Crop Area Estimation Research Articles

Kharif rice crop acreage and yield estimation using microwave and optical remote sensing time series satellite data: A case study of the eastern region of Maharashtra

Aim of the studyThe study aims to assess the feasibility of using remote sensing to estimate crop area and yield in a major rice-growing region of Maharashtra.Material and methodsRecent advancements in remote sensing technology, including improvements in image resolution and availability, allowed for timely data collection. The study employs a random forest classifier to identify rice crop using sentinel-1A SAR temporal backscatter satellite images. Additionally, a semi-physical method that incorporates remote sensing and physiological concepts such as Photo-synthetically Active Radiation and a fraction of PAR absorbed by the crop is used to estimate crop yield.Results and conclusionsNet Primary Product was calculated using the Monteith model. The calculation of Kharif rice yield involved considering the actual NPP, Radiation use efficiency, and Harvest index. The present study was conducted throughout two kharif seasons, 2020 and 2021. Although there are minor differences in kharif rice area and yield estimations, the model is still applicable in other significant kharif rice-growing regions of India.

Characterization of the typology of producers in the “sembrando vida” program in Oaxaca, Mexico

In Mexico, research work documented at plot in the field or producer level is uncommon and with limited information, due to the fact that it is expensive and requires specialized personnel. This research work carried out by the National Institute of Forestry, Agriculture and Livestock Research (INIFAP) and financed by the National Council of Humanities, Science and Technology (CONAHCYT) was based on a field diagnosis to determine the description of the typology of the participating "sowers'' producers enrolled in the Sembrando Vida Program (PSV), of the Federal Goverment of Mexico belonging to the state of Oaxaca, and thus, be able to implement strategies for training and follow-up attention on a massive scale. A total of 383 sample surveys were analyzed from a population of 37,719 small producers representing an estimated crop area of 56,578 hectares distributed in two regions of the state of Oaxaca. It is concluded that the diagnosis continues to be a fundamental information tool to identify and address more precisely and timely the problems at the farmer's plot level and with any type of crop. Another fundamental activity was the visit or technical visits to the territories that are representative of the regions in order to validate the information obtained. With the results obtained in this research, it was possible to address the main problems through personalized and directed technical assistance under the scheme of field schools (learning-by-doing) throughout the development of annual and fruit crops that the planters have in the MIAF within their PUPs (Small Production Units). Likewise, insert the main training needs at the express and consensual request of all program stakeholders: researchers-trainers, PSV technicians and sowers.

Multi-Model Ensemble Approach for Soybean Crop Yield Estimation (Kharif-2023) in Latur District at Macroscale level

Crop area estimation is a critical aspect of agricultural monitoring and management, providing essential information for decision-making in the agricultural sector. Study was carried out at Semantic Technologies and Agritech services Pvt. Ltd., GIS and Remote sensing team, Pune during Kharif-2023. All methodology given by YESTECH manual under Pradhan Mantri Fasal Bima Yojana (PFMBY) was followed. Latur district facing more weather-based yield losses during last few of years. In this case study we tried to estimate yield of soybean crop for agriculture-based stake holders, insurance companies, Government polices at Revenue circle level (RC). Multimodal approach is beneficial over single model yield estimation approach as it takes ensemble yield for perfect forecasting of crop yield. Accuracy was in the range as given in YESTECH manual at RC level. Thus, overall results show that use of such model for yield estimation is one of the best approach to take the decisions for insurance based stake holders in rainfed regions where more negative consequences on soybean productivity under different climate change scenario was observed.

Comparison of Crop Area Estimation for the Winter Season Using the Google Earth Engine (GEE) Platform and Traditional Method of Supervisor Classification

There is a significant development in the methods and techniques for classifying satellite images during these years. One of the most recently launched methods is using the Google Earth Engine platform for its contribution in providing a huge amount of satellite data with different dates and multiple satellite images sources, with a method of work that is easy to deal with, but it needed to learn the computer language such as Java and Python, which is the method of work depends on writing a script that gives application commands to classify the different satellite image. Therefore, this research aims to apply classification of satellite images using the Google Earth Engine platform for three models Classification and Regression Trees (CART), Support Vector Machine (SVM), and Random Forest (RF), then compare the results calculated from using images classification (Supervised classification: Maximum likelihood) in terms of the degree of accuracy of the classification and matching the results of the classified satellite images in addition. to boundary clarify of the classification methods. This study showed that, using the Google Earth Engine (GEE) platform is considered one of the important methods that facilitated researchers interested in using remote sensing techniques and geographic information systems, due to the availability of this platform of free satellite sources with multiple spatial resolutions, in addition to the presence of a powerful system that collects, processes, and classifies satellite images using different classification models and indexes. But we need to apply these modern techniques at a number of levels, such as at Markas, Governorates, International, and regional levels, to judge their accuracy and suitability for large areas.

