China's Gaofen-3 satellites to form Earth-observation network

Demonstration by China National Space Administration

A new Earth-observation satellite was successfully launched by China on Thursday, 7th April 2022, from the Jiuquan Satellite Launch Center in the north-western part of the country.

Long March-4C rocket launched Gaofen-3 03 at 7:47 a.m. (Beijing Time) and it went into orbit as planned.

Together with two satellites from the Gaofen-3 series that have already been launched, the three will work together to make a "sky eye" in space.

https://news.cgtn.com/news/2022-04-07/China-launches-new-satellite-for-Earth-ob…
China

Today: International Day of Human Space Flight 2022

Space Collage. Image: United Nations and Roscosmos

The beginning of the space era for mankind is marked on 12th April, ‘the International Day of Human Space Flight’, which reaffirms the vital role of space research and technology in accomplishing sustainable development objectives and increasing the well-being of nations and peoples as well as assuring the realization of their wish to keep outer space peaceful.

https://www.un.org/en/observances/human-spaceflight-day

In Detail: Agriculture Drought Monitoring and Hazard Assessment using Google Earth Engine

Droughts make considerable effects on agricultural and agro-pastoral areas due to their substantial dependency on rainfall. Agricultural drought monitoring is very important to maintain food security in the world. Satellite remote sensing is widely used for vegetation health monitoring and has become a powerful drought detection approach, because of its use at the global level. Indices have been developed using remote sensing data like the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI). These are employed to onset and monitor the agriculture drought in the relation to the plant growth.

The recommended practice is prepared to monitor and perform early warning of agriculture drought and can be easily adapted using Google Earth Engine. 
 

Background

Drought is a hydro-meteorological hazard often observed in the southern part of Pakistan. It is a slow onset disaster and is caused by below normal rainfall over a prolonged period. Besides rainfall, other indicators such as low soil moisture, irregular stream flow, less accessibility of groundwater or canal water supply, and higher evapotranspiration (ET) increase the intensity of drought. Droughts are recurring events that can disturb large areas, continuing for either a period of a few weeks or lasting up to several years. The effects are non-structural, grow gradually, and can remain for a long time. Droughts affect vegetation, crops, livestock, humans, and eventually the economy of the country.

The traditional method of drought monitoring includes the downloading of satellite images and long pre-post processing steps. By using cloud computing and machine learning algorithms, it is possible to efficiently perform the tasks without downloading the satellite images.

In this procedure, the cloud computing platform Google Earth Engine is used to derive the indices from satellite products of MODIS data which are MOD13Q1 and MOD11A2.
 

Agriculture Drought

There are various types of drought concerning the differences in requirements, regions, and disciplinary approaches. Agricultural drought refers to situations where the soil moisture level is inadequate to meet the water requirements of plants during the vegetation period. 

Different characteristics of meteorological (or hydrological) drought are related by agricultural drought to agricultural impacts, concentrating on shortages of precipitation, variations between real and potential evapotranspiration, deficits of surface water, decreased levels of groundwater or reservoirs, etc. The demand for plant water depends on prevailing weather conditions, the biological features of the particular plant, its stage of growth, and the physical and biological characteristics of the soil.


Drought Indices

Remote sensing has been widely used to monitor and perform early warnings of natural disasters via indicators and indices. To study droughts, satellite-derived drought indicators measured from satellite-derived surface parameters have been commonly used. 

1. Normalized Difference Vegetation Index (NDVI)

The NDVI is an excellent sign of green biomass, leaf area index, and patterns of production as, when sunlight hits a plant, mostly the red bandwidth in the visible part of the electromagnetic radiation spectrum (0.4–0.7 mm) is absorbed by chlorophyll in the leaves, whereas the cell formation of leaves reflects the bulk of near-infrared (NIR) radiation (0.7–1.1 mm). Healthy vegetation absorbs the red light and reflects NIR radiation. Usually, if there is extra reflected radiation in the NIR range than the visible, then vegetation will be healthy (dense). The NDVI range varies from −1 to +1, with values near zero representing no green vegetation and values near +1 showing the highest possible density of vegetation. Areas of barren rock, sand, and snow produce NDVI values of <0.1, while shrub and grassland typically produce NDVI values of 0.2–0.3, and temperate and tropical rainforests produce values in the 0.6–0.8 range. The NDVI is calculated with the following formula: 

