eLEAF (Netherlands)

Abstract Appropriate determination of actual evapotranspiration (ETa) is crucial to improve crop water productivity and optimizing water resource consumption. Satellite data enables us to calculate ETa for a large spatial extent with higher granularity, but the temporal frequency of non-commercial satellite data is often a limitation. This research proposes a method that combines crop coefficients with satellite data to fill temporal data gaps and calculate ETa on a daily basis. The study was conducted on sugarcane crops in the Amirkabir Agro-industries area in the southern part of Khuzestan Province, southwestern Iran. First, Landsat-8 data with the 8- day temporal resolution is acquired to estimate Land Surface Temperature (LST) using Single-Channel Algorithm. The estimated LST is validated with the in-situ canopy temperature measurement via Infrared Thermometer (IRT). Then, the validated LST is used to predict the crop stress coefficient (Ks) based on its relationship with the crop water stress index (CWSI). The crop coefficient (Kc) is obtained from the Surface Energy Balance Algorithm for Land (SEBAL) algorithm. The predicted Ks and Kc with the 8-day temporal resolution are assumed to be constant during the eight days and are utilized to calculate daily ETa by multiplying by the daily reference evapotranspiration (ET0) obtained from local meteorological data. The calculated Ks based on the LST result showed that nRMSE ranged from 0.03 to 0.07 from April to September. The results indicate that the crop coefficients of sugarcane in the initial and mid-stage are 12% and 18%, respectively, higher than the proposed figures by the FAO56 guideline. The aggregated decadal and monthly ETa have shown remarkable similarity with the WaPOR datasets, represented by an RMSE of 8.7 and 1.93 mm, respectively. We think this naval approach can significantly overcome the challenge of remote sensing data availability with the desired higher temporal resolution.

Irrigation represents one of the most impactful human interventions in the terrestrial water cycle. Knowing the distribution and extent of irrigated areas as well as the amount of water used for irrigation plays a central role in modeling irrigation water requirements and quantifying the impact of irrigation on regional climate, river discharge, and groundwater depletion. Obtaining high-quality global information about irrigation is challenging, especially in terms of quantification of the water actually used for irrigation. Here, we review existing Earth observation datasets, models, and algorithms used for irrigation mapping and quantification from the field to the global scale. The current observation capacities are confronted with the results of a survey on user requirements on satellite-observed irrigation for agricultural water resources’ management. Based on this information, we identify current shortcomings of irrigation monitoring capabilities from space and phrase guidelines for potential future satellite missions and observation strategies.

The FAO Water Productivity Open Access Portal (WaPOR) offers continuous actual evapotranspiration and interception (ETIa-WPR) data at a 10-day basis across Africa and the Middle East from 2009 onwards at three spatial resolutions. The continental level (250m) covers Africa and the Middle East (L1). The national level (100m) covers 21 countries and four river basins (L2). The third level (30m) covers eight irrigation areas (L3). To quantify the uncertainty of WaPOR version 2 (V2.0) ETIa-WPR in Africa, we used a number of validation methods. We checked the physical consistency against water availability and the long term water balance and then verify the continental spatial and temporal trends for the major climates in Africa. We directly validated ETIa-WPR against in-situ data of 14 eddy covariance stations (EC). Finally, we checked the level consistency between the different spatial resolutions. Our findings indicate that ETIa-WPR is performing well, but with some noticeable overestimation. The ETIa-WPR is showing expected spatial and temporal consistency with respect to climate classes. ETIa-WPR shows mixed results at point scale as compared to EC flux towers with an overall R2 of 0.61, and a root mean square error of 1.04 mm/day. The level consistency is very high between L1 and L2. However, the consistency between L1 and L3 varies significantly between irrigation areas. In rainfed areas, the ETIa-WPR is overestimating at low ETIa-WPR and underestimating when ETIa is high. In irrigated areas, ETIa-WPR values appear to be consistently overestimating ETa. The soil moisture content, the input of quality layers and local advection effects were some of the identified causes. The quality assessment of ETIa-WPR product is enhanced by combining multiple evaluation methods. Based on the results, the ETIa-WaPOR dataset is of enough quality to contribute to the understanding and monitoring of local and continental water processes and water management.

It is often difficult to retrieve timely and reliable information on agricultural production in areas under conflict. Security conditions disrupt the normal data collection and agricultural statistics are often not available, or of questionable accuracy. Satellite Earth observation (EO) is a powerful solution, as it can assess agricultural production in areas difficult to access and provide unbiased information on a wide range of spatial and temporal scales. The objective of this study was to analyse changes in cultivated area and agricultural productivity in Syria prior to and during conflict. As EO provides both historical and near-real time operational data, changes can be rapidly identified in a cost-effective, consistent and repeatable manner. Overall, we found that agricultural productivity in irrigated land in Syria has decreased since the start of the conflict. The results provided input to an integrated multi-sector analysis carried out by the World Bank to inform future policy decisions, reconstruction efforts and shape future development response. Analysis of status, changes, and trends in agricultural area extent and productivity helped to identify the need and most suitable types of intervention, the timing and the targeting criteria. Combining the results with other datasets allows the identification of drivers and aggregators in agriculture, particularly in the context of conflict and economy. Furthermore, the results could also be used to focus rehabilitation actions after the conflict ends, for example the planning of re-establishing agricultural supply chains.