Remote Sensing Applications in Agriculture. Component-I(A) - Personal Details. Component-I (B) - Description of Module. Role Name Affiliation

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1 Component-I(A) - Personal Details Role Name Affiliation Principal Investigator Prof.MasoodAhsanSiddiqui Department of Geography, JamiaMilliaIslamia, New Delhi Paper Coordinator, if any Dr. M P Punia Head, Department of Remote Sensing, Birla Institute of Scientific Research, Jaipur Content Writer/Author (CW) Kaushal Panwar Senior Research Fellow, Birla Institute of Scientific Research, Jaipur Content Reviewer (CR) Dr. M P Punia Head, Department of Remote Sensing, Birla Institute of Scientific Research, Jaipur Language Editor (LE) Component-I (B) - Description of Module Items Subject Name Paper Name Module Name/Title Description of Module Geography Remote Sensing, GIS, GPS Remote Sensing Applications in Agriculture Module Id RS/GIS 15 Pre-requisites Objectives Student will get to know RS analysis helps in agriculture field. Student will acquire skill how to study data and its algorithms in agriculture sector. Student will be equipped with knowledge to study further about the applications.

2 Keywords 1. Remote Sensing Applications in Agriculture Outline Introduction about role of remote sensing in agriculture and impact on Indian economy. Remote Sensing application in agriculture. Crop production forecasting. Assessment of crop damage and crop progress. Horticulture, Cropping Systems Analysis. Environmental Impact Assessment of Agricultural System.

3 Non-point source pollution. Impact of Climate Change on Agricultural System. Biophysical parameter retrieval and process modeling. Demonstration of technique for forewarning Pests and Diseases. National Mission National production forecast (FASAL). Crop Identification Technology Assessment for Remote Sensing (CITARS). Conclusion. Introduction Earth observations (EO), comprising satellite, aerial, and in situ systems, are increasingly recognized as critical tool for studying and monitoring of natural resources and unfolding the intricate behavior of the complex earth s dynamic processes. The Remote Sensing research has evolved as multidisciplinary theme dedicated to developing the applications of remote sensing technology of Land Ocean and atmosphere addressing geologic, botanic, and hydrologic issues at national, regional, and site-specific scales. Agriculture plays dominant role in economy of almost every nation. Whether agriculture represents a substantial trading industry for an economically strong country or simply sustenance for a hungry, overpopulated one, it plays a significant role in almost every nation. The production of food is important to everyone and

4 producing food in a cost-effective manner is the goal of every farmer, large-scale farm manager and regional agricultural agency. A farmer needs to be informed to be efficient, and that includes having the knowledge and information products to forge a viable strategy for farming operations. These tools will help him understand the health of his crop, extent of infestation or stress damage, or potential yield and soil conditions. Commodity brokers are also very interested in how well farms are producing, as yield (both quantity and quality) estimates for all products control price and worldwide trading. The policy makers are interested in harnessing the best available tools for optimizing the resource use, minimizing the damage/losses and ensuring the societal benefit. Most important component of such decisions is the agricultural and allied information at best possible resolution of spatial and temporal scales. Remote sensing techniques come handy for achieving such an objective. The diverse agro climatic conditions give rise to cultivations of large number of crops. The information on long term sustainability vis-à-vis environmental impacts demands seasonal and yearly information of variant as well as invariant resources. The diverse crop growing conditions coupled with uncertainties of climate situation demands much more finer crop information needs on temporal and spatial scale. During the last two decades remote sensing and GIS techniques are applied to explore agricultural applications such as crop identification, area estimation, crop condition assessment, soil moisture estimation, yield estimation, agriculture water management, agro meteorological and agro advisories. The application of remote sensing in agriculture, i.e. in crops and soils is extremely complex because of highly dynamic and inherent complexity of biological materials and soils (Myers, 1983). However, remote-sensing technology provides many advantages over the traditional methods in agricultural resources survey. The advantages include,

5 i) capability of synoptic view ii) potential for fast survey iii) capability of repetitive coverage to detect the changes iv) low cost involvement v) higher accuracy vi) Use of multispectral data for increased information, and so on. Now almost all part of the electromagnetic spectrum is utilized for agricultural applications at various scales. Crop growth and associated factors change dynamically and need continuous monitoring. The different sub themes of agriculture are shown in fig. 1 Remote sensing (RS) technology has potential to estimate crop area and forecast productivity at district and regional level due to its multispectral, large area and repetitive coverage. The following sections explain the developments taken place during the last three decades with special reference to agricultural remote sensing in India.

