scholarly journals The Detection of Wetlands And Wetland Fragmentation Using Sentinel 1 And 2 Imagery: The Example of Southern Nigeria

2021 ◽  
Author(s):  
Sani Idris Garba ◽  
Susanna Ebmeier ◽  
Jean-François Bastin ◽  
Danilo Mollicone ◽  
Joseph Holden
Keyword(s):  
Shallow Water ◽  
Land Surface ◽  
Climate Models ◽  
Google Earth ◽  
Visual Interpretation ◽  
Wetland Ecosystems ◽  
Spatial Coverage ◽  
Southern Nigeria ◽  
Wetland Fragmentation ◽  
Sentinel 2

Abstract Wetland ecosystems play key roles in global biogeochemical cycling, but their spatial extent and connectivity is often not well known. Here, we describe an approach suitable for application on a 1000 km scale using Sentinel-1 and Sentinel-2 imagery, exploiting the implementation of Random Forest algorithm in Google Earth Engine. The approach was used to detect the spatial coverage of wetland types focusing on the case of southern Nigeria, thought to be one of the most wetland-rich areas of Africa. We compiled 1050 wetland and non-wetland control points for algorithm training and validation, primarily from visual interpretation of high-resolution (<1 m pixel) imagery. This allowed us to establish the relative importance of 18 input channels derived from Sentinel-1 polarimetric and Sentinel-2 indices for classification of wetland. We estimate that the swamps, marshes, mangroves, and shallow water wetlands of southern Nigeria cover 29,900 km² with 2% uncertainty of 460 km². We found larger mangrove and smaller marsh extent than suggested by earlier, coarser spatial resolution studies. Average continuous wetland patch areas were 120 km², 11 km², 55 km² and 13 km² for mangrove, marsh, swamp, and shallow water respectively. Our final map with 10 m pixels also captures small patches of wetland, with 20% of wetland patches being <1 km2; these were clustered around urban centres, suggesting anthropogenic wetland fragmentation. Our approach can now be used across rest of Africa and globally to detect wetlands and wetland change which, in turn, will be crucial for improved land-surface climate models and wetland conservation.

scholarly journals Accurate extraction of surface water in complex environment based on Google Earth Engine and Sentinel-2

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253209
Author(s):  
Jianfeng Li ◽  
Biao Peng ◽  
Yulu Wei ◽  
Huping Ye
Keyword(s):  
Surface Water ◽  
Google Earth ◽  
Water Extraction ◽  
Practical Significance ◽  
Commission Error ◽  
Complex Environment ◽  
Visual Interpretation ◽  
Water Index ◽  
Google Earth Engine ◽  
Sentinel 2

To realize the accurate extraction of surface water in complex environment, this study takes Sri Lanka as the study area owing to the complex geography and various types of water bodies. Based on Google Earth engine and Sentinel-2 images, an automatic water extraction model in complex environment(AWECE) was developed. The accuracy of water extraction by AWECE, NDWI, MNDWI and the revised version of multi-spectral water index (MuWI-R) models was evaluated from visual interpretation and quantitative analysis. The results show that the AWECE model could significantly improve the accuracy of water extraction in complex environment, with an overall accuracy of 97.16%, and an extremely low omission error (0.74%) and commission error (2.35%). The AEWCE model could effectively avoid the influence of cloud shadow, mountain shadow and paddy soil on water extraction accuracy. The model can be widely applied in cloudy, mountainous and other areas with complex environments, which has important practical significance for water resources investigation, monitoring and protection.

scholarly journals Simulation of land surface temperatures: comparison of two climate models and satellite retrievals

2009 ◽  
Vol 2 (1) ◽  
pp. 309-340
Author(s):  
J. M. Edwards
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Diurnal Cycle ◽  
Land Surface ◽  
General Circulation ◽  
Climate Models ◽  
Surface Flux ◽  
Spatial Coverage ◽  
Diurnal Range ◽  
Satellite Retrievals

