Gross primary productivity of Brazilian Savanna (Cerrado) estimated by different remote sensing-based models

Biudes, Marcelo Sacardi; Vourlitis, George L.; Velasque, Maísa Caldas Souza; Machado, Nadja Gomes; Danelichen, Victor Hugo de Morais; Pavão, Vagner Marques; Arruda, Paulo Henrique Zanella de; Nogueira, José de Souza

doi:10.1016/j.agrformet.2021.108456

Cited by 21 publications

(12 citation statements)

References 51 publications

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“…Thus, rather than directly adopting satellite products, researchers prefer to develop specific models for the study area to obtain more robust estimates. These models mainly belong to the above approaches and require numerous parameters to drive and calibrate [79][80][81][82][83]. However, similar to the problems in [45], most parameters are uncertain and sparse and are difficult to obtain by same measuring methods.…”

Section: Primary Productivitymentioning

confidence: 99%

Review of Remote Sensing Applications in Grassland Monitoring

Wang

Zhang

et al. 2022

Remote Sensing

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The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite.

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Section: Primary Productivitymentioning

confidence: 99%

Review of Remote Sensing Applications in Grassland Monitoring

Wang

Zhang

et al. 2022

Remote Sensing

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“…The MODIS NDVI dataset is based on specific spectral bands (red and infrared) designed to offer stable global vegetation leaves greenness monitoring. It has been widely used in land cover classification, carbon exchange modeling, and phenology studies (Shen et al, 2014;Aredehey et al, 2017;Biudes et al, 2021). In addition, EVI is more sensitive to high biomass areas and is minimally impacted by soil and atmosphere due to the increased blue band information (Huete et al, 2002;Jiang et al, 2008).…”

Section: Vegetation Indices Datamentioning

confidence: 99%

Comparison of Phenology Estimated From Monthly Vegetation Indices and Solar-Induced Chlorophyll Fluorescence in China

Wang

Sun

et al. 2022

Front. Earth Sci.

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Phenology is an important biological indicator for monitoring terrestrial ecosystems and global change. Solar-induced chlorophyll fluorescence (SIF) emitted by chlorophyll has been proven to characterize vegetation photosynthesis and phenology. In this study, we used monthly normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and SIF products to qualitatively compare the effectiveness at detecting the phenological characteristics (SOS (start-of-season), EOS (end-of-season), and LOS (length-of-season)) over China during 2007–2013. The phenological characteristics determined by gross primary productivity (GPP) were applied as the reference to validate the phenological characteristics derived from NDVI, EVI, and SIF. The results demonstrated that the phenological characteristics derived from SIF were more consistent with that of GPP than VIs (NDVI and EVI) when considering all latitude grades, different elevation grades, and different land cover types in China. In the middle- and high-latitude regions, SOS derived from the vegetation indices (SOSVIs) did not deviate from those from GPP (SOSGPP) and SIF (SOSSIF), while in low latitudes, SOSVIs were about 20 d later than SOSSIF and SOSGPP. The VIs (EOSVIs) had a severe lag behind those of SIF (EOSSIF) in estimating the EOS at all latitudes. The EOSSIF had a deviation of fewer than 5 d compared with EOS estimated by GPP (EOSGPP), whereas the deviation of EOSVIs from EOSGPP was about 10–31 d across low to high latitude regions. The biases of SIF and VIs were due to the inconsistency between vegetation photosynthesis and leaf greenness. Also, VIs overestimated the LOS at all latitudes, the difference of LOS between estimated by NDVI and estimated by GPP was as long as 39 d in the high-latitude region. Our study suggests that SIF is suitable for estimating the phenological characteristics of vegetation regardless of different latitudes, elevation grades, and land cover types in China, providing a basis for SIF to study the vegetation phenological characteristics in a regional scope.

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“…From the land use changes detected in the study and the carbon concentrations determined for each category, it was possible to understand that the loss of páramo causes a loss of carbon sequestration in the Andean ecosystem. The release of organic carbon into the atmosphere contributes to climate change issues [24].…”

Section: Systematic Land Use Changementioning

confidence: 99%

“…The evaluation of the different land uses through satellite imaging has been widely performed by many authors [21] using different nonparametric statistical techniques, which allow the creation of predictive algorithms from basic remote sensing data [22][23][24]. Thus, neural networks have been used to determine land use through images from different satellites [25,26] and other authors have used different machine learning algorithms to interpret satellite data [27].…”

Section: Introductionmentioning

confidence: 99%

Identification of a Set of Variables for the Classification of Páramo Soils Using a Nonparametric Model, Remote Sensing, and Organic Carbon

et al. 2021

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Páramo ecosystems harbor important biodiversity and provide essential environmental services such as water regulation and carbon sequestration. Unfortunately, the scarcity of information on their land uses makes it difficult to generate sustainable strategies for their conservation. The purpose of this study is to develop a methodology to easily monitor and document the conservation status, degradation rates, and land use changes in the páramo. We analyzed the performance of two nonparametric models (the CART decision tree, CDT, and multivariate adaptive regression curves, MARS) in the páramos of the Chambo sub-basin (Ecuador). We used three types of attributes: digital elevation model (DEM), land use cover (Sentinel 2), and organic carbon content (Global Soil Organic Carbon Map data, GSOC) and a categorical variable, land use. We obtained a set of selected variables which perform well with both models, and which let us monitor the land uses of the páramos. Comparing our results with the last report of the Ecuadorian Ministry of Environment (2012), we found that 9% of the páramo has been lost in the last 8 years.

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Gross primary productivity of Brazilian Savanna (Cerrado) estimated by different remote sensing-based models

Cited by 21 publications

References 51 publications

Review of Remote Sensing Applications in Grassland Monitoring

Review of Remote Sensing Applications in Grassland Monitoring

Comparison of Phenology Estimated From Monthly Vegetation Indices and Solar-Induced Chlorophyll Fluorescence in China

Identification of a Set of Variables for the Classification of Páramo Soils Using a Nonparametric Model, Remote Sensing, and Organic Carbon

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