODIS Remote Sensing Imagery

Optical and radar data integration for land use and land cover mapping in the Brazilian Amazon

Analysis of Vegetation Behavior in a North African Semi-Arid Region, Using SPOT-VEGETATION NDVI Data

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