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Remote Sensing of Primary Productivity

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (28 June 2019) | Viewed by 38435

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Dr. Micol Rossini

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Remote Sensing of Environmental Dynamics Laboratory, Department of Earth and Environmental Sciences (DISAT), University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
Interests: GIS and remote sensing; unmanned aerial systems (UAV); earth observation; vegetation; ecology; environment; cryosphere
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Prof. Dr. Alexander Damm

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Department of Geography, University of Zurich, Winterthurerstrasse, 1908057 Zurich, Switzerland
Interests: fluorescence spectroscopy; remote sensing of vegetation; plant–water relations; carbon and water cycle; plant photosynthesis; ecosystem functioning and environmental change
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Prof. Dr. Yongguang Zhang

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Ecology and Remote Sensing Reseach Group, Nanjing University, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China
Interests: remote sensing of chlorophyll fluorescence; carbon cyle; photosynthesis; climate change; bioshphere-atmoshpere interactions
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Special Issue Information

Dear Colleagues,

Primary productivity of vegetation is a key indicator to understand the functioning of terrestrial ecosystems in the context of global change. Primary productivity is crucial to explore the dynamics of ecosystem processes (e.g., photosynthetic CO₂ fixation, plant water relations), estimate the provisioning of ecosystem services (e.g., food and fiber, CO₂ regulation), and is suggested as a candidate essential biodiversity variable.

Assessments of primary productivity using observations and models are of major scientific significance in carbon cycle research and crucial for predicting carbon dynamics. Remote sensing opens several pathways to facilitate advanced estimates of primary productivity with spatial and temporal information. This includes the provisioning of proxies to estimate primary productivity at coarse scales or the retrieval of relevant ecosystem parameters driving this complex process across scales. Causality matters and innovative strategies are needed to relate observations from ground, airborne and satellite systems with models to reliably constrain estimates of primary productivity across scales.

In this Special Issue on “Remote Sensing of Primary Productivity”, we welcome contributions that make use of remote sensing observations to advance estimates of primary productivity. We particularly welcome contributions using novel observations (e.g., sun-induced chlorophyll fluorescence), new algorithms (e.g., machine learning, physically based approaches), advanced modelling frameworks for the estimation of primary productivity at different spatial scales, and new experimental activities. Review articles are also welcome. Submissions may cover a wide range of topics including (but not limited to):

the use of sun-induced fluorescence to constrain estimates and improve modelling of primary productivity,
the exploitation of new algorithms to assess relations between optical measurements and primary productivity
modelling of primary productivity building upon resource-use-efficiency theory;
activities to assimilate remote sensing in global models of primary productivity;
relationship between productivity and biodiversity;
novel spectral sensors to monitor primary productivity;
novel experimental activities to assess and monitor primary productivity across scales

Dr. Micol Rossini
Prof. Dr. Alexander Damm
Prof. Yongguang Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Gross primary production
Light-use-efficiency model
Process-based models
Sun-induced chlorophyll fluorescence
Spectroradiometry
Remote Sensing
Machine learning methods
Biodiversity
Physically based approaches

Published Papers (7 papers)

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Research

28 pages, 5478 KiB

Open AccessArticle

Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems

by Liangxia Zhang, Decheng Zhou, Jiangwen Fan, Qun Guo, Shiping Chen, Ranghui Wang and Yuzhe Li

Remote Sens. 2019, 11(11), 1333; https://doi.org/10.3390/rs11111333 - 03 Jun 2019

Cited by 28 | Viewed by 4648

Abstract

Models constitute the primary approaches for predicting terrestrial ecosystem gross primary production (GPP) at regional and global scales. Many satellite-based GPP models have been developed due to the simple algorithms and the low requirements of model inputs. The performances of these models are well documented at the biome level. However, their performances among vegetation subtypes limited by different environmental stresses within a biome remains largely unexplored. Taking grasslands in northern China as an example, we compared the performance of eight satellite-based GPP models, including three light-use efficiency (LUE) models (vegetation photosynthesis model (VPM), modified VPM (MVPM), and moderate resolution imaging spectroradiometer GPP algorithm (MODIS-GPP)) and five statistical models (temperature and greenness model (TG), greenness and radiation model (GR), vegetation index model (VI), alpine vegetation model (AVM), and photosynthetic capacity model (PCM)), between the water-limited temperate steppe and the temperature-limited alpine meadow based on 16 site-year GPP estimates at four eddy covariance (EC) flux towers. The results showed that all the GPP models performed better in the alpine meadow, particularly in the alpine shrub meadow (R² ≥ 0.84), than in the temperate steppe (R² ≤ 0.68). The performance varied greatly among the models in the temperate steppe, while slight intermodel differences existed in the alpine meadow. Overall, MVPM (of the LUE models) and VI (of the statistical models) were the two best-performing models in the temperate steppe due to their better representation of the effect of water stress on vegetation productivity. Additionally, we found that the relatively worse model performances in the temperate steppe were seriously exaggerated by drought events, which may occur more frequently in the future. This study highlights the varying performances of satellite-based GPP models among vegetation subtypes of a biome in different precipitation years and suggests priorities for improving the water stress variables of these models in future efforts. Full article

