Drought (agricultural)

Satellite(s) Landsat, MODIS, Sentinel-1, AVHRR.	Monitoring element Land surface reflectance, SAR backscatter.

Satellite(s)

Landsat, MODIS, Sentinel-1, AVHRR.

Monitoring element

Land surface reflectance, SAR backscatter.

Description technique

To date, agricultural drought monitoring has generally been achieved via multispectral time series, particularly Landsat, by exploiting the relationship between optical reflectance and soil moisture (Sánchez et al, 2018). To resolve a measure of drought stress, a range of indices have been proposed which take advantage of the spectral range of multispectral sensors such as Landsat, in particular near infra-red (NIR) and shortwave infrared (SWIR), the degree of reflectance within which has been shown to strongly correlated with vegetation health (Brown et al, 2006). These include NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index) and NDWI (Normalized Difference Vegetation Index). A number of other indices including VCI (Vegetation Condition Index), VHI (Vegetation Health Index) and TCI (Temperature Condition Index) and also take advantage of thermal bands carried by Landsat and other sensors, such as AVHRR but still use NDVI as an input (Liu et al, 2016, Zambrano et al, 2016). By comparing a stack of observations through time, changes in crop drought stress can be monitored.

Furthermore, a range of indices using meteorological data have also been developed, the most popular of which is SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index), which also factors in temperature data (Zambrano et al, 2016). These models benefit from using records which often date back further than optical remote sensing data, but do not offer the spatial granularity of optical multispectral based applications due to the need for significant interpolation between measurement stations.

Accuracy / Resolution

Variable spatial and temporal resolution according to sensors.

Case study

Several regional to global scale studies have monitored agricultural drought at relatively coarse resolutions (i.e. 250m or greater). Zambrano et al (2016) demonstrate the applicability of MODIS time series to monitor agricultural drought, with 16 year VCI time series covering the BioBio region of Chile and concluded that the results correlated well with qualitative reports of drought through the monitoring period.

At finer resolutions (30m), Ghaleb et al (2015) show how a simple Landsat derived VCI and TCI time series can be used to describe both spatial and temporal trends in drought within Lebanon, from 1982 to 2014.

Also fits domain

Atmosphere.

Benefits

Can provide a regional overview of agricultural drought status and a better understanding on the spatial patterns of drought within a region, supporting response and resource management on the ground;
Depending on the temporal granularity of selected approach, it may be possible to monitor for irrigation by looking for dramatic reductions in the drought stress measure;
More cost effective than field monitoring.

Limitations

Limitations in spatial, temporal and spectral resolution of remote sensing platforms, requiring compromise.
Microwave (SAR) can only penetrate 2-5 cm into the soil layer, significantly more shallow than the depth at which crop roots reside (Liu et al, 2016).
Requires optical observations with no atmospheric interference, which may skew results.

Applicability for Northland

Yes, likely.

The extent and magnitude of recent droughts should be readily apparent within a range of satellite data sources, assuming cloud free optical data is available through the summer months. A relatively deep time series will be required to distinguish short term variability from longer term changes, but the Landsat and MODIS coverage over Northland should facilitate this.

Techniques applying optical data will be limited in coverage and temporal granularity by the persistent cloud cover in the region, particularly during the winter months. Mature cloud-masking techniques are directly available for open access multispectral data (e.g. Landsat and Sentinel-2). When using commercial data, care must be taken to ensure that there is sufficiently cloud free imagery available, as cloud masking is not as mature and ordering a large volume of imagery to ensure complete cloud free coverage between multiple observations can become cost prohibitive.

Publication references

West, H., Quinn, N. and Horswell, M., 2019. Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities. Remote Sensing of Environment, 232, pp.1-14.;

https://www.sciencedirect.com/science/article/pii/S0034425719303104

Gouveia, C., Trigo, R.M. and DaCamara, C.C., 2009. Drought and vegetation stress monitoring in Portugal using satellite data. Natural Hazards and Earth System Sciences, 9(1), pp.185-195.

https://nhess.copernicus.org/articles/9/185/2009/

Keshavarz, M.R., Vazifedoust, M. and Alizadeh, A., 2014. Drought monitoring using a Soil Wetness Deficit Index (SWDI) derived from MODIS satellite data. Agricultural water management, 132, pp.37-45.
Liu, X., Zhu, X., Pan, Y., Li, S., Liu, Y. and Ma, Y., 2016. Agricultural drought monitoring: Progress, challenges, and prospects. Journal of Geographical Sciences, 26(6), pp.750-767.

https://www.sciencedirect.com/science/article/pii/S0378377413002746?via%3Dihub

Brown, M.E., Pinzón, J.E., Didan, K., Morisette, J.T. and Tucker, C.J., 2006. Evaluation of the consistency of long-term NDVI time series derived from AVHRR, SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors. IEEE Transactions on geoscience and remote sensing, 44(7), pp.1787-1793.

https://ieeexplore.ieee.org/document/1645279

Sánchez, N., González-Zamora, Á., Martínez-Fernández, J., Piles, M. and Pablos, M., 2018. Integrated remote sensing approach to global agricultural drought monitoring. Agricultural and forest meteorology, 259, pp.141-153.

https://daneshyari.com/article/preview/6536638.pdf

Zambrano, F., Lillo-Saavedra, M., Verbist, K. and Lagos, O., 2016. Sixteen years of agricultural drought assessment of the BioBío region in Chile using a 250 m resolution Vegetation Condition Index (VCI). Remote Sensing, 8(6), p.530.

https://www.mdpi.com/2072-4292/8/6/530

Ghaleb, F., Mario, M. and Sandra, A.N., 2015. Regional landsat-based drought monitoring from 1982 to 2014. Climate, 3(3), pp.563-577.

https://www.mdpi.com/2225-1154/3/3/563

Other comments or information

The ability of cloud based processing platforms such as Google Earth Engine to rapidly apply cutting edge cloud masking algorithms to large time series datasets has significantly reduced the complexity of undertaking such analyses when compared to traditional desktop processing.