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The association of climatic variables and climatic oscillations with wheat yield across 26 canal command areas (CCAs) in Punjab, Pakistan, is studied, for recent 34 years (1982–2015). Climatic variables include precipitation, temperature, dry/wet spell lengths, hot/cold spell lengths, wet/dry spell ratio along with climatic oscillations, and satellite-based vegetation index for the months of January to April (Rabi season). During Rabi season, temperature increase rate was 0.14 °C decade−1, whereas precipitation trends were spatially inhomogeneous and varied between – 2.7 mm decade−1 and + 5.4 mm decade−1. The precipitation, temperature, cold spell length, smoothed NDVI (ND), and standard deviation of ND (SDND) displayed significant correlations (0.31–0.96) with wheat yield at varying time scales. The inter-annual wheat yield variability displayed a gain of 415.7 kg ha−1 under present climatic conditions for all CCAs. Worse to extreme climatic conditions based on percentiles obtained from present climate displayed a reduction of wheat yield by 252.0 to 954.9 kg ha−1, respectively. The good to best climate in comparison to present climatic conditions added up 108.1 to 251.9 kg ha−1, respectively. The 11-year period of 1993–2003 was the most drought prone period with consecutive five years of drought (1998–2002), having decreased precipitation, small wet/dry spell ratio and increased temperature, and long dry/hot spell length during wheat growing season. The wheat yield is predicted based on multiple regression models with sizable skill (having an explained variance of 0.79 and a root mean squared error of 121.3 kg ha−1).

An integrated approach is developed to assess major crops water productivity (CWP) in relation to water supply across the irrigated areas of Punjab province in central Pakistan during 1982–2015. The role of precipitation (effective rainfall) in term of add on to water supply and impacts on wheat yield is also studied. The water supply from canals displayed a significant declining trend for Rabi season (wheat mainly) during 1996−2015. Crop water productivity for harvestable area (CWPA) has decreased; however, it has increased in case of production (CWPP) and yield (CWPY). The role of tube wells is analyzed to assess the improved CWP even under decreased canal water supply. Contribution of water supply through effective rainfall showed a significant add on of 7.6 mm to 291.8 mm to water supply and 0.48 kg ha−1 mm−1 to 8.19 kg ha−1 mm−1 towards CWPY during the studied 34-year period. The precipitation (effective rainfall) contribute water supply of 10.6 mm (6.64 mm), 23.4 mm (14.6 mm), and 25.5 mm (16.0 mm) for January, February, and March, respectively. Average canal water supply for irrigation is observed to be 254.9 mm against irrigation need of 418.6 mm with a deficit of 39.1%, across irrigated areas. The multiple regression models are developed with seasonal predictive capacity for wheat harvestable area and yields with an explained variability of ~ 81% and ~ 78%, respectively. A decrease in temporal satellite-based weekly composite NDVI profile during 1986, 1992, 1994, 1997, 2002, and 2010 corresponded to El Nino episodes, since negative phases of ENSO are known to cause droughts across Punjab province. The synthesized information calls for a more comprehensive management of declining canal water supply for sustainable major crops water productivity such as wheat which is a staple food crop.

Major threat that Pakistan faces today is water scarcity and any significant change in water availability from storage reservoirs coupled with below normal precipitation threatens food security of more than 207 million people. Two major reservoirs of Tarbela and Mangla on Indus and Jhelum rivers are studied. Landsat satellite’s data are used to estimate the water extents of these reservoirs during 1981–2017. A long-term significant decrease of 15–25% decade−1 in water extent is found for Tarbela as compared to 37–70% decade−1 for Mangla, mainly during March to June. Significant water extents reductions are observed in the range of −23.9 to −53.4 km2 (1991–2017) and −63.1 to −52.3 km2 (2001–2010 and 2011–2017) for Tarbela and Mangla, respectively. The precipitation amount and areas receiving this precipitation show a significant decreasing trend of −4.68 to −8.40 mm year−1 and −358.1 to −309.9 km2 year−1 for basins of Mangla and Tarbela, respectively. The precipitation and climatic oscillations are playing roles in variability of water extents. The ensuing multiple linear regression models predict water extents with an average error of 13% and 16% for Tarbela and Mangla, respectively.

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent.