Monitoring Drought at Indirasagar Reservoir in India using Multispectral Satellite Imagery
- Arpit Shah
- Nov 18, 2022
- 15 min read
Updated: Apr 22
INTRODUCTION
As a city-dweller residing for most parts in the tropical wetland that is Kolkata, I've always found the city to be water-positive - the South-Westerlies bring copious quantities of rainfall, the murky Hooghly river never ceases to appear menacing, the vegetation is evergreen, and the soft soil seems to emanate a distinct watery-smell throughout the year. Yet, underneath this seeming abundance lies a worrying prospect - the city's groundwater levels are receding.
HYPERLINKS TO SECTIONS
Credits: RUS Copernicus for the tutorial & ArcGIS Pro for the trial software.
BACKGROUND
For all the misery that a Drought brings (it is the most costly weather-event after a Cyclone), it is rather innocuously defined by USGS as a period of drier-than-normal conditions that results in water-related problems. While this phenomena can largely be attributed to natural causes such as dearth of adequate precipitation, humans are exacerbating it through their misuse of resources which contribute towards global warming and broader climate change.
Having read National Geographic's succinct explanation as to why tracking the onset of Drought is complicated, I can draw parallels to my study on Mapping Deformation at Volcanic sites as this phenomena also develops gradually.
Droughts can be categorized as-
Meteorological - when precipitation is below average for an extended period of time
Hydrological - when the water level in natural or man-made storage sites such as aquifers, lakes and reservoirs fall below a threshold
Agricultural/Ecological - when poor farming practises or excessive heat or low precipitation reduces soil moisture availability for crops
The repercussions from Droughts can be wide-ranging, from destruction of crops and vegetation, famine, migration, soil erosion and resulting wildfires to loss of biodiversity, increase in water pollution, scarcity of water for irrigation and urban consumption, and low power generation at dams. There is actually a category of drought known as Socio-economic Drought which occurs when there is a shortage of agricultural produce that is dependent on water supply i.e. when the demand for products such as grains and vegetables exceeds its supply.
Does a Drought end when it rains?
Not really. The continuity of rains matters. Essentially, the precipitation needs to soak the soil and replenish the groundwater for the drought conditions to subside and for the vegetation to reappear.
Developing and underdeveloped economies are most vulnerable to Drought due to their high dependence on Agriculture, as can be ascertained from UNCCD's report. I was taken aback to know the prevalent Drought conditions in India - nearly 50% of land area in India was affected by it as of 2019 (30% as of 2023) and in the five years from 2016-17 to 2021-22, an astounding 35 million hectares of farmlands had been damaged due to it. To give you a perspective of how large this figure is, it is roughly 22% of India's total arable land which stood at 160 million hectares (2011 estimate). The vagaries of weather - too much precipitation at certain locations and too little at others - is particularly brutal.
All is not lost however. Gujarat, a western state of India at the receiving end of prolonged dry weather, has taken substantial measures to monitor and combat Drought and can be a role model for other states affected by it across the country. Globally, there is an emphasis on developing Early Warning Systems to proactively identify this phenomena as well as on practical methods such as irrigation planning, rainwater harvesting, resistant crops cultivation, sustainable farming practises and even funky techniques such as cloud-seeding to combat it.
DROUGHT MONITORING USING REMOTE SENSING
I have used Sentinel-2 Multispectral Satellite Imagery to monitor Hydrological Drought conditions at Indirasagar Reservoir in the state of Madhya Pradesh, India over the last four years (2019-2022). This man-made reservoir borne out of Indirasagar Dam is the largest in the country in terms of area and water storage capacity and the water is utilized extensively for irrigation purposes across the state which is experiencing increasing mean annual temperatures making it susceptible to Drought.