NDVI = NIR – RED / NIR + RED


2. Vegetation Condition Index (VCI)

The VCI is an indicator of the status of vegetation cover as a function of NDVI minima and maxima encountered for a given ecosystem over many years. It is a better indicator of water stress condition than the NDVI. The deviation of the vegetation condition is an indicator of the intensity of the impact of drought on vegetation growth. The VCI is calculated using the following formula: 

VCIj = (NDVIj - NDVImin) / (NDVImax - NDVImin ) × 100

NDVImax and NDVImin are the maximum and minimum NDVI values in a multi-year dataset. The ‘j’ is the NDVI value for the current month. 


3. Temperature Condition Index (TCI)

Land surface temperature (LST) derived from thermal radiance bands is a good indicator of the energy balance of the Earth’s surface, because temperatures can rise quickly under water stress. The TCI is an initial indicator of water stress and drought. It is calculated using the following formula. 

TCIj = (TCIj - TCImin) / (TCImax - TCImin) × 100

TCImax and TCImin are the maximum and minimum TCI values in a multi-year dataset. The ‘j’ is the TCI value for the current month.


4. Vegetation Health Index (VHI)

The VHI is a combination of the constructed VCI and TCI and can be used effectively for drought assessments. It can be calculated using the following formula. 

VHI = α × VCI + (1 - α) × TCI

where α is the weight to measure the contribution of the VCI and TCI for assessing the status of drought. Generally, α is set as 0.5 because it is difficult to distinguish the contribution of the surface temperature and the NDVI when measuring drought stress. 
 

This practice can be applied to vegetation drought events anywhere in the world. 

However, the procedure works best for rainfed agriculture. For irrigated agriculture, the results may not necessarily be satisfying. 

Advantages

  • The user interested in applying this recommended practice has high flexibility in choosing the preferred area for the analysis interest.
  • The VCI distinguishes the variations in short-term weather-related NDVI from the long-term shifts in the ecology. So, while the NDVI shows seasonal dynamics of vegetation, the VCI rescales the dynamics of vegetation between 0 and 100 to represent relative changes in the state of moisture from extremely poor to optimal. Because optimal moisture conditions are given by favourable weather, high VCI values are consistent with safe and unstressed vegetation. On the other hand, due to the high temperature and dryness, low TCI values correspond to vegetation stress. Due to the thermal influence, the TCI offers the ability to detect subtle changes in plant health as drought proliferates when moisture shortages are followed by high temperatures.
  • VHI studies both vegetation status (named VCI) and thermal condition of vegetation (TCI) observation period. VHI therefore subsequently tests the drought of vegetation stressed by temperature.
  • VHI has been found to be useful in identifying the spatiotemporal extent of agricultural drought. By performing a composite analysis of both vegetation status and thermal condition of vegetation, it can also be used to clarify drought intensity classes in the research areas. As an early warning system, the results of VHI estimates will contribute to the monitoring of the onset of agricultural drought.

 

Disadvantages

  • It may be noted here that VCI and TCI take into account only one indicator of NDVI and LST respectively. VCI only measures the vegetation vigor while the TCI provides the measurement of LST.
  • The procedure works best for rainfed agriculture. For irrigated agriculture, the results may not necessarily be satisfying. 
  • Areas with high and/or frequent cloud-coverage are prone to lower quality results.
  • Agricultural areas in the observed study region should be large enough to sufficiently covered by the relatively coarse spatial resolution of MODIS data. Small, scattered fields will not deliver good results and would require higher resolution data sets.
  • It has been studied that VHI can only be applied successfully at low latitudes, mainly in arid, semi-arid, and sub-humid climatic regions where water is the main limiting factor for the growth of vegetation. In the tropics around the Equator and in the humid regions of high latitudes, where vegetation development is primarily limited by energy, another physiological mechanism exists. Higher temperatures in these regions accelerate the development of plants and, therefore, the VHI must be used with caution to assess the state and condition of vegetation.
     

