Agricultural Area Diversification and Crop Water Demand Analysis: A Remote Sensing and GIS Approach

Large scale adoption of input intensive rice–wheat cropping system in the centrally located Jalandhar district of Indian Punjab has led to over-exploitation of ground water resources, intensive use of chemical fertilizers and deterioration of soil health. To overcome these shortfalls, in the present study, agricultural area diversification plan has been generated from agricultural area and crop rotation maps derived from remote sensing data (IRS P6-AWiFS and RADARSAT ScanSAR) along with few agro-physical parameters in GIS environment. Cropping system indices (area diversity, multiple cropping and cultivated land utilization) were also worked out from remote sensing data .Analysis of remote sensing data (2004–05) revealed that rice and wheat individually remained the dominant crops, occupy 57.8% and 64.9% of total agricultural area (TAA), respectively. Therefore, in the diversified plan, it is suggested that at least 39% of the current 40% TAA under rice–wheat rotation should be replaced by other low water requiring, high value and soil enriching crops, particularly in coarse textured alluvial plain having good quality ground water zones with low annual rainfall(<700 mm). This will reduce water requirement to the tune of 15,660 cm depth while stabilizing the production and profitability by crop area diversification without further degradation of natural resources.     

Mapping of Cropping System for the Indo-Gangetic Plain Using Multi-Date SPOT NDVI-VGT Data

The present study has been carried out to delineate the existing cropping systems in the Indo-Gangetic Plains (IGP) using 10 day composite SPOT VEGETATION (VGT) NDVI data acquired over a crop year (June–May). Results showed that it is feasible to identify the major crops like rice, wheat, sugarcane, potato, and cotton in the dominant growing areas with good accuracy. Double cropping pattern is the most prevalent. Rice-wheat, sugarcane based, cotton-wheat, rice-potato, rice-rice, maize/millet-wheat are some of the major rotations followed. Rice-wheat is the dominant rotation accounting for around 40% of the net sown area. Triple crop rotations was less than 5% of the area and observed in some parts of Uttar Pradesh, Bihar and West Bengal. Single crop rotation of rice-fallow is significant only in West Bengal.     

Results on potential use of simulated IRS-1C WiFS data for crop monitoring

Some of the basic requirements for cropping system analysis are updated information on crops grown, their phenological behaviour, method and duration of establishment and harvest, inter and intra crop variability, sequential cropping patterns. The next generation Indian Remote Sensing Satellite with high repeat cycle opens new possibility of crop surveys to derive such information. In this study, an attempt has been made to analyse cropping system at district level using simulated IRS-1C Wide Field Sensor (WiFS) data. Data acquired for nineteen dates during 1992–93 season for Bardhaman district, West Bengal has been used. It was feasible to derive accurate information on cropping pattern, crop rotation, crop duration, progress of harvest, crop growth profiles and annual crop acreage using multidate data. It was observed that even a seven to eight day interval of data acquisition during critical growth periods significantly affected classification and identification accuracy.     

利用Landsat TM和IRS P6 LISS III卫星影像及GIS对耕地和耕种模式的时空动态分析(英文)

The study of the spatial patterns and temporal changes of cropland is important to understand the underlying factors and the functional effects of the agricultural landscape. On the other hand, crop dynamics mapping is essential to know the overall agro-spatial diversity of the area. Therefore, this paper addressed a spatio-temporal analysis of cropland and cropping pattern change in the Bogra district of Bangladesh over the last 16 years (between 1988/89 and 2004/05). In this paper, crop mapping from multi-temporal and multi-sensor satellite images was described. Landsat TM and IRS P6 LISS III satellite images were used with GIS for spatial dynamics of cropland and cropping pattern change analysis. First, seasonal cropland maps were derived from object-based classification of satellite images, then two-date classified image differencing with GIS overlay technique and decision rules were applied. Cropping pattern change was analyzed in a spatial and quantitative way for the 16 years and for this, Integrated Land and Water Information System (ILWIS) and Land Change Modular (LCM) of IDRISI Andes were used. The results showed that in the area, mono crop cultivation was found in summer, but in winter, areas under different crop cultivation had changed dramatically. Change analysis showed that the changes mainly occurred in the north northwest and southwest of the areas, and during the time the highest change area was found under the rice-potato pattern.      