The video below contains an elaborate walkthrough demonstrating the processing chain involved in Satellite Imagery Analytics for Drought Monitoring - from downloading the Multispectral datasets, processing it using an open source Imagery Analytics tool, and mapping it out using GIS software-
Prior to monitoring Drought conditions at Indirasagar Reservoir, I had opted to study Theewaterskloof Dam's reservoir in South Africa, the time period being the same (2019-2022). This was for me an extension to the tutorial which monitored Drought conditions at this location for five years until 2020. Theewaterskloof, which stores and supplies water to the city of Cape Town, was affected by severe Drought from 2016 to 2018 and water levels were estimated to have receded by as much as 89%. I will include snippets from the Theewaterskloof study in this post as well.
a. TIMELINE OF IMAGERY USED
For the Indirasagar study, I had selected two Sentinel-2 Satellite Imagery datasets from each of the four years, acquired during summer and monsoon respectively.
For the Theewaterskloof study, I had selected three Sentinel-2 Satellite Imagery datasets from each of the four years, two acquired during summer and one during monsoon (P.S. the seasons are reversed in the Southern Hemisphere and Drought conditions can exist during the monsoons (less or no precipitation).
Maintaining consistency in terms of the timeline felt appropriate - not just in the selection of the month but also in the selection of the day from the month. For example, one can reasonably expect May 15th - 31st to be reasonably warmer than May 1st - 15th in an Indian summer. To compare a May 28th image to a May 2nd image from the previous year wouldn't be a like-for-like comparison: the water body would likely be drier due to more evaporation, all else remaining constant.
Also, while the Sentinel-2 datasets are available cyclically throughout the year, the tricky aspect to deal with is the presence of Clouds in the imagery - it is very important that the Cloud cover over the study area in particular (the reservoir) is negligible as it hinders the analysis of surface features due to unavailability of adequate solar reflectance data. I had to be mindful of this and had to move to selecting another date if there were no Cloud-free images in the desired date-range. For the Indirasagar study, most of the datasets which I had selected were relatively cloud-free (<1% cloud cover for most and 2-3% for the rest of them).
Just to clarify, Earth Observation satellites such as Sentinel-2 travel on a fixed path and offer standard-sized coverage (based on swath width). If the study area is very large or does not lie within a single pass of the satellite, one may need to stitch two or more Imagery datasets together prior to processing. In contrast, if the study area lies within the imagery, one prefers to clip the geographic extent of the image as it helps in reducing the size of the dataset (Sentinel-2 L2A dataset is ~1 GB per image) allowing subsequent processing steps to run quicker. Technically known as Subset, clipping is not restricted to geographic extent alone - multispectral datasets capture surface reflectances across multiple ranges of wavelengths (called Spectral bands) and one can choose to remove the bands that are not relevant to the study, thereby making the dataset lighter which saves up on processing time.
The polygon on the left in Figure 6 depicts the geographic extent of a Sentinel-2 multispectral dataset - you can observe the water body which lies within - it is the Indirasagar Reservoir, our area of interest. Notice the clouds that lie south-west in the satellite view on the right. While the Cloud cover percentage in the image is miniscule (<1 % as mentioned in the product page), had that chunk of clouds been directly over the reservoir, I would be hesitant to select this dataset for the study.
By contrast, this product (Figure 7) is not useful at all though-
b. PROCESSING TOOLS UTILIZED
Given that Sentinel-2 L2A products over this region have begun to be released for public consumption relatively recently (December 2018), I was fortunate that I could use them for my study instead of Level-1C products. L2A products are atmospherically-corrected i.e. they contain Bottom of Atmosphere (BoA) reflectance values instead of Top of Atmosphere (ToA) radiance which L1C carries - this makes the analysis of surface features much more accurate, as desired in the Drought Monitoring study. Converting Level-1C product to Level-2A product manually would entail deploying the Sen2Cor processing algorithm which is a time-consuming process.
To analyse and subsequently perform Drought mapping on the L2A products, I have used three applications - SNAP, QGIS and ArcGIS Pro. SNAP software was predominantly used for the main analysis (identifying water pixels) whereas the two GIS software were used to refine, organize and map the extracted output from SNAP. All the steps have been demonstrated in the video walkthrough.
c. PROCESSING THE IMAGERY ON SNAP
SNAP, acronym for Sentinel Application Platform, is a powerful and free-to-use software that can perform a wide variety of analytics workflows on Satellite Imagery datasets. Let me highlight the processing steps for this Drought Mapping study.
