Irrigation plays a crucial role in maize cultivation, as watering is essential for optimizing crop yield and quality, particularly given maize’s sensitivity to soil moisture variations. In the current study, a hybrid Long Short-Term Memory (LSTM) approach is presented aiming to predict irrigation scheduling in maize fields in Bursa, Turkey. A critical aspect of the study was the use of the Aquacrop 7.0 model to simulate soil moisture content (MC) data due to data limitations in the investigated fields. This simulation model, developed by the Food and Agriculture Organization (FAO), helped overcome gaps in soil sensor data, enhancing the LSTM model’s predictions. The LSTM model was trained and tuned using a combination of soil, weather, and satellite-based plant vegetation data in order to predict soil moisture content (MC) reductions. The study’s results indicated that the LSTM model, supported by Aquacrop 7.0 simulations, was effective in predicting MC reduction across various time phases of the maize growing season, attaining R² values ranging from 0.8163 to 0.9181 for Field 1 and from 0.7602 to 0.8417 for Field 2, demonstrating the potential of this approach for precise and efficient agricultural irrigation practices.

Traditional methods based on field campaigns are generally used to assess the performance of irrigation schemes in Burkina Faso, resulting in labor-intensive, time-consuming, and costly processes. Despite their extensive application for such performance assessment, remote sensing (RS)-based approaches remain very much underutilized in Burkina Faso. Using multi-temporal Landsat images within the Python module for the Surface Energy Balance Algorithm for Land model, we investigated the spatiotemporal performance patterns of the Kou Valley irrigation scheme (KVIS) during two consecutive cropping seasons. Four performance indicators (depleted fraction, relative evapotranspiration, uniformity of water consumption, and crop water productivity) for rice, maize, and sweet potato were calculated and compared against standard values. Overall, the performance of the KVIS varied depending on year, crop, and the crop’s geographical position in the irrigation scheme. A gradient of spatially varied relative evapotranspiration was observed across the scheme, with the uniformity of water consumption being fair to good. Although rice was the most cultivated, a shift to more sweet potato farming could be adopted to benefit more from irrigation, given the relatively good performance achieved by this crop. Our findings ascertain the potential of such RS-based cost-effective methodologies to serve as basis for improved irrigation water management in decision support tools.

Accurate estimation of evapotranspiration (ET) is an important factor in water management, especially in irrigated agriculture. Accurate irrigation scheduling requires accurate estimation of ET. The objective of this study was to estimate the actual evapotranspiration (ETa) by the pySEBAL model and to compare it with the actual evapotranspiration measured by the lysimeter method of soybean crop in Adana, Turkey. Five Landsat 5 Thematic Mapper (TM) images and weather data were used for this study to estimate actual evapotranspiration by the pySEBAL model. The results showed a good relationship between ETa estimated by the pySEBAL model and ETa measured by the lysimeter method, with an R2 of 0.73, an RMSE of 0.51 mm.day−1, an MBE of 0.04 mm.day−1 and a Willmott’s index of agreement (d) of 0.90. Based on this study, there is a good relationship between the actual evapotranspiration estimated by the pySEBAL model and the actual evapotranspiration measured by the lysimeter method. Consequently, ETa of soybean crop can be estimated with high accuracy by the pySEBAL model in Adana, Turkey.

ÖZET : Tarımsal üretimde, uzaktan algılama teknikleri kullanılarak bitki gelişimin takibi yaygın olarak kullanılmaya başlanılmıştır. Uydu ve hava görüntüleri ile insansız hava araçlarından alınan görüntüler, bitkinin arazide gelişiminin konumsal takibinde kolaylıklar sağlamaktadır. Bitki yeşil aksamının yoğunluğunun ve türünün belirlenmesinde bitkilerden yansıyan kırmızı ve yakın kızılötesi bant görüntüleri kullanılarak bitki örtüsü indeksi olarak NDVI değeri hesaplanmaktadır. Bu çalışmada, Uludağ Üniversitesi Ziraat Fakültesi çiftlik arazisinde 2015-2016 üretim döneminde Pehlivan çeşidi buğday ekili alanlarda NDVI değerlerinin zamana ve konuma göre değişiminin belirlenmesi amaçlanmıştır. Çalışmada farklı tarihlerde çekilmiş Landsat 8 görüntüleri kullanılmıştır. Parsel bazında yapılan değerlendirme sonucunda parsel içerisinde bitki örtü indeksi değerlerinin homojen bir dağılım göstermediği görülmüştür.