Kharif rice acreage estimation in Orissa using NOAA-AVHRR data

Rice-acreage estimation of Orissa state was carried out using single-date NOAA-AVHRR data. Selection of optimum date of data acquisition for this purpose was studied using data of six acquisition dates viz. October, 3, 12, 21, 29, November 7 and 26, 1989. Comparative performance of MXL classification of two NOAA bands (Band-1: 0.58–0.68 μm and Band-2: 0.73–1.10 μm) and Normalised Difference Vegetation Index (NDVI) derived from these two-band data was examined. Acreage thus estimated was compared against Bureau of Economics and Statistics (BES) estimate of the same year. The acreage estimation obtained by two band classification was closer to BES estimate than that based on NDVI. Data acquired in the month of October have given better estimate for state level rice acreage than that acquired in the month of November.     

Multiple forecasts of kharif rice in orissa state-four year experience of fasal pilot study

Considering the requirement of multiple pre-harvest crop forecasts, the concept of Forecasting Agricultural output using Space, Agrometeorology and Land based observations (FASAL) has been formulated. Development of procedure and demonstration of this technique for four in-season forecasts for kharif rice has been carried out as a pilot study in Orissa State since 1998. As the availability of cloud-free optical remote sensing data during kharif season is very poor for Orissa state, multi-date RADARSAT SCANSAR data were used for acreage estimation of kharif rice. Meteorological models have been developed for early assessment of acreage and prediction of yield at mid and late crop growth season. Four in-season forecasts were made during four kharif seasons (1998-2001); the first forecast of zone level rice acreage at the beginning of kharif crop season using meteorological models, second forecast of district level acreage at mid growth season using two-date RADARSAT SCANSAR data and yield using meteorological models, third forecast at late growth season of district level acreage using three-date RADARSAT SCANSAR data and yield using meteorological models and revised forecast incorporating field observations at maturity. The results of multiple forecasts have shown rice acreage estimation and yield prediction with deviation up to 14 and 11 per cent respectively. This study has demonstrated the potential of FASAL concept to provide inseason multiple forecasts using data of remote sensing, meteorology and land based observations.     

District-level rice-yield estimation for Orissa using satellite data

Remotely-sensed data transformed into a vegetation index (radiance ratio of near infrared to red) has been related to district rice yields for Orissa using IRS-1A LISS-I data of kharif seasons 1988–89 and 1989–90. Using the empirical relation of the first year, estimation of rice yield has been done for the 1989–90 kharif season. Deviations in the districts of coastal tract and central tableland ranged from 1.9 to 11.1 percent whereas deviations were larger in Eastern Ghat and Northern plateau of Orissa.     

Wheat acreage estimation for Haryana using satellite digital data

This paper summarizes the procedures adopted and results obtained since 1985–86 for wheat inventory for Haryana using satellite digital data (MSS: 1985–86 to 1987–88, LISS-I: 1988–89 onwards). The approach followed is based on sample segments (10 × 10 km during 1985–86 to 1988–89, 7.5 × 7.5 km during 1989–90) and 10 percent sampling fraction and stratified sample design. There has been consistent improvement in accuracy over the years as judged from lower biases when compared with Bureau of Economics and Statistics (BES) acreage estimates and higher precision. In 1989–90, the state-level estimate achieved an accuracy goal of 90 percent at 90 percent confidence interval. A number of studies which have been carried out to study effect of choice of sensor, acquisition date, stratification approach, classification procedure on wheat inventory are also mentioned.     

