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

2008 ◽  
Vol 5 (4) ◽  
pp. 2985-3011 ◽  
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 ◽  
Arid Ecosystems ◽  
Stress Index ◽  
Accurate Information ◽  
Water Stress Index ◽  
Production Modelling

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 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 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 modelling within a water limited environment. Results show a strong correlation between EVI against gross primary production (GPP), demonstrating the significance of EVI 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 modelling in similar semi-arid ecosystems is limited.

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 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.

scholarly journals Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture

Agriculture ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. 116 ◽  
Author(s):  
Alessandro Matese ◽  
Salvatore Di Gennaro
Keyword(s):  
Water Stress ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Stress Index ◽  
Crop Water Stress Index ◽  
Practical Applications ◽  
Water Stress Index ◽  
Sensing Applications ◽  
Wide Range ◽  
Precision Viticulture

High spatial ground resolution and highly flexible and timely control due to reduced planning time are the strengths of unmanned aerial vehicle (UAV) platforms for remote sensing applications. These characteristics make them ideal especially in the medium–small agricultural systems typical of many Italian viticulture areas of excellence. UAV can be equipped with a wide range of sensors useful for several applications. Numerous assessments have been made using several imaging sensors with different flight times. This paper describes the implementation of a multisensor UAV system capable of flying with three sensors simultaneously to perform different monitoring options. The intra-vineyard variability was assessed in terms of characterization of the state of vines vigor using a multispectral camera, leaf temperature with a thermal camera and an innovative approach of missing plants analysis with a high spatial resolution RGB camera. The normalized difference vegetation index (NDVI) values detected in different vigor blocks were compared with shoot weights, obtaining a good regression (R2 = 0.69). The crop water stress index (CWSI) map, produced after canopy pure pixel filtering, highlighted the homogeneous water stress areas. The performance index developed from RGB images shows that the method identified 80% of total missing plants. The applicability of a UAV platform to use RGB, multispectral and thermal sensors was tested for specific purposes in precision viticulture and was demonstrated to be a valuable tool for fast multipurpose monitoring in a vineyard.

scholarly journals Upscaling Evapotranspiration with Parsimonious Models in a North Carolina Vineyard

Agronomy ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 152 ◽  
Author(s):  
Christian Dold ◽  
Joshua Heitman ◽  
Gill Giese ◽  
Adam Howard ◽  
John Havlin ◽  
...  
Keyword(s):  
North Carolina ◽  
Water Stress ◽  
Water Use ◽  
Land Surface ◽  
Vegetation Index ◽  
Multiple Linear Regression Model ◽  
Water Dynamics ◽  
Stress Index ◽  
Crop Water Stress Index ◽  
Water Stress Index

Water stress can positively or negatively impact grape yield and yield quality, and there is a need for wine growers to accurately regulate water use. In a four-year study (2010–2013), energy balance fluxes were measured with an eddy-covariance (EC) system in a North Carolina vineyard (Vitis vinifera cv. Chardonnay), and evapotranspiration (ET) and the Crop Water Stress Index (CWSI) calculated. A multiple linear regression model was developed to upscale ET using air temperature (Ta), vapor pressure deficit (VPD), and Landsat-derived Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI). Daily ET reached values of up to 7.7 mm day−1, and the annual ET was 752 ± 59 mm, as measured with the EC system. The grapevine CWSI was between 0.53–0.85, which indicated moderate water stress levels. Median vineyard EVI was between 0.22 and 0.72, and the EVI range (max–min) within the vineyard was 0.18. The empirical models explained 75%–84% of the variation in ET, and all parameters had a positive linear relationship to ET. The Root Mean Square Error (RMSE) was 0.52–0.62 mm. This study presents easily applicable approaches to analyzing water dynamics and ET. This may help wine growers to cost-effectively quantify water use in vineyards.

scholarly journals Integrating Early Growth Information to Monitor Winter Wheat Powdery Mildew Using Multi-Temporal Landsat-8 Imagery

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3290 ◽  
Author(s):  
Huiqin Ma ◽  
Yuanshu Jing ◽  
Wenjiang Huang ◽  
Yue Shi ◽  
Yingying Dong ◽  
...  
Keyword(s):  
Water Stress ◽  
Powdery Mildew ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Stress Index ◽  
Wheat Growth ◽  
Wheat Powdery Mildew ◽  
Water Stress Index ◽  
Multi Temporal ◽  
Regional Disease

Powdery mildew is one of the dominant diseases in winter wheat. The accurate monitoring of powdery mildew is important for crop management and production. Satellite-based remote sensing monitoring has been proven as an efficient tool for regional disease detection and monitoring. However, the information provided by single-date satellite scene is hard to achieve acceptable accuracy for powdery mildew disease, and incorporation of early period contextual information of winter wheat can improve this situation. In this study, a multi-temporal satellite data based powdery mildew detecting approach had been developed for regional disease mapping. Firstly, the Lansat-8 scenes that covered six winter wheat growth periods (expressed in chronological order as periods 1 to 6) were collected to calculate typical vegetation indices (VIs), which include disease water stress index (DSWI), optimized soil adjusted vegetation index (OSAVI), shortwave infrared water stress index (SIWSI), and triangular vegetation index (TVI). A multi-temporal VIs-based k-nearest neighbors (KNN) approach was then developed to produce the regional disease distribution. Meanwhile, a backward stepwise elimination method was used to confirm the optimal multi-temporal combination for KNN monitoring model. A classification and regression tree (CART) and back propagation neural networks (BPNN) approaches were used for comparison and validation of initial results. VIs of all periods except 1 and 3 provided the best multi-temporal data set for winter wheat powdery mildew monitoring. Compared with the traditional single-date (period 6) image, the multi-temporal images based KNN approach provided more disease information during the disease development, and had an accuracy of 84.6%. Meanwhile, the accuracy of the proposed approach had 11.5% and 3.8% higher than the multi-temporal images-based CART and BPNN models’, respectively. These results suggest that the use of satellite images for early critical disease infection periods is essential for improving the accuracy of monitoring models. Additionally, satellite imagery also assists in monitoring powdery mildew in late wheat growth periods.