Amalo, L. , Hidayat, R. & Haris (2017). Comparison between remote-sensing-based drought indices in East Java. IOP Conference Series: Earth and Environmental Science. 54. 012009. 10.1088/1755-1315/54/1/012009.  https://www.researchgate.net/publication/313622824_Comparison_between_remote-sensing-based_drought_indices_in_East_Java

Belal, AA., El-Ramady, H.R., Mohamed, E.S. et al. (2014). Drought risk assessment using remote sensing and GIS techniques. Arab J Geosci 7, 35–53. https://doi.org/10.1007/s12517-012-0707-2 Available at https://link.springer.com/content/pdf/10.1007%2Fs12517-012-0707-2.pdf

Bhuiyan, C. (2008). Desert Vegetation during Droughts Response and Sensitivity [online] Available at https://www.researchgate.net/publication/228452114_Desert_Vegetation_during_Droughts_Response_and_Sensiti-vity.

Chang, S., Wu, B., Yan, N., Davdai, B., & Nasanbat, E. (2017). Suitability Assessment of Satellite-Derived Drought Indices for Mongolian Grassland. Remote Sensing, 9(7), 650. https://doi.org/10.3390/rs9070650.  

Online Resource, NASA Terra MODIS : https://terra.nasa.gov/about/terra-instruments/modis

Servir Global: Google Earth Engine Change detection training https://servirglobal.net/Portals/0/Documents/Articles/ChangeDetectionTraining/Module2_Intro_Google_Earth_Engine_Exercise.pdf
 

/advisory-support/recommended-practices/recommended-practice-agriculture-drough…
/advisory-support/recommended-practices/recommended-practice-agriculture-drough…

Step by Step: Agriculture Drought Monitoring and Hazard Assessment using Google Earth Engine

The MODIS sensor onboard the Aqua and Terra satellites acquires data in 36 spectral bands: two bands at 250 meters, five bands at 500 meters, and twenty-nine bands at 1000 meters spatial resolution.

MODIS data is useful to track changes in the landscape over time due to its high temporal resolution, which allows for the monitoring of vegetation health by means of time-series analyses with vegetation indices.

Figure 1 shows MODIS spectral bands and their bandwidth. MODIS data is available free of charge and can be downloaded from USGS (https://earthexplorer.usgs.gov/) or AppEEARS (https://lpdaacsvc.cr.usgs.gov/appeears/) once the user is registered.

In this recommended practice the data will be loaded directly in the Google Earth Engine environment and does not need to be downloaded.

MODIS spectral bands and its bandwidth
Figure 1: MODIS spectral bands and its bandwidth

Indices Selection 

1. Normalized Difference Vegetation Index (NDVI)

The NDVI is an excellent sign of green biomass, leaf area index, and patterns of production as, when sunlight hits a plant, mostly the red bandwidth in the visible part of the electromagnetic radiation spectrum (0.4–0.7 mm) is absorbed by chlorophyll in the leaves, whereas the cell formation of leaves reflects the bulk of near-infrared (NIR) radiation (0.7–1.1 mm). Healthy vegetation absorbs the red light and reflects NIR radiation. Usually, if there is extra reflected radiation in the NIR range than the visible, then vegetation will be healthy (dense). The NDVI range varies from −1 to +1, with values near zero representing no green vegetation and values near +1 showing the highest possible density of vegetation. Areas of barren rock, sand, and snow produce NDVI values of <0.1, while shrub and grassland typically produce NDVI values of 0.2–0.3, and temperate and tropical rainforests produce values in the 0.6–0.8 range. The NDVI is calculated with the following formula: 

NDVI = NIR – RED / NIR + RED


2. Vegetation Condition Index (VCI)

The VCI is an indicator of the status of vegetation cover as a function of NDVI minima and maxima encountered for a given ecosystem over many years. It is a better indicator of water stress condition than the NDVI. The deviation of the vegetation condition is an indicator of the intensity of the impact of drought on vegetation growth. The VCI is calculated using the following formula: 

VCIj = (NDVIj - NDVImin) / (NDVImax - NDVImin ) × 100

NDVImax and NDVImin are the maximum and minimum NDVI values in a multi-year dataset. The ‘j’ is the NDVI value for the current month. 


