Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield

Dente, Laura; Satalino, G.; Mattia, F.; Rinaldi, Michele

doi:10.1016/j.rse.2007.05.023

Cited by 271 publications

(161 citation statements)

References 26 publications

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“…The HH, HV, VV, HH/VV, HH/HV, and VV/HV indices were strongly correlated with LAI and biomass. Previous studies have found that polarization ratios (HH/VV, HH/HV, and VV/HV) and backscattering coefficients (HH, HV, and VV) are suitable for LAI and biomass estimations in some crops and forests [9,24,36]. Our results were in agreement with these studies.…”

Section: Discussionsupporting

confidence: 92%

“…Two important indicators of these parameters-leaf area index (LAI) and above ground biomass (AGB)-were used to monitor crop canopy structural development and growth changes and to estimate yield. The reasonable and reliable estimation of LAI and biomass can improve crop fertilizer applications [2], water irrigation [3,4], disease and weed control [5,6], and grain production marketing [7][8][9]. LAI and biomass change seasonally under different environmental conditions, and therefore, it is important to timely estimate their values.…”

Section: Introductionmentioning

confidence: 99%

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Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

et al. 2015

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Leaf area index (LAI) and biomass are frequently used target variables for agricultural and ecological remote sensing applications. Ground measurements of winter wheat LAI and biomass were made from March to May 2014 in the Yangling district, Shaanxi, Northwest China. The corresponding remotely sensed data were obtained from the earth-observation satellites Huanjing (HJ) and RADARSAT-2. The objectives of this study were (1) to investigate the relationships of LAI and biomass with several optical spectral vegetation indices (OSVIs) and radar polarimetric parameters (RPPs), (2) , p < 0.01) were highly correlated with biomass. The estimation accuracy of LAI and biomass was better using the COSVI-RPPs than using the OSVIs and RPPs alone. The results revealed that the PLSR regression equation better estimated LAI and biomass than the MSR regression equation based on all the COSVI-RPPs, OSVIs, and RPPs. Our results indicated that the COSVI-RPPs can be used to robustly estimate LAI and biomass. This study may provide a guideline for improving the estimations of LAI and biomass of winter wheat using multisource remote sensing data.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

et al. 2015

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“…Through data assimilation, the MODIS LAI product and extracted vegetation indices of NDVI and EVI forecast crop yield, using only a partial year of data, with relative deviations from reference data less than 3.5% (Fang et al 2011). Passive MODIS, AVHRR, and Medium Resolution Imaging Spectrometer (MERIS), and active ASAR data, have been used to estimate wheat or maize yield with relative differences less than 11% (Moriondo et al 2007;Ren et al 2008;Yan et al 2009;Dente et al 2008). Furthermore, LiDAR data, either airborne (Jaskierniak et al 2011;Tonolli et al 2011;Latifi et al 2010) or spaceborne ), have been the primary sources to estimate timber volume.…”

Section: Provisioning Servicesmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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“…SPAc simulations are less reliable for yield than NEE and LAI, and improvements in representing yield formation are warranted for further study. As Dente et al (2008) show, considerable improvements can be expected through DA. Nonetheless, we are confident that our methodology provides a representative upscaled estimate of agroecosystem C cycling.…”

Section: Model Improvement Through Sequential Modis Damentioning

confidence: 96%

“…sowing or emergence date, Brown and de Beurs, 2008;Dente et al, 2008;Doraiswamy et al, 2004) and initial conditions (e.g. soil water content, Inoue and Olioso, 2006), mostly applying batch-calibration data assimilation (DA) techniques.…”

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confidence: 99%

A data assimilation framework for constraining upscaled cropland carbon flux seasonality and biometry with MODIS

et al. 2013

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Agroecosystem models are strongly dependent on information on land management patterns for regional applications. Land management practices play a major role in determining global yield variability, and add an anthropogenic signal to the observed seasonality of atmospheric CO₂ concentrations. However, there is still little knowledge on spatial and temporal variability of important farmland activities such as crop sowing dates, and thus these remain rather crudely approximated within carbon cycle studies. In this study, we present a framework allowing for spatio-temporally resolved simulation of cropland carbon fluxes under observational constraints on land management and canopy greenness. We apply data assimilation methodology in order to explicitly account for information on sowing dates and model leaf area index. MODIS 250 m vegetation index data were assimilated both in batch-calibration for sowing date estimation and sequentially for improved model state estimation, using the ensemble Kalman filter (EnKF), into a crop carbon mass balance model (SPAc). In doing so, we are able to quantify the multiannual (2000–2006) regional carbon flux and biometry seasonality of maize–soybean crop rotations surrounding the Bondville Ameriflux eddy covariance site, averaged over 104 pixel locations within the wider area. (1) Validation at the Bondville site shows that growing season C cycling is simulated accurately with MODIS-derived sowing dates, and we expect that this framework allows for accurate simulations of C cycling at locations for which ground-truth data are not available. Thus, this framework enables modellers to simulate current (i.e. last 10 yr) carbon cycling of major agricultural regions. Averaged over the 104 field patches analysed, relative spatial variability for biometry and net ecosystem exchange ranges from ∼7% to ∼18%. The annual sign of net biome productivity is not significantly different from carbon neutrality. (2) Moreover, observing carbon cycling at one single field with its individual sowing pattern is not sufficient to constrain large-scale agroecosystem carbon flux seasonality. Study area average growing season length is 20 days longer than observed at Bondville, primarily because of an earlier estimated start of season. (3) For carbon budgeting, additional information on cropland soil management and belowground carbon cycling has to be considered, as such constraints are not provided by MODIS

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Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield

Cited by 271 publications

References 26 publications

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

A data assimilation framework for constraining upscaled cropland carbon flux seasonality and biometry with MODIS

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