Remote Sensing

Farmonaut Overview: 2021-2022

Farmonaut was a vision seen in 2018 to bridge the technological gap between the farmers and their outputs. It also offers a satellite-based crop health monitoring system to agricultural companies to map more than thousands and lakhs of farmers’ fields. Farmanaut uses geotagging to map fields and provide smart data to farmers through its robust and advanced web app. 

As per data, organizations like Godrej Agrovet mapped several thousand field pieces initially using Farmonaut’s technology. The organization had already mapped over several thousand hectares last year.

Ankur Omar, CEO and founder of Farmonaut, said “Geotagging of farmer’s field has become easy, fast and more streamlined. There is an ecosystem of interconnected apps which enables our clients to perform multiple tasks like monitoring data, managing it without going to fields and accessing the data from anywhere across the world. During Covid 19, Farmonaut was a game-changer when it provided flawless data with interoperability that had become a game-changer for the organizations that have bigger pieces of land with huge numbers.” He added, “If flawless data is provided, it helps organizations perform farm-level actions in advance. Several organizations are using this technology. We welcome more to be on board across the world.” 

 

The challenge of agriculture in India is to get support and education for the right crop as per the weather conditions. Remote sensing technology provides data that mitigate challenges cost-effectively.

“We provide support and data to our clients cost-effectively. Indian farmers spend Rs. 42,000 per year on each hectare of land suitable for growing crops. If a farmer uses high processed remote sensing data results, 30% of the expense on chemicals, insecticides, pesticides and plant growth regulators can be saved.” said Aakash Omar, co-founder of Farmonaut.

Aakash Omar added, “We provide updated data, every 2 to 5 days with the resolution of 10m.

 

What data Farmonaut provides, let us figure out in detail.

Crop Health – To monitor crop health using geo sensing technology is a game-changer when it comes to production. Using geo sensing, a farmer can identify underperforming crops. Farmonout does provide support to farmers to take out underperforming crops from trouble. The organization provides farmers’ education for better crop yielding. Expert advice raises farmers’ education that helps them at every step to achieve profit. 

 

Evapotranspiration – Geo sensing technology also brings data to farmers when and where water is highly required for the crops. It also provides data for excess water in a field. 

Monitor evapotranspiration is an important task as it gives an estimate to the farmer that how much the soil contains water. A farmer can regulate the irrigation system as per the requirement of soil. It also identifies the location from where water is getting lost into the atmosphere at a higher rate. 

 

Radar data – A client can question what if there are clouds in the sky, how do we get data of our field then? 

To answer this question, Farmonaut also provides radar data that works even if it is extremely cloudy conditions. Organizations rely on Farmanout as it provides flawless data in harsh atmospheric conditions.

 

Digital Elevation Model – India is an agricultural country. Agriculture is a widespread occupation across the country. Monitoring data in Gangetic plans as well as hilly farms of Uttrakhand shows the commitment towards flawless data representation to our clients in India. Farmonaut uses technology that identifies tropical slopes at a micro-level.

During Covid 19 pandemic productivity and efficiency have been some important issues. In that case, useful information helps to improve farming. Digital Elevation Model is an important geospatial product that provides data on every slope and very useful tool for farming in hilly areas.

 

Colourblind visualization – Farmanout understands that a farmer’s productivity is our utmost priority. The organization treats all its farmers with respect. Farmanout also provides colour neutral satellite data specifically for colour blind users.  

 

Water Stress – Water stress is a key factor in the growth of crops. The app identifies the location where the water stress is low. It provides that data to the farmer by which water circulation can be managed effectively. 

 

Soil Moisture Identification – Breaking some barriers ahead, the app Farmanout also provides services in the identification of soil moisture. It provides data to the farmer where the moisture of the soil is low.

 

Soil Organic Carbon – If the organic carbon level of the soil is in check, fertilization can be done in a controlled way for better yielding. It also identifies the location where the soil organic carbon level is low and high. Experts suggest farmers rotate the fertilizers for a better outcome.

 

Weather Forecast – Conducive weather conditions are an essential part of the growth of crops. Weather is an uncontrollable phenomenon. However, damage to crops can be mitigated if the user knows the forthcoming situation. Farmonaut provides upcoming weather conditions so that farmers can prepare themselves.

 

All the above-mentioned services are being used by individual farmers as well as agricultural organizations.  Several agribusinesses have partnered with Farmonaut to avail satellite-based data for their farmers.

Some big names, who are the prominent clients of Farmonaut, are Godrej Agrovet, LINXAg, ZR3i.com, Crop United, Ubin Core, and Fashion for biodiversity. 

 

In India, very few organizations are putting efforts to change the face of agriculture using advanced technology. Farmonaut is one of them by taking baby steps and empowering farmers with technology.

 

All the above-mentioned services are being used by individual farmers as well as agricultural organizations.  Several agribusinesses have partnered with Farmonaut to avail satellite-based data for their farmers.

Some big names, who are the prominent clients of Farmonaut, are Godrej Agrovet, LINXAg, ZR3i.com, Crop United, Ubin Core, and Fashion for biodiversity. 

 

In India, very few organizations are putting efforts to change the face of agriculture using advanced technology. Farmonaut is one of them by taking baby steps and empowering farmers with technology.

 

We will keep posting about any such informative information on to our blogs, to help as many people as possible. Farmonaut is built upon a vision to bridge the technological gap between farmers and strives to bring state-of-the-art technologies in the hands of each and every farmer. For any queries/suggestions, please contact us at support@farmonaut.com.

We have some more interesting articles coming up soon. Stay tuned!

Wait!!