Spatiotemporal Landsat-Sentinel-2 satellite imagery-based Hybrid Deep Neural network for paddy crop prediction using Google Earth engine

Rice is one of the predominant food sources to fulfill the dietary requirements of well-being in India. Therefore, accurate and timely paddy crop yield prediction is crucial to ensure the food security of the country. In this direction, the present study proposed a hybrid deep-learning method based on Conv-1D and LSTM layers using the classification-derived phenological with meteorological parameters for paddy crop yield prediction. The paddy crop classification has been conducted using high-resolution (10 m) multispectral imagery based on GPS coordinates collected during the paddy field visits to extract the phenological parameters for input to the prediction model. In this context, the efficiency of Random Forest, Naïve Bayes, SVM, and Gradient Tree boost classifiers was assessed. Furthermore, we have also analyzed the accuracy of Landsat-8, Sentinel-1 GRD, and Sentinel-2 satellite imagery in paddy crop classification based on area estimation. The Statistical Abstract of Haryana was utilized to validate the paddy crop area estimation and yield prediction. The classification outcomes showed that the Random Forest method attained the highest accuracy of 96.6 % compared to other GEE-based classifiers. The proposed Hybrid Deep learning approach achieved an RMSE value of 0.219 t/ha compared to CNN, LSTM, CNN-Bi-LSTM, and Regression techniques for crop yield prediction. The study conclusion highlighted that the sentinel-2 satellite imagery performed well and found that the proposed hybrid approach provided an alternative for paddy crop yield prediction.

Unveiling Agricultural Landscapes: A Methodological Review of SAR for Crop Area Estimation

Accurate and timely information on crop areas is essential for informed decision-making in agriculture, from farm management to food security assessments. Synthetic Aperture Radar (SAR) data has emerged as a game-changer in this field due to its ability to image landscapes through cloud cover and its sensitivity to vegetation structure. This review paper delves into the various methodologies employed to utilize SAR data for effective crop area estimation. We begin by the significance of crop area information and progressively dive into the crucial image processing techniques used in SAR data analysis. These techniques encompass preprocessing steps to remove noise and artifacts, segmentation to delineate individual crop fields, and feature extraction to capture relevant crop characteristics that influence the radar backscatter. Following this, we examine the classification algorithms employed to identify and differentiate crop types within the segmented regions. We discuss both established supervised machine learning algorithms like Support Vector Machines (SVM) and Random Forest (RF) and unsupervised machine learning algorithms like K-means clustering and cutting-edge deep learning approaches like Convolutional Neural Networks (CNNs) for robust and automated crop classification. Furthermore, we explore the concept of time series analysis in the context of SAR data. By incorporating data acquired at different points throughout the growing season, these methods leverage the dynamic changes in backscatter associated with various crop growth stages. This improves differentiation between different crop types and facilitates more precise area estimation. Finally, we acknowledge the limitations and challenges associated with SAR-based crop area mapping, such as the sensitivity to soil moisture content and the potential confusion between crops with similar characteristics.

Estimation of Wheat Crop Production using Multispectral Information Fusion.

