scholarly journals Investigation of Spatial and Temporal Changes in the Land Surface Albedo for the Entire Chinese Territory

Geosciences ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 362
Author(s):  
Jihui Yuan
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Global Climate ◽  
Temporal Changes ◽  
Spatial And Temporal Changes ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Satellite

Currently, global climate change (GCC) and the urban heat island (UHI) phenomena are becoming serious problems, partly due to the artificial construction of the land surface. When sunlight reaches the land surface, some of it is absorbed and some is reflected. The state of the land surface directly affects the surface albedo, which determines the magnitude of solar radiation reflected by the land surface in the daytime. In order to better understand the spatial and temporal changes in surface albedo, this study investigated and analyzed the surface albedo from 2000 to 2016 (2000, 2008, and 2016) in the entire Chinese territory, based on the measurement database obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, aboard NASA’s Terra satellite. It was shown that the Northeast China exhibited the largest decline in surface albedo and North China showed the largest rising trend of surface albedo from 2000 to 2016. The correlation between changes in surface albedo and the Normalized Difference Vegetation Index (NDVI) indicated that the change trend of surface albedo was opposite to that of NDVI. In addition, in order to better understand the distribution of surface albedo in the entire Chinese territory, the classifications of surface albedo in three years (2000, 2008, and 2016) were implemented using five classification methods in this study.

scholarly journals Reconstruction of Spatiotemporally Continuous MODIS-Band Reflectance in East and South Asia from 2012 to 2015

2020 ◽  
Vol 12 (21) ◽  
pp. 3674
Author(s):  
Bo Gao ◽  
Huili Gong ◽  
Jie Zhou ◽  
Tianxing Wang ◽  
Yuanyuan Liu ◽  
...  
Keyword(s):  
South Asia ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Basic Model ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Intermediate Variables ◽  
Bidirectional Reflectance Distribution

To reconstruct Moderate Resolution Imaging Spectroradiometer (MODIS) band reflectance with optimal spatiotemporal continuity, three bidirectional reflectance distribution function (BRDF) models—the Ross-Thick-Li-Sparse Reciprocal (RTLSR) model, Gao model, and adjusted BF model—were used to retrieve MODIS-band reflectance for cloudy MODIS pixels according to different inversion conditions with a proposed filling algorithm. Then, a spatiotemporally continuous MODIS-band reflectance dataset for most of Asia with more than 98% spatiotemporal coverage was reconstructed from 2012 to 2015. The validation highlighted an evident improvement in filling cloudy MODIS observations; a reasonable spatial distribution, such as in South Asia and Southeast Asia; and acceptable precision for the filled MODIS pixels, with the root mean square error percentage (RMSE%) at 9.7–9.8% and 12–16% for the Gao and adjusted BF models, respectively. In the course of reconstructing the spatiotemporal continuous MODIS-band reflectance, the differences among the three models were discussed further. For a 16-day period with a stable and unchanged land surface, the RTLSR model, as a basic model, accurately derived land surface reflectance (no more than 10% RMSE% for MCD43C1 V006 band 1) and outperformed the other two models. When the inversion period is sufficiently long (e.g., 108 days, 188 days, 268 days, or a full year), the Gao/adjusted BF model provides better precision than the RTLSR model by considering the normalized difference vegetation index (NDVI) and soil moisture/NDVI as intermediate variables used to adjust the BRDF parameters in real time. The Gao model is optimal when the inversion period is sufficiently long. Based on combining the RTLSR model and Gao/adjusted BF model, we proposed a filling algorithm to derive a dataset of MODIS-band reflectance with optimal spatiotemporal continuity.

scholarly journals MAPPING RICE CROPPING SYSTEM IN THE LOWER GANGETIC PLAIN USING LANDASAT 8 (OLI) AND MODIS IMAGERY

2018 ◽  
Vol XLII-5 ◽  
pp. 271-274
Author(s):  
A. Maiti ◽  
P. Acharya
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cropping System ◽  
Rice Fields ◽  
Gangetic Plain ◽  
Water Index ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

