scholarly journals Understanding the Land Surface Phenology and Gross Primary Production of Sugarcane Plantations by Eddy Flux Measurements, MODIS Images, and Data-Driven Models

2020 ◽  
Vol 12 (14) ◽  
pp. 2186
Author(s):  
Fengfei Xin ◽  
Xiangming Xiao ◽  
Osvaldo M.R. Cabral ◽  
Paul M. White ◽  
Haiqiang Guo ◽  
...  
Keyword(s):  
Primary Production ◽  
Seasonal Dynamics ◽  
Land Surface ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Data Driven ◽  
Flux Tower ◽  
The Usa ◽  
The Relationship

Sugarcane (complex hybrids of Saccharum spp., C4 plant) croplands provide cane stalk feedstock for sugar and biofuel (ethanol) production. It is critical for us to analyze the phenology and gross primary production (GPP) of sugarcane croplands, which would help us to better understand and monitor the sugarcane growing condition and the carbon cycle. In this study, we combined the data from two sugarcane EC flux tower sites in Brazil and the USA, images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and data-driven models to study the phenology and GPP of sugarcane croplands. The seasonal dynamics of climate, vegetation indices from MODIS images, and GPP from two sugarcane flux tower sites (GPPEC) reveal the temporal consistency in sugarcane phenology (crop calendar: green-up dates and harvesting dates) as estimated by the vegetation indices and GPPEC data. The Land Surface Water Index (LSWI) is shown to be useful to delineate the phenology of sugarcane croplands. The relationship between the sugarcane GPPEC and the Enhanced Vegetation Index (EVI) is stronger than the relationship between the GPPEC and the Normalized Difference Vegetation Index (NDVI). We ran the Vegetation Photosynthesis Model (VPM), which uses the light use efficiency (LUE) concept and is driven by climate data and MODIS images, to estimate the daily GPP at the two sugarcane sites (GPPVPM). The seasonal dynamics of the GPPVPM and GPPEC at the two sites agreed reasonably well with each other, which indicates that VPM is a powerful tool for estimating the GPP of sugarcane croplands in Brazil and the USA. This study clearly highlights the potential of combining eddy covariance technology, satellite-based remote sensing technology, and data-driven models for better understanding and monitoring the phenology and GPP of sugarcane croplands under different climate and management practices.

scholarly journals Canopy Near-infrared Observation Project

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Garrett Michael Shuldes
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Infrared Observation ◽  
Old Growth Forest ◽  
Forest Regions

The Canopy Near-infrared Observing Project (CaNOP) will utilize a multispectral pushbroom imager in a 3U CubeSat to carry out spectrally resolved imaging of global forest regions with spectral resolution sufficient to reproduce the LandSat 8 mission and calculate vegetation indices. The project provides an educational experience for undergraduate students to design, build, test, and operate a 3U CubeSat. Using concepts from fields such as physics, engineering, and computer science, students were to link the electrical, mechanical, and software components of the satellite together to create a flawless flow of information and power across the entirety of the satellite and its corresponding ground stations to which the images of the global forest regions will be transferred and analyzed. Two vegetation indices; the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) will be used in combination with the Net Primary Production (NPP) of any forest to produce the Gross Primary Production (GPP), which will then provide the information required to answer the argument that the CaNOP team’s hypothesis proposes: An old-growth forest will absorb more carbon than a newly grown secondary forest.

scholarly journals Plant Traits Help Explain the Tight Relationship between Vegetation Indices and Gross Primary Production

2020 ◽  
Vol 12 (9) ◽  
pp. 1405 ◽  
Author(s):  
César Hinojo-Hinojo ◽  
Michael L. Goulden
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Plant Traits ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Terrestrial Ecosystem ◽  
Leaf Mass Per Area ◽  
Co2 Uptake ◽  
Area Index

Remotely-sensed Vegetation Indices (VIs) are often tightly correlated with terrestrial ecosystem CO2 uptake (Gross Primary Production or GPP). These correlations have been exploited to infer GPP at local to global scales and over half-hour to decadal periods, though the underlying mechanisms remain incompletely understood. We used satellite remote sensing and eddy covariance observations at 10 sites across a California climate gradient to explore the relationships between GPP, the Enhanced Vegetation Index (EVI), the Normalized Difference Vegetation Index (NDVI), and the Near InfraRed Vegetation (NIRv) index. EVI and NIRv were linearly correlated with GPP across both space and time, whereas the relationship between NDVI and GPP was less general. We explored these interactions using radiative transfer and GPP models forced with in-situ plant trait and soil reflectance observations. GPP ultimately reflects the product of Leaf Area Index (LAI) and leaf level CO2 uptake (Aleaf); a VI that is sensitive mainly to LAI will lack generality across ecosystems that differ in Aleaf. EVI and NIRv showed a strong, multiplicative sensitivity to LAI and Leaf Mass per Area (LMA). LMA was correlated with Aleaf, and EVI and NIRv consequently mimic GPP’s multiplicative sensitivity to LAI and Aleaf, as mediated by LMA. NDVI was most sensitive to LAI, and was relatively insensitive to leaf properties over realistic conditions; NDVI lacked EVI and NIRv’s sensitivity to both LAI and Aleaf. These findings carry implications for understanding the limitations of current VIs for predicting GPP, and also for devising strategies to improve predictions of GPP.

