scholarly journals Relationship between Vegetation Biophysical Properties and Surface Temperature Using Multisensor Satellite Data

2007 ◽  
Vol 20 (22) ◽  
pp. 5593-5606 ◽  
Author(s):  
Seungbum Hong ◽  
Venkat Lakshmi ◽  
Eric E. Small
Keyword(s):  
Surface Temperature ◽  
Water Content ◽  
Land Surface ◽  
Vegetation Index ◽  
Climatic Variability ◽  
Regional Distribution ◽  
Spatial And Temporal Variability ◽  
Area Index ◽  
Vegetation Water Content ◽  
Vegetation Water

Abstract Vegetation is an important factor in global climatic variability and plays a key role in the complex interactions between the land surface and the atmosphere. This study focuses on the spatial and temporal variability of vegetation and its relationship with land–atmosphere interactions. The authors have analyzed the vegetation water content (VegWC) from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), the leaf area index (LAI), the normalized difference vegetation index (NDVI), the land surface temperature (Ts), and the Moderate Resolution Imaging Spectroradiometer (MODIS). Three regions, which have climatically differing characteristics, have been selected: the North America Monsoon System (NAMS) region, the Southern Great Plains (SGP) region, and the Little River Watershed in Tifton, Georgia. Temporal analyses were performed by comparing satellite observations from 2003 and 2004. The introduction of the normalized vegetation water content (NVegWC) derived as the ratio of VegWC and LAI corresponding to the amount of water in individual leaves has been estimated and this yields significant correlation with NDVI and Ts. The analysis of the NVegWC–NDVI relationship in the above listed three regions displays a negative exponential relation, and the Ts–NDVI relationship (TvX relationship) is inversely proportional. The correlation between these variables is higher in arid areas such as the NAMS region, and becomes less correlated in the more humid and more vegetated regions such as the area of eastern Georgia. A land-cover map is used to examine the influence of vegetation types on the vegetation biophysical and surface temperature relationships. The regional distribution of vegetation reflects the relationship between the vegetation biological characteristics of water and the growing environment.

scholarly journals Monitoring Cotton (Gossypium sps.) Crop Condition through Synergy of Optical and Radar Remote Sensing

2018 ◽  
Author(s):  
Dipanwita Haldar ◽  
Rojalin Tripathy ◽  
Viral Dave ◽  
Rucha Dave ◽  
Bimal Bhattacharya ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Water Content ◽  
Vegetation Index ◽  
Broad Band ◽  
Plant Water ◽  
Area Index ◽  
Vegetation Water Content ◽  
Vegetation Water ◽  
Crop Condition

Morphological parameters like cotton height, branches, Leaf Area Index and biomass are mainly affected by the vegetation water content (VWC). Periodical assessment of the VWC and crop parameters is required for timely management of the crop for maximizing yield. The study aimed at using both optical and microwave remotely sensed data to assess cotton crop condition based on the above mentioned traits. Vegetation indices (VI) derived from ground based measurements (5 narrow band and 2 broad band VIs) as well as satellite derived reflectance (2 broad band VIs) were assessed. Regression models were derived for estimating LAI, biomass and plant water content using the ground based indices and applied to the satellite derived spectral index (from LISS-III) map to estimate the respective parameters. HH and HV polarization from RISAT-1 were used to derive Radar Vegetation Index (RVI). The coefficient of determination of the model for estimating LAI, biomass and vegetation water content of cotton with optical vegetation index as input parameter were found to be 0.42, 0.51 and 0.52, respectively. The correlation between RVI and plant height, date of planting in terms of the age of the crop and vegetation water content were found to range between 0.4 to 0.6. The fresh biomass from RVI showed spatial variability from 100 gm-2 to 4000 gm-2 while the dry biomass map derived from NDVI showed spatial variability of 50 to 950 g m-2 for the study area. Plant water content in the district varied from 65 to 85%. The correlation between optical vegetation index and RVI was not significant. Hence a multiple linear regression model using both optical index (NDVI and LSWI) and SAR index (RVI) was developed to assess the LAI, biomass and plant water content. The model showed a R2 of 0.5 for LAI estimation but not significant for biomass and water content. This study show cased the use of combined optical and microwave (C band) remote sensing for cotton condition assessment.

