Bayesian networks for raster data (BayNeRD): plausible reasoning from observations

This paper describes the basis functioning and implementation of a computer-aided Bayesian Network (BN) method that is able to incorporate experts’ knowledge for the benefit of remote sensing applications and other raster data analyses: Bayesian Network for Raster Data (BayNeRD). Using a case study of soybean mapping in Mato Grosso State, Brazil, BayNeRD was tested to evaluate its capability to support the understanding of a complex phenomenon through plausible reasoning based on data observation. Observations made upon Crop Enhanced Index (CEI) values for the current and previous crop years, soil type, terrain slope, and distance to the nearest road and water body were used to calculate the probability of soybean presence for the entire Mato Grosso State, showing strong adherence to the official data. CEI values were the most influencial variables in the calculated probability of soybean presence, stating the potential of remote sensing as a source of data. Moreover, the overall accuracy of over 91% confirmed the high accuracy of the thematic map derived from the calculated probability values. BayNeRD allows the expert to model the relationship among several observed variables, outputs variable importance information, handles incomplete and disparate forms of data, and offers a basis for plausible reasoning from observations. The BayNeRD algorithm has been implemented in R software and can be found on the internet. \ an

A novel Greenness and Water Content Composite Index (GWCCI) for soybean mapping from single remotely sensed multispectral images

As a critical source of food and one of the most economically significant crops in the world, soybean plays an important role in achieving food security. Large area accurate mapping of soybean has long been a vital, but challenging issue in remote sensing, relying heavily on large-volume and representative training samples, whose collection is time-consuming and inefficient, especially for large areas (e.g., national scale). Thus, methods are needed that can map soybean automatically and accurately from single-date remotely sensed imagery. In this research, a novel Greenness and Water Content Composite Index (GWCCI) was proposed to map soybean from just a single Sentinel-2 multispectral image in an end-to-end manner without employing training samples. By capitalizing on the product of the NDVI (related to greenness) and the short-wave infrared (SWIR) band (related to canopy water content), the GWCCI provides the required information with which to discriminate between soybean and other land cover types. The effectiveness of the proposed GWCCI was investigated in seven typical soybean planting regions within four major soybean-producing countries across the world (i.e., China, the United States, Brazil and Argentina), with diverse climates, cropping systems and agricultural landscapes. In the experiments, an optimal threshold of 0.17 was estimated and adopted by the GWCCI in the first study site (S1) in 2021, and then generalised to the other study sites over multiple years for soybean mapping. The GWCCI method achieved a consistently higher accuracy in 2021 compared to two conventional comparative classifiers (support vector machine (SVM) and random forest (RF)), with an average overall accuracy (OA) of 88.30% and a Kappa coefficient (k) of 0.77; significantly greater than those of RF (OA: 80.92%, k: 0.62) and SVM (OA: 80.29%, k: 0.60). Furthermore, the OA of the extended years was highly consistent with that of 2021 for study sites S2 to S7, demonstrating the great generalisation capability and robustness of the proposed approach over multiple years. The proposed GWCCI method is straightforward, reliable and robust, and represents an important step forward for mapping soybean, one of the most significant crops grown globally

Systems mapping: how to improve the genetic mapping of complex traits through design principles of biological systems

<p>Abstract</p> <p>Background</p> <p>Every phenotypic trait can be viewed as a "system" in which a group of interconnected components function synergistically to yield a unified whole. Once a system's components and their interactions have been delineated according to biological principles, we can manipulate and engineer functionally relevant components to produce a desirable system phenotype.</p> <p>Results</p> <p>We describe a conceptual framework for mapping quantitative trait loci (QTLs) that control complex traits by treating trait formation as a dynamic system. This framework, called systems mapping, incorporates a system of differential equations that quantifies how alterations of different components lead to the global change of trait development and function through genes, and provides a quantitative and testable platform for assessing the interplay between gene action and development. We applied systems mapping to analyze biomass growth data in a mapping population of soybeans and identified specific loci that are responsible for the dynamics of biomass partitioning to leaves, stem, and roots.</p> <p>Conclusions</p> <p>We show that systems mapping implemented by design principles of biological systems is quite versatile for deciphering the genetic machineries for size-shape, structural-functional, sink-source and pleiotropic relationships underlying plant physiology and development. Systems mapping should enable geneticists to shed light on the genetic complexity of any biological system in plants and other organisms and predict its physiological and pathological states.</p