Before that…

Follow us at:

Facebook: https://facebook.com/farmonaut

Instagram: https://instagram.com/farmonaut

Twitter: https://twitter.com/farmonaut

LinkedIn: https://www.linkedin.com/company/farmonaut/

Pinterest: https://in.pinterest.com/farmonaut/

Tumblr: https://farmonaut.tumblr.com/

Youtube: https://www.youtube.com/channel/UCYWOOPPKATLgh4L6YRlYFOQ

AppLink: https://play.google.com/store/apps/details?id=com.farmonaut.android

Website: https://farmonaut.com

Satellite Imagery: https://farmonaut.com/satellite-imagery

Satellite Imagery Samples: https://farmonaut.com/satellite-imagery-samples

 

 

Wheat Yield Calculation Based on Remote Sensing in Saharsa (Bihar) - 2022

Yield calculation of the crop is very important for assessing the production and it depends on many variables like soil, weather, agricultural practices (date of planting, amount of irrigation and fertilizer use), seeds, pests, weeds and biotic stress.

Remote sensing provides an effective and efficient way to forecast yield. Remote sensing has been widely used by many institutions across world to calculate area and yield of a crop.

In this paper, we focus on forecasting yield of wheat based on data from Sentinel-2 satellite data. The study area was wheat crop in Saharsa (Bihar). The crop is in advanced stage and has vegetated fully and harvest is expected in next 20-30 days.

Methodology:

Data from Sentinel-2 was used and random fields were selected from the mapped area and yield was calculated using NDVI (Normalized Difference Vegetation Index) and LAI (Leaf Area Index).

Farmonaut platform was used to map fields to create bounded regions (fields) as shown in Figure-1. On the dates of observations the sample fields were free from clouds and normal data was observed. Crop classification was done manually by ground truthing to ascertain the crop is standing in the field.

Figure-1              Comparison of peak NDVI in 2022 Vs 2021 wheat crop

Maximum NDVI was used to calculate yields for the given fields which means peak vegetative growth rate. Peak vegetative growth helps to forecast yields more accurately.

Below are the observations of NDVI of the various fields. The maximum NDVI was reached on 8-March-2022 compared to 21 Feb 2021 which was used to calculate yield. From the maximum NDVI, LAI was calculated which was subsequently used to calculate yield. This approach is being used after going through a lot of available texts and final model was considered.

In this procedure the estimated LAI was used with WTGROWS model for yield mapping. This model suggests direct correlation between grain yields and LAI. This relationship was applied to all the fields to calculate yields.

NDVI started to rise after planting of crop in November continued to rise until 08-March-2022 making the data as best date for calculating yield. The NDVI started falling after 08-March-2022 and will fall until harvest. Best results of yield is obtained when the NDVI is maximum.  

NDVI and LAI showed the best correlation for the estimated yields. The yields ranged from 2.87 Tons/ha to 2.98 Tons/ha compared to 3.5 Tons/ha to 3.7 Tons/ha last year (2021).  

 

Figure-2

 

Area (sq. m.)

26-Feb-22

03-Mar-22

08-Mar-22

13-Mar-22

Maximum LAI

Estimated Yield  Kg/ha

Field 1

3600

0.4

0.5

0.5

0.44

1.827376043

2981

Field 2

7700

0.4

0.5

0.5

0.43

1.827376043

2981

Field 3

1500

0.4

0.48

0.5

0.45

1.827376043

2981

Field 4

3100

0.4

0.49

0.5

0.43

1.827376043

2981

Field 5

6600

0.4

0.44

0.49

0.4

1.785290384

2944

Field 6

2700

0.39

0.43

0.47

0.4

1.704004521

2871

Field 7

2000

0.4

0.48

0.49

0.42

1.785290384

2944

Field 8

4300

0.4

0.44

0.48

0.41

1.744173984

2908

Field 9

1700

0.4

0.46

0.5

0.44

1.827376043

2981

Field 10

3700

0.4

0.49

0.5

0.48

1.827376043

2981

 

Figure-3

Observations

The delay in Max NDVI occurred due to late planting of wheat crop as rains in during the planting due to cyclones led to delay in planting. Moreover, rains in vegetative stage of crop led to worsening of condition of the crop.

Also, the average max temperature in the crop season was below corresponding period last year and humidity and cloud cover was significantly higher in 2021-22 leading to less sunlight on the crop thereby affecting the quality of crop and lower yields. Higher humidity leads to frost and less sunlight in winter. Also, more cloud cover leads to lower temperature and less sunlight which directly affects the crop. Even wind speed was higher in the season.

In the Figure 1, there is comparison of peak NDVI of 2021-22 wheat crop vs 2020-21 white crop showing that last year wheat crop was in much better condition that current year and the average yield was 3.7 Tonnes/Hectare in the above plot while this year the average yield in the above 10 fields are 3.0 Tonnes/Hectare which is approximately 20% below last year. The data was corroborated with discussion with Krishi Vigyan Kendra- Saharsa which said the peak crop yield will fall from 4.5 Tonnes/Hectare to 3.7 Tonnes/Hectare which is approximately 20% lower than last year. Late planting, rains before planting, after planting, higher cloud cover, lower temperature, higher humidity and higher wind speed led to lower yield in current year. The crop will be ready to harvest by mid-April-2022 compared to start of April last year.

 

Date

Min. Temp (C)

Max. Temp (C)

Humidity (%)

Pressure (hPa)

Cloud Cover (%)

Wind Speed (m/s)

21-02-2021

21

27

27

1018

0

4.43

08-03-2021

21

31

39

1010

0

4.08

13-03-2021

23

32

49

1013

68

4.22

 

 

 

 

 

 

 

21-02-2022

17

24

38

1012

1

2.68

08-03-2022

18

33

32

1009

0

2.64

13-03-2022

23

28

26

1009

0

2.86

 

 

 

 

 

 

 

2020-21 (1 Dec-Mar 10)

18.80

22.98

41.99

1014.00

7.68

2.39

2021-22(1 Dec-Mar 10)

18.02

20.87

50.95

1014.54

25.88

2.85

 

 

 

 

 

 

 

2021 (1 Feb-10 March)

20.41

25.00

37.38

1013.03

6.14

2.47

2022 (1 Feb-10 March)

18.06

21.43

48.83

1012.43

16.86

3.14

 

 

 

 

 

 

 

2021 (Jan)

16.93

21.14

47.79

1014.21

14.00

2.58

2022 (Jan)

16.41

18.68

57.09

1015.64

31.41

2.76

Figure-4                                Weather parameters responsible for lower yield

 

 

Results:

Data from our observation show that the average yield is 3.0 Tonnes/Hectare compared to 3.7 Tonnes/Hectare in the same fields last year. The yield range from 2.87 Tonnes/Hectare to 2.94 Tonnes/Hectare compared to 3.49 Tonnes/Hectare to 3.71 Tonnes/Hectare.