Abstract. Recently there has been significant progress in the retrieval of land surface temperature from satellite observations. Satellite retrievals of surface temperature offer several advantages, including broad spatial coverage, and such data are potentially of great value in assessing general circulation models of the atmosphere. Here, retrievals of the land surface temperature over the contiguous United States are compared with simulations from two climate models. The models generally simulate the diurnal range realistically, but show significant warm biases during the summer. The models' diurnal cycle of surface temperature is related to their surface flux budgets. Differences in the diurnal cycle of the surface flux budget between the models are found to be more pronounced than those in the diurnal cycle of surface temperature.

scholarly journals Optimizing Top Dressing Nitrogen Fertilization Using VENμS and Sentinel-2 L1 Data

2021 ◽  
Vol 13 (19) ◽  
pp. 3934
Author(s):  
David J. Bonfil ◽  
Yaron Michael ◽  
Shilo Shiff ◽  
Itamar M. Lensky
Keyword(s):  
Extreme Values ◽  
N Uptake ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Fertilizer Application ◽  
Google Earth ◽  
N Application ◽  
L1 Data ◽  
Spatial Coverage ◽  
Sentinel 2

Environmental and economic constraints are forcing farmers to be more precise in the rates and timing of nitrogen (N) fertilizer application to wheat. In practice, N is frequently applied without knowledge of the precise amount needed or the likelihood of significant protein enhancement. The objective of this study was to help farmers optimize top dress N application by adopting the use of within-field reference N strips. We developed an assisting app on the Google Earth Engine (GEE) platform to map the spatial variability of four different vegetation indices (VIs) in each field by calculating the mean VI, masking extreme values (three standard deviations, 3σ) of each field, and presenting the anomaly as a deviation of ±σ and ±2σ or deviation of percentage. VIs based on red-edge bands (REIP, NDRE, ICCI) were very useful for the detection of wheat above ground N uptake and in-field anomalies. VENµS high temporal and spatial resolutions provide advantages over Sentinel-2 in monitoring agricultural fields during the growing season, representing the within-field variations and for decision making, but the spatial coverage and accessibility of Sentinel-2 data are much better. Sentinel-2 data is already available on the GEE platform and was found to be of much help for the farmers in optimizing topdressing N application to wheat, applying it only where it will increase grain yield and/or grain quality. Therefore, the GEE anomaly app can be used for top-N dressing application decisions. Nevertheless, there are some issues that must be tested, and more research is required. To conclude, satellite images can be used in the GEE platform for anomaly detection, rendering results within a few seconds. The ability to use L1 VENµS or Sentinel-2 data without atmospheric correction through GEE opens the opportunity to use these data for several applications by farmers and others.

scholarly journals Sentinel-2 Analysis of Spruce Crown Transparency Levels and Their Environmental Drivers After Summer Drought in the Northern Eifel (Germany)

2021 ◽  
Vol 4 ◽  
Author(s):  
Carsten Montzka ◽  
Bagher Bayat ◽  
Andreas Tewes ◽  
David Mengen ◽  
Harry Vereecken
Keyword(s):  
Satellite Data ◽  
Land Surface ◽  
Google Earth ◽  
Training Data ◽  
Cumulative Distribution ◽  
Environmental Drivers ◽  
Summer Drought ◽  
Data Set ◽  
Crown Transparency ◽  
Sentinel 2

Droughts in recent years weaken the forest stands in Central Europe, where especially the spruce suffers from an increase in defoliation and mortality. Forest surveys monitor this trend based on sample trees at the local scale, whereas earth observation is able to provide area-wide information. With freely available cloud computing infrastructures such as Google Earth Engine, access to satellite data and high-performance computing resources has become straightforward. In this study, a simple approach for supporting the spruce monitoring by Sentinel-2 satellite data is developed. Based on forest statistics and the spruce NDVI cumulative distribution function of a reference year, a training data set is obtained to classify the satellite data of a target year. This provides insights into the changes in tree crown transparency levels. For the Northern Eifel region, Germany, the evaluation shows an increase in damaged trees from 2018 to 2020, which is in line with the forest inventory of North Rhine-Westphalia. An analysis of tree damages according to precipitation, land surface temperature, elevation, aspect, and slope provides insights into vulnerable spruce habitats of the region and enables to identify locations where the forest management may focus on a transformation from spruce monocultures to mixed forests with higher biodiversity and resilience to further changes in the climate system.

