Estimating Actual Evapotranspiration over Croplands Using Vegetation Index Methods and Dynamic Harvested Area

Advances in estimating actual evapotranspiration (ETa) with remote sensing (RS) have contributed to improving hydrological, agricultural, and climatological studies. In this study, we evaluated the applicability of Vegetation-Index (VI) -based ETa (ET-VI) for mapping and monitoring drought in arid agricultural systems in a region where a lack of ground data hampers ETa work. To map ETa (2000–2019), ET-VIs were translated and localized using Landsat-derived 3- and 2-band Enhanced Vegetation Indices (EVI and EVI2) over croplands in the Zayandehrud River Basin (ZRB) in Iran. Since EVI and EVI2 were optimized for the MODerate Imaging Spectroradiometer (MODIS), using these VIs with Landsat sensors required a cross-sensor transformation to allow for their use in the ET-VI algorithm. The before- and after- impact of applying these empirical translation methods on the ETa estimations was examined. We also compared the effect of cropping patterns’ interannual change on the annual ETa rate using the maximum Normalized Difference Vegetation Index (NDVI) time series. The performance of the different ET-VIs products was then evaluated. Our results show that ETa estimates agreed well with each other and are all suitable to monitor ETa in the ZRB. Compared to ETc values, ETa estimations from MODIS-based continuity corrected Landsat-EVI (EVI2) (EVIMccL and EVI2MccL) performed slightly better across croplands than those of Landsat-EVI (EVI2) without transformation. The analysis of harvested areas and ET-VIs anomalies revealed a decline in the extent of cultivated areas and a loss of corresponding water resources downstream. The findings show the importance of continuity correction across sensors when using empirical algorithms designed and optimized for specific sensors. Our comprehensive ETa estimation of agricultural water use at 30 m spatial resolution provides an inexpensive monitoring tool for cropping areas and their water consumption.</jats:p

Reduce blue water scarcity and increase nutritional and economic water productivity through changing the cropping pattern in a catchment

Water-stressed countries need to plan their food security and reduce the pressure on their limited water resources. Agriculture, the largest water-using sector, has a major role in addressing water scarcity and food security challenges. While there has been quite some attention to water management solutions like soil mulching and improved irrigation, less attention has been paid to adapting the cropping pattern to save water. Here, we investigate how a change in which crops are grown where and when can influence the green and blue water footprint (WF) of crop production, save blue water, reduce blue water scarcity and increase both food and cash crop production, using FAO's AquaCrop model. The performance of two potential solutions, first a strategy of mulching plus drip irrigation, and second a strategy with changing the cropping pattern in addition to mulching and drip irrigation, were compared in one of the most water-stressed catchments in the world, the Upper Litani Basin in Lebanon. Our results show a substantial potential for more efficient use of green water resources for food production while saving scarce blue water resources. Whereas mulching and drip irrigation together decrease the blue WF in the basin by 4.5%, changing the cropping pattern as well can decrease it by 20.3%. Food and cash production could increase by 3% and 50% by changing the cropping pattern, compared to 1.5% and 2.1% by mulching and drip irrigation. Changing the cropping pattern could thus significantly reduce water scarcity and enlarge food and cash production in the basin. © 2020Authors are thankful for the support from the UN-FAO headquarter and FAO-Lebanon. The ground data used for the simulation and modelling were collected during the field visit of the Litani Basin funded by the FAO-WaPOR project (FRAME consortium

Mapping Vegetation Index-Derived Actual Evapotranspiration across Croplands Using the Google Earth Engine Platform

Precise knowledge of crop water consumption is essential to better manage agricultural water use, particularly in regions where most countries struggle with increasing water and food insecurity. Approaches such as cloud computing and remote sensing (RS) have facilitated access, process, and visualization of big geospatial data to map and monitor crop water requirements. To find the most reliable Vegetation Index (VI)-based evapotranspiration (ETa) for croplands in drylands, we modeled and mapped ETa using empirical RS methods across the Zayandehrud river basin in Iran for two decades (2000–2019) on the Google Earth Engine platform using the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index 2 (EVI2). Developed ET-VI products in this study comprise three NDVI-based ETa (ET-NDVI*, ET-NDVI*scaled, and ET-NDVIKc) and an EVI2-based ETa (ET-EVI2). We (a) applied, for the first time, the ET-NDVI* method to croplands as a crop-independent index and then compared its performance with the ET-EVI2 and crop ET, and (b) assessed the ease and feasibility of the transferability of these methods to other regions. Comparing four ET-VI products showed that annual ET-EVI2 and ET-NDVI*scaled estimations were close. ET-NDVIKc consistently overestimated ETa. Our findings indicate that ET-EVI2 and ET-NDVIKc were easy to parametrize and adopt to other regions, while ET-NDVI* and ET-NDVI*scaled are site-dependent and sensitive to image acquisition time. ET-EVI2 performed robustly in arid and semi-arid regions making it a better tool. Future research should further develop and confirm these findings by characterizing the accuracy of VI-based ETa over croplands in drylands by comparing them with available ETa products and examining their performance using crop-specific comparisons