scholarly journals Evaluation of satellite based indices for gross primary production estimates in a sparse savanna in the Sudan

2009 ◽  
Vol 6 (1) ◽  
pp. 129-138 ◽  
Author(s):  
M. Sjöström ◽  
J. Ardö ◽  
L. Eklundh ◽  
B. A. El-Tahir ◽  
H. A. M. El-Khidir ◽  
...  
Keyword(s):  
Water Stress ◽  
Primary Production ◽  
Satellite Data ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Arid Ecosystems ◽  
Stress Index ◽  
Accurate Information ◽  
Production Modeling

Abstract. One of the more frequently applied methods for integrating controls on primary production through satellite data is the Light Use Efficiency (LUE) approach. Satellite indices such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and the Shortwave Infrared Water Stress Index (SIWSI) have previously shown promise as predictors of primary production in several different environments. In this study, we evaluate NDVI, EVI and SIWSI derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor against in-situ measurements from central Sudan in order to asses their applicability in LUE-based primary production modeling within a water limited environment. Results show a strong correlation between vegetation indices and gross primary production (GPP), demonstrating the significance of vegetation indices for deriving information on primary production with relatively high accuracy at similar areas. Evaluation of SIWSI however, reveal that the fraction of vegetation apparently is to low for the index to provide accurate information on canopy water content, indicating that the use of SIWSI as a predictor of water stress in satellite data-driven primary production modeling in similar semi-arid ecosystems is limited.

scholarly journals Upscaling of gross ecosystem production to the landscape scale using multi-temporal Landsat images, eddy covariance measurements and a footprint model

2009 ◽  
Vol 6 (6) ◽  
pp. 11317-11345 ◽  
Author(s):  
B. Chen ◽  
Q. Ge ◽  
D. Fu ◽  
G. Liu ◽  
G. Yu ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Land Surface ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Co2 Flux ◽  
Landsat Images ◽  
Flux Footprint ◽  
Water Index ◽  
Multi Temporal

Abstract. In order to use the global available eddy-covariance (EC) flux dataset and remote sensing measurements to provide estimates of gross primary production (GPP) at landscape (101–102km2), regional (103–106km2) and global land surface scales, we developed a satellite-based GPP algorithm using Landsat data and an upscaling framework. The satellite-based GPP algorithm uses two improved vegetation indices (Enhanced Vegetation Index – EVI, Land Surface Water Index – LSWI). The upscalling framework involves flux footprint climatology modeling and data-model fusion. This approach was first applied to an evergreen coniferous stand in South China subtropical monsoon climatic zone. The EC measurements at Qian Yinzhou tower site (26° 44'48'' N, 115° 04'13'' E), which belongs to the Chinaflux network, and the Landsat images for this region in 2004 were used in this study. The seasonal dynamics of GPP predicted by the satellite-based algorithm agreed well with observed GPP in 2004 at this site. These results demonstrate the potential of combining of the satellite-based algorithm, flux footprint modeling and data-fusion, for scaling-up of GPP at the CO2 flux tower sites, a key component for the study of the carbon cycle at regional and global scales.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

scholarly journals Modeling the Radial Stem Growth of the Pine (Pinus sylvestris L.) Forests Using the Satellite-Derived NDVI and LST (MODIS/AQUA) Data

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Yulia Ivanova ◽  
Anton Kovalev ◽  
Vlad Soukhovolsky
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Principal Component ◽  
Stem Growth ◽  
Forest Growth ◽  
Parabolic Approximation ◽  
New Approach ◽  
The Relationship ◽  
Tree Increment

The paper considers a new approach to modeling the relationship between the increase in woody phytomass in the pine forest and satellite-derived Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (MODIS/AQUA) data. The developed model combines the phenological and forest growth processes. For the analysis, NDVI and LST (MODIS) satellite data were used together with the measurements of tree-ring widths (TRW). NDVI data contain features of each growing season. The models include parameters of parabolic approximation of NDVI and LST time series transformed using principal component analysis. The study shows that the current rate of TRW is determined by the total values of principal components of the satellite indices over the season and the rate of tree increment in the preceding year.

scholarly journals Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

2021 ◽  
Vol 13 (11) ◽  
pp. 2060
Author(s):  
Trylee Nyasha Matongera ◽  
Onisimo Mutanga ◽  
Mbulisi Sibanda ◽  
John Odindi
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Critical Role ◽  
Spectral Characteristics ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Special Focus ◽  
Land Surface Phenology ◽  
Spectral Vegetation Indices ◽  
Satellite Sensors

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.

scholarly journals Can vegetation index track the interannual variation in gross primary production of temperate deciduous forests?