Remote Sensing and Geographical Information System for canopy cover mapping

Availability of remote sensing data from earth observation satellites has made it convenient to map and monitor land use/land cover at regional to local scales. A land cover map is very critical for a various planning activities including watershed planning. The spectral and spatial resolutions are major constraints for mapping the crop resources at microlevel. The cropping pattern zones have been mapped using the false color composite, physiography, irrigation and toposheets. The IRS LISS-III data is classified into various categories depending on spectral reflectance from crop canopy and are overlaid on cropping zones map. The re-classified resultant map provides land use/land cover information including dominant cropping systems. The canopy cover is estimated monthly considering the crop calendar for the area.     

Environmental aspects in Dalli-Rajhara iron ore mine area,M.P. using digital image processing techniques

Landsat MSS (1982) and IRS LISS-II (1989) data have been used to study the land use/land cover changes in Dalli-Rajhara iron ore mine area. Supervised classification has been performed on the temporal data to generate land use/land cover maps. Land use/land cover categories generated from IRS LISS-II data of 36 m resolution has been resampled to 80 m and areal statistics have been computed for 2, 4, 8 and 10 km wide strips around Dalli-Rajhara iron ore mine. The environmental impact due to on-going mining activities in the area has been analysed. The results of this study indicate that due to increase in mine-related and agricultural activities, forests have been degraded and also forest areas have been reduced considerably.     

Assessing gaps in irrigated agricultural productivity through satellite earth observations—A case study of the Fergana Valley,Central Asia

Improving crop area and/or crop yields in agricultural regions is one of the foremost scientific challenges for the next decades. This is especially true in irrigated areas because sustainable intensification of irrigated crop production is virtually the sole means to enhance food supply and contribute to meeting food demands of a growing population. Yet, irrigated crop production worldwide is suffering from soil degradation and salinity, reduced soil fertility, and water scarcity rendering the performance of irrigation schemes often below potential. On the other hand, the scope for improving irrigated agricultural productivity remains obscure also due to the lack of spatial data on agricultural production (e.g. crop acreage and yield). To fill this gap, satellite earth observations and a replicable methodology were used to estimate crop yields at the field level for the period 2010/2014 in the Fergana Valley, Central Asia, to understand the response of agricultural productivity to factors related to the irrigation and drainage infrastructure and environment. The results showed that cropping pattern, i.e. the presence or absence of multi-annual crop rotations, and spatial diversity of crops had the most persistent effects on crop yields across observation years suggesting the need for introducing sustainable cropping systems. On the other hand, areas with a lower crop diversity or abundance of crop rotation tended to have lower crop yields, with differences of partly more than one t/ha yield. It is argued that factors related to the infrastructure, for example, the distance of farms to the next settlement or the density of roads, had a persistent effect on crop yield dynamics over time. The improvement potential of cotton and wheat yields were estimated at 5%, compared to crop yields of farms in the direct vicinity of settlements or roads. In this study it is highlighted how remotely sensed estimates of crop production in combination with geospatial technologies provide a unique perspective that, when combined with field surveys, can support planners to identify management priorities for improving regional production and/or reducing environmental impacts.     

Evaluation of multisensor data for delineating salt affected soils in arid rajasthan

The Landsat (MSS and TM), SPOT (PLA and MLA) and IRS (LISS-I and LISS-II) images of crop free period (April, May), rainfed crop (October) and rabi irrigated crop (January, February) have been evaluated for their capabilities of mapping (1) primary salt affected soils: (slightly, moderately and severely) (2) saline water irrigated saline soils, (3) sodic water irrigated sodic soils and (4) salt affected soils due to tank seepage in the arid region of Rajasthan. The moderately and severe salt affected soils could be mapped with Landsat, (IRS LISS-I) and SPOT, images of any season. However, the summer season imagery provided maximum extent of salt affected soils. The LISS-II imagery also provided delineation of slightly salt affected soils in addition to the moderate and severely salt affected soils. The delineation of saline and sodic water irrigated areas was possible by using Landsat False Colour Composite for the January month by their characteristic reflectance, existing cropping pattern and the quality of irrigation water being used in the area. The IRS (LISS-II) and SPOT PLA images for the May month were also used for mapping of saline and sodic water irrigated soils.     