Early Crop Mapping Using Dynamic Ecoregion Clustering: A USA-Wide Study

peer reviewedMapping target crops earlier than the harvest period is an essential task for improving agricultural productivity and decision-making. This paper presents a new method for early crop mapping for the entire conterminous USA (CONUS) land area using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data with a dynamic ecoregion clustering approach. Ecoregions, geographically distinct areas with unique ecological patterns and processes, provide a valuable framework for large-scale crop mapping. We conducted our dynamic ecoregion clustering by analyzing soil, climate, elevation, and slope data. This analysis facilitated the division of the cropland area within the CONUS into distinct ecoregions. Unlike static ecoregion clustering, which generates a single ecoregion map that remains unchanged over time, our dynamic ecoregion approach produces a unique ecoregion map for each year. This dynamic approach enables us to consider the year-to-year climate variations that significantly impact crop growth, enhancing the accuracy of our crop mapping process. Subsequently, a Random Forest classifier was employed to train individual models for each ecoregion. These models were trained using the time-series MODIS (Moderate Resolution Imaging Spectroradiometer) 250-m NDVI and EVI data retrieved from Google Earth Engine, covering the crop growth periods spanning from 2013 to 2017, and evaluated from 2018 to 2022. Ground truth data were sourced from the US Department of Agriculture’s (USDA) Cropland Data Layer (CDL) products. The evaluation results showed that the dynamic clustering method achieved higher accuracy than the static clustering method in early crop mapping in the entire CONUS. This study’s findings can be helpful for improving crop management and decision-making for agricultural activities by providing early and accurate crop mapping

First Large Extent and High Resolution Cropland and Crop Type Map of Argentina

Trabajo presentado al 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS 2020), 22–26 March 2020, Santiago, Chile.The availability of spatially explicit information about agricultural crops for large regions in Argentina is scarce. In particular, due to temporal dynamics of agricultural production (i.e. changes in planted crops from year to year) and spectral similarities among herbaceous crops it is difficult to generate crop type maps from remote sensing. Large regions with marked climatic variations, like the main agricultural areas of Argentina, represent an additional challenge. Here we generated a map based on supervised classifications using field samples along 14 agricultural zones. Best classification accuracies were obtained by combining seasonal indices (year, summer and winter), with indices that describe the temporal dynamics of vegetation. Accuracy was increased at regions with high and balanced number of samples and with longer growing seasons. The map allows to identify areas with clusters of one, two or three crops and to characterize areas with different spatial distribution between cropland and no cropland areas.EEA Salta.Fil: De Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Veron, Santiago Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos; Argentina.Fil: Banchero, Santiago. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Mosciaro, Maria Jesus. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Propato, Tamara. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Ferraina, Antonela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Gomez Taffarel, Maria Cielo. Actividad privada; Argentina.Fil: Dacunto, Luciana. Actividad privada; Argentina.Fil: Franzoni, Agustin. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Volante, Jose Norberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; Argentin

Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain)

A workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary preclassification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (Fβ around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8

Use of satellite remote-sensing techniques to predict the variation of the nutritional composition of corn (Zea mays L) for silage

The nutritional composition of corn (Zea mays L) silage can vary substantially within a same silo. Environmental differences within the cornfield could contribute to this variability. We explored using green vegetation index maps, known as normalized difference vegetation index (NDVI) maps, to identify differences in the nutritional composition of corn at the field level. We hypothesized that the nutritional composition of the corn plant differs within the corn- field according to the vegetation index maps as detected by satellite remote-sensing techniques. Three cornfields from 3 commercial dairy farms located within the state of Virginia were utilized in this study. Landsat satellite data were obtained from the US Geological Survey to develop NDVI maps. Each cornfield was segregated in 3 regions classified as low NDVI, mid NDVI, and high NDVI. Corn plants from each region were harvested to determine their nutritional composition. At harvesting, corn plants were cut, weighed, chopped, and analyzed in the laboratory. Data were analyzed as for a complete block design, where fields and NDVI regions were considered blocks and treatments, respectively. The concentrations of ash (40 g kg-1), crude protein (102 g kg-1), neutral detergent fiber (398 g kg-1), acid detergent fiber (232 g kg-1), acid detergent lignin (14 g kg-1), and starch (304 g kg-1) did not differ at different NDVI regions. In our study none of the cornfields seemed to be environmentally stressed during the growing season of 2014. Therefore, it is plausible that the intrinsic variation of the cornfields was minimum due to the adequate growing conditions

MODIFICATION OF INPUT IMAGES FOR IMPROVING THE ACCURACY OF RICE FIELD CLASSIFICATION USING MODIS DATA

The standard image classification method typically uses multispectral imageryon one acquisition date as an input for classification. Rice fields exhibit high variability inland cover states, which influences their reflectance. Using the existing standard method forrice field classification may increase errors of commission and omission, thereby reducingclassification accuracy. This study utilised temporal variance in a vegetation index as amodified input image for rice field classification. The results showed that classification ofrice fields using modified input images provided a better result. Using the modifiedclassification input improved the correspondence between rice field area obtained from theclassification result and reference data (R2 increased from 0.2557 to 0.9656 for regencylevelcomparisons and from 0.5045 to 0.8698 for district-level comparisons). Theclassification accuracy and the estimated Kappa value also increased when using themodified classification input compared to the standard method, from 66.33 to 83.73 andfrom 0.49 to 0.77, respectively. The commission error, omission error, and Kappa variancedecreased from 68.11 to 42.36, 28.48 to 27.97, and 0.00159 to 0.00039, respectively, whenusing modified input images compared to the standard method. The Kappa analysisconcluded that there are significant differences between the procedure developed in thisstudy and the standard method for rice field classification. Consequently, the modifiedclassification method developed here is significant improvement over the standardprocedure