The above mentioned forecasted yields can vary depending upon weather and other unavoidable factors before harvest. If normal condition prevail then the above yields can be achieved subject to error of 10%.

The researches done in the area of yield forecasting of field crops by remote sensing has demonstrated good results. With the help of new sensors and indexes, researchers can calculate yields with less errors in future.

We will keep posting about any such informative information on to our blogs, to help as many people as possible. Farmonaut is built upon a vision to bridge the technological gap between farmers and strives to bring state-of-the-art technologies in the hands of each and every farmer. For any queries/suggestions, please contact us at support@farmonaut.com.

We have some more interesting articles coming up soon. Stay tuned!

Wait!!

Before that…

Follow us at:

Facebook: https://facebook.com/farmonaut

Instagram: https://instagram.com/farmonaut

Twitter: https://twitter.com/farmonaut

LinkedIn: https://www.linkedin.com/company/farmonaut/

Pinterest: https://in.pinterest.com/farmonaut/

Tumblr: https://farmonaut.tumblr.com/

Youtube: https://www.youtube.com/channel/UCYWOOPPKATLgh4L6YRlYFOQ

AppLink: https://play.google.com/store/apps/details?id=com.farmonaut.android

Website: https://farmonaut.com

Satellite Imagery: https://farmonaut.com/satellite-imagery

Satellite Imagery Samples: https://farmonaut.com/satellite-imagery-samples

 

 

Impact Analysis (April - October 2021)

Last 6 Months have taught us so much at Farmonaut. We faced the dangerous 2nd wave of covid-19 which affected all of us on the team as well as family level. For a bootstrapped startup like Farmonaut® it was a very important battle to be fought. It feels great to see us achieve so many milestones even in these tough times.


From serving more than 20,000 users directly through our platforms, to onboarding so many API users globally (Zr3i.com زرعي دوت, Godrej Agrovet Ltd, Ubincore, Fashion For Biodiversity Solutions GmbH, Linx Spatial Systems) and having more than 250,000 API Hits, to mapping 22,000+ farms for Godrej Agrovet Ltd, to successfully completely a large scale soybean area estimation for The Soybean Processors Association of India (SOPA), to conducting several internal and corporate researches, and receiving multiple awards and recognitions (Ramaiah Evolute, CE Worldwide, The Startup Pill, GIS Resources, Global Launch Base), it surely was a splendid 6 months period.


These are just a few steps towards a greater goal we plan to achieve in the coming months. Stay tuned! A lot more announcements are on the way.


Happy Farming!

#farming #farm #harvest #agrotech #agriculture #satellite #crophealth #farmonaut #fruit #vegetables #crops #agritech #precisionagriculture #organicfarming #greentech #technology #tech #foodsecurity #farmerlife #urbanfarmer #urbanfarming #nasa #remotesensing #gis #earth

We will keep posting about any such informative information on to our blogs, to help as many people as possible. Farmonaut is built upon a vision to bridge the technological gap between farmers and strives to bring state-of-the-art technologies in the hands of each and every farmer. For any queries/suggestions, please contact us at support@farmonaut.com.

We have some more interesting articles coming up soon. Stay tuned!

Wait!!

Before that…

Follow us at:

Facebook: https://facebook.com/farmonaut

Instagram: https://instagram.com/farmonaut

Twitter: https://twitter.com/farmonaut

LinkedIn: https://www.linkedin.com/company/farmonaut/

Pinterest: https://in.pinterest.com/farmonaut/

Tumblr: https://farmonaut.tumblr.com/

Youtube: https://www.youtube.com/channel/UCYWOOPPKATLgh4L6YRlYFOQ

AppLink: https://play.google.com/store/apps/details?id=com.farmonaut.android

Website: https://farmonaut.com

Satellite Imagery: https://farmonaut.com/satellite-imagery

Satellite Imagery Samples: https://farmonaut.com/satellite-imagery-samples

 

 

Rice Yield Calculation Based on Remote Sensing in Saharsa (Bihar) - 2021

Yield calculation of the crop is very important for assessing the production and it depends on many variables like soil, location, weather, crop variety, agricultural practices (date of planting, amount of irrigation, fertilizer use, weedicide and pesticide use), seeds and biotic stress. 

Remote sensing provides an effective and efficient way to forecast yield. Remote sensing has been widely used by many institutions across world to calculate area and yield of a crop.

In this paper, we focus on forecasting yield of paddy based on data from Sentinel-2 satellite data and Farmonaut’s Satellite Based Crop Monitoring System available on android, iOS, and web-app. The study area was rice crop in Saharsa (Bihar). The crop is in advanced stage and has vegetated fully and harvest is expected in next 10-30 days depending on crop variety of rice.

 

Methodology:

Data from Sentinel-2 was used and fields with highest vegetative index were selected from the mapped area and yield was calculated using NDVI (Normalized Difference Vegetation Index).

Farmonaut platform was used to map fields to create bounded regions (fields) as shown in Figure-2. On the dates of observations the sample fields were free from clouds and normal data was observed. Crop classification was done manually by ground truthing to ascertain the crop is standing in the field.