At first, I will visualize the Sentinel-2 L2A product in RGB mode (Natural color)-
Sentinel-2A products capture data across 12 Spectral bands (ranges of wavelength) as can be seen in Figure 11 (L1C products have an additional B10 band - Cirrus).
Besides Spectral bands, S-2 L2A package has atmospherical corrections information stored in separate masks - such as the extracted Cloud pixels (refer Figure 12 below).
The raw L2A dataset will undergo three processing phases - I'd like to call them refining, analysing and extracting for the purposes of this Drought Mapping study-
In Phase 1 (Refining), the L2A imagery product will be resampled first so that all the Spectral bands have the same 10m Spatial resolution. This is because certain processing steps on SNAP only work if there is an equivalence in Spatial resolution across the bands that are being used i.e. it is not a multi-sized product. Thereafter, I will proceed to subset the product discarding the unnecessary geographic extent from the product making it lighter to process.
Phase 2 (Analysis) involves manipulating the resampled spectral bands to isolate the pixels over water bodies from the dataset. For this Hydrological Drought Mapping study, what I am essentially intending to monitor is how the area of the reservoir has changed over a period of time - if it has shrunk by a significant margin, then it is indicative of Drought.
I've utilized a few type of Water radiometric indices to identify the water pixels-
Each indice has its own advantages and disadvantages - typically a suitable indice is chosen based on the type of water body and/or based on surrounding features (urban area, vegetated land).
The way to input these indices on SNAP is just like applying an arithmetic formula. For example, NDWI can be derived using the formula (B3 band − B8 band) ÷ (B3 band + B8 band) where B3, the Green band has a central wavelength of 560 nanometers and B8, the Near Infrared band has a central wavelength of 842 nanometers.
But what is the rationale?, you may wonder,
As you'll infer from Figure 14 above, Water reflects wavelengths within the Visible Light spectrum in low amounts (~10% ), however it does not reflect Near Infrared wavelengths at all i.e. absorbs NIR completely. Meanwhile, Soil and Green Vegetation demonstrate a considerable increase in reflectivity when exposed to NIR waves in comparison to Visible Light. What NDWI essentially does is it magnifies this contrasting reflectance characteristic: (Green minus NIR) ÷ (Green + NIR) in order to make it easy for the researcher to detect and isolate Water pixels from Land pixels from the dataset.
Please note that the Earth Observation satellites which acquire Multispectral datasets do so passively i.e. by capturing solar reflectance after it has interacted with surface features on Earth. Solar radiation comprises wavelengths from the Visible Light, Near Infrared (NIR), Short Wave Infrared (SWIR) and Ultraviolet (UV) portion of the electromagnetic spectrum which allows NDWI and other water radiometric indices to function on it.
In Phase 3 (Extraction), I will ask SNAP to isolate those pixels that have been identified as water across all the four Water Radiometric indices outputs. The advantage of doing so is that all the shortlisted pixels are highly likely to be water pixels, however, the disadvantage is the removal of false negatives i.e. those pixels which contain water fully or partially but are (incorrectly) removed as one or more water indice wasn't able to detect/classify it as so.
Thereafter, I will create a Mask layer which will remove all NaN pixels i.e those with no data values (these are the pixels that were not classified as water across all the four outputs) and preserve the information of only the isolated water pixels. After repeating the previous steps on the other seven datasets across the four years of the study, I will export them to my desktop as GeoTIFF files and use them as inputs in the processing phase on QGIS software subsequently.
SNAP allows to perform basic quantitative measurements as well. In the figure below, I am computing the area occupied by the water pixels in a mask layer-
Next, I'll perform Time Series Analysis - refer to the video below where I get to compare the pixel values of the NDWI output of all the eight images acquired over a span of four years simultaneously on a graph upon hovering the cursor over a single image - [10] NDWI.