<p><strong>Abstract.</strong> The Indo-Gangetic basin is one of the productive rice growing areas in South-East Asia. Within this extensive flat fertile land, lower Gangetic basin, especially the south Bengal, is most intensively cultivated. In this study we map the rice growing areas using Moderate Resolution Imaging Spectroradiometer (MODIS) derived 8-day surface reflectance product from 2014 to 2015. The time series vegetation and wetness indices such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) were used in the decision tree (DT) approach to detect the rice fields. The extracted rice pixels were compared with Landsat OLI derived rice pixels. The accuracy of the derived rice fields were computed with 163 field locations, and further compared with statistics derived from Directorate of Economics and Statistics (DES). The results of the estimation shows a high degree of correlation (<i>r</i><span class="thinspace"></span>=<span class="thinspace"></span>0.9) with DES reported area statistics. The estimated error of the area statistics while compared with the Landsat OLI was &amp;plusmn;15%. The method, however, shows its efficiency in tracing the periodic changes in rice cropping area in this part of Gangetic basin and its neighboring areas.</p>

scholarly journals Land Surface Temperature Assessment in Central Sumatra, Indonesia

2020 ◽  
Vol 52 (2) ◽  
pp. 239
Author(s):  
Tofan Agung Eka Prasetya ◽  
Munawar Munawar ◽  
Muhammad Rifki Taufik ◽  
Sarawuth Chesoh ◽  
Apiradee Lim ◽  
...  
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Regression Model ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multiple Linear Regression Model ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Land Surface Temperature (LST) assessment can explain temperature variation, which may be influenced by factors such as elevation, land cover, and the normalized difference vegetation index (NDVI). In this study, a multiple linear regression model of LST variation was constructed based on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Terra satellite, relating to the period, 2000-2018. The highest LST variation of nearly 1.3 °C/decade was found in savanna areas while the lowest variation was in the evergreen broadleaf forest and woody savanna, which experienced a decrease of 2.1 °C/decade. The overall mean change of LST was -0.4 °C/decade and the regression model with LST as the dependent variable and elevation, land cover type, and NVDI as independent variables produced an R square of 0.376. The variation in LST was different depending upon the NDVI.

scholarly journals A Fused Variable by Night Light Images and MODIS Products for Improving Urban Built-Up Area Extraction

Technologies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 40
Author(s):  
Guang Yang ◽  
Yuntao Ma ◽  
Jiaqi Hu
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Extraction Methods ◽  
Average Error ◽  
Geographic Coordinate System ◽  
Optimal Thresholds ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Spatial Locations

The boundary of urban built-up areas is the baseline data of a city. Rapid and accurate monitoring of urban built-up areas is the prerequisite for the boundary control and the layout of urban spaces. In recent years, the night light satellite sensors have been employed in urban built-up area extraction. However, the existing extraction methods have not fully considered the properties that directly reflect the urban built-up areas, like the land surface temperature. This research first converted multi-source data into a uniform projection, geographic coordinate system and resampling size. Then, a fused variable that integrated the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night light images, the Moderate-resolution Imaging Spectroradiometer (MODIS) surface temperature product and the normalized difference vegetation index (NDVI) product was designed to extract the built-up areas. The fusion results showed that the values of the proposed index presented a sharper gradient within a smaller spatial range, compared with the only night light images. The extraction results were tested in both the area sizes and the spatial locations. The proposed index performed better in both accuracies (average error rate 1.10%) and visual perspective. We further discussed the regularity of the optimal thresholds in the final boundary determination. The optimal thresholds of the proposed index were more stable in different cases on the premise of higher accuracies.

scholarly journals Quantification of the Environmental Impacts of Highway Construction Using Remote Sensing Approach

2021 ◽  
Vol 13 (7) ◽  
pp. 1340
Author(s):  
Shuailong Feng ◽  
Shuguang Liu ◽  
Lei Jing ◽  
Yu Zhu ◽  
Wende Yan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Environmental Impacts ◽  
Land Surface ◽  
Vegetation Index ◽  
Highway Construction ◽  
Temporal Changes ◽  
Moisture Index ◽  
Buffer Zones ◽  
Wide Range ◽  
Spatial And Temporal Changes

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.

scholarly journals An LSWI-Based Method for Mapping Irrigated Areas in China Using Moderate-Resolution Satellite Data

2020 ◽  
Vol 12 (24) ◽  
pp. 4181
Author(s):  
Kunlun Xiang ◽  
Wenping Yuan ◽  
Liwen Wang ◽  
Yujiao Deng
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Statistical Data ◽  
Spatial Information ◽  
Hydrological Cycle ◽  
Mainland China ◽  
Mean Annual Precipitation ◽  
Irrigated Area ◽  
Moderate Resolution