Maximum NDVI was used to calculate yields for the given fields which means peak vegetative growth rate. Peak vegetative growth helps to forecast yields more accurately.

Below are the observations of NDVI of the various fields. The maximum NDVI was reached on 4-Oct-2021 which was used to calculate yield. This approach is being used after going through a lot of available texts and final model was considered.

This model suggests direct correlation between grain yields and NDVI. This relationship was applied to all the fields to calculate yields.

NDVI started to rise after planting of crop in June continued to rise until 4-Oct-2021 making the data as best date for calculating yield. The NDVI started falling after 4-Oct-2021 and will fall until harvest. Best results of yield is obtained when the NDVI is maximum. In Figure 1 NDVI figures of three dates were taken and the date when the NDVI was maximum was considered.

NDVI showed the best correlation for the estimated yields. The yields ranged from 4.7 Tons/ha to 6.28 Tons/ha as shown in Figure 1 implying that most of the fields are having of long duration rice crop thereby generating higher NDVI. Also, the highest NDVI fields were selected meaning that most of the fields assessed is having long duration rice variety.

 Figure-1

Farmer

Area ( sq. m.)

24-09-2021

04-10-2021

09-10-2021

Estimated Yield (Tons/ha)

Field-1

32600

0.6

0.63

0.54

4.70

Field-2

9000

0.63

0.64

0.56

4.90

Field-3

12000

0.62

0.69

0.58

6.03

Field-4

3800

0.62

0.7

0.6

6.28

Field-5

22300

0.65

0.7

0.43

6.28

Field-6

5600

0.64

0.7

0.48

6.28

Field-7

5300

0.63

0.68

0.43

5.78

Field-8

4200

0.65

0.7

0.55

6.28

Field-9

6300

0.51

0.7

0.54

6.28

Field-10

12000

0.62

0.69

0.51

6.03

Results:

Our analysis of prediction of average yield was reported at 5.88 tons/ha which is in line with yield reported by Krishi Vigyan Kendra- Saharsa(ICAR) of 5-6 tons/ha for long duration crops. Our analysis show that the rice field used for yield prediction is mostly of long duration. Also, the prime reason of higher yield prediction was due to selection of fields which are producing highest NDVI skewing the data towards higher average yield. Which means the fields selected in our study is in upper quartile of yield implying long duration rice variety.

Data from Krishi Vigyan Kendra (KVK-Saharsa) showed that yields range between 5-6 tons/ha for long duration crop of 150-155 days (MTU 7029, Rajendra Masuri, Sabaur Shree, Sabaur Sampann) which will be harvested in November. For short duration rice crop of 90-100 days (Prabhat, Rajendra Bhagwati, Sabaur Surabhi etc) show mean yield of 3 tons/ha and it is harvested from mid-October. Also, yields in Saharsa is lower as it is situated on flood belts and around 80% of field is effected by water logging leading to lower yields compared to other districts. The above mentioned yields can vary depending upon weather before harvest as the crop is still standing while making the report. If conducive condition prevail then the above yields can be achieved.

The researches done in the area of yield forecasting of field crops by remote sensing has demonstrated good results. With the help of new sensors and indexes, researchers can calculate yields with less errors in future. Also, data by ground truthing will benefit the yield forecasting.

We will keep posting about any such informative information on to our blogs, to help as many people as possible. Farmonaut is built upon a vision to bridge the technological gap between farmers and strives to bring state-of-the-art technologies in the hands of each and every farmer. For any queries/suggestions, please contact us at support@farmonaut.com.

We have some more interesting articles coming up soon. Stay tuned!

Wait!!

Before that…

Follow us at:

Facebook: https://facebook.com/farmonaut

Instagram: https://instagram.com/farmonaut

Twitter: https://twitter.com/farmonaut

LinkedIn: https://www.linkedin.com/company/farmonaut/

Pinterest: https://in.pinterest.com/farmonaut/

Tumblr: https://farmonaut.tumblr.com/

Youtube: https://www.youtube.com/channel/UCYWOOPPKATLgh4L6YRlYFOQ

AppLink: https://play.google.com/store/apps/details?id=com.farmonaut.android

Website: https://farmonaut.com

Satellite Imagery: https://farmonaut.com/satellite-imagery

Satellite Imagery Samples: https://farmonaut.com/satellite-imagery-samples

 

 

Snow Cover Analysis Using NDSI

INTRODUCTION

Soil organic carbon is the carbon that remains in the soil after partial decomposition of any material produced by living organisms. Soil organic carbon is present as a main component of soil organic matter. Soil organic carbon is
believed to play crucial role for many soil functions and ecological properties. The amount of organic carbon present in a soil depends upon the local geology, climatic conditions, land use and management. Organic carbon is
mainly present in the top soil (2500 pg of c to 2-m depth).The amount of carbon that is present in the soil is twice larger the amount present in atmosphere hence soil act as an important reservoir of carbon. 

IMPACT ON AGRICULTURE

Soil organic carbon is the basis of sustainable agriculture. Farmers are interested in retaining and increasing soil organic carbon for individual fields in order to improve soil health and yield. One of the main reasons behind this is the ability of soil organic carbon in maintaining the soil fertility. SOC improves soil aeration, water retention capacity, drainage, and enhances microbial growth. As carbon stored in the soil is increased carbon i “sequestered” (long -term storage) and risk of loss of nutrients through leaching and erosion is reduced. When the amount of carbon in the soil is increased it reduces the amount of carbon dioxide present in the atmosphere which provides a better climatic condition for plant growth. An increase in soil organic carbon results in more stable carbon cycle and enhanced overall agricultural productivity. 