Study of irrigation and crop water requirements and growth of two <Emphasis Type="Italic">Rabi</Emphasis> crops grown in a semi arid region using agrometeorology and remote sensing

Irrigation water requirements of wheat and mustard crops grown in Western Yamuna Canal Command area were estimated using FAO model CROPWAT with the help of agrometeorological and remote sensing data (1986–1998 and 2008). The variations in irrigation water requirements of these two crops were judged by calculating coefficient of Variations (CVs) of yearly data. Crop coefficient values were obtained through FAO (1993) method. Supervised Maximum Likelihood Classification (MXL) of IRS 1B image was done to estimate area under wheat and mustard in the canal command. Water need was calculated from amount of supply and water requirement for the whole area. Results showed that ET_crop values of both wheat and mustard varied very little over different years (CVs 4.7% and 5.6% respectively). Irrigation water requirements of both these crops were having relatively large variations (CVs 14.1% and 22.6% respectively) which were mainly because of high variations of their effective rainfall (CVs 61.1% and 69.2% respectively). In general, increase in amount of irrigation enhanced the growth performance of the wheat crop. Increase in distribution equity within soil associations slightly improved the growth performance of the wheat crop. Agro-climatic data merged with satellite image approximated the deficiency of applied irrigation amount (549.5 ha-m for wheat and 692.7 ha-m for mustard) as compared to requirement.     

Use of GIS and remote sensing for crop diversification — a case study for Punjab state

In this study, an attempt has been made to suggest crop diversification based on soil and weather requirements of different crops. State level spatial databases of various agro-physical parameters such as rainfall, soil texture, physiography and problem soil along with the agricultural area derived from remote sensing data were integrated using GIS. A raster based modelling approach was followed to arrive at suitable zones for practicing different cropping systems. The results showed that the south-western Punjab is suitable for low water requiring crops such as desi cotton, pearl millet, gram etc., where as north-eastern Punjab with high rainfall and excess drainage should practice maize based cropping system. Rice can be substituted by maize and other crops in Central Punjab, where water table is going down fast. Using this approach the area of rice based cropping system can be reduced from present 24.7 lakh ha to 19.6 lakh ha, thereby reducing the degradation of valuable land and water resources.     

Landuse/land cover mapping and change detection using satellite data — a case study of dehlon block, district Ludhiana, Punjab

The land use information collected for Dehlon block of Ludhiana district, Punjab from the analysis of the IRS-1B LISS-II data for the year 1993 and IRS PAN data for the year 1997 and SOI topographical maps for 1964 revealed a large change in the area of different land use categories during the period from 1964 to 1997. The agricultural land covering an area of about 94.14 per cent in 1964 reduced to 90.26 per cent in 1997. while the area under rural settlements increased from 312 ha in 1964 to 1162 ha in 1997. An extra area of about 169 ha under waste land was added during the period under study making total waste land area to about 400 ha in 1997. However, the block lacks the forest cover of the required limit. Considerable change in living environment was observed in the block. Number of persons per unit settlement area (ha) being 213.3 in 1964 reduced to 97.1 in 1991; it indicate that the living standard of the people of the block has improved with the changed cropping pattern and increased agricultural production during the period from 1964 to 1991.     

Impact Assessment of Watershed Programme Through Remote Sensing and Geographical Information System

Impact assessment of watershed development activity assumes greater importance in present day agriculture. Considering the ability of remote sensing technology in watershed monitoring and impact assessment, a study was carried out to investigate the Impact Assessment of Karnataka Watershed Development Project (DANIDA) in Koralahallihalla Sub watershed in Sindagi taluk of Bijapur district in Northern Karnataka using satellite data of two periods i.e., IRS 1?C, LISS-III data of 30 December, 1997 (pre-treatment) and IRS P6, LISS-III data of 17 December, 2004 (post-treatment). The land use/land cover map was derived from the supervised classification. The results revealed that there has been no major shift in cropping patterns over a period of 7?years (1997?C2004). However, rabi cropped area has decreased drastically (187?ha), which might be due to the continuous droughts that occurred during the implementation period. On the other hand, kharif and double cropped area have increased marginally (103?ha and 96?ha, respectively). Increase in double cropped area showed that there was increase in irrigated land, which were earlier being used as rainfed and wastelands turned in to cultivated lands as seen in scrub lands and rabi cropped areas of the sub watershed. Wastelands in the sub-watershed has decreased marginally (36?ha). The vegetation vigour of the sub-watershed has been derived from the NDVI maps of both the periods. These NDVI maps indicate that there was a significant change in biomass status of the sub watershed. The vegetation vigour of the area was classified into three classes using NDVI. Substantial increase in the area under high and low biomass levels was observed (319?ha and 77?ha, respectively). The benefit-cost analysis indicates that the use of remote sensing technology was 2 times cheaper than the conventional methods. Thus, the repetitive coverage of the satellite data provides an excellent opportunity to monitor the land resources and evaluate the land cover changes through comparison of images for the watershed at different periods.     