(This article belongs to the Special Issue Remote Sensing of Primary Productivity)

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20 pages, 5252 KiB

Open AccessFeature PaperArticle

Assimilation of Earth Observation Data Over Cropland and Grassland Sites into a Simple GPP Model

by Michele Meroni, Dominique Fasbender, Raul Lopez-Lozano and Mirco Migliavacca

Remote Sens. 2019, 11(7), 749; https://doi.org/10.3390/rs11070749 - 27 Mar 2019

Cited by 5 | Viewed by 3999

Abstract

The application of detailed process-oriented simulation models for gross primary production (GPP) estimation is constrained by the scarcity of the data needed for their parametrization. In this manuscript, we present the development and test of the assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Normalized Difference Vegetation Index (NDVI) observations into a simple process-based model driven by basic meteorological variables (i.e., global radiation, temperature, precipitation and reference evapotranspiration, all from global circulation models of the European Centre for Medium-Range Weather Forecasts). The model is run at daily time-step using meteorological forcing and provides estimates of GPP and LAI, the latter used to simulate MODIS NDVI though the coupling with the radiative transfer model PROSAIL5B. Modelled GPP is compared with the remote sensing-driven MODIS GPP product (MOD17) and the quality of both estimates are assessed against GPP from European eddy covariance flux sites over crops and grasslands. Model performances in GPP estimation (R² = 0.67, RMSE = 2.45 gC m⁻² d⁻¹, MBE = −0.16 gC m⁻² d⁻¹) were shown to outperform those of MOD17 for the investigated sites (R² = 0.53, RMSE = 3.15 gC m⁻² d⁻¹, MBE = −1.08 gC m⁻² d⁻¹). Full article

(This article belongs to the Special Issue Remote Sensing of Primary Productivity)

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21 pages, 11354 KiB

Open AccessArticle

Estimation of Carbon Fluxes from Eddy Covariance Data and Satellite-Derived Vegetation Indices in a Karst Grassland (Podgorski Kras, Slovenia)

by Koffi Dodji Noumonvi, Mitja Ferlan, Klemen Eler, Giorgio Alberti, Alessandro Peressotti and Sofia Cerasoli

Remote Sens. 2019, 11(6), 649; https://doi.org/10.3390/rs11060649 - 16 Mar 2019

Cited by 9 | Viewed by 7112

Abstract

The Eddy Covariance method (EC) is widely used for measuring carbon (C) and energy fluxes at high frequency between the atmosphere and the ecosystem, but has some methodological limitations and a spatial restriction to an area, called a footprint. Remotely sensed information is usually used in combination with eddy covariance data in order to estimate C fluxes over larger areas. In fact, spectral vegetation indices derived from available satellite data can be combined with EC measurements to estimate C fluxes outside of the tower footprint. Following this approach, the present study aimed to model C fluxes for a karst grassland in Slovenia. Three types of model were considered: (1) a linear relationship between Net Ecosystem Exchange (NEE) or Gross Primary Production (GPP) and each vegetation index; (2) a linear relationship between GPP and the product of a vegetation index with PAR (Photosynthetically Active Radiation); and (3) a simplified LUE (Light Use-Efficiency) model assuming a constant LUE. We compared the performance of several vegetation indices derived from two remote platforms (Landsat and Proba-V) as predictors of NEE and GPP, based on three accuracy metrics, the coefficient of determination (R²), the Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC). Two types of aggregation of flux data were explored: midday average and daily average fluxes. The vapor pressure deficit (VPD) was used to separate the growing season into two phases, a wet and a dry phase, which were considered separately in the modelling process, in addition to the growing season as a whole. The results showed that NDVI is the best predictor of GPP and NEE during the wet phase, whereas water-related vegetation indices, namely LSWI and MNDWI, were the best predictors during the dry phase, both for midday and daily aggregates. Model 1 (linear relationship) was found to be the best in many cases. The best regression equations obtained were used to map GPP and NEE for the whole study area. Digital maps obtained can practically contribute, in a cost-effective way to the management of karst grasslands. Full article

(This article belongs to the Special Issue Remote Sensing of Primary Productivity)