 

DEPLETION OF SOIL ORGANIC CARBON

According to the study conducted in Sweden, nationwide the 270 TG c stocks in agriculture surface soil is rapidly decreasing at a rate of 1 TG per year. One of the reasons behind this according to the study of GUO and GIFFORD is change in land use pattern. There is a chance of reduction of 10% of c stock when there is change in land use from forest to crop land. Unsustainable management practices like excessive irrigation, over grazing, deforestation, excessive tillage, practice of burning agricultural fields also causes soc losses. A large amount of carbon in the soil is reduced due to plant harvesting processes. The process of decomposition done by micro-organisms present in the soil where half of the organic carbon is released in the form of carbon dioxide is a major reason behind soil organic carbon depletion. Greater root bio-mass also result in carbon loss due to increased rate of respiration that take place through these roots. The amount of organic carbon present in the soil is affected by factors like climate, texture, hydrology (water content), land use and vegetation. When the amount of carbon in soil is reduced it affects the ability of soil to supply nutrients to the plant which in turn leads to low yield and affect food security. It also reduces the soil bio-diversity since it effects the growth of microbes. Global warming also contributes in depletion of organic matter present in the soil.

PRACTICES TO PROMOTE SOIL CARBON STORAGE 

Soil carbon storage is a vital ecosystem service. In an agricultural land soil carbon loss take place as a result of improper methods of soil managements such as excessive tillage, increased rate of irrigation, increased use of chemical fertilizers etc. One of the most effective methods for leaving the soil undisturbed is the practice of zero-tillage. Soil fertility can be maintained by introducing proper management strategy for grazing and by reducing the use of chemical fertilizers. Replacing chemical fertilizers with organic fertilizers and manures will help to restore the soil health. Erosion of top soil which bring the down the amount of carbon present in soil can be controlled by maintaining the ground cover. Growing cover crops like eucalyptus can reduce the wash away of top soil. Excessive irrigation can deteriorate soil health. So the amount of water supplied to the plants should be according to its needs, not more, not less. Another method of increasing carbon storage is by growing high yield, high biomass crops. The amount of carbon present in the soil will increase if the crop frequency of a place is maxi-mum. 

 

HOW TO ACHIEVE THIS 

Monitoring the field to assess whether the change in management is restoring or depleting the carbon resource is an important step towards protecting the soil organic carbon content. This can be done using the technology of remote sensing. Quantitative and qualitative estimation of soil using the conventional method is difficult since soil show variability form site to site even within the same field. The method of remote sensing is cost-effective and rapid. FARMONAUT app uses remote sensing technology to create a SOC image that provides color map of percentage of organic matter present in the selected field. If the content of SOC is more than 5% the area appears dark green in the color map and it appears red if the SOC content is less than 1%.Change in SOC content with time is also  noted with the help of remote sensing in FARMONAUT. This provides precise information to the farmers which help them to take the right measures, in the right time, and in the right place hence ensuring productivity and soil health.

 

NDVI vs NDRE And Their Applications In Agriculture

NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge) are known as “index products” which are primarily used to estimate crop health in an agricultural field. Both of these indexes are constructed from a combination of two distinct frequencies of light. NDVI is built with a combination of visual red light and near-infrared (NIR) light. NDVI is discussed in detail in a separate article. You can read the detailed article on this link below:

https://farmonaut.com/blogs/remote-sensing/normalized-difference-vegetation-index-ndvi/

 

NDRE uses a combination of near-infrared light and a frequency band that is in the transition region between visual red and NIR light.

One of the most common question being asked is NDVI or NDRE, which one should I use?  And what we say to this is that it depends upon the growth stage of your crops in the farming field.

 

NDVI is a more commonly used index to estimate crop health of a given field. In simple words, NDVI correlates with chlorophyll, which then in turn correlates with plant health. Having NDVI information of a particular field can help us identify crop health in their earlier growth stages.

However, it isn’t perfect and accurate for all crops and for all stages of crop growth. The visual-band red content is absorbed quite strongly by the top of the plant canopy, which means that the NDVI measurements do not have contribution from the lower levels of the canopy. Hence, leaf area index (LAI) and its correlation with NDVI is partially impaired. If the plants have more layers of leaves (for example, tree canopies), this impairment of correlation of LAI with NDVI increases.

Furthermore, in grasses, cereal crops, permanent crops and in certain row crops which are in their later growth stages, chlorophyll content reaches a point at which NDVI reaches a maximum value of 1.0 and hence saturates. Hence, any crop health issue is hard to detect with NDVI until any such problem becomes strong enough to reduce the NDVI value below 1.0. This may happen at a point at which damage has already occurred.

NDRE

NDRE OFFERS A SOLUTION

By substituting NDVI’s red band with NDRE’s red edge band we can mitigate this issue of saturation discussed abovev. NDRE’s red edge band provides a measurement that is not as strongly absorbed by just the topmost layers of leaves. By using NDRE, one can get better insight into crops in their later stage because it is able to observe further down into the canopy as well.

NDRE = (NIR – RE)/(NIR + RE)

NDRE is also less prone to saturation in the presence of dense vegetation. This will help us get much accurate results in pasture biomass estimation measurements. Thus, in situations like these, NDRE can provide a much accurate and better measurement of variability in an area in which the NDVI measurement would come simply as 1.0

So, in conclusion, if the crops of observation are permanent or dense, you should use NDRE right away. Ofcourse, using both the indices together is often the most ideal solution. A lot of farmers with crops that transition from seed to thick canopies in a single season make use of both NDVI and NDRE.

Farmonaut’s Automated Satellite Based Crop Health Monitoring System provides a farmer with both NDVI and NDRE results for crop health status measurements everytime the satellite crosses the farmer’s field.

We will keep posting about any such informative information on to our blogs, to help as many people as possible. Farmonaut is built upon a vision to bridge the technological gap between farmers and strives to bring state-of-the-art technologies in the hands of each and every farmer. For any queries/suggestions, please contact us at support@farmonaut.com.

We have some more interesting articles coming up soon. Stay tuned!

Wait!!