6 Fig. 1: Remote Sensing Applications in Agriculture Source: Remote Sensing Application in Agriculture Crop Production Forecasting

7 Fig.2: Crop Production Forecasting Graph Source: Agricultural crop identification and area estimation has been the focus ever since the inception of civilian RS program in the U.S. in the early 1960s. Some of the early studies conducted were experiments such as Crop Identification Technology Assessment for Remote Sensing (CITARS) and Large Area Crop Inventory Experiment (LACIE). Exploring the use of remote sensing for agricultural application in India started with the use of multi band and colour infra red (CIR) aerial photographs as early as Further knowledge on crop signature was gathered through scientifically designed field experiments using multi band

8 radiometer under the Indian Remote sensing Satellite-Utilisation Programme (IRS- UP) on: i) Crop production forecasting ii) Crop stress detection, and iii) Crop yield modelling. Many of these studies have led to the operationalisation of the methodology and conduct of national-level projects. Launch of the Indian Remote Sensing Satellites (IRS-1A, IB & 1C) carrying linear imaging self-scanning sensors (LISS I,II &III) provided a much-required impetus to agricultural applications. IRS-1C carried onboard a unique combination of three sensors viz., (i) Wide Field Sensor (WiFS) with 188m spatial, two spectral bands red and near infrared, 810km swath and a repetivity of 5 days, (ii) Linear Imaging Self scanning Sensor (LISS-III) with 23.5m spatial resolution in the green red and near infrared region, and 70.5 m in the middle infrared region, and 140 km swath, and (iii) Panchromatic (PAN) camera with 5.8m spatial resolution, 70km swath and stereo capability. The launch of RISAT has filled another dimension to the agricultural remote sensing as all weather capability of data is now reality from Indian satellite. Crop production forecasting comprises identification of crops, acreage estimation and forecasting their yield. Crop identification and discrimination is based upon the fact that each crop has a unique reflectance pattern in different parts of the electromagnetic spectrum which is termed as spectral signature. The general spectral response of a crop canopy in the visible and NIR region is characterised by absorption in the 0.35 to 0.5 um and 0.6 to 0.7 um regions (due to chlorophyll pigments), high reflectance in the green region (around 0.54 um), a steep increase in the reflection in the 0.7 to 0.74 um and high reflectance in 0.74 to 1.3 um region

9 due to internal cellular structure of the leaves. The absorption at 1.45, 1.95 and 2.6 um spectral bands is due to leaf water content. The varying response of the crops stems from the fact that various factors such as type of crop, stage of the crop, canopy architecture, per cent ground cover, differences in cultural practices, crop stress conditions, background soil/water etc., contribute to the composite response. Each crop has its own architecture, growing period, etc. thus enabling discrimination through remote sensing data. If there are two crops with similar spectral signatures on a given date (confusion crops), multidate data are used to discriminate them Vigour of the crop is manifested in the absorption in the red and reflectance in the near infrared spectral regions. It has been observed that the ratio of near infrared to red radiance is a good indicator of the vigour of the crop. Some of these properties are utilised in crop identification, crop condition assessment and yield forecasting. The broad procedure used for crop acreage estimation is shown in illustration below. The data is analysed applying Maximum Likelihood Supervised classification technique (other classifiers are also used), where limited field information called Ground truth is used to generate the training signature. In case of complete enumeration data for analysis was selected by overlaying the boundary mask of the area over the remote sensing images. When it was extended to large area stratified sampling technique was developed where area was first divided with a grid representing sampling frame size (5 X 5 km) and then data belonging to selected sample segments is extracted (20%) are analysed. The samples are randomly drawn proportionate to size of each stratum. Area estimate is made from the proportion of crop present in the sample.