Land Surface Temperature and Miombo forest canopy phenophases: what induces leaf fall and leaf flush?

2020 ◽  
Author(s):  
Henry Zimba ◽  
Miriam Coenders-Gerrits ◽  
Banda Kawawa ◽  
Imasiku Nyambe ◽  
Hubert Savenije ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Water Content ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Indices ◽  
Leaf Fall ◽  
Vegetation Water Content ◽  
Vegetation Water ◽  
Leaf Flush ◽  
Photosynthetic Activities

<p>Miombo woodland is the most widespread tropical seasonal woodland and dry forest formation in Africa covering between 2.7 and 3.6 million km<sup>2</sup> in eleven countries. Leaf fall and leaf flush during the dry season is a major characteristic feature of the various Miombo species. However, the question on what induces the leaf fall process is by far inconclusive. Different studies indicate either moisture or temperature or both elements as inducers for leaf fall. Knowing what induces leaf fall is important for studying the consequence of e.g., climate change on the Miombo forest. To better understand the driver of leaf fall in Miombo forest we employed a simple remote sensing and statistical analysis approach using long term averages (2009 – 2018) of Land Surface Temperature (LST) of the Miombo forest, various vegetation indices (VI), actual evaporation (E<sub>a</sub>), and root zone soil moisture (SM). The vegetation indices (VI) included the Normalised Difference Water Index (NDWI) as indicator of vegetation water content and the Normalised Difference Vegetation Index (NDVI) as indicator of plant photosynthetic activities and leaf cover. Results showed that the NDWI, NDVI, E<sub>a</sub> and SM begun to decline immediately following the end of the rainy season in early April while the LST remained relatively constant before it began to decline in May when leaf fall in some Miombo species begins. Hysteresis graphs revealed that vegetation water content (i.e. NDWI) responded quicker to changes in both LST and SM. Furthermore, high rates of decrease in NDWI and NDVI values were observed between July and September the same period when LST increased. This is also the same period when leaf fall intensifies in Miombo forest. Correlation analysis revealed strong season-dependent LST relationship with VI and SM with the rainy season exhibiting strong negative linear correlations (R<sup>2</sup> = 0.77, 0.91, 0.88; for the NDWI, NDVI and SM respectively). In the dry season relatively weaker negative correlations (R<sup>2</sup> = 0.52, 0.60, 0.55; for NDWI, NDVI and SM respectively) were observed. On the other hand SM showed strong positive linear correlations (R<sup>2</sup> > 0.6) with NDWI and NDVI (for the rainy and dry seasons respectively). The correlations imply that in Miombo forest soil water content (i.e. SM), vegetation water content (i.e. NDWI) and the photosynthetic activities and leaf cover (i.e. NDVI) declines with increase in LST. These relationships show the possibility of land surface temperature being a major inducing element of leaf fall and changes in canopy structure in the Miombo woodland.</p>

scholarly journals Estimating malaria burden in Nigeria: a geostatistical modelling approach

2015 ◽  
Vol 10 (2) ◽  
Author(s):  
Nnadozie Onyiri
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Model ◽  
Vegetation Index ◽  
Malaria Prevalence ◽  
Malaria Risk ◽  
Water Bodies ◽  
Area Index ◽  
Environmental Covariates

This study has produced a map of malaria prevalence in Nigeria based on available data from the Mapping Malaria Risk in Africa (MARA) database, including all malaria prevalence surveys in Nigeria that could be geolocated, as well as data collected during fieldwork in Nigeria between March and June 2007. Logistic regression was fitted to malaria prevalence to identify significant demographic (age) and environmental covariates in STATA. The following environmental covariates were included in the spatial model: the normalized difference vegetation index, the enhanced vegetation index, the leaf area index, the land surface temperature for day and night, land use/landcover (LULC), distance to water bodies, and rainfall. The spatial model created suggests that the two main environmental covariates correlating with malaria presence were land surface temperature for day and rainfall. It was also found that malaria prevalence increased with distance to water bodies up to 4 km. The malaria risk map estimated from the spatial model shows that malaria prevalence in Nigeria varies from 20% in certain areas to 70% in others. The highest prevalence rates were found in the Niger Delta states of Rivers and Bayelsa, the areas surrounding the confluence of the rivers Niger and Benue, and also isolated parts of the north-eastern and north-western parts of the country. Isolated patches of low malaria prevalence were found to be scattered around the country with northern Nigeria having more such areas than the rest of the country. Nigeria’s belt of middle regions generally has malaria prevalence of 40% and above.