Before that…

Follow us at:

Cloud Coverage Analysis From Satellite Imagery

Cloud coverage essentially means the fraction of sky covered with clouds. In this article, we will illustrate a technique to find cloud cover percentage explicitly for beginners in image processing and discuss the limitations of the technique.

Naturally, the first question that comes into mind is why do we need to find the cloud cover percentage? Why is it so important? Lets review some of the reasons.

The most basic reason, is that cloud reflects roughly 90 percent of radiation back into space and at the same time it traps the outgoing radiation, which is known as the greenhouse effect. Hence, cloud cover has a large influence on the climate.

Climate change in turn can be the reason of natural hazards. Global warming decreases the temperature difference between the poles and the equator and hence, can increase the number of intense storms.

In conclusion, cloud cover distribution affects the weather system, heat situation of the surface, atmospheric conditions and other factors therefore, making it important for us to keep a check on it.

himalayan clouds

METHOD

Cloud cover percentage of a satellite image can be found basic python codes and a decent amount of manual work.

Consider a satellite image with a fair cloud coverage. First of all, we need to check the pixel values of the cloudy area in the image in order to narrow down the range of pixel values of the cloud.

It is important to pass a grayscale image, so that instead of dealing with three values for each pixel in an RGB image, you get equivalent one pixel value in the grayscale image. Since the clouds are white in color, the closer the pixel value is to 255, greater is the intensity of the cloud.

Since the density of cloud may be thick and thin in different regions, the intensity of whiteness at different regions will be different as well. Hence, the generated pixel values are to be interpreted and we need to find the range of pixel values the cloud region lies within. The estimated upper and lower limit values will be a rough range to identify any pixel as cloudy or not.

Although this part is quite cumbersome and lengthy, it is important to find the right pixel range of cloud to get an accurate cloud cover percentage.

Once we have the range, finding the cloud cover percentage is straight forward.

Following are some pseudo codes to perform the above.

Pseudo Code #1

The program will allow user to print pixel values of a grayscale image.

#import the libraries

#read a grayscale image

#access the pixel value in the range of the height and width of the image

#print pixel values

Pseudo Code #2

2. The program will allow user to calculate cloud cover percentage

#import the libraries

#read a grayscale image

#access the pixel value in the range of the height and width of the image

#use if statement to iterate through pixel values that lie between the selected range

#add the number of such pixel values

# use the mathematical expression to find the percentage

# print the result

STUDY AND OBSERVATION

We processed the satellite images of Thaltej, Ahmedabad of 2nd june, 2018. We captured Band 3 ,Band 4, Band 5, Band 11 and ETCI image. The estimated cloud coverage of that day is 16.7579%.

We performed the operations and for each bandwidth we found out the cloud cover percentage.

 

After analyzing the pixel value of the image, we considered the pixel range of cloud to be between 86-255. Following are the results we calculated.

 

BAND

BAND 3

BAND 4

BAND 5

BAND 11

ETCI

CLOUD COVER %

16.105

15.169

14.011

20.34

27.38

 

 

The clear observation here is that we are getting varying results from each bandwidth. Since the penetration level of each bandwidth is different, we see the difference in each image. Lets go through each bandwidth briefly.

 

Band 3: The Green Band

It is used for imaging peak vegetation and water structures that can penetrate up to 90 feet deep in clear water.

Band 4: The Red Band

It is used for discriminating the vegetation slopes.

Band 5:

It is used primarily for imaging vegetation.

Band 11 :Thermal 2

It measures the heat of the ground instead of temperature of air. The ground temperature is often more hotter.

 

The idea is to go through all the results and find an estimated result. One method is that results can be referred with SWIR result to get a proper estimation. SWIR can give more accurate results as SWIR can penetrate through clouds.

 

 LIMITATIONS

The variation in results infers that the cloud cover technique has limitations.

Sometimes the overly bright surfaces such as some desert surfaces and sands, having higher pixel value gets considered in the cloud coverage is one reason for inaccurate result.

 The SWIR results can be referred with to get a proper estimation. The limitation here is that resolution of SWIR is low which makes this method not so ideal.

It is also possible that sometimes the darker or smaller “popcorn” clouds go undetected.

Another reason is that snow and clouds are hard to differentiate and very often results in inaccurate results.

In the next article, we will be focusing on differentiating between snow and clouds and discussing the importance and techniques to achieve the desired result.

We will keep posting about any such informative information on to our blogs, to help as many people as possible. Farmonaut is built upon a vision to bridge the technological gap between farmers and strives to bring state-of-the-art technologies in the hands of each and every farmer. For any queries/suggestions, please contact us at support@farmonaut.com.

We have some more interesting articles coming up soon. Stay tuned!

Wait!!

Before that…

Follow us at:

Analyzing Impact of Cyclone Fani On Odisha Using Satellite Imagery And Remote Sensing Techniques

On the 3rd of May,2019, Odisha witnessed the strongest April cyclone in 43 years. With winds of more than 200 kmph, Cyclone Fani took over 64 lives and affected the lives of over 1.6 crore people in roughly 18,388 villages and 51 towns.

WHAT MAKES IT DIFFERENT?
The disastrous storm started to develop around the equator and moved upwards. It was supposed to hit Tamil Nadu, according to the IMD prediction but the cyclone Fani changed its course and traveled a longer time over the Bay of Bengal, resulting in the increased strength of the cyclone.
The cyclone finally made landfall in east India, specifically in Puri , Odisha at 8 am, also affecting Kendrapara, Bhubaneswar, Cuttack, and Khurda. It gradually made its way to Bangladesh which forced the evacuation of over 5 lakh people.

orissa-farmonaut

TOTAL LOSS
The state government of Odisha estimated a loss of Rs 12,000 crore. About 5,00,000 people have lost their houses and would need reconstruction.

Cyclone Fani uprooted over 10 million trees in total and damaged at least two million trees in Bhubaneswar alone. 