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21 pages, 10419 KiB

Open AccessArticle

Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China

by Meng Zhang, Hui Lin, Hua Sun and Yaotong Cai

Remote Sens. 2019, 11(2), 133; https://doi.org/10.3390/rs11020133 - 11 Jan 2019

Cited by 25 | Viewed by 6048 | Correction | Retraction

Abstract

Estimating the net primary production (NPP) of vegetation is essential for eco-environment conservation and carbon cycle research. Remote sensing techniques, combined with algorithm models, have been proven to be promising methods for NPP estimation. High-precision and real-time NPP monitoring in heterogeneous areas requires high spatio-temporal resolution remote sensing data, which are not easy to acquire by single remote sensors, especially in cloudy weather. This study proposes to fuse images of different sensors to provide high spatio-temporal resolution data for NPP estimation in cloud-prone areas. Firstly, the time series Normalized Difference Vegetation Index (NDVI) with a spatial resolution of 30 m and a temporal resolution of 16 days, are obtained by the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Then, the time series NDVI data, combined with meteorological data are input into an improved Carnegie–Ames–Stanford Approach (CASA) model for NPP estimation. This method is validated by a case study of a heavily urbanized area, in the middle reaches of the Yangtze River in China. The results indicate that the NPP estimated by the fused NDVI data has more detailed spatial information than by using the MODIS data. The results show a strong correlation between the actual Landsat8 NDVI and the fused NDVI images, which means that the accuracy of synthetic NDVI images (a 16 day interval and a 30 m resolution) is reliable, and it can provide superior inputs for accurate estimations of a NPP time series. The correlation coefficient (R) and root mean square error between the NPP, based on the fused NDVI and the measured NPP, are 0.66 and 14.280 g C/(m²·yr), respectively, indicating a good consistency. The small discrepancy is caused by the uncertainties of fused NDVI, measurement errors, conversion errors, and other factors in the CASA model. In this study, we achieved NPP with high spatial and temporal resolutions, which can provide higher accuracies of NPP data for analyzing the carbon cycling heavily urbanized areas, compared with similar studies using mono-temporal NPP data. The spatio-temporal fusion technique is an effective way of generating high spatio-temporal resolution images from different sensors, thereby providing enough data for NPP monitoring in urbanized areas. Full article

(This article belongs to the Special Issue Remote Sensing of Primary Productivity)

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22 pages, 11574 KiB

Open AccessArticle

Generation of High Resolution Vegetation Productivity from a Downscaling Method

by Tao Yu, Rui Sun, Zhiqiang Xiao, Qiang Zhang, Juanmin Wang and Gang Liu

Remote Sens. 2018, 10(11), 1748; https://doi.org/10.3390/rs10111748 - 06 Nov 2018

Cited by 14 | Viewed by 4597

Abstract

Accurately estimating vegetation productivity is important in the research of terrestrial ecosystems, carbon cycles and climate change. Although several gross primary production (GPP) and net primary production (NPP) products have been generated and many algorithms developed, advances are still needed to exploit multi-scale data streams for producing GPP and NPP with higher spatial and temporal resolution. In this paper, a method to generate high spatial resolution (30 m) GPP and NPP products was developed based on multi-scale remote sensing data and a downscaling method. First, high resolution fraction photosynthetically active radiation (FPAR) and leaf area index (LAI) were obtained by using a regression tree approach and the spatial and temporal adaptive reflectance fusion model (STARFM). Second, the GPP and NPP were estimated from a multi-source data synergized quantitative algorithm. Finally, the vegetation productivity estimates were validated with the ground-based field data, and were compared with MODerate Resolution Imaging Spectroradiometer (MODIS) and estimated Global LAnd Surface Satellite (GLASS) products. Results of this paper indicated that downscaling methods have great potential in generating high resolution GPP and NPP. Full article

(This article belongs to the Special Issue Remote Sensing of Primary Productivity)

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27 pages, 16882 KiB

Open AccessArticle

Drought-Induced Reduction in Net Primary Productivity across Mainland China from 1982 to 2015

by Chengguang Lai, Jun Li, Zhaoli Wang, Xiaoqing Wu, Zhaoyang Zeng, Xiaohong Chen, Yanqing Lian, Haijun Yu, Peng Wang and Xiaoyan Bai