Identification of Aman Rice Crop in Bangladesh Using Temporal C-Band SAR – A Feasibility Study

In-season rice area estimation using C-band Synthetic Aperture Radar (SAR) data from RADARSAT-1 is being done in India for more than a decade. Decision rule based models in backscatter domain have been calibrated and validated using extensive field data and a long term backscatter signature bank of rice fields has been developed. Since the rice crop growing environment in India is a diverse one in the world having all the rice cultural types, the rice backscatter is quite exhaustive. This paper highlights the results of classification of rice lands in Bangladesh using the signature bank of India. The results showed that the Aman rice crop of Bangladesh has a typical temporal backscatter of shallow and intermediate rice fields of that of West Bengal state. The mean backscatter of the intermediate/deep water fields in southern Bangladesh was ?19?dB, while that of shallow cultural types mostly in northern Bangladesh was ?17?dB. The signature of the rice crop in Southern Bangladesh matched well with that of Gangetic West Bengal, particularly that of the 24 Parganas, Howrah and Hughli districts. The signature of rice crop in the Sub-Himalayan West Bengal particularly that of Dinajpur and Maldah districts matched well with that of the northern area of Bangladesh. State level rice area estimated using the selected models was found with in 5% deviation from that of the reported acreage.     

In-season time series analysis of Resourcesat-1 AWiFS data for estimating irrigation water requirement

AWiFS sensor on board IRS-P6 (Resourcesat-1), with its unique features—wide swath and 5-day revisit capability provides excellent opportunities to carry out in-season analysis of irrigated agriculture. The study carried out in Hirakud command area, Orissa State indicated that the progression of rice crop acreage could be mapped through analysis of time series AWiFS data set. The spectral emergence pattern of rice crop was found useful to identify the period of rice transplantation and its variability across the command area. This information, integrated with agro-meteorological data, was used to quantify 10-daily canal-wise irrigation water requirement. A comparison with field measured actual irrigation supplies indicated an overall supply adequacy of 88% and showed wide variability at lateral canal level ranging between 18% and 109%. The supply pattern also did not correspond with the chronological variations associated with crop water requirement, supplies were 15% excess during initial part of season (December and January) and were 20.1% deficit during later part of season (February to April). Rescheduling the excess supplies of the initial period could have reduced the deficit to 15% during peak season. The study has demonstrated the usefulness of AWiFS data to generate the irrigation water requirement by mid-season, subsequent to which 38% supplies were yet to be allocated. This would support the irrigation managers to reschedule the irrigation water supplies to achieve better synchronization between requirement and supply leading to improved water use efficiency.     

Rapeseed-mustard acreage estimation using IRS LISS-II data

The present paper describes the remote sensing-based acreage estimation of rapeseed-mustard crop in Mehsana and Banaskantha districts of Gujarat, using four-band data and Maximum Likelihood classification. IRS LISS-II data of November 25, 1989 has been used to estimate the acreage of rapeseed-mustard. It is found that the data of November 25 is useful in discriminating rapeseedmustard from other rabi crops. Talukawise acreage estimation has also been done for three talukas of Mehsana and two talukas of Banaskantha district.     

基于两个独立抽样框架的农作物种植面积遥感估算方法

通过分析遥感技术在中国农作物种植面积估算中所遇到的难点 ,针对运行化的农作物遥感估产系统对主要农作物种植面积估算的需求 ,提出在农作物种植结构区划的基础上 ,采用整群抽样和样条采样技术相结合的方法 ,进行农作物种植面积估算。整群抽样技术利用遥感影像估算农作物总种植成数 ,样条采样是一种适合中国农作物种植结构特征的采样技术 ,用于调查不同农作物类别在所有播种作物中的分类成数。在中国现有的耕地数据库基础上 ,根据两次抽样获得的成数 ,计算得到具体某一种农作物类别的种植面积。最后给出了 2 0 0 3年早稻种植面积估算的实例。     

Synergistic use of multi-temporal Radarsat SAR and AWiFS data for <Emphasis Type="Italic">Rabi</Emphasis> rice identification