The Balukhand wildlife sanctuary , which was home for over 4,000 spotted deer, a large number of wolves and lizards have lost over 4.5 million trees. However, there is no report of large-scale wildlife loss from any other affected districts so far.

 SATELLITE IMAGERY

Cyclone Fani has proved to be profoundly unique from other cyclones which led us to analyse the affected areas of the East India coast line. For the same, we gathered satellite imagery accessible through our website (https://farmonaut.com/satellite-imagery) for two different locations which were the most affected during the cyclone. To perform these observations, we retrieved images of the following bands:

1. TCI: True Color Image, Resolution: 10 m

2. NDVI: Normalized Difference Vegetation Index, Resolution: 

10 m

3. MNDVI: Masked Normalized Difference Vegetation Index, Resolution: 10 m

4. NDWI: Normalized Difference Water Index, Resolution: 

20 m

3. MNDWI: Masked Normalized Difference Water Index, Resolution: 20 m

 

KHURDA

For Khurda, we processed the satellite images from 23rd of April till 8th of May to study the impact of the cyclone. The processed images helps enumerate the total loss and realize the scale of devastation.

LEFT TO RIGHT :23rd April to 8th May -ETCI

LEFT TO RIGHT :23rd April to 8th May -NDVI

LEFT TO RIGHT :23rd April to 8th May -MNDVI

 

The green colour in the above images indicates vegetation and the red represents construction and material objects. In the rightmost image we can, very distinctly, notice a lot orange and green at the bottom and some on the top is replaced with brown which represents the destruction of vegetation and construction. This is the purpose of satellite imagery, it is the key to tally the loss in the area after the hazard. 

We also processed the images of Kendrapara district, to gather more information about the land.

KENDRAPARA

LEFT TO RIGHT :25th April to 15th May -ETCI

LEFT TO RIGHT :25th April to 15th May -MNDVI

 In the above images, the green colour represents the vegetation. We can observe in  the bottom left corner of the rightmost image i.e after the cyclone, the green colour has faded noticebly which means that area suffered huge vegetation loss.

 

LEFT TO RIGHT :25th April to 15th May -MNDWI

In the above set of images, the green colour represents the water content in the land. The colour green has increased remarkably in the rightmost image which indicates that after the cyclone has occurred, the water content in the land has increased by quite a lot.

 WHAT DID THE GOVERNMENT DO?

Around 8 lakh people from low-slung area of Odisha were evacuated before the cyclone had occurred. The helicopters were deployed by the government to enable aerial rescue and for immediate help. Because of inaccessible road connectivity helicopters were responsible for providing relief materials such as food items, medical aid and clothing in inaccessible areas. 

Cyclones are common in many coastal areas and quite disastrous. Since precautions against a natural calamity is limited , it is necessary to develop proper plans to reduce the intensity of the destruction. This is when remote sensing comes into picture.

 

WHAT IS REMOTE SENSING, GIS AND GPS?

Remote sensing and geographic information system(GIS) is an organizing technology used to gather information. Remote sensing and spatial analysis is an effectual way to gather information from previously occurred hazard and implement plans based on that information.

WHY REMOTE SENSING TECHNOLOGY?

• We can obtain vulnerability and risk maps of a calamity that has already happened. Using them, we acquire enough information on which vulnerable parts has less or no cyclone shelters so they can be prioritized to reduce damage of lives and property in the future cyclones.

• It helps find the critical area during hazard which reduces the response time , hence reducing damage.

• The satellite images and aerial shots provides synoptic overview for a wide range of scales. The satellite provide regular images and can be used  to detect early stages of the cyclone. 

• It helps to monitor the occurrence of the cyclone. GIS or geographical information system is then used for planning evacuation routes and emergency rescue shelters.

• GIS and Global Positioning Systems (GPS) is use to study the left behind damaged area and the information gathered is used to suitably plan and build the disaster prone area accordingly.

 

Natural hazards are always disastrous and mostly unpredictable. Although hurricanes and cyclones can be tracked one day before they hit the land, it is virtually impossible to predict earthquakes. There is always a need to understand every natural hazard that occurs, and prepare for the next one.

 

The app is available for android on Google PlayStore: 

https://play.google.com/store/apps/details?id=com.farmonaut.android

We will keep posting about any such informative information on to our blogs, to help as many people as possible. Farmonaut is built upon a vision to bridge the technological gap between farmers and strives to bring state-of-the-art technologies in the hands of each and every farmer. For any queries/suggestions, please contact us at support@farmonaut.com.

We have some more interesting articles coming up soon. Stay tuned!

Wait!!

Before that…

Follow us at:

Normalized Difference Water Index - NDWI

Vegetation cover on the earth’s surface undergoes severe stress during a drought. If affected areas are not identified in time, entire crops may be damaged. Hence, the early detection of water stress can prevent many of the negative impacts on crops.  NDWI index can help us control irrigation in real time, significantly improving agriculture, especially in areas where meeting the need for water is difficult.

To calculated NDWI index, we need imagery from two different wavelengths, namely Near Infrared and Shortwave Infrared band imagery. And the NDWI ratio is calculated as:

NDWI = (NIR – SWIR)/(NIR + SWIR)

A short wave infrared imagery band is used because the high absorption of light by water occurs at this wavelength. NIR band is used because water does not absorb this part of the electromagnetic spectrum, thus the calculated NDWI index is resistant to atmospheric effects, making it distinguishable from NDVI.

The NDWI index is characterized by a more stable decrease in value upon reaching critical anthropogenic load, which can give an indication of the ecological state of forests.

farmonaut_ndwi2

The value of the NDVI index can range from -1.0 to 1.0. 

The high NDWI values correspond to high plant water content and coating of high plant fraction, whereas the low NDWI values correspond to low vegetation content and cover with low vegetation. NDWI rate will decrease during periods of water stress.