Remote Sens. 2018, 10(9), 1433; https://doi.org/10.3390/rs10091433 - 08 Sep 2018

Cited by 40 | Viewed by 5232

Abstract

Terrestrial net primary productivity (NPP) plays an essential role in the global carbon cycle as well as for climate change. However, in the past three decades, terrestrial ecosystems across mainland China suffered from frequent drought and, to date, the adverse impacts on NPP remain uncertain. This study explored the spatiotemporal features of NPP and discussed the influences of drought on NPP across mainland China from 1982 to 2015 using the Carnegie Ames Stanford Application (CASA) model and the standardized precipitation evapotranspiration index (SPEI). The obtained results indicate that: (1) The total annual NPP across mainland China showed an non-significantly increasing trend from 1982 to 2015, with annual increase of 0.025 Pg C; the spring NPP exhibited a significant increasing trend (0.031 Pg C year⁻¹, p < 0.05) while the summer NPP showed a higher decreasing trend (0.019 Pg C year⁻¹). (2) Most areas of mainland China were spatially dominated by a positive correlation between annual NPP and SPEI and a significant positive correlation was mainly observed for Northern China; specific to the nine sub-regions, annual NPP and SPEI shared similar temporal patterns with a significant positive relation in Northeastern China, Huang-Huai-Hai, Inner Mongolia, and the Gan-Xin Region. (3) During the five typical drought events, more than 23% areas of mainland China experienced drought ravage; the drought events generally caused about 30% of the NPP reduction in most of the sub-regions while the NPP in the Qinghai-Tibet Plateau Region generally decreased by about 10%. Full article

(This article belongs to the Special Issue Remote Sensing of Primary Productivity)

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22 pages, 4052 KiB

Open AccessArticle

Variation of Net Primary Production and Its Correlation with Climate Change and Anthropogenic Activities over the Tibetan Plateau

by Zhaohui Luo, Wenchen Wu, Xijun Yu, Qingmei Song, Jian Yang, Jiahui Wu and Hengjun Zhang

Remote Sens. 2018, 10(9), 1352; https://doi.org/10.3390/rs10091352 - 25 Aug 2018

Cited by 67 | Viewed by 4831

Abstract

Grasslands in the Tibetan Plateau are claimed to be sensitive and vulnerable to climate change and anthropogenic activities. Quantifying the impacts of climate change and anthropogenic activities on grassland growth is an essential step for developing sustainable grassland ecosystem management strategies under the background of climate change and increasing anthropogenic activities occurring in the plateau. Net primary productivity (NPP) is one of the key components in the carbon cycle of terrestrial ecosystems, and can serve an important role in the assessment of vegetation growth. In this study, a modified Carnegie–Ames–Stanford Approach (CASA) model, which considers remote sensing information for the estimation of the water stress coefficient and time-lag effects of climatic factors on NPP simulation, was applied to simulate NPP in the Tibetan Plateau from 2001 to 2015. Then, the spatiotemporal variations of NPP and its correlation with climatic factors and anthropogenic activities were analyzed. The results showed that the mean values of NPP were 0.18 kg∙C∙m⁻²∙a⁻¹ and 0.16 kg∙C∙m⁻²∙a⁻¹ for the original CASA model and modified CASA model, respectively. The modified CASA model performed well in estimating NPP compared with field-observed data, with root mean square error (RMSE) and mean absolute error (MAE) of 0.13 kg∙C∙m⁻²∙a⁻¹ and 0.10 kg∙C∙m⁻²∙a⁻¹, respectively. Relative RMSE and MAE decreased by 45.8% and 44.4%, respectively, compared to the original CASA model. The variation of NPP showed gradients decreasing from southeast to northwest spatially, and displayed an overall decreasing trend for the study area temporally, with a mean value of −0.02 × 10⁻² kg∙C∙m⁻²∙a⁻¹ due to climate change and increasing anthropogenic activities (i.e., land use and land cover change). Generally, 54% and 89% of the total pixels displayed a negative relationship between NPP and mean annual temperature, as well as annual cumulative precipitation, respectively, with average values of –0.0003 (kg∙C∙m⁻² a⁻¹)/°C and −0.254 (g∙C∙m⁻²∙a⁻¹)/mm for mean annual temperature and annual cumulative precipitation, respectively. Additionally, about 68% of the total pixels displayed a positive relationship between annual cumulative solar radiation and NPP, with a mean value of 0.038 (g∙C∙m⁻²·a⁻¹)/(MJ m⁻²). Anthropogenic activities had a negative effect on NPP variation, and it was larger than that of climate change, implying that human intervention plays a critical role in mitigating the degenerating ecosystem. In terms of human intervention, ecological destruction has a significantly negative effect on the NPP trend, and the absolute value was larger than that of ecological restoration, which has a significantly positive effect on NPP the trend. Our results indicate that ecological destruction should be paid more attention, and ecological restoration should be conducted to mitigate the overall decreasing trend of NPP in the plateau. Full article

(This article belongs to the Special Issue Remote Sensing of Primary Productivity)

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