A national level project on kharif rice identification and acreage estimation is being carried out successfully for several states in the country. A similar methodology based on the temporal profile for identification and delineation of various land cover classes has been followed for the Rabi rice acreage estimation. To define rabi rice, rabi season in India starts from November — February to March — June. Though the main growing season is predominantly winter but the uncertainty of getting cloud free data during the season has resulted in the use of microwave data. A feasibility study was taken up for early forecasting of the rabi rice area using microwave data. Hierarchical decision rule classification technique was used for the identification of the different land cover classes. Land preparation, puddling and transplantation were the reasons for the specific backscatter of rice growing areas. The increase or decrease in the SAR backscatter due to progress in the crop phenology or due to delayed sowing respectively forms the basis for identifying the rice areas. In addition the potential of optical data of a later date has been utilized in the form of various indices from bands including MIR to distinctly separate the late sown areas and also the puddled areas from other areas. This study emphasizes the synergistic use of SAR and optical data for delineating the rabi rice areas which is of immense use in giving an early forecast.     

Accuracy assessment in cotton acreage estimation using Indian remote sensing satellite data

The accuracy of cotton crop classification using satellite data has been assessed with respect to a detailed land cover map prepared by field survey. The effect of spatial resolution on classification accuracy was studied using LISS-I (spatial resolution 72.6 m) and LISS-II data (spatial resolution 36.25 m) of the Indian remote sensing satellite IRS-1B. The performances of the maximum likelihood and the minimum distance to mean as classifiers have also been assessed. LISS-II data have been found to give a higher classification accuracy. The estimate of cotton acreage using LISS-II data was closer to that obtained from the base map. The maximum likelihood classifier (MXL) and the minimum distance to mean (MDM) classifier performed equally well.     

Crop classification and acreage estimation in North Korea using phenology features

In North Korea, reliable and timely information on crop acreage and spatial distribution is hard to obtain. In this study, we developed a fast and robust method to estimate crop acreage in North Korea using time-series normalized difference vegetation index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. We proposed a method to identify crop type based on NDVI phenology features using data collected in other areas with similar agri-environmental conditions to mitigate the shortage of ground truth data. Eventually the classification map (MODIScrop) was assessed using the Food and Agriculture Organization (FAO) statistical data and high-resolution crop classification maps derived from one Landsat scene (LScrop). The Pareto boundary method was used to assess the accuracy and crop distribution of the MODIScrop maps. Results showed that acreage derived from the MODIScrop maps was generally consistent with that reported in the FAO data (a relative error <4.1% for rice and <6.1% for maize, and <9.0% for soybean except for in 2004, 2008, and 2009) and the maps derived from the LScrop (a relative error about 5% in 2013, and 7% in 2008 and 2014). The classification accuracy reached 74.4%, 69.8%, and 73.1% of the areas covered by the Landsat images in 2008, 2013, and 2014, respectively. This indicates that features derived from NDVI profiles were able to characterize major crops, and the approaches developed in this study are feasible for crop mapping and acreage estimation in regions with limited ground truth data.     

District-level mustard crop estimation for rajasthan: improving precision by in-season digital stratification

The acreage and yield of mustard crop in Rajasthan shows year to year variation. In the present study CAPE, analysis by incorporating digital stratification with current season data and comparison of coefficient variation (CV) at district level using conventional stratification with previous season data was undertaken. The stratification approach using current year’s data for mustard acreage estimation was adopted during 1994-95 and 1995-96 crop seasons and regional CV of less than 2 per cent was attained. A comparison of CV at district level for the years 1994-95 and 1995-96 with those obtained in previous two seasons (1992-93, 1993-94) indicated considerable improvement in precision (lower CV) is 7 out of 11 study districts. Mustard acreage estimate for Bharatpur (1995-96) had CV of 10.1 percent when conventional approach (past year data) for stratification was used. However, with the use of current year data for stratification CV reduced to 4.4 per cent The study suggests that use of in-season data for stratification improves precision for acreage estimation of crops like mustard which has high year to year variation in area.     