In the coming days, the Farmonaut’s Satellite Imagery for Farmers application will expand to include more remote sensing features beneficial for farmers. We will keep you posted about any new developments in this regards.  Stay tuned!!

For agricultural purposes, Farmonaut provides satellite based crop health monitoring system on our android app, through which farmers can select their field and identify the regions of the field at which the crop growth is not normal. Upon identifying that region of their fields, they can simply pay a visit to that part of the field and identify if the problem has already started. If it has not, the farmer can take preventive remedies by applying more fertilizers, plant growth regulators etc. If the problem has already started, they can simply explain their problem to Farmonaut’s crop issue identification system and get real-time govt. approved remedies. The satellite imagery is updated every 2-5 days and has a resolution of 10 meters which is 2 times better than google maps in rural India.

The app is available for android on Google PlayStore: 

https://play.google.com/store/apps/details?id=com.farmonaut.android

We will keep posting about any such informative information on to our blogs, to help as many people as possible. Farmonaut is built upon a vision to bridge the technological gap between farmers and strives to bring state-of-the-art technologies in the hands of each and every farmer. For any queries/suggestions, please contact us at support@farmonaut.com.

We have some more interesting articles coming up soon. Stay tuned!

Wait!!

Before that…

Follow us at:

Facebook: https://facebook.com/farmonaut

Instagram: https://instagram.com/farmonaut

Twitter: https://twitter.com/farmonaut

LinkedIn: https://www.linkedin.com/company/farmonaut/

Pinterest: https://in.pinterest.com/farmonaut/

Tumblr: https://farmonaut.tumblr.com/

Youtube: https://www.youtube.com/channel/UCYWOOPPKATLgh4L6YRlYFOQ

AppLink: https://play.google.com/store/apps/details?id=com.farmonaut.android

Website: https://farmonaut.com

Satellite Imagery: https://farmonaut.com/satellite-imagery

Satellite Imagery Samples: https://farmonaut.com/satellite-imagery-samples

Normalized Difference Vegetation Index - NDVI

It has been quite a long time since scientists and agronomists are using Normalized Difference Vegetation Index (NDVI) to monitor and examine health of crops. The extent and benefits of NDVI have increased manifold with so many earth mapping satellites being launched every year to monitor earth’s surface in different wavelengths.

To calculated NDVI index, we need imagery from two different wavelengths, namely Near Infrared and Red band imagery. And the NDVI ratio is calculated as:

NDVI = (NIR – RED)/(NIR + RED)

From this formula, it is apparent that the density of vegetation (NDVI) at a given point in the generated image is equal to the difference in the intensities of reflected light in the red and infrared range divided by the sum of these two intensities.

 

farmonaut_ndvi2

The value of the NDVI index can range from -1.0 to 1.0. 

  • The negative NDVI values are mainly due to clouds, snow and water. 
  • The values of NDVI close to zero are mainly due to rocks and bare soil. 
  • NDVI values ranging from 0 to 0.1 correspond to sand, snow or empty areas of rocks. 
  • NDVI values ranging from 0.2 to 0.3 represent shrubs and meadows, and
  • NDVI values ranging from 0.6 to 0.8 indicate tropical and temperate forests.
In simple words, NDVI measures status of plant health based on how plants reflect light at certain frequencies. Though we cannot perceive it with our eyes, everything around us (including plants) reflect wavelengths of light in visible and non-visible spectrum. Taking into account how much amount of a certain wavelength is reflected, we can access the current status of plants. 

We know that plants have chlorophyll which absorb sunlight for photosynthesis .Chlorophyll immensely absorbs visible light (0.4 to 0.7 microns) for photosynthesis, whereas structure of cells of leaves immensely reflect near-infrared light (0.7 to 1.1 microns). If a plant is healthy, it will have large amount of chlorophyll on it’s leaves and will absorb good amount of visible light from 0.4 to 0.7 microns and reflect quite less of it and vice-versa. 

Farmonaut’s Crop Health Monitoring system is provided to farmers and monitors how much sunlight is reflected by the plants. We take into account this basic principle in identifying crop health status of an agricultural land.

For research purposes (non-farming usage), Farmonaut provides access to satellite imagery of any place around the through our android app.

For agricultural purposes, Farmonaut provides satellite based crop health monitoring system on our android app, through which farmers can select their field and identify the regions of the field at which the crop growth is not normal. Upon identifying that region of their fields, they can simply pay a visit to that part of the field and identify if the problem has already started. If it has not, the farmer can take preventive remedies by applying more fertilizers, plant growth regulators etc. If the problem has already started, they can simply explain their problem to Farmonaut’s crop issue identification system and get real-time govt. approved remedies. The satellite imagery is updated every 2-5 days and has a resolution of 10 meters which is 2 times better than google maps in rural India.

The app is available for android on Google PlayStore: 

https://play.google.com/store/apps/details?id=com.farmonaut.android

We will keep posting about any such informative information on to our blogs, to help as many people as possible. Farmonaut is built upon a vision to bridge the technological gap between farmers and strives to bring state-of-the-art technologies in the hands of each and every farmer. For any queries/suggestions, please contact us at support@farmonaut.com.

We have some more interesting articles coming up soon. Stay tuned!

Wait!!

Before that…

Follow us at:

Facebook: https://facebook.com/farmonaut

Instagram: https://instagram.com/farmonaut

Twitter: https://twitter.com/farmonaut

LinkedIn: https://www.linkedin.com/company/farmonaut/

Pinterest: https://in.pinterest.com/farmonaut/

Tumblr: https://farmonaut.tumblr.com/

Youtube: https://www.youtube.com/channel/UCYWOOPPKATLgh4L6YRlYFOQ

AppLink: https://play.google.com/store/apps/details?id=com.farmonaut.android

Website: https://farmonaut.com

Satellite Imagery: https://farmonaut.com/satellite-imagery

Satellite Imagery Samples: https://farmonaut.com/satellite-imagery-samples