Precision Irrigation: Spatial variability in crop production is observed because of the spatio-temporal variation of soil structure and properties. As water remains one of the limiting factor in Indian agriculture, calculating irrigation requirement of the soil types spatio-temporally would be advantageous. Proper usage of water can increase the yield and crop health. Precision irrigation is the practice of irrigating the crops with optimal amount of water.
High-resolution soil moisture data calculated using the innovative algorithm developed by Satyukt Analytics Pvt Ltd is used to determine the optimal amount of water required for each crop based on the soil type and properties. Irrigation requirement varies with time and hence near real-time monitoring of the soil moisture, soil type and other agrohydrological parameters such as evapotranspiration, rainfall, vegetation index etc would help to determine the amount of irrigation required at various seasons.
Crop yield: The prediction of crop yield in near real-time helps to tackle adverse climatic changes and reduce its effect on crop yield. The knowledge about the variation of these changes with time is of utmost importance as this helps to adapt different agricultural practices for the betterment of yield. With the emergence of satellite sensors, many parameters influencing agricultural yield can be monitored and analyzed. Agro-hydrological variables influencing agricultural yields such as Soil Moisture, rainfall, vegetation indices, and evapotranspiration are used for the estimation.
Detecting how these parameters vary over time helps to predict the changes well before its occurrence and thus provides a possibility of preparedness without compromising on the yield. The conventional method of crop yield estimation is based on Crop Cutting Experiments CCE which is cumbersome, resources consuming and prone to bias due to the limited number of observations. There have been various attempts to estimate the crop yield using the satellite data. These attempts have failed due to any of these reasons: Focusing only on physical modeling which fails due to insufficient data, using Big data by assuming the system to be a black box and in the process ignoring the physical laws, Gaps in the date due to limited spatial/temporal resolution and clouds.
Satyukt Analytics Pvt Ltd has the expertise for the development of various agro-hydrological variables such as soil moisture and actual Evapotranspiration and gridded precipitation data sets by combining the multi-satellite data accounting for spatiotemporal resolution gaps. After estimating the multi-satellite agro-hydrological variables, physical based modeling will be performed to estimate the crop yield. Big data would be used to minimize the bias and error in the output of the physics-based model by utilizing the different satellite data and other auxiliary information. Crop yield is predicted for the cropping season according to the agro hydrological variables which are monitored and analyzed using multi-sensor data.
Crop Acreage: Accurate and faster estimation of crop area is a great utility in the farming process as it helps in estimating crop production in advance of the harvest. As Indian agriculture is mainly characterized by fragmentation and land holdings whose size is less than 1 ha a much ﬁner spatial resolution is necessary. The required spatiotemporal resolution of the satellite products used in the study is obtained by integrating multi-sensor data with an algorithm developed at Satyukt analytics, as most of the satellite products have compromised spatial or temporal resolution.
- Know the total crop area within a catchment/administrative unit
- Make optimal plan for the procurement
- Potential impact on the price
Crop acreage is the determining factor in crop production as it is helpful in determining the national or regional food demand and supply balance.
Satyukt Analytics Pvt Ltd has developed an innovative methodology based on Dynamic time warping algorithm to estimate the temporal signatures of the area. Each crop has a phenological cycle that is relevant for space-time classification. Remote sensing satellites overpass a particular place at regular intervals, and thus their data can be recorded as a three-dimensional array in space-time. Spectral signatures of the area under study are also calculated as different crops exhibit significant variability due to several factors like climate, soil, farming practices. These signatures are used to estimate the different crop types for each of the pixel (spatial resolution of 30 m) of satellite data. The crops grown over time at a particular place can be analyzed and identified accurately and thereby area covered by individual crops can be calculated with much ease. The model is calibrated using 60% of the field data samples, whereas the remaining is used for validation of the results.
Crop Health: Many times, the spatiotemporal variation of yield is observed in the same agriculture land because of the variation in soil structure and properties. The land-atmosphere interactions responsible for these variations can be modeled and analyzed to predict crop-water requirement, crop health and agricultural yield. The underlying parameters(agrohydrological parameters) that change with these interactions, such as the soil moisture, Evapotranspiration, normalized difference vegetation index (NDVI), Vegetation Condition Index (VCI) can be monitored to analyze the crop health.
VATI, a web-based interactive map developed at Satyukt Analytics Pvt Ltd provides these agro-hydrological variables in an easily accessible and understandable interface. VATI is now updated with a new feature, which allows one to upload their farm details and then can download the agrohydrological parameters specific to the farm in near real-time under all weather conditions. Farm details include the location file, Soil type, crop type, and sowing date. VATI also provides access to historical data since 1975.