Accurate spatial information about irrigation is crucial to a variety of applications, such as water resources management, water exchange between the land surface and atmosphere, climate change, hydrological cycle, food security, and agricultural planning. Our study proposes a new method for extracting cropland irrigation information using statistical data, mean annual precipitation and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover type data and surface reflectance data. The approach is based on comparing the land surface water index (LSWI) of cropland pixels to that of adjacent forest pixels with similar normalized difference vegetation index (NDVI). In our study, we validated the approach over mainland China with 612 reference samples (231 irrigated and 381 non-irrigated) and found the accuracy of 62.09%. Validation with statistical data also showed that our method explained 86.67 and 58.87% of the spatial variation in irrigated area at the provincial and prefecture levels, respectively. We further compared our new map to existing datasets of FAO/UF, IWMI, Zhu and statistical data, and found a good agreement with the irrigated area distribution from Zhu’s dataset. Results show that our method is an effective method apply to mapping irrigated regions and monitoring their yearly changes. Because the method does not depend on training samples, it can be easily repeated to other regions.

scholarly journals An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products

2021 ◽  
Vol 13 (4) ◽  
pp. 719
Author(s):  
Xiuxia Li ◽  
Shunlin Liang ◽  
Huaan Jin
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Continuous Data ◽  
Landsat Data ◽  
Area Index ◽  
Free Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Resolution Data

Leaf area index (LAI) and normalized difference vegetation index (NDVI) are key parameters for various applications. However, due to sensor tradeoff and cloud contaminations, these data are often temporally intermittent and spatially discontinuous. To address the discontinuities, this study proposed a method based on spectral matching of 30 m discontinuous values from Landsat data and 500 m temporally continuous values from Moderate-resolution Imaging Spectroradiometer (MODIS) data. Experiments have proven that the proposed method can effectively yield spatiotemporally continuous vegetation products at 30 m spatial resolution. The results for three different study areas with NDVI and LAI showed that the method performs well in restoring the time series, fills in the missing data, and reasonably predicts the images. Remarkably, the proposed method could address the issue when no cloud-free data pairs are available close to the prediction date, because of the temporal information “borrowed” from coarser resolution data. Hence, the proposed method can make better use of partially obscured images. The reconstructed spatiotemporally continuous data have great potential for monitoring vegetation, agriculture, and environmental dynamics.

scholarly journals Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor

2016 ◽  
Vol 51 (7) ◽  
pp. 858-868
Author(s):  
Marcos Cicarini Hott ◽  
Luis Marcelo Tavares de Carvalho ◽  
Mauro Antonio Homem Antunes ◽  
Polyanne Aguiar dos Santos ◽  
Tássia Borges Arantes ◽  
...  
Keyword(s):  
Vegetative Growth ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Growth ◽  
Growth Index ◽  
Data Series ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Very High

Abstract: The objective of this work was to analyze the development of grasslands in Zona da Mata, in the state of Minas Gerais, Brazil, between 2000 and 2013, using a parameter based on the growth index of the normalized difference vegetation index (NDVI) from the moderate resolution imaging spectroradiometer (Modis) data series. Based on temporal NDVI profiles, which were used as indicators of edaphoclimatic conditions, the growth index (GI) was estimated for 16-day periods throughout the spring season of 2012 to early 2013, being compared with the average GI from 2000 to 2011, used as the reference period. Currently, the grassland areas in Zona da Mata occupy approximately 1.2 million hectares. According to the used methods, 177,322 ha (14.61%) of these grassland areas have very low vegetative growth; 577,698 ha (45.96%) have low growth; 433,475 ha (35.72%) have balanced growth; 39,980 ha (3.29%) have high growth; and 5,032 ha (0.41%) have very high vegetative growth. The grasslands had predominantly low vegetative growth during the studied period, and the NDVI/Modis series is a useful source of data for regional assessments.

scholarly journals Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere

2015 ◽  
Vol 9 (5) ◽  
pp. 1879-1893 ◽  
Author(s):  
K. Atlaskina ◽  
F. Berninger ◽  
G. de Leeuw
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Precipitation Amount ◽  
Snow Cover Fraction ◽  
Air Temperatures ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March–May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50° N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56 % of variation of albedo in March, 76 % in April and 92 % in May. Therefore the effects of other parameters were investigated only for areas with 100 % SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between −15 and −10 °C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100 % SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.

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