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Fan Liu ◽  
Chuankuan Wang ◽  
Xingchang Wang
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Deciduous Forest ◽  
Gross Primary Production ◽  
Growing Season ◽  
Temperate Deciduous Forest ◽  
Near Infrared Reflectance ◽  
Modis Ndvi ◽  
Temperate Deciduous

Abstract Background Vegetation indices (VIs) by remote sensing are widely used as simple proxies of the gross primary production (GPP) of vegetation, but their performances in capturing the inter-annual variation (IAV) in GPP remain uncertain. Methods We evaluated the performances of various VIs in tracking the IAV in GPP estimated by eddy covariance in a temperate deciduous forest of Northeast China. The VIs assessed included the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the near-infrared reflectance of vegetation (NIRv) obtained from tower-radiometers (broadband) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. Results We found that 25%–35% amplitude of the broadband EVI tracked the start of growing season derived by GPP (R2: 0.56–0.60, bias < 4 d), while 45% (or 50%) amplitudes of broadband (or MODIS) NDVI represented the end of growing season estimated by GPP (R2: 0.58–0.67, bias < 3 d). However, all the VIs failed to characterize the summer peaks of GPP. The growing-season integrals but not averaged values of the broadband NDVI, MODIS NIRv and EVI were robust surrogates of the IAV in GPP (R2: 0.40–0.67). Conclusion These findings illustrate that specific VIs are effective only to capture the GPP phenology but not the GPP peak, while the integral VIs have the potential to mirror the IAV in GPP.

scholarly journals Modelling spatial and temporal dynamics of gross primary production in the Sahel from earth-observation-based photosynthetic capacity and quantum efficiency

2017 ◽  
Vol 14 (5) ◽  
pp. 1333-1348 ◽  
Author(s):  
Torbern Tagesson ◽  
Jonas Ardö ◽  
Bernard Cappelaere ◽  
Laurent Kergoat ◽  
Abdulhakim Abdi ◽  
...  
Keyword(s):  
Primary Production ◽  
Quantum Efficiency ◽  
Vegetation Index ◽  
Photosynthetic Capacity ◽  
Gross Primary Production ◽  
Earth Observation ◽  
Arid Ecosystems ◽  
Spatially Explicit ◽  
Equivalent Water Thickness ◽  
Semi Arid

Abstract. It has been shown that vegetation growth in semi-arid regions is important to the global terrestrial CO2 sink, which indicates the strong need for improved understanding and spatially explicit estimates of CO2 uptake (gross primary production; GPP) in semi-arid ecosystems. This study has three aims: (1) to evaluate the MOD17A2H GPP (collection 6) product against GPP based on eddy covariance (EC) for six sites across the Sahel; (2) to characterize relationships between spatial and temporal variability in EC-based photosynthetic capacity (Fopt) and quantum efficiency (α) and vegetation indices based on earth observation (EO) (normalized difference vegetation index (NDVI), renormalized difference vegetation index (RDVI), enhanced vegetation index (EVI) and shortwave infrared water stress index (SIWSI)); and (3) to study the applicability of EO upscaled Fopt and α for GPP modelling purposes. MOD17A2H GPP (collection 6) drastically underestimated GPP, most likely because maximum light use efficiency is set too low for semi-arid ecosystems in the MODIS algorithm. Intra-annual dynamics in Fopt were closely related to SIWSI being sensitive to equivalent water thickness, whereas α was closely related to RDVI being affected by chlorophyll abundance. Spatial and inter-annual dynamics in Fopt and α were closely coupled to NDVI and RDVI, respectively. Modelled GPP based on Fopt and α upscaled using EO-based indices reproduced in situ GPP well for all except a cropped site that was strongly impacted by anthropogenic land use. Upscaled GPP for the Sahel 2001–2014 was 736 ± 39 g C m−2 yr−1. This study indicates the strong applicability of EO as a tool for spatially explicit estimates of GPP, Fopt and α; incorporating EO-based Fopt and α in dynamic global vegetation models could improve estimates of vegetation production and simulations of ecosystem processes and hydro-biochemical cycles.

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