10 Fig. 3: Procedure For Acreage Estimation using Sampling Techniques Source: self Since the space technology has advanced and variety of sensors of different spatial, spectral and temporal resolutions is available and there is a continual need for crop information throughout the growing period (Fig. 3), frequent monitoring is feasible at various scales. Realizing the importance of multiple source information like weather, econometric and field survey towards a robust approach for multiple forecasts of a number of crops, a new concept was formulated: FASAL (Forecasting Agricultural output using Space, Agro- meteorology and Land based observations) (fig. 4). Implementation of FASAL was initiated in

11 Fig. 4: Information need and sources in frequent crop monitoring Source: Clay/p/book/ Assessment of crop damage and crop progress Damage to crop due to moisture stress is a common occurrence in rain fed rice growing region. The characteristic backscatter profile of rice using temporal SAR data is useful in characterising the crop condition as normal and sub-normal. Flood is a common phenomenon in many rice-growing regions, particularly in monsoon season. Temporal SAR data is found not only to map flood affected rice fields, but also to model duration and degree of submergence. Complete submergence of rice at any given period of growth lower the backscatter. The degree of submergence was modelled with reference to crop height and its deviation from the reference normal growth profile. The model thus can detect the completely submerged fields as well as partially submerged fields. It is well established that sowing dates have a significant effect on crop biomass and yield. Temporal SAR data is used to

12 categorise the fields as normal, late and very late sown which is additional component of crop assessment need that enables identifying the reasons for delay. Similar efforts have been made which for deriving the regional variation of rice growth profile using optical data (fig. 6) Fig: 5a.crop growth pattern in feb 2016 Fig: 5b. Crop growth pattern in April 2016

13 Fig: 5c.crop growth pattern in oct 2016 O OBSERVED PUNJAB M - MODELLED J&k AP TN Fig. 6: Rice spectral profile of different regions in India derived using multidate optical data Source: Remote Sensing of the Environment, John R. Jensen

14 Crop yield models Fig.7: Crop yield model Source: Out of the two constituents of crop production, namely crop acreage and crop yield, the assessment of the latter is most complex because of the high variability involved. The information on crop yield is an important input for production estimation shown in fig(7). Every crop genotype has certain yield potential, which can be achieved (to an approximation) in experimental field with optimal conditions. However, in the real world, the crop yield is conditioned by various parameters like soil, weather and cultivation practices, like date of sowing, irrigation and fertiliser. Crop yield is also influenced by biotic stresses like disease and pest. While the variability of the weather explain most of the annual variability

15 over a short period of time, the cultivation practices and new varieties explain most of the variability over a period of 10 to 20 years. For longer periods of time, climatic changes or soil improvements or degradation are the main factors influencing the crop yield However, the fact that all these factors are interdependent makes the yield assessment a more complex task. Hence, one way of forecasting of the yield is understanding the variability in the above parameters and defining their relationship with the final crop yield. Satellite based remote sensing provides a suitable alternative for crop condition and yield assessment/ forecasting, as it gives a timely, accurate, synoptic and objective estimation of various crop parameters. The time series based trend and arima models were developed in the beginning of CAPE project based on district-wise yield data of DES which were used to compute production by multiplying with the RS derived area estimates. Further agro meteorology model, spectral models and combination of these models were tested and used for a variety of conditions, crops and regions. The agro meteorology inputs were predominantly significant rainfall at fortnightly intervals, minimum and maximum temperatures etc that would form part of correlation weighted regression model. The RS input, NDVI (single date or derived from profiles integration) in such models were used for model development. The rice biomass has high correlation with SAR backscatter and therefore the yield. The crop vigour is an indication of crop yield. The vigour of crop can be assessed using vegetation indices derived from different parts of the spectrum. The normalized vegetation index is one such index which represents the green biomass of the plant. The NDVI can be directly correlated to the yield of the crop and this relation can be used for estimating yield. A variety of models involving combination of factors of weather and spectral parameters have been developed and used in conjugation with remote sensing derived acreage for providing

16 production estimates. Plant growth simulation models have been used for monitoring crop growth, health shown in fig(8) and predicting yield.however, their use in large areas has been limited because most plant growth models were developed at the field scales and the performance of the models is not so satisfactory when they are extended from field to regional scales. Fig.8: Crop Stress Detection Source: Remote Sensing Applications - Horticulture India is bestowed with varied agro-climate which is highly favourable for growing a large number of horticultural crops such as fruits, vegetables, root tuber, ornamental, aromatic plants, medicinal, spices and plantation crops like coconut, areca nut, cashew and cocoa. India is the largest producer of fruits (49.36 MT) and second largest producer of vegetables (93 MT) in the world. Horticulture occupies about 12 per cent of the total cultivated area in the country, and contributes about