Relative performance of landsat MSS,TM and IRS LISS-I data for acreage estimation of Rabi sorghum

Acreage estimation of Rabi sorghum crop in Ahmadnagar, Pune and Solapur districts of central Maharashtra has been attempted using synchronously acquired Landsat MSS and TM data of 1987–88 season and IRS LISS-I data of 1988–89 season; in conjuction with near-synchronous ground truth data. The remote-sensing-based acreage estimations for the districts were compared with the respective estimates by Bureau of Economics and Statistics (BES). As the acreages were underestimated with the classification of standard four-band MSS data, the atmospheric correction of fourband MSS data and normalised differencing (ND) of the atmospheric-corrected MSS data were attempted. The main observations are: (1) the use of Landsat MSS data results in underestimation of sorghum acreage in comparison with BES estimation, (2) the atmospheric correction and ND transformation of MSS data are necessary for bringing acreage estimates in agreement with BES estimates, (3) Mid-IR data in band 1.55 to 1.75 μm are useful in improving the separability of land-use classes, and (4) remote sensing data with radiometric sensitivity comparable to LISS-I or Landsat TM and Signal-to-Noise ratios comparable to LISS-I data are suitable for accurate acreage estimation of sorghum.     

Temporal behaviour of surface waterlogged areas using spaceborne multispectral multitemporal measurements

Waterlogging and subsequent salinization and/or alkalization is the major land degradation problem in the irrigation commands of the semi-arid regions. Information on the nature, extent and spatial distribution of waterlogged areas is a pre-requisite for restoration of fertility, which has hitherto been generated conventionally. Realising the potential of spaceborne multispectral measurements in providing reliable information on spatial patterns of waterlogged areas in a timely and cost-effective manner, a study was taken up to delineate and monitor the spatial distribution pattern of waterlogged areas in Mahanadi command Stage-I covering parts of Orissa state, eastern India using Landsat-TM, Indian Remote Sensing satellite (IRS-1A) Linear Imaging Self-Scanning Sensor (LISS-II) and IRS-ID LISS-III data. A systematic on-the-screen visual interpretation approach after geo-referencing and radiometric normalization of digital multispectral data in a Silicon Graphics work station using ERDAS/ IMAGINE software was followed to realize the objectives. Results point to a significant increase in the spatial extent of waterlogged areas. Seasonally waterlogged areas increased from 29330 ha to 33421 ha and permanent waterlogged areas from 10870 ha to 12973 ha during the period 1988–89 to 1999–2000. Methodology and results are discussed in detail.     

Irrigation Water Requirements and Supply Analysis in Dehradun Region—An Integrated Remote Sensing and GIS Approach

The paper focuses on analysing the irrigation water supply and demand of different crops under three main canals for kharif and rabi seasons in Dehradun region of Uttaranchal state. Crop acreage maps of rabi and kharif seasons have been prepared using LANDSAT TM 5 digital data by applying different image processing and classification techniques. Crop water and irrigation water requirements of different crops have been computed using CROPWAT computer program. Canal discharges have been compared with the irrigation water planning and management and found to be more than the irrigation water requirements in many months, that shows the need of revising the irrigation water management.     

An integrated approach for prioritization of reservoir catchment using remote sensing and geographic information system techniques

This study is aimed at evolving a watershed prioritization of reservoir catchment based on vegetation, morphological and topographical parameters, and average annual soil loss using geographic information system (GIS) and remote sensing techniques. A large multipurpose river valley project, Upper Indravati reservoir, situated in the state of Orissa, India, has been chosen for the present work. Watershed prioritization is useful to soil conservationist and decision makers. This study integrates the watershed erosion response model (WERM) and universal soil loss equation (USLE) with a geographic information system (GIS) to estimate the erosion risk assessment parameters of the catchment. The total catchment is divided into 15 sub-watersheds. Various erosion risk parameters are determined for all the sub-watersheds separately. Average annual soil loss is also estimated for the sub-watersheds using USLE. The integrated effect of all these parameters is evaluated to recommend the priority rating of the watersheds for soil conservation planning.     