Notice the cyclical patterns when I hover around the edge of the reservoir - this is because during the summer months, the water in the reservoir dries up and the edges turn into land i.e. negative NDWI value. During the monsoons, those pixels contain predominantly water and NDWI becomes positive.
d. SUBSEQUENT PROCESSING ON QGIS
I'd have preferred to perform the ensuing processing steps on SNAP itself as moving data from one application to another is slightly discomforting besides the fact that one needs to know how to operate different applications as well. This isn't a slight on QGIS though - it is a very powerful software adept at performing a wide variety of geospatial workflows. The best aspect is that it is open-source and free-to-use just like SNAP, making it highly popular in the geospatial community.
In QGIS, I will perform data transformation and simultaneous water pixels extraction across all the eight images in the timeline. The data transformation entails the conversion of the data in Raster format in the GeoTIFF file to a Vector format (Vector vs Raster explained). In a Raster image, each pixel has a data value but there are no containers for clusters of similar data values. Converting the imagery products into Vector format helps to have distinct shapes in the visualization - all of which would contain water pixels and which would allow me to extract the Indirasager reservoir from the images as it will be wrapped in a distinct container/shape (the extraction step shall be performed on ArcGIS Pro software later).
The next steps could have been done on QGIS itself, however, I am more comfortable using ArcGIS Pro software to perform these type of steps.
f. SUBSEQUENT PROCESSING ON ARCGIS Pro
Esri's ArcGIS Pro is a premium application which is adept at performing a wide variety of mapping, location analytics and imagery analytics workflows. It is highly user-friendly and a trial version is also available (which I have utilized myself). The software also offers several dedicated extensions for performing specialized workflows specific to certain sectors and industries such as solar, airports, military, urban planning among others. The workflows performed on SNAP earlier can also be performed on ArcGIS Pro by using the Image Analyst extension.
Watching my video demonstration would be the ideal in case you'd like to know the processing steps performed on ArcGIS Pro in detail - there are several minor steps involved which may not be all that evident from this post.
Explore the slider below - what have I done here?
Slider 1: Before and After comparison of an important processing step in ArcGIS Pro
I have refined the vector file to do away with several small water pools surrounding the reservoir in the mask layer of the imagery dataset. Doing so will allow me to derive an accurate computation of the area of water within Indirasagar reservoir - directly relevant to the Drought Assessment study.
I could have chosen the easier way which is to source a historical shapefile of the reservoir from within the software (using ArcGIS Online or Living Atlas) or from other online sources on the web and use it to clip out the other water features. But that would have been a less accurate approach for this time-dependent (temporal) study. Instead, I've opted to manually extract the reservoir from the dataset. This isn't all that difficult to do as Indirasagar reservoir is the largest shape in the whole extent by a distance and I can isolate it by deleting all the water body vector shapes in the dataset besides the largest one. For doing so, I've used the Buffer tool which allows me to generate an outline around all the vector shapes within the layer and subsequently I've used the Clip tool to keep only the largest outline i.e. the one that represents the Indirasagar reservoir.
Besides helping to clean up, organize, and refine the data, ArcGIS Pro is particularly useful when it comes to symbolizing a layer. I can visualize the output in multiple ways using a variety of colors, shapes and effects. In many ways, this is the essence of map-making which is to present the information in a highly impactful way to the viewer.
Figure 20 helps me to ascertain the presence of Drought at Theewaterskloof and if so, which portions of the reservoir receded the most. It is evident that the reservoir was recovering from the Drought which it had faced between 2016-18 as there is an increase in the water area almost every year between 2019-22.
Besides visual storytelling, one can also analyze the data with dynamic charts, statistical tools and infographs on ArcGIS Pro-
At Indirasagar, it becomes evident from Figure 21 that there was a significant decline in water area between 2021 monsoon and 2022 summer when compared to the previous years.
The charts below help to enhance our understanding-
ArcGIS Pro allows me to combine all the components I'd like to convey to the reader into a beautiful printable map-
From this study, while I cannot technically validate the presence of Drought - a hydrological expert with access to historical data and benchmarks would be the right person to make that assessment - the dip in the water area at Indirasagar Reservoir is very evident nonetheless.
I hope you found this post to be interesting and appreciated the importance of water as a precious resource. Feel free to share your feedback on projects@mapmyops.com or on the demonstration video's comment section on YouTube.
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