The present work estimates the area and the corresponding produce of wheat crop in the study area, which comprises Etah region of Uttar Pradesh, India. For this purpose, multispectral images of multiple sensors, viz., Sentinel-2, Landsat-8, and Landsat-9 during the preharvesting period, i.e., March for the years 2021 and 2022 are used. A multispectral information fusion approach has been proposed which involves image classification as well as vegetation index-based information extraction. For imposing the information fusion, appropriate image bands are identified with the help of separability analysis followed by land cover classification for wheat crop class extraction. Support Vector Machine (SVM), Artificial Neural Network (ANN) and Maximum Likelihood (ML) are used for classification whereas Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC) are used for index-based crop area extraction. The maximum accuracy of 98.34% is achieved for Sentinel-2 data using ANN whereas minimum accuracy of 80.21% is achieved for Landsat-9 using ML classifier. The estimated area for Sentinel-2 data for the year 2021 is 260540 hectares using ANN and 203240 hectares using ML which is near to the reference data, i.e., 238600 hectares. SVM also shows good performance and calculates least error in estimated crop area for the year 2022 on Sentinel-2 data. It calculates 8408490 tons of wheat for the same year. The proposed method utilizes a single image per year for extraction of information supported by the ground truth data which makes it a novel approach of information extraction for crop produce monitoring. This article is protected by copyright. All rights reserved.

Transformative Role of Remote Sensing in Advancing Horticulture: Optimizing Sustainability, Efficiency and Resilience

The field of horticulture, vital for addressing global challenges like food security and sustainable agriculture, has been revolutionized by remote sensing technology. This comprehensive review explores the transformative impact of remote sensing on horticulture, emphasizing its role in optimizing resource utilization, promoting environmental sustainability, and mitigating the effects of climate change. Remote sensing, encompassing a range of sensors, satellites, and data analysis techniques, enables the collection of critical information from a distance, providing insights into crop health, soil conditions, water availability, and more. Precision agriculture, including the use of GPS and GIS, is integrated with remote sensing to enhance agricultural efficiency while minimizing environmental impacts. Site-Specific Crop Management (SSCM) is highlighted as a key component of precision agriculture, enabled by geospatial technologies, including remote sensing. It discusses how remote sensing systems, with their multispectral and multi-temporal capabilities, support various horticultural applications such as crop yield estimation, abiotic and biotic stress management, crop classification, canopy measurement, crop area estimation, and even crop insurance validation. The use of Geographic Information Systems (GIS) and the Global Positioning System (GPS) in tandem with remote sensing is explored in the context of spatial analysis, mapping, and precise navigation.

PARCS: A Deployment-Oriented AI System for Robust Parcel-Level Cropland Segmentation of Satellite Images

Cropland segmentation of satellite images is an essential basis for crop area and yield estimation tasks in the remote sensing and computer vision interdisciplinary community. Instead of common pixel-level segmentation results with salt-and-pepper effects, a parcel-level output conforming to human recognition is required according to the clients' needs during the model deployment. However, leveraging CNN-based models requires fine-grained parcel-level labels, which is an unacceptable annotation burden. To cure these practical pain points, in this paper, we present PARCS, a holistic deployment-oriented AI system for PARcel-level Cropland Segmentation. By consolidating multi-disciplinary knowledge, PARCS has two algorithm branches. The first branch performs pixel-level crop segmentation by learning from limited labeled pixel samples with an active learning strategy to avoid parcel-level annotation costs. The second branch aims at generating the parcel regions without a learning procedure. The final parcel-level segmentation result is achieved by integrating the outputs of these two branches in tandem. The robust effectiveness of PARCS is demonstrated by its outstanding performance on public and in-house datasets (an overall accuracy of 85.3% and an mIoU of 61.7% on the public PASTIS dataset, and an mIoU of 65.16% on the in-house dataset). We also include subjective feedback from clients and discuss the lessons learned from deployment.