17 25 per cent of the total agricultural export. Remote sensing technology helps in generation of crop Inventory of major horticultural crops, site suitability analysis for expansion/introduction, infra-structure planning for post harvest requirement, disease detection and precision planning for horticulture. The general approach involves the use of high resolution/high temporal data (LISS-III) for identifying the crop of interest and relevant collateral information (i.e soil, water, climatic, infrastructure etc) and processing for logical clustering for decision-making. Feasibility studies demonstrating the remote sensing technology in horticultural sector have been carried out. Inventory of orchards like apple, grape, mango, coconut, banana and vegetables like potato, onion has been carried out in different agro climatic regions of the country. The post-harvest infrastructure planning and optimization of cold store facility for post-harvest management of potato has been demonstrated. Apart from these early trends in national winter potato production from the country is regularly brought out to infer about National/state production prospects and identifying areas with significant change. Cropping Systems Analysis A cropping system is defined as the cropping pattern and its management to derive benefits from a given resource base under a specific environmental condition. This requires identification of crops and areas where changes in cropping patterns are desirable. This calls for an initial step of creating an updated database of the present cropping systems of the country and simulate the long-term effects, taking into consideration the resource base and agro climatic condition. Satellite remote sensing (RS) and Geographical Information System have a crucial role to play in this direction. The multidate satellite data is helpful in deriving seasonal cropping

18 pattern, sowing pattern, crop rotation, efficiency indicators and other related parameters. Environmental Impact Assessment of Agricultural System Fig. 9:Concept of Environmental Impact Assessment of Agricultural System Source: Environmental Impact Review.v.28 The agriculture has transformed from simple sustenance objective to intensivecommercial form thus depleting and degrading the environmental resources. Pressure on high production has led to intensification of agriculture. Intensive Agriculture, long term sustainability and quality of natural resources, thus is matter of compromise and concern. Agricultural is a major reservoir and transformer in global cycles of carbon, nitrogen and water. Intensive agriculture leads to erosion of soil resources, loss of biodiversity, alienation of ecological niches, temporary imbalance in soil microbial functioning, associated long-term effects on microbial

19 processes and changes in biogeochemical Cycles. Agricultural activities contribute about 70% of all anthropogenic N2O emissions and about 65% of all anthropogenic CH4 emissions. Nutrient leakage from agriculture is a prime cause of degradation of groundwater, surface waters and estuarine and coastal marine systems, and via the atmosphere affects other terrestrial systems. Nitrate contamination of groundwater is common in agricultural areas around the world. Some of the specific components include fertilizer and pesticide residual toxicity, plant/soil metabolic exudates such as methane/nitrous oxide in the immediate micro/macro environment. Methane and nitrous oxide form important components of such an interface. The GHG pattern which is also available using sensors on board satellites is also being studied in detail which is clearly shown in fig(8). The methodology was developed to generate total annual methane emission map from the rice areas of India and its temporal pattern taking into consideration the diverse conditions under which the rice is grown. The methodology was developed for the variety components on use of RS data for stratification, spatial and temporal sampling strategy, development of indigenous method of sampling and up-scaling of methane. Results showed that the major stratum emerged as the rain fed drought prone with 42.8 per cent of total rice lands (wet season) and found in many states. Livestock constitutes an integral component of Indian agriculture. India possesses the world s largest total livestock population of 485 million, which accounts for about 57% and 16% of the world s buffalo and cattle populations, respectively. A detailed state/ district-level methane emission inventory for different livestock categories was made using the country-specific and Indian feed standard based methane emission coefficients, which are based on IPCC guidelines, and the latest available livestock census. The total methane emission including enteric fermentation and manure management has been estimated as Tg for the year