Radar Vegetation Index for Estimating the Vegetation Water Content of Rice and Soybean

2012 ◽  
Vol 9 (4) ◽  
pp. 564-568 ◽  
Author(s):  
Yihyun Kim ◽  
T. Jackson ◽  
R. Bindlish ◽  
Hoonyol Lee ◽  
Sukyoung Hong
Keyword(s):  
Water Content ◽  
Vegetation Index ◽  
Vegetation Water Content ◽  
Vegetation Water

scholarly journals Combining the trapezoidal relationship between land surface temperature and vegetation index with the Priestley-Taylor equation to estimate evapotranspiration

2015 ◽  
Vol 368 ◽  
pp. 379-384
Author(s):  
X. Wang ◽  
W. Wang ◽  
Y. Jiang
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Hydrological Cycle ◽  
Bare Soil ◽  
Landsat 8 ◽  
Extreme Conditions ◽  
Enhanced Vegetation Index ◽  
Vegetation Water

Abstract. Evapotranspiration (ET) plays an important role in the hydrological cycle. A method of combining the Priestley-Taylor (P-T) equation with a trapezoidal space between land surface temperature (Ts) and enhanced vegetation index (EVI) is proposed based on the principle of energy balance. Generally, this method is divided into three major parts: (1) construct the Ts versus EVI (Ts-VI) trapezoidal space for calculating the Ts at four extreme conditions (i.e. well-watered vegetation, water-stressed vegetation, saturated bare soil and dry bare soil); (2) calculate the P-T coefficient for each pixel according to the position of the observed (EVI, Ts) point in the trapezoid space; (3) calculate actual ET of the pixel using the P-T equation. The method is validated using Landsat-8 images and ground-observed data for a semi-humid area in China. The result shows that the ET estimates match the observations well, which indicates the effectiveness the proposed method here.

scholarly journals Seasonal variation in vegetation water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

2015 ◽  
Vol 12 (7) ◽  
pp. 5503-5533
Author(s):  
G. Mendiguren ◽  
M. P. Martín ◽  
H. Nieto ◽  
J. Pacheco-Labrador ◽  
S. Jurdao
Keyword(s):  
Spatial Variation ◽  
Water Content ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Spectral Indices ◽  
Proximal Sensing ◽  
Area Index ◽  
Vegetation Water Content ◽  
Equivalent Water Thickness ◽  
Vegetation Water

Abstract. This study evaluates three different metrics of vegetation water content estimated from proximal sensing and MODIS satellite imagery: Fuel Moisture Content (FMC), Equivalent Water Thickness (EWT) and Canopy Water Content (CWC). Dry matter (Dm) and Leaf area Index (LAI) were also analyzed in order to connect FMC with EWT and EWT with CWC, respectively. This research took place in a Fluxnet site located in Mediterranean wooded grassland (dehesa) ecosystem in Las Majadas del Tietar (Spain). Results indicated that FMC and EWT showed lower spatial variation than CWC. The spatial variation within the MODIS pixel was not as critical as its temporal trend, so to capture better the variability, fewer plots should be sampled but more times. Due to the high seasonal Dm variability, a constant annual value would not work to predict EWT from FMC. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. VARI provided the worst results in all cases. For proximal sensing, GEMI worked best for both FMC (RRMSE = 34.5%) and EWT (RRMSE = 27.43%) while NDII and GVMI performed best for CWC (RRMSE =30.27% and 31.58% respectively). For MODIS data, results were a bit better with EVI as the best predictor for FMC (RRMSE = 33.81%) and CWC (RRMSE = 27.56%) and GEMI for EWT (RRMSE = 24.6%). To explain these differences, proximal sensing measures only grasslands at nadir view angle, but MODIS includes also trees, their shades, and other artifacts at up to 20° view angle. CWC was better predicted than the other two water content variables, probably because CWC depends on LAI, which is highly correlated to the spectral indices. Finally, these empirical methods outperformed FMC and CWC products based on radiative transfer model inversion.

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