3. Temperature Condition Index (TCI)

Land surface temperature (LST) derived from thermal radiance bands is a good indicator of the energy balance of the Earth’s surface, because temperatures can rise quickly under water stress. The TCI is an initial indicator of water stress and drought. It is calculated using the following formula. 

TCIj = (TCIj - TCImin) / (TCImax - TCImin) × 100

TCImax and TCImin are the maximum and minimum TCI values in a multi-year dataset. The ‘j’ is the TCI value for the current month.


4. Vegetation Health Index (VHI)

The VHI is a combination of the constructed VCI and TCI and can be used effectively for drought assessments. It can be calculated using the following formula. 

VHI = α × VCI + (1 - α) × TCI

where α is the weight to measure the contribution of the VCI and TCI for assessing the status of drought. Generally, α is set as 0.5 because it is difficult to distinguish the contribution of the surface temperature and the NDVI when measuring drought stress. 
 

 

This Recommended Practice will utilize Google Earth Engine for the preparation of the NDVI, VCI, TCI, and VHI for the assessment of drought conditions using MODIS data. Initial access to Google Earth Engine can be requested by using one’s Gmail credential. The Google Earth Engine editor can then be accessed via the following URL: https://code.earthengine.google.com/. The interface of Google Earth Engine is shown in figure 2. 

Google Earth Engine Interface
Figure 2: Google Earth Engine Interface

 

The step-by-step methodology for the calculation of the VCI, TCI, and VHI indices is given below. 

 

Step 1. To calculate the VCI, TCI & VHI using Google Earth Engine, copy the code from annex 1 (TCI), annex 2 (VCI) or annex 3 (VHI), and paste it into the Code Editor window. The codes can also be imported by clicking these links:TCI, VCI and VHI. Each index and its output is calculated in an individual script.

 

Step 2. This script can be saved by clicking the save option shown in figure 2. The saved script will appear in the left window under the Script Manager and owner option.

Please note: In order to be able to save the script, you will need to make a change to it. E.g.: Add a line or a comment.  

 

Step 3. The study area can be added manually or uploaded as a shapefile (see description in the code). However, it is recommended that the study area may be defined by importing a shapefile as shown in figure 3. For adding a shapefile, click the Assets menu (located in the left corner), then click NEW and select Shape files from the drop-down menu to upload desired Area of Interest (AOI). A popup window (Upload a new shapefile Asset) will appear to upload the desired shapefile. Now select all components of shapefile i.e. .shp, .prj, .dbf, .cpg, and shp.xml and click upload. The upload process will take a few seconds and can be seen under the Task menu as shown in figure 3. A shapefile for demonstration purposes is provided at the bottom of this page (annex 4), which consists of the adminstrative unit of the Sindh Province, Pakistan.

 

Process of importing a shapefile
Figure 3: Process of importing a shapefile

 

Step 4. Now click on the AOI in the left panel and import the shapefile into the Code Editor. Then change the variable name “table” to “AOI” in the Code Editor (Red Box) to run the script as shown in figure 4. Finally, click Run to execute the code.  

Note: In case of the variable name was not changed, the script will end up with an error.

 

Adding AOI and renaming variable
Figure 4: Adding AOI and renaming variable

 

Step 5. The VCI, TCI and VHI is calculated for the month of March 2019 to assess the vegetation condition in the region. The calculations are based on the entire MODIS operation period (2000-today or selected end time). The output is available in the map window.

The output can be exported to the user’s Google Drive account, by running the task for the creation of the output file in the "Tasks" tab, as shown in figure 5. Afterwards, the output file can be used or downloaded from the user’s Google Drive.

