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. 


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: 


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: 

VCI= (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. 


  • 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.



  • 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.

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. Available at

Bhuiyan, C. (2008). Desert Vegetation during Droughts Response and Sensitivity [online] Available at

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.  

Online Resource, NASA Terra MODIS :

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