Estimation of Water Stress on Rice Crop Using Ecological Parameters.

Smallholder farmers are threatened by various vulnerable risks which include hostile weather conditions, rainfall at odd times, disease outbreaks and the market shocks. Crop insurance is the only solution to mitigate these risks. Crop yield records are of great importance to predict the crop yield/area into a region but the developing countries like Pakistan, have limited availability of crop yield records. Crop Reporting Service (CRS) in Punjab province of Pakistan has taken this initiative to save crop related data. We obtained the CRS based datasets of rice crop from (2008-2018) to predict the rice yield/area for the fiscal year 2019. The CRS based datasets were incorporated in collaboration with remotely sensed dataset to obtain more accurate results. The spectral responses of rice crop were taken as input to compute NDVI/RVI values of each year. We applied linear regression to NDVI/RVI and the CRS based yield to generate regression equations for prediction of rice yield for the year 2019 which was computed as 2.09 (ton/ha). The area under rice cultivation was estimated using supervised classification that was 139616 hectors. The net rice production was estimated as 219797 tons. Spectral responses of rice crop canopy proved efficient to determine the net productions.

Section: Area Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Net Rice Production for the Fiscal year 2019 using Multisource Datasets.

Rehman

Ayyaz

Riaz

et al. 2019

“…This scheme of provision of water is quite different for other crops like wheat, maize or sugarcane etc. The remote sensing satellites capture the crop canopy responses in form of solar energy reflected in visible and microwave ranges [15]. This determine that each crop has unique spectral signature which varies depending upon the canopy structure, chlorophyll level, and the available water content.…”

Section: Introductionmentioning

confidence: 99%

Step-by-Step Processing of Sentinel-1 data for Estimation of Rice Area.

Karamat

Nawaz

Mirza

et al. 2019

Rice has become an essential part of four pillars of food security, especially in Asia, where it is produced over large spatial extents and also consumed widely. About 89 % of the global rice production is targeted and achieved from Asian countries. We downloaded Sentinel-1 datasets from official website of European Space Agency (ESA) for identification of rice patterns in the study site. The data was selected in Ground Range Detection (GRD) format and applied the toolbox in Sentinel Application Platform (SNAP) for further processing. We applied the orbit file for geometric and radiometric corrections, LEE filter for removal of spackles, resampling to convert 20*20m2 to 10*10m2 pixel size and finally the Random Forest Classification (RFC) to classify the satellite image. The classification results of Sentinel image for the year 2018, show that the total area of the study site was 360021 ha, including 144991 ha as rice area, 130598 as other vegetation, 19339 ha as water body and the built-up area was estimated as 5693 ha. Kappa statistics resulted the overall accuracy of 85% which is in strong agreement to ground reality. We observed that the rice area was increased from 140403 ha in 2017 to 144991 ha in 2018. The main reason of this increase in rice area was observed as the preference of local farmers to grow rice in comparison to other crops because the local government was offering high subsidy to rice farmers. Moreover, district Nankana-Sahib produces rice of expert quality which is famous throughout the world therefore, it is considered as cash crop.

“…Ultimate crop productivity can be increased by identifying suitable sites for growth and development of a crop [6]. Therefore, it is essential to analyze the availability of nutrients in a particular soil needed for a particular crop [7].…”

Section: Introductionmentioning

confidence: 99%

Identification of Potential Sites for Rice Plant Growth using Multi Criteria Decision (MCE) Technique Through Remote Sensing and GIS

Mahmood

Saifullah

Zafar

et al. 2019

Self Cite

Rice is one of major corps grown in Pakistan. It is considered as the backbone of Pakistan’s economy. Almost all the rural population of Pakistan is involved in rice preparation from its germination to final harvest. Although its contribution is less in Gross Domestic Product (GDP) of Pakistan, however efforts are on the way to enhance its productivity. The land of Punjab Pakistan produces rice of export quality which is famous throughout the world. Rice crop growth promoting parameters are considered essential to achieve high yield e.g., temperature, humidity, pressure, soil pH, soil type, drainage and electric conductivity. We used Multi-Criteria decision analysis to map temperature base and soil-based rice friendly zones. The results show that the total area under investigation was 3151km2, out of which 2075 km2 was observed highly suitable, 772 km2 was moderately suitable and 303 km2 was not suitable for rice cultivation. On ground validation, it was observed that the areas which were not suitable for rice cultivation, were actually urban area. The urban areas had high temperatures due to anthropogenic activities and fossil fuel emissions. Remote sensing and GIS techniques proved efficient in mapping the suitability zones for rice crop.