Crop Water Condition Mapping by Optical Remote Sensing

Abstract

Crop water stress monitoring represents a fundamental step in agricultural production. In order to increase water savings and enhance agricultural sustainability, implementation of suitable irrigation scheduling methods is essential, and requires early detection of water stress in crops, before it causes irreversible damage and yield loss. There are different methods to measure water stress, some of them are based on soil moisture measurements while others are based on calculations of vegetation indices, evapotranspiration or soil water balance. Currently, the use of remote sensing technologies for the analysis of plant water status comprises a wide range of available methods such as infrared thermometry for canopy temperature measures, microwave radiation for soil water content assessment, and spectral vegetation indices for the study of the reflectance responses of canopies to different environmental conditions. The aim of the presented work is to investigate the applicability of the optical trapezoid model (OPtical TRApezoid Model) in mapping the moisture content within agricultural field. The model ability to provide vegetation characteristics, and crop water status at the canopy scale can improve the site-specific decision-making process in a precision agriculture.

Technogenic impact assessment on the environment of Pavlodar region using GIS technologies

“…The consequence of this is a decrease in biological diversity, climate change, and water problems. The consequence of environmental degradation is every fourth death in the world, which means 12.6 million people a year [7][8][9].…”

Improving the methods of Agricultural mapping using remote sensing data

“…In addition, the program has a set of tools for working with raster data, which allows you to process RS data (space images), and perform analytical work using analytical functions. The full capabilities of ArcGIS include the ability to convert spatial raster data from one projection to another, transform images and link coordinates, and export from raster format to vector format [20][21].…”

“…Because these errors can limit a person's ability to process, interpret, and analyse images. Recent scientific studies have argued that controlled classification algorithms show better results [13,20]. Controlled classification algorithms, in particular the Maximum Similarity Algorithm (MLC), Random Forest Algorithm (Random Forest), Base Vector Method (SVM), and Artificial Neutral Networks (ANN) algorithms create unique classification capabilities [13].…”

Surface Soil Moisture Determination of Irrigated and Drained Agricultural Lands with the OPTRAM Method and Sentinel-2 Observations

“…The OPTRAM model was mainly used to estimate soil moisture and has been used in various climatic conditions (e.g., United States [30,44], Italy [32], China [45], Hungary [46], Iran [47], Ethiopia [48], etc.). OPTRAM has also been used successfully in other applications, i.e., for estimating actual evapotranspiration [49], for water table depth monitoring in bogs [50], or for estimation of dust emission probability in the lake basin [51].…”

“…As we can see in Figure 8, the presence of plants masks the actual soil moisture; therefore, the best SSM estimation results are obtained with low NDVI index values (see Figure 10). The OPTRAM model requires the determination and fit of wet and dry edges of the model, which is most often conducted arbitrarily through visual inspection [30,32,46], which could affect accuracy.…”