Farm-Level Yield mapping for precision crop management by linking remote sensing inputs and a crop simulation model

A study aimed at generating wheat yield maps of farmer’s fields by using remote sensing (RS) inputs was undertaken during the rabi season of 1998-99 in six villages of Alipur Block of Delhi State. RS derived leaf area index (LAI) were linked to wheat simulation model WTGROWS by adopting a strategy christened “Modified Corrective Approach”. This essentially uses an empirical relation of grain yield and LAI, which was derived from WTGROWS simulation model by running model for a combination of input resources, management practices and soil types occurring in the area. This biometric relationship was applied to all the wheat fields of the study area for which the LAI was derived from single acquisition of IRS LISS-III data (Jan 27, 99). The LAI-NDVI relation adopted was logarithmic in nature (R²=0.83) and was based on ground measurements of LAI in farmer’s fields in the same area. A comparison of predicted grain yield by the modified corrective approach and actual observed yield for the 22 farmer’s fields showed high correlation coefficient of 0.8 and a root mean square error (RMSE) of 597 kg ha^-1 which was 17% of the observed mean yield. Thus linking of RS information and crop simulation model provides an alternative for mapping and forecasting crop yield under highly variable cropping environment of Indian farms, which is a pre-requisite for implementing Precision Crop Management (PCM).     

Evaluation of simulated IRS-1C WiFS data for concurrent monitoring of command areas at disaggregated level

Data of Wide Field Sensor (WiFS) to go onboard Indian Remote Sensing Satellite, IRS-1C, in December 1995, is simulated mainly from IRS IB LISS I data of Bhadra command area, Karnataka (India) during 1993–94 summer season, to evaluate its capability in concurrent monitoring of irrigated crops at disaggregated level Crop area, crop-growth profiles of homogeneous crops like paddy, as obtained from both simulated WiFS data and LISS I data are very close for almost all the distributary commands of Bhadra project Though non-paddy-crop groups could also be classified satisfactorily, the Workability with small-extent-individual crops like groundnut, garden and sugarcane is found to be less due to coarse resolution of WiFS data and hence the individual crops could not be separated out. This study proves the potential of WiFS in concurrent monitoring of fairly-large-extent irrigated crops at distributary level. The basic feasibility of WiFS had been established in an earlier work at broad level and this study demonstrates the feasibility of information extraction at distributary command level from WiFS data.     

Evaluation of spectral bands and spatial resolution of LISS II and LISS III sensors on-board IRS satellites for crop identification

The present study evaluates the performance of Indian Remote Sensing (JRS) LISS Jl and LISS III data having spatial resolutions of 36 m and 23.5 m respectively in the Classification accuracy of rice, mustard and potato crops grown in West Bengal, India. The role of Middle infra-red (MIR.) band, of IRS 1C LISS III was also investigated in this context. The results indicated that in case of crop like rice which was grown over large contiguous fields, no significant change in classification accuracy was observed between LISS II and LISS III data. However, the accuracy increased by 5–7 per cent with the inclusion of MIR band mainly due to better separability between lowland rice and other hill vegetation. In case of crops like mustard and potato which were grown on small size or less contiguous fields, the classification accuracy increased by 5–8 per cent due to higher spatial resolution of LISS III. Inclusion of MIR band did not improve the accuracy of these crops.     

Application of remote sensing technology for Impact Assessment of Watershed Development Programme

The current study was taken up to investigate the utility of remote sensing and GIS tools for evaluation of Integrated Wasteland Development Programme (IWDP) implemented during 1997–2001 in Katangidda Nala watershed, Chincholi taluk, Gulbarga district, Karnataka. The study was carried out using IRS 1C, LISS III data of December 11, 1997 (pre-treatment) and November 15, 2002 (post-treatment) covering the watershed to assess the changes in land use / land cover and biomass that have changed over a period of five years (1997–2002). The images were classified into different land use/land cover categories using supervised classification by maximum likelihood algorithm. They were also classified into different biomass levels using Normalized Difference Vegetation Index (NDVI) approach. The results indicated that the area under agriculture crops and forest land were increased by 671 ha (5.7%) and 1,414 ha (11.94%) respectively. This is due to the fact that parts of wastelands and fallow lands were brought into cultivation. This increase in the area may be attributed to better utilization of surface and ground waters, adoption of soil and water conservation practices and changes in cropping pattern. The area under waste lands and fallow lands decreased by 1,667 ha (14.07%) and 467 ha (3.94%), respectively. The vegetation vigour of the area was classified into three classes using NDVI. Substantial increase in the area under high and low biomass levels was observed (502 ha and 19 ha respectively). The benefit-cost analysis indicates that the use of remote sensing and GIS was 2.2 times cheaper than the conventional methods. Thus, the repetitive coverage of the satellite data provides an excellent opportunity to monitor the land resources and evaluate the land cover changes through comparison of images for the watershed at different periods.     