20 2003 (Chhabra et al. 2008, 2009). Enteric fermentation accounts for ~92% or Tg of the total, while manure management contributes only 8% or 1.09 Tg. Non-point source pollution Agriculture has been identified as the largest contributor of non-point source (NPS) pollution of surface and ground water systems globally (Thorburn et al., 2003). Fertilizers, which are used as important inputs in agriculture to supply essential nutrients like nitrogen (N), phosphorus (P), and potassium (K) also, serve as a major non-point source pollutant. An integrated methodology was developed for quantification of different forms of nitrogen losses from rice crop using remote sensing derived inputs, field data of fertilizer application, collateral data of soil and rainfall and nitrogen loss coefficients derived from published nitrogen dynamics of kharif and rabi seasons. The nitrogen losses through leaching in form of urea-n, ammonium-n (NH4-N) and nitrate-n (NO3-N) are dominant over ammonia volatilization loss. The study results indicate that nitrogen loss through leaching in kharif and rabi rice is of the order of 34.9% and 39.8% of the applied nitrogenous fertilizer in the Indo-Gangetic plain region. This study provides a significant insight to the role of nitrogenous fertilizer as a major non-point source pollutant from agriculture.

21 Impact of Climate Change on Agricultural System Fig.10: Impact of Climate Change on Agriculture Source: Climate change is one of the most discussed topics of the last two decades shown in the fig(10). It impact on agricultural systems with inputs from multiple sources were studies with simulation models. The CropSyst and water balance models along with the climate forecasts using GCMs, RCM and statistical downscaled model (under different scenario) for understanding the impact of climate changes on agricultural systems were studied. The impact of climate change as projected by the RCM-PRECIS (A1B scenario) on the rice-rice system of West Bengal showed yield reduction ranging from 7.88 to %.The adaptation study in rice-rice

22 system was early sowing by a period of 12 days to increase the yield in 2020 and compensate the yield reduction in Analysis of the climatic extreme events under climate change scenario of HadCM3 through different temperature and precipitation indices was carried out. Biophysical parameter retrieval and process modeling The satellite derived biophysical products is one of the key developments taken place during last two decades. The investigations on deriving these products were carried out in India using Indian and other sensors. The NDVI, fapar, insolation, LAI, LST etc are some of them on which R & D were carried out. The Leaf Area Index (LAI) is a key biophysical variable used by plant physiologists and modellers for estimating foliage cover and plant growth and biomass. The regional modeling of growth processes such as evapotranspiration (ET) and net CO2 assimilation require retrieval of some core variables such as land surface temperature (LST), leaf area index (LAI), albedo and soil moisture etc.. Field-scale (local) and regional-scale (agro-climatic zone) non-linear empirical models are developed for wheat leaf area index (LAI) based on normalized difference vegetation index (NDVI) using IRS 1D LISS-III. Demonstration of technique for forewarning Pests and Diseases The remote sensing pests and diseases has remained a challenge due to the complexity of occurrence, overlapping with other factors, varying magnitudes and subtle manifestation. The mustard aphid (Lypaphis erysimi) infestation models have been developed from near-surface air temperature and relative humidity from sounder data (e.g. TOVS), and sowing dates (Bhattacharya et al, 2007) and validated through a collaborative study with National Research Centre on Rapeseeds and Mustard (NRCRM), Bharatpur, Rajasthan. These models were later up scaled and extrapolated using SPOT-VGT to map aphid onset dates (Dutta et al,

23 2008, Fig.).A new methodology of multi-stage tracking of Sclerotinia rot (Sclerotinia sclerotiorum) disease in a large mustard growing region over in Bharatpur district has been conceptualized and demonstrated. National Missions: FASAL: National production forecast Land Observations Cropped area Crop condition Crop area & Production Crop area & Production Crop acreage Crop yield MULTIPLE IN-SEASON FORECAST Pre- Season Early- Season Mid- Season State Pre- Harvest State Pre- Harvest District Revised Assessing Damage Fig.11: Concept of FASAL Source: Multiple in-season wheat forecasts using multi-date IRS WiFS data and weekly weather variables at meteorological sub-divisions in major wheat producing states of India are being made at national level shown in fig(11). Seven major wheatproducing states, Uttar Pradesh, Punjab, Madhya Pradesh, Haryana, Rajasthan, Gujarat and Bihar form the study area. These states account for more than 90% of