scholarly journals Automatic High-Resolution Land Cover Production in Madagascar Using Sentinel-2 Time Series, Tile-Based Image Classification and Google Earth Engine

2020 ◽  
Vol 12 (21) ◽  
pp. 3663
Author(s):  
Meinan Zhang ◽  
Huabing Huang ◽  
Zhichao Li ◽  
Kwame Oppong Hackman ◽  
Chong Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Google Earth ◽  
Visual Interpretation ◽  
Entire Study ◽  
Heterogeneous Landscapes ◽  
Land Cover Map ◽  
Google Earth Engine ◽  
The Tropics ◽  
Sentinel 2

Madagascar, one of Earth’s biodiversity hotpots, is characterized by heterogeneous landscapes and huge land cover change. To date, fine, reliable and timely land cover information is scarce in Madagascar. However, mapping high-resolution land cover map in the tropics has been challenging due to limitations associated with heterogeneous landscapes, the volume of satellite data used, and the design of methodology. In this study, we proposed an automatic approach in which the tile-based model was used on each tile (defining an extent of 1° × 1° as a tile) for mapping land cover in Madagascar. We combined spectral-temporal, textural and topographical features derived from all available Sentinel-2 observations (i.e., 11,083 images) on Google Earth Engine (GEE). We generated a 10-m land cover map for Madagascar, with an overall accuracy of 89.2% based on independent validation samples obtained from a field survey and visual interpretation of very high-resolution (0.5–5 m) images. Compared with the conventional approach (i.e., the overall model used in the entire study area), our method enables reduce the misclassifications between several land cover types, including impervious land, grassland and wetland. The proposed approach demonstrates a great potential for mapping land cover in other tropical or subtropical regions.

scholarly journals Improved Estimates of Arctic Land Surface Phenology Using Sentinel-2 Time Series

2020 ◽  
Vol 12 (22) ◽  
pp. 3738
Author(s):  
Adrià Descals ◽  
Aleixandre Verger ◽  
Gaofei Yin ◽  
Josep Peñuelas
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Surface ◽  
Vegetation Index ◽  
Mean Squared Error ◽  
Google Earth ◽  
The Arctic ◽  
Threshold Method ◽  
Land Surface Phenology ◽  
Sentinel 2

The high spatial resolution and revisit time of Sentinel-2A/B tandem satellites allow a potentially improved retrieval of land surface phenology (LSP). The biome and regional characteristics, however, greatly constrain the design of the LSP algorithms. In the Arctic, such biome-specific characteristics include prolonged periods of snow cover, persistent cloud cover, and shortness of the growing season. Here, we evaluate the feasibility of Sentinel-2 for deriving high-resolution LSP maps of the Arctic. We extracted the timing of the start and end of season (SoS and EoS, respectively) for the years 2019 and 2020 with a simple implementation of the threshold method in Google Earth Engine (GEE). We found a high level of similarity between Sentinel-2 and PhenoCam metrics; the best results were observed with Sentinel-2 enhanced vegetation index (EVI) (root mean squared error (RMSE) and mean error (ME) of 3.0 d and −0.3 d for the SoS, and 6.5 d and −3.8 d for the EoS, respectively), although other vegetation indices presented similar performances. The phenological maps of Sentinel-2 EVI compared well with the same maps extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) in homogeneous landscapes (RMSE and ME of 9.2 d and 2.9 d for the SoS, and 6.4 and −0.9 d for the EoS, respectively). Unreliable LSP estimates were filtered and a quality flag indicator was activated when the Sentinel-2 time series presented a long period (>40 d) of missing data; discontinuities were lower in spring and early summer (9.2%) than in late summer and autumn (39.4%). The Sentinel-2 high-resolution LSP maps and the GEE phenological extraction method will support vegetation monitoring and contribute to improving the representation of Artic vegetation phenology in land surface models.

scholarly journals Development of a New Phenology Algorithm for Fine Mapping of Cropping Intensity in Complex Planting Areas Using Sentinel-2 and Google Earth Engine