 

Drought Index (VHI)
Figure 5: Exporting Data to Google Drive

 

Step 6. The output raster of the previous steps needs to be categorized into the drought severity zones so that the extent of drought can be analyzed in the affected region. VHI values vary between 0 and +1(100) and the raster image was classified as per the criteria mentioned in table 1 and implemented in the code.

 

Drought Zonation based on VCI, TCI & VHI values
Table 1: Drought Zonation based on VCI, TCI & VHI values

 

The classified image (tiff) is shown in figure 5. Subsequently, the classified image was recoded as per the criteria placed in table 2. 

 

Raster Values in the Final Product
Table 2: Raster Values in the Final Product

 

Final products can be used for further analysis of agriculture drought assessment and monitoring in a GIS environment along with other datasets.

 

Step 7. Users can also prepare maps based on VCI and TCI indices by repeating the procedure mentioned in Step 6. VCI and TCI outputs are shown in figures 6 and 7 respectively.

 

Vegetation Condition Index (VCI)
Figure 6: Vegetation Condition Index (VCI)

 

Temperature Condition Index (TCI)
Figure 7: Temperature Condition Index (TCI)

 

Conclusion 

The scientific community uses VCI and VHI to analyze the greenness effect in agriculture to assess drought. While TCI is utilized to assess vegetation stress due to the temperature. Hence, VCI and VHI can be used for drought monitoring and assessment. However, VHI is more robust and effective due to its good representation of drought occurrence phenomena. VHI time series products pertaining to the study area are placed in Figure 9. This figure clearly shows the evolution of a drought-like situation, which evolved gradually from 2016 to 2019 respectively.

 

VHI Time Series Products
Figure 8: VHI Time Series Products (a) March 2016 (b) March 2017 (c) March 2018 (d) March 2019


 

Recommended Practice: Drought monitoring using the Vegetation Condition Index (VCI)
Recommended Practice: Drought monitoring using the Standard Vegetation Index (SVI)
Recommended Practice: Drought monitoring using the Standardized Precipitation Index (SPI)
MODIS Vegetation Product (NASA)
Google Earth Engine (Google)
Detail Page
Overview

Recommended Practice: Agriculture Drought Monitoring and Hazard Assessment using Google Earth Engine

Drought is slow on-set disaster which affects agriculture and associated livelihood in many parts of the world. Drought causes stress to vegetation; hence the extent of a drought can be reflected by changes in the vegetation condition. Geospatial scientists have developed drought indices based on remote sensing data to effectively detect drought conditions. The Normalized Difference Vegetation Index (NDVI) is the normalized difference between near-infrared (NIR) and red reflectance. Simple and effective, it is one of the most widely used indices for detecting drought conditions. More recently, many other indices have been developed, such as the Vegetation Condition Index (VCI), which is an indicator of the status of vegetation cover as a function of NDVI minima and maxima encountered for a given ecosystem over many years, or the Vegetation Health Index (VHI), which combines the VCI and land surface temperature.

This Recommended Practice will utilize Google Earth Engine for the preparation of indices. Google Earth Engine is a cloud-based platform and can perform large-scale geospatial analysis to solve real world spatial problems based open-source data. The major advantage of Google Earth Engine is its use of Google’s extensive computational capabilities, resources and datasets, which generally do not require high-performance computing systems at the user end. Therefore, Google Earth Engine appears to be a unique and integrated platform designed to empower not only traditional remote sensing scientists but also a much wider audience that lacks the technical capacity needed to utilize traditional supercomputers or cloud-computing resources.

In Detail Drought VCI
Step by Step Drought VCI
Recommended Practice: Drought monitoring using the Vegetation Condition Index (VCI)
Recommended Practice: Drought monitoring using the Standard Vegetation Index (SVI)
Recommended Practice: Drought monitoring using the Standardized Precipitation Index (SPI)
Google Earth Engine (Google)

The objective of this practice is to identify the drought extent and severity in the affected region. This information can be used by disaster management agencies and other stakeholders to mitigate, rescue, and relief activities in affected areas.

