Studies on irrigation distribution equity and crop growth performance in a Canal Command using remote sensing

Irrigation distribution equity and crop growth were studied in Delhi Sub-branch of Western Yamuna Canal Command. Total irrigation was estimated from the canal and tube well discharge data and irrigation distribution equity was expressed in terms of Theil’s and Christiansen’s Coefficients for nearly 140 wheat fields randomly chosen over the command. Crop growth performance for these plots was assessed from the Normalized Difference Vegetation Index (NDVI) obtained from the IRS, LISS II data. Four soil associations viz., Nabha-Ghoga, Daryapur-Hissar, Holambi-Nabha and Khampur-Hissar mainly represented the study area. 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 crop. Over and above, the irrigation equity, quality and quantity constraints to irrigation, the other soil parameters like CEC, applied P also contributed to differences in wheat growth as observed from the stepwise multiple regression analysis. Irrigation performance indices were estimated from water distribution between soil associations and from water requirement of crop, indicated performance slightly below the critical level.     

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.     

Wavelet-Based Technique to Extract Convective Clouds From Infrared Satellite Images

Extraction of all cumuliform clouds from infrared satellite images is important for cloud studies. Existing methods have focused on extracting only the cumulonimbus clouds. Over monsoon Asia, warm cumulus and cumulus congestus clouds are a large fraction of total cumuliform clouds and are covered by cirrus. An extraction method based only on brightness temperatures (BT) is not sufficient for the detection of these cumuliform clouds. In this letter, a new cloud extraction technique based on spatial characteristics of the convective clouds is presented. The À trous wavelet transform (ATWT) is shown here to successfully extract both shallow and deep convective clouds. The depression in BT caused by the cold cloud tops corresponds to negative wavelet components found by the ATWT.