2021 ◽  
Vol 10 (9) ◽  
pp. 587
Author(s):  
Yan Guo ◽  
Haoming Xia ◽  
Li Pan ◽  
Xiaoyang Zhao ◽  
Rumeng Li ◽  
...  
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growth Cycle ◽  
Google Earth ◽  
Henan Province ◽  
Cropping Intensity ◽  
Water Index ◽  
Google Earth Engine ◽  
Sentinel 2

Cropping intensity is a key indicator for evaluating grain production and intensive use of cropland. Timely and accurately monitoring of cropping intensity is of great significance for ensuring national food security and improving the level of national land management. In this study, we used all Sentinel-2 images on the Google Earth Engine cloud platform, and constructed an improved peak point detection method to extract the cropping intensity of a heterogeneous planting area combined with crop phenology. The crop growth cycle profiles were extracted from the multi-temporal normalized difference vegetation index (NDVI) and land surface water index (LSWI) datasets. Results show that by 2020, the area of single cropping, double cropping, and triple cropping in the Henan Province are 52,236.9 km2, 74,334.1 km2, and 1927.1 km2, respectively; the corresponding producer accuracies are 86.12%, 93.72%, and 91.41%, respectively; the corresponding user accuracies are 88.99%, 92.29%, and 71.26%, respectively. The overall accuracy is 90.95%, and the Kappa coefficient is 0.81. Using the sown area in the statistical yearbook data of cities in the Henan Province to verify the extraction results of this paper, the R2 is 0.9717, and the root mean square error is 1715.9 km2. This study shows that using all the Sentinel-2 data, the phenology algorithm, and cloud computing technology has great potential in producing a high spatio-temporal resolution dataset for crop remote sensing monitoring and agricultural policymaking in complex planting areas.

scholarly journals Simulation of land surface temperatures: comparison of two climate models and satellite retrievals

2009 ◽  
Vol 2 (2) ◽  
pp. 123-136 ◽  
Author(s):  
J. M. Edwards
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Diurnal Cycle ◽  
Land Surface ◽  
General Circulation ◽  
Climate Models ◽  
Surface Flux ◽  
Spatial Coverage ◽  
Diurnal Range ◽  
Satellite Retrievals

Abstract. Recently there has been significant progress in the retrieval of land surface temperature from satellite observations. Satellite retrievals of surface temperature offer several advantages, including broad spatial coverage, and such data are potentially of great value in assessing general circulation models of the atmosphere. Here, retrievals of the land surface temperature over the contiguous United States are compared with simulations from two climate models. It is found that these two models generally simulate the diurnal range of surface temperature realistically, but show significant warm biases during the summer. The models' diurnal cycle of surface temperature is related to their surface flux budgets. Differences in the diurnal cycle of the surface flux budget between the models are found to be more pronounced than those in the diurnal cycle of surface temperature.

scholarly journals Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1469
Author(s):  
Jiwei Li ◽  
David E. Knapp ◽  
Mitchell Lyons ◽  
Chris Roelfsema ◽  
Stuart Phinn ◽  
...  
Keyword(s):  
Dominican Republic ◽  
Shallow Water ◽  
Estimation Algorithm ◽  
Virgin Islands ◽  
Global Scale ◽  
Google Earth ◽  
Environment Protection ◽  
Extensive Coverage ◽  
Google Earth Engine ◽  
Sentinel 2

Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Methods that employ satellite-based bathymetric modeling provide an alternative to conventional shipborne measurements, offering high spatial resolution combined with extensive coverage. We developed an automated bathymetry mapping approach based on the Sentinel-2 surface reflectance dataset in Google Earth Engine. We created a new method for generating a clean-water mosaic and a tailored automatic bathymetric estimation algorithm. We then evaluated the performance of the models at six globally diverse sites (Heron Island, Australia; West Coast of Hawaiʻi Island, Hawaiʻi; Saona Island, Dominican Republic; Punta Cana, Dominican Republic; St. Croix, United States Virgin Islands; and The Grenadines) using 113,520 field bathymetry sampling points. Our approach derived accurate bathymetry maps in shallow waters, with Root Mean Square Error (RMSE) values ranging from 1.2 to 1.9 m. This automatic, efficient, and robust method was applied to map shallow water bathymetry at the global scale, especially in areas which have high biodiversity (i.e., coral reefs).

Sign in / Sign up

Export Citation Format

Share Document