FAO 56 Model and Remote Sensing for the Estimation of Crop-Water Requirement in Main Branch Canal of the Bhadra Command area, Karnataka State

Remote sensing and FAO 56 crop water model are used for estimating crop water requirement for paddy crop located in the main branch canal of Bhadra Command Area in Karnataka, India. The estimation of crop-water requirement depends on the meteorological factors, soil type and crop coefficients. The result obtained showed that water requirements of rabi crops higher than those of the kariff crops. The total irrigated area estimated from the IRS image is 29,353 ha. It is found that the total paddy crop acreage is 18,257 ha covering 62 % in the total irrigated area of the command area, Arecanut 20 %, coconut 15 % and sugarcane with other crops 3 %. The water requirement for paddy is 1180.4 mm for its entire growth period. The total water requirement for irrigation supply for crops in the entire command area is 5,790 at a demand of 0.10501 cusecs per ha.     

Phenological metrics-based crop classification using HJ-1 CCD images and Landsat 8 imagery

Crop type data are an important piece of information for many applications in agriculture. Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions. In this research, we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device (CCD) data. We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a long-term time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution. To increase accuracy, four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images. These phenological metrics were used to further identify each of the crop types with less, but easier to access, ancillary field survey data. We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment. The results show that our classification accuracy was 92% when compared with the highly accurate but limited ZY-3 images and matched up to 80% to the statistical crop areas.     

Accuracy of mustard and wheat inventory at village-level using liss-II digital data in pali district, Rajasthan

A study was carried out to estimate the accuracy of crop discrimination and area inventory for wheat and mustard using IRS LISS-II digital data of two acquisition dates D1 (Dec. 28, 1994) and D2 (Feb. 10, 1995) over a test site (1413 ha) comprising of two villages in Pali district, Rajasthan, The D1 and D2 were optimal acquisitions for mustard and wheat respectively with deviations in aereage estimates of less than five per cent in comparison to field survey. The percent correctly classified pixels ter training site for optimal dates of each crop ranged between 85 and 86 per cent and they were much lower for other dates. These results with lower accuracies than reported earlier for sites with single dominant crop are indicative of accuracies for discrimination and area inventory fot sites having two crops and also sensitivity to acquisition period.     

Satellite-based investigation of flood-affected rice cultivation areas in Chao Phraya River Delta,Thailand

The occurrence of catastrophic floods in Thailand in 2011 caused significant damage to rice agriculture. This study investigated flood-affected rice cultivation areas in the Chao Phraya River Delta (CRD) rice bowl, Thailand using time-series moderate resolution imaging spectroradiometer (MODIS) data. The data were processed for 2008 (normal flood year) and 2011, comprising four main steps: (1) data pre-processing to construct time-series MODIS vegetation indices (VIs), to filter noise from the time-series VIs by the empirical mode decomposition (EMD), and to mask out non-agricultural areas in respect to water-related cropping areas; (2) flood-affected area classification using the unsupervised linear mixture model (ULMM); (3) rice crop classification using the support vector machines (SVM); and (4) accuracy assessment of flood and rice crop mapping results. The comparisons between the flood mapping results and the ground reference data indicated an overall accuracy of 97.9% and Kappa coefficient of 0.62 achieved for 2008, and 95.7% and 0.77 for 2011, respectively. These results were reaffirmed by close agreement (R² > 0.8) between comparisons of the two datasets at the provincial level. The crop mapping results compared with the ground reference data revealed that the overall accuracies and Kappa coefficients obtained for 2008 were 88.5% and 0.82, and for 2011 were 84.1% and 0.76, respectively. A strong correlation was also found between MODIS-derived rice area and rice area statistics at the provincial level (R² > 0.7). Rice crop maps overlaid on the flood-affected area maps showed that approximately 16.8% of the rice cultivation area was affected by floods in 2011 compared to 4.9% in 2008. A majority of the flood-expanded area was observed for the double-cropped rice (10.5%), probably due to flood-induced effects to the autumn–summer and rainy season crops. Information achieved from this study could be useful for agricultural planners to mitigate possible impacts of floods on rice production.