中国农情遥感速报系统

介绍了中国农情遥感速报系统的建设情况 ,系统内容包括农作物长势监测、农作物种植面积监测、农作物单产预测与粮食产量估算、作物时空结构监测和粮食供需平衡预警等。简要介绍了 1998年以来中国农情遥感速报系统在监测内容与监测范围、监测频率、技术发展以及质量控制与过程检验体系建立等方面的进展 ,并就中国农情遥感速报系统的发展方向提出了展望。     

Evaluation of RADARSAT Standard Beam data for identification of potato and rice crops in India

The Canadian satellite RADARSAT launched in November 1995 acquires C-band HH polarisation Synthetic Aperture Radar (SAR) data in various incident angles and spatial resolutions. In this study, the Standard Beam S7 SAR data with 45°–49° incidence angle has been used to discriminate rice and potato crops grown in the Gangetic plains of West Bengal state. Four-date data acquired in the 24-day repeat cycle between January 2 and March 15, 1997 was used to study the temporal backscatter characteristics of these crops in relation to the growth stages. Two, three and four-date data were used to classify the crops. The results show that the backscatter was the lowest during puddling of rice fields and increased as the crop growth progressed. The backscatter during this period changed from −18 dB to −8 dB. This temporal behaviour was similar to that observed in case of ERS-SAR data. The classification accuracy of rice areas was 94% using four-date data. Two-date data, one corresponding to pre-field preparation and the other corresponding to transplantation stage, resulted in 92% accuracy. The last observation is of particular interest as one may estimate the crop area as early as within 20–30 days of transplantation. Such an early estimate is not feasible using optical remote sensing data or ERS-SAR data. The backscatter of potato crop varied from −9 dB to −6 dB during the growth phase and showed large variations during early vegetative stage. Two-date data, one acquired during 40–45 days of planting and another at maturing stage, resulted in 93% classification accuracy for potato. All other combinations of two-date data resulted in less than 90% classification accuracy for potato.     

The use of MODIS data to derive acreage estimations for larger fields: A case study in the south-western Rostov region of Russia

Recent developments in remote sensing technology, in particular improved spatial and temporal resolution, open new possibilities for estimating crop acreage over larger areas. Remotely sensed data allow in some cases the estimation of crop acreage statistics independently of sub-national survey statistics, which are sometimes biased and incomplete. This work focuses on the use of MODIS data acquired in 2001/2002 over the Rostov Oblast in Russia, by the Azov Sea. The region is characterised by large agricultural fields of around 75 ha on average. This paper presents a methodology to estimate crop acreage using the MODIS 16-day composite NDVI product. Particular emphasis is placed on a good quality crop mask and a good quality validation dataset. In order to have a second dataset which can be used for cross-checking the MODIS classification a Landsat ETM time series for four different dates in the season of 2002 was acquired and classified. We attempted to distinguish five different crop types and achieved satisfactory and good results for winter crops. Three hundred and sixty fields were identified to be suitable for the training and validation of the MODIS classification using a maximum likelihood classification. A novel method based on a pure pixel field sampling is introduced. This novel method is compared with the traditional hard classification of mixed pixels and was found to be superior.     

Irrigation command area inventory and assessment of water requirement using IRS-1B satellite data

Monitoring the crop acreage and irrigation water requirements vis-a-vis irrigation water supplies is important to obtain a realistic view of the “irrigation potential” and “potential utilised”. Satellite data provides information on crop area and thereby net irrigation water requirements of crops. A pilot study was taken up in Mahendragarh distributary canal in Haryana State to estimate net irrigation water requirement of crops under 17 minors for kharif and rabi seasons of 1992–93 period using IRS-1B satellite geocoded FCC images. These water requirements, when analysed with canal and tubewell water supplies for crops, show largescale deficiencies in the irrigation command area.