Monitoring Crop Water Status and Irrigation Needs Using Multispectral Airborne Sensors

HortScience ◽  
1995 ◽  
Vol 30 (4) ◽  
pp. 905D-905
Author(s):  
Thomas R. Clarke ◽  
M. Susan Moran
Keyword(s):  
Water Stress ◽  
Near Infrared ◽  
Water Status ◽  
Canopy Cover ◽  
Stress Index ◽  
Crop Water ◽  
Vegetative Cover ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Subsurface Drip

Water application efficiency can be improved by directly monitoring plant water status rather than depending on soil moisture measurements or modeled ET estimates. Plants receiving sufficient water through their roots have cooler leaves than those that are water-stressed, leading to the development of the Crop Water Stress Index based on hand-held infrared thermometry. Substantial error can occur in partial canopies, however, as exposed hot soil contributes to deceptively warm temperature readings. Mathematically comparing red and near-infrared reflectances provides a measure of vegetative cover, and this information was combined with thermal radiance to give a two-dimensional index capable of detecting water stress even with a low percentage of canopy cover. Thermal, red, and near-infrared images acquired over subsurface drip-irrigated cantaloupe fields demonstrated the method's ability to detect areas with clogged emitters, insufficient irrigation rate, and system water leaks.

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.

An insight to the performance of crop water stress index for olive trees

2013 ◽  
Vol 118 ◽  
pp. 79-86 ◽  
Author(s):  
N. Agam ◽  
Y. Cohen ◽  
J.A.J. Berni ◽  
V. Alchanatis ◽  
D. Kool ◽  
...  
Keyword(s):  
Water Stress ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Olive Trees ◽  
Crop Water Stress

scholarly journals Water stress indices for the sugarcane crop on different irrigated surfaces

2016 ◽  
Vol 20 (10) ◽  
pp. 925-929 ◽  
Author(s):  
Rodrigo G. Brunini ◽  
José E. P. Turco
Keyword(s):  
Water Stress ◽  
Raw Materials ◽  
Irrigation System ◽  
Canopy Temperature ◽  
Ambient Air ◽  
Saccharum Officinarum ◽  
Stress Index ◽  
Stress Indices ◽  
Sugarcane Crop ◽  
Water Stress Index

ABSTRACT Sugarcane (Saccharum officinarum L.) is a crop of vital importance to Brazil, in the production of sugar and ethanol, power generation and raw materials for various purposes. Strategic information such as topography and canopy temperature can provide management technologies accessible to farmers. The objective of this study was to determine water stress indices for sugarcane in irrigated areas, with different exposures and slopes. The daily water stress index of the plants and the water potential in the soil were evaluated and the production system was analyzed. The experiment was carried out in an “Experimental Watershed”, using six surfaces, two horizontal and the other ones with 20 and 40% North and South exposure slopes. Water stress level was determined by measuring the temperatures of the vegetation cover and the ambient air. Watering was carried out using a drip irrigation system. The results showed that water stress index of sugarcane varies according to exposure and slope of the terrain, while areas whose water stress index was above 5.0 oC had lower yield values.

scholarly journals Satellites reveal an increase in gross primary production in a greenlandic high arctic fen 1992–2008

2010 ◽  
Vol 7 (1) ◽  
pp. 1101-1129 ◽  
Author(s):  
T. Tagesson ◽  
M. Mastepanov ◽  
M. P. Tamstorf ◽  
L. Eklundh ◽  
P. Schubert ◽  
...  
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Gross Primary Production ◽  
Field Measurements ◽  
High Arctic ◽  
Research Area ◽  
Strong Increase ◽  
Co2 Uptake ◽  
Flux Measurements

Abstract. Arctic wetlands play a key role in the terrestrial carbon cycle. Recent studies have shown a greening trend and indicated an increase in CO2 uptake in boreal and sub- to low-arctic areas. Our aim was to combine satellite-based normalized difference vegetation index (NDVI) with ground-based flux measurements of CO2 to investigate a possible greening trend and potential changes in gross primary production (GPP) between 1992 and 2008 in a high arctic fen area. The study took place in Rylekaerene in the Zackenberg Research Area (74°28' N 20°34' W), located in the National park of North Eastern Greenland. We estimated the light use efficiency (ε) for the dominant vegetation types from field measured fractions of photosynthetic active radiation (FAPAR) and ground-based flux measurements of GPP. Measured FAPAR were correlated to satellite-based NDVI. The FAPAR-NDVI relationship in combination with ε was applied to satellite data to model GPP 1992–2008. The model was evaluated against field measured GPP. The model was a useful tool for up-scaling GPP and all basic requirements for the model were well met, e.g., FAPAR was well correlated to NDVI and modeled GPP was well correlated to field measurements. The studied high arctic fen area has experienced a strong increase in GPP between 1992 and 2008. The area has during this period also experienced a substantial increase in local air temperature. Consequently, the observed greening trend is most likely due to ongoing climatic change possibly in combination with CO2 fertilization, due to increasing atmospheric concentrations of CO2.

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