24 the wheat production. A three level stratification of the area based on agro-climatic zones, an agricultural area and crop proportion is made. A grid of 5X5 km is overlaid on the satellite images and fifteen per cent random sample (each of size 5*5 km) is selected within a state. The sample segments are classified using inseason ground truth and a hierarchical (decision rule based) classifier. The state level acreage estimates are then statistically aggregated to arrive at national level wheat acreage estimates. The methodology for state level yield forecasting is multiple regression models based on temperature, using a correlation weighted regression approach. The National level acreage and yield estimates are then combined to provide National Production Forecast. Investigations using space borne SAR data started with limited use of data from JERS, SIR C sensors. However, the possibility of examining space borne radar data for large area agricultural application was realised with the successful launch of ERS-1 Synthetic Aperture Radar (SAR) (SAC, 1997). Due to the problem of cloudy weather during rainy season, Kharif rice crop production estimates in the major rice growing states are being generated using multi-date Synthetic Aperture Radar data (fig. 11a). Rice growing environment or management practices ensures that there is standing water beneath the canopy at least for a short duration during crop season. This information is used to characterize the rice crop on a temporal domain (fig. 11b). The rate of change and direction of change of SAR response aids in building decision rule for classification of rice pixels. A stratified random sampling approach for each state is adopted for acreage estimation with a sample size of 5*5 km. A fifteen per cent sampling fraction and in-season ground truth information of the selected sample segments are used. The segments are classified using a decision rule classifier followed by statistical aggregation of state level acreages to national level rice acreage estimate. The statistical relationship between

25 yield and rainfall during the cropping season is used for yield forecasts. The district level models are combined with acreage and production forecasts for the country is made. Temporal backscattering profile of Rice & Non-Rice Area a h e d i c b f Backscatter co-efficient (db) Days ( July 1=1) Rice Water Urban Homestead Other Crops g Field-preparation Puddling Transplanted Vegetative Peak-vegetative Heading Maturity Fig. 12a: Three date colour Fig. 12b: Temporal pattern of rice crop composite of Scan SAR data ( R:G:B: Date1:Date2: Date 3) over the study district showing distinct signature of rice (b,c,d,e,f) in different growth stage and other land cover classes (a: water, h: forest and i: urban). Source: me_weather_events_and_indian_agricultu. Crop Identification Technology Assessment for Remote Sensing (CITARS) CITRAS (Crop Identification Technology Assessment for Remote Sensing) was an experiment to quantify the crop identification performance achievable with several automatic data processing classification technique.it was conducted from April 1973 to April 1975.The five specific objectives of CITARS were:

26 1) To assess the effects of Landsat data acquisition during the corn and soybean growing season on crop-identification performance. 2) To access the effect of differing geographical locations having differing soil, weather, management practice, crop distributions, and field sizes on cropidentification performance. 3) To quantify the variation in crop-identification performance using the differing automatic data processing (ADP) classification procedure. 4) To test the ability to extend training signatures, selected within the test area, to train the classifiers in other areas. 5) To access crop identification benefits to be derived from classifying with multiple Landsat data acquire multi-temporally during the crop growing season. Technical Approach Six major tasks were completed during CITARS they were: i) Design of sampling scheme in Illinois and India for corn and soybeans using Landsat MSS data. ii) Acquisition and preparation of a Landsat-1 data set with ancillary data sufficient to support the experimental objectives and design. iii) Computer added processing of this data set with the selected classification algorithms and procedures. iv) Quantification of the crop-identification performances to evaluate the ability of these procedures to satisfy agricultural applications requirements. v) Statistical analysis to quantitatively evaluate the impact of major factors known to affect crop identification performance.

27 vi) Interpretation of the results to ascertain the underlying factors responsible for the result and to drawn inference as to the status of the technology as it relates to agricultural applications. Conclusions Remote sensing applications of agriculture expanded into different domains and further many of them grew to higher levels of maturity during last twenty-five years. The crop production forecasting for example started from experimental stage and moved up to operational stage. The newer applications of agriculture and its environment assessment were also explored. With availability of SAR sensors, monitoring of crop during kharif season became a reality. India is a global leader in agricultural applications of remote sensing and has carried out capacity building on not only Indian scientist, but also of scientists of other countries in India as well as outside. The specific tools and techniques have been developed to cater to above needs and operationalisation. Almost all sensors spanning the entire range of EMS used in RS application have been studied and host of them have been showcased. The emphasis in future should be on products and services sector encompassing more decipherable and ready to used knowledge based RS products. The agro ecosystems analysis and climate change impacts would be the focal theme in which variety of components can come from RS data. The horticulture and site specific management would demand much more complex algorithms and service oriented products. The RS and communications technologies would fuse in the future to deliver the near-real time service to all stakeholders of agriculture.