In regions where water is more scarce than land, the water productivity concept (e.g. crop yield per unit of water utilized) provides a useful framework to analyse crop production increase or water savings in irrigated agriculture. Generic crop and soil models were applied at field and regional scale, together with geographical and satellite data to analyse water productivity in Sirsa District (India). In this district certain parts show a serious decline in groundwater levels and water shortage, while other parts experience a serious rise of groundwater levels, causing waterlogging and salinization. The regional analysis showed a large spatial variability of water productivity, net groundwater recharge and salinization. Scenario analysis showed that improved crop husbandry, reallocation of canal water from fresh to saline groundwater areas and reduction of seepage losses in saline groundwater areas are effective measures to increase the overall water productivity and to attain sustainable irrigation in Sirsa District.

There is a growing effort to use access to remote sensing data (RS) in conjunction with crop model simulation capability to improve the accuracy of crop growth and yield estimates. This is critical for sustainable agricultural management and food security, especially in farming communities with limited resources and data. Therefore, the objective of this study was to provide a systematic review of research on data assimilation and summarize how its application varies by country, crop, and farming systems. In addition, we highlight the implications of using process-based crop models (PBCMs) and data assimilation in small-scale farming systems. Using a strict search term, we searched the Scopus and Web of Science databases and found 497 potential publications. After screening for relevance using predefined inclusion and exclusion criteria, 123 publications were included in the final review. Our results show increasing global interest in RS data assimilation approaches; however, 81% of the studies were from countries with relatively high levels of agricultural production, technology, and innovation. There is increasing development of crop models, availability of RS data sources, and characterization of crop parameters assimilated into PBCMs. Most studies used recalibration or updating methods to mainly incorporate remotely sensed leaf area index from MODIS or Landsat into the WOrld FOod STudies (WOFOST) model to improve yield estimates for staple crops in large-scale and irrigated farming systems. However, these methods cannot compensate for the uncertainties in RS data and crop models. We concluded that further research on data assimilation using newly available high-resolution RS datasets, such as Sentinel-2, should be conducted to significantly improve simulations of rare crops and small-scale rainfed farming systems. This is critical for informing local crop management decisions to improve policy and food security assessments.