<p><strong>Abstract.</strong> Spatio-temporal crop phenological information helps in understanding trends in food supply, planning of seed/fertilizer inputs, etc. in a region. Rice is one of the major food sources for many regions of the world especially in monsoon Asia and accounts for more than 11<span class="thinspace"></span>% of the global cropland. Accurate, on-time and early information on spatial distribution of rice would be useful for stakeholders (cultivators, fertilizer/pesticide manufacturers and agriculture extension agencies) to effectively plan supply of inputs, market activities. Also, government agencies can plan and formulate policies regarding food security. Conventional methods involves manual surveying for developing spatio-temporal crop datasets while remote sensing satellite observations provide cost effective alternatives with better spatial extent and temporal frequency. Remote sensing is one of the effective technologies to map the areal extent of the crops using optical as well as microwave/Synthetic Aperture RADAR (SAR) sensors. Cloud cover is the major problem faced in using the optical datasets during monsoon (June to Sept. locally called <i>Kharif</i> season). Hence, Sentinel-1 C-band (center frequency: 5.405<span class="thinspace"></span>GHz) RADAR sensor launched by European Space Agency (ESA) which has an Interferometric Wide-swath mode (IW) with dual polarization (VV and VH) has been used for rice area mapping. Limited studies have attempted to establish operational early season rice area mapping to facilitate local governance, agri-input management and crop growers. The key contribution of this work is towards operational near real time and early season rice area mapping using multi-temporal SAR data on GEE platform. The study has been carried out in four districts viz., Guntur, Krishna, East Godavari andWest Godavari from Andhra Pradesh (AP), India during the period of <i>Kharif</i> 2017. The study region is also called as coastal AP where rice transplanting during the <i>Kharif</i> season is carried out during mid Jun. till Aug. and harvesting during Oct. to mid Dec. months. The training data for various classes viz, Rice, NonRice-Agriculture, Waterbodies, Settlements, Forest and Aquaculture have been obtained from GEE, Global Land Cover (GLC) layers developed by ESA and field observations. We have evaluated the performance of Random Forest (RF) classifier by varying the number of trees and incrementally adding the SAR images for model training. Initially the model has been trained considering two images available from mid June 2017. Further, various models have been trained by adding one consecutive image till end of August 2017 and classification performance has been evaluated on validation dataset. The classified output has been further masked with agriculture non-agriculture layer derived from global land-cover layer obtained from ESA. Analysis shows that incremental addition of temporal observations improves the performance of the classifier. The overall classification accuracy ranges between 78.11 to 87.00<span class="thinspace"></span>%. We have found that RF classifier with 30 trees trained on six images available from mid June till end August performed better with classification accuracy of 87.00<span class="thinspace"></span>%. However, accuracy assessment performed using independent stratified random sampling approach showed the classification accuracy of 84.45<span class="thinspace"></span>%. An attempt is being made to follow the proposed approach for current (i.e. 2018) season and provide incremental rice area estimates in near real-time.</p>
With advances in sensing systems attempts are continuously being made to design Internet of Things (IoT) based interoperable sensing systems. The important issues (water, pest / disease, nutrient management, etc.) pertaining to cropweather-soil continuum can be addressed through high resolution monitoring of agro-meteorological parameters. Presently the designed sensing systems have syntactic and semantic heterogeneity and face underlying limitations for achieving interoperability among these distributed sensing systems. In this study an attempt has been made to develop KrishiSense. A semantically aware web enabled wireless sensing system for precision agriculture applications. Through integration of Open Geospatial Consortium (OGC) specified Sensor Web Enablement (SWE) standards on sensing system has enabled the interoperability between different standardized sensing systems. KrishiSense acts as interconnection between multiple users (researchers / scientists, farmers and extension community) through multiple protocols and distributed web connected platforms, thus facilitating human participatory sensing.Index Terms-Internet of Things, interoperability, precision agriculture, Sensor Web Enablement, syntactic and semantic heterogeneity.
ABSTRACT:Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (http://earthexplorer.usgs.gov/). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.
ABSTRACT:Rapid advances in Wireless Sensor Network (WSN) for agricultural applications has provided a platform for better decision making for crop planning and management, particularly in precision agriculture aspects. Due to the ever-increasing spread of WSNs there is a need for standards, i.e. a set of specifications and encodings to bring multiple sensor networks on common platform. Distributed sensor systems when brought together can facilitate better decision making in agricultural domain. The Open Geospatial Consortium (OGC) through Sensor Web Enablement (SWE) provides guidelines for semantic and syntactic standardization of sensor networks. In this work two distributed sensing systems (Agrisens and FieldServer) were selected to implement OGC SWE standards through a Service Oriented Architecture (SOA) approach. Online interoperable data processing was developed through SWE components such as Sensor Model Language (SensorML) and Sensor Observation Service (SOS). An integrated web client was developed to visualize the sensor observations and measurements that enables the retrieval of crop water resources availability and requirements in a systematic manner for both the sensing devices. Further, the client has also the ability to operate in an interoperable manner with any other OGC standardized WSN systems. The study of WSN systems has shown that there is need to augment the operations / processing capabilities of SOS in order to understand about collected sensor data and implement the modelling services. Also, the very low cost availability of WSN systems in future, it is possible to implement the OGC standardized SWE framework for agricultural applications with open source software tools.
Abstract. The current study focuses on the estimation of cloud-free Normalized Difference Vegetation Index (NDVI) using the Synthetic Aperture Radar (SAR) observations obtained from Sentinel-1 (A and B) sensor. South-West Summer Monsoon over the Indian sub-continent lasts for four months (mid-June to mid-October). During this time, optical remote sensing observations are affected by dense cloud cover. Therefore, there is a need for methodology to estimate state of vegetation during the cloud cover. The crops considered in this study are Paddy (Rice) from Punjab and Haryana, whereas Cotton, Turmeric, and Banana from Andhra Pradesh, India. We have considered, observations of Sentinel-1 and Sentinel-2 sensors with the same overpass day and non-cloudy pixels for each crop. We used Google Earth Engine to extract surface reflectance for the Sentinel-2 and Ground Range Detected (GRD) backscatter for Sentinel-1. The Red and NIR bands of Sentinel 2 were used to estimate NDVI. Sentinel-1 based VV, and VH backscatter was used for estimation of Normalized Ratio Procedure between Bands (NRPB). Regression analysis was performed by using NDVI as an independent variable, and VV, VH, NRPB, and radar incidence angle as dependant variables. We evaluated the performance of Linear regression with tuned Support Vector Regression (SVR) as well as tuned Random Forest Regression (RFR) using the independent data. Results showed that the RFR produced the lowest RMSE for all the crops in the study. The average RMSE using the RFR was 0.08, 0.09, 0.11, and 0.10 for Rice, Cotton, Banana, and Turmeric, respectively. Similarly, we have obtained R2 values of 0.79, 0.76, 0.69, and 0.71 for the same crops using the RFR. A model with 80 trees produced the best results for Rice and Cotton, whereas the model with 90 trees produced the best results for Banana and Turmeric. Analysis with NDVI threshold of 0.25 showed improved R2 and RMSE. We found that for grown crop canopy, SAR based NDVI estimates are reasonably matching with the optical NDVI. A good agreement was observed between the actual and estimated NDVI using the tuned RFR model.
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