Preparedness
Drought
Sindh Province, Pakistan

The Recommended Practice was initially applied to the drought-like situation observed in March 2019 using MODIS data for the period 2000 to 2018. The drought affecting the area was extracted from the visible bands (R, G, B, NIR) of MODIS products, which have a 1000 meters resolution, by using Normalized Difference Indices (NDVI, VCI, TCI, and VHI). The resolution can be enhanced up to 500m depending on the spatial extent of AOI. 

This practice can be applied to vegetation drought events anywhere in the world. 

However, the procedure works best for rainfed agriculture. For irrigated agriculture, the results may not necessarily be satisfying. 

Advanced Geospatial Technologies for Disaster Risk Reduction (DRR)

This is event is available for participation on an ongoing basis
Advanced Geospatial Technologies for DRR (IIRS)

Natural disasters are becoming more frequent and intense across the globe. Enhancing resilience to increasing hazards, exposure, and vulnerability therefore requires leveraging of advanced geospatial technologies for better disaster mitigation and management. With continuous improvements in satellite data sensor acquisition parameters together with geo-computational approaches, geospatial technologies have emerged as the most powerful technology for all phases of disaster management.

0
0
disaster risk reduction
online training
Indian Space Research Organisation (ISRO)
Indian Space Research Organisation
IIRS
Professionals from Central / State Govt. / Private Organizations / NGO engaged in geospatial data analysis related research and projects; students and researchers aligned to research in Disaster Risk Reduction (DRR).
No certificate issued or not known

Familiarization with Basics of Remote Sensing and GIS.

0.00
USD

The programme is sponsored by IIRS, Indian Space Research Organisation, Department of Space, Government of India, Dehradun

04/18/2022, 12:00am - 04/29/2022, 12:00am
not visible

Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO)

https://www.iirs.gov.in/Edusat-News/
1

German EnMAP Hyperspectral Satellite Launched Successfully

EnMAP Logo © DLR

The EnMAP (Environmental Mapping and Analysis Program) satellite launched into space from Cape Canaveral, Florida, at 18:24 CEST on April 1, 2022. EnMAP will use 242 spectral channels (hyperspectral) to record Earth from a distance of 650 kilometers.

https://www.dlr.de/content/en/articles/news/2022/02/20220401_german-satellite-e…

ICPAC Joins the Risk-informed Early Action Partnership (REAP) as the 62nd Member

ICPAC and REAP Logos

The IGAD Climate Prediction and Applications Centre (ICPAC) has become an official member of the Risk-informed Early Action Partnership (REAP), a worldwide project that brings together major actors in the climate, humanitarian, and development sectors.

https://africanews.space/icpac-becomes-the-62nd-member-of-the-risk-informed-ear…
Eastern Africa

World Meteorological Day 2022: Early Warning Systems Must Protect Everyone Within Five Years

World Meteorological Day 2022

The United Nations sets an ambitious goal for adapting to climate change and increasingly severe weather. Everyone on Earth should be protected against more severe weather and climate change over the next five years, according to an ambitious new United Nations target published a few days ago.

UN Secretary-General António Guterres has charged the World Meteorological Organization (WMO) with leading the effort and presenting an action plan to accomplish this aim during this November's UN climate summit in Egypt.

https://unfccc.int/news/un-early-warning-systems-must-protect-everyone-within-f…

Ministry of Communications and Multimedia (KKMM) in Malaysia to finalize satellite technology for disaster management this year

AEHF (Advanced Extremely High Frequency) Satellite, US Space Force.

The Deputy Communications and Multimedia Minister of Malaysia announced that the selection of satellite technology to strengthen the country's communications infrastructure during disasters, particularly floods, is likely to be completed this year. It was stated that the government is acquiring superior satellite technology to aid the Meteorological Department and other associated organizations in the event of a disaster.

https://www.theborneopost.com/2022/03/08/kkmm-to-finlise-satellite-technology-f…
Malaysia
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