Development of a 10-m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation

Spatially explicit information on crop distribution is essential for market information, food security, and agricultural sustainability. However, high-resolution crop maps are unavailable for most countries of the world. In this study, we developed an operational workflow and produced the first openly-available 10-m resolution maize and soybean map over China. We also derived area estimates for maize and soybean extent for 2019 using a stratified, two-stage, cluster sampling design and ground data collected for the entire country. We developed a multi-scale, multi-temporal procedure for mapping, in which field data were used as training to map maize and soybean over the first-stage sample of 10 km × 10 km equal-area blocks with PlanetScope and Sentinel-2 data. Then, the classified blocks were used as training to map maize and soybean for the country with wall-to-wall Sentinel-2 data using a random forests approach. We used all available Sentinel-2 surface reflectance data acquired between April and October 2019, applied quality assurance, including cloud and shadow masking, and created monthly image composites as inputs for the random forests analysis. We derived maize and soybean area estimates using the field sample data and a regression estimator. Utilizing the probability output layer of the random forests models, we found and applied empirical probability thresholds that matched map-based crop area estimates with sample-based area estimates. Maize area in China in 2019 was estimated to be 330,609 ± 34,109 km2 (± value is the standard error), and soybean area was estimated to be 78,107 ± 12,969 km2. Validated using the field sample data as reference, our crop map had an overall accuracy of 91.8 ± 1.2%. The user's and producer's accuracies for the maize class were 93.9 ± 2.5% and 79.2 ± 3.6%, and for the soybean class were 63.6 ± 12.1% and 61.9 ± 11.8%. Our map-based maize and soybean area estimates had close agreement with government reports at the provincial and prefectural levels, with r2 of 0.90 and 0.92 for maize, and 0.93 and 0.94 for soybean, respectively. Our workflow can generate internally consistent results for crop area estimation and crop mapping simultaneously. As Sentinel-2 data are being acquired consistently and very-high-resolution commercial satellite data are increasingly available, our established workflow may be applied in an operational setting for annual crop mapping in China and other countries.

A method of yield monitoring based on neural networks using deep learning

Estimation of crop area is an important task in agriculture and can be used to provide accurate information on many issues such as crop yield estimation, food policy development, adjustment of planting patterns, which is of great importance for national food security. This article discusses yield monitoring based on the image segmentation method based on the work of neural networks using deep learning. For this purpose, a neural network based on the U-net architecture was selected and trained, and an algorithm was created for subsequent analysis of processed images.

Development of an Adaptive Linear Mixture Model for Decomposition of Mixed Pixels to Improve Crop Area Estimation Using Artificial Neural Network

Precise spatial information of crop distribution is vital for government and research organizations to monitor agriculture activities like crop health monitoring, crop yield prediction, and food security. Mapping of crop area is challenging in smallholder farming like India, where crop parcels are smaller than two hectares. With an extension of artificial intelligence, an artificial neural network has ability to learn the spectral feature of multispectral satellite images and map them to a land cover class. However, mixed pixel is a challenging problem in pixel wise classification of coarse resolution satellite images. The linear mixture model is successfully utilized to unmix the signals of a mixed classes. The success of linear mixture model is depending on the selection of endmembers of a mixed class. Therefore, this paper presents an adaptive approach for automatic selection of endmembers of a mixed pixel in linear mixture model using spectral and spatial information. The proposed approach is capable of extracting the fraction area cover of each class by using a constrained least-squares error solution. The GPS field surveys, and drone images are employed to create reference data for the accuracy assessment of proposed algorithm. The experimentation results indicate that the solution of the proposed approach outperformed recent baseline methods in terms of efficiency and accuracy of pixelwise estimated area and overall estimated area of various land cover classes.

Fine crop classification in high resolution remote sensing based on deep learning

Mapping the crop type can provide a basis for extracting information on crop planting structure, and area and yield estimation. Obtaining large-scale crop-type mapping by field investigation is inefficient and expensive. Traditional classification methods have low classification accuracy due to the fragmentation and heterogeneity of crop planting. However, the deep learning algorithm has a strong feature extraction ability and can effectively identify and classify crop types. This study uses GF-1 high-resolution remote sensing images as the data source for the Shuangcheng district, Harbin city, Heilongjiang Province, China. Two spectral feature data sets are constructed through field sampling and employed for training and verification, combined with basic survey data of grain production functional areas at the plot scale. Traditional machine learning algorithms, such as random forest (RF) and support vector machine (SVM), and a popular deep learning algorithm, convolution neural network have been utilized. The results show that the fusion of multi-spectral information and vegetation index features helps improve classification accuracy. The deep learning algorithm is superior to the machine learning algorithm in both classification accuracy and classification effect. The highest classification accuracy of Crop Segmentation Network (CSNet) based on fine-tuning Resnet-50 is 91.2%, kappa coefficient is 0.882, and mean intersection over union is 0.834. The classification accuracy is 13.3% and 9.5% points higher than RF and SVM, respectively, and the best classification performance is obtained. The classification accuracy and execution efficiency of the model are suitable for a wide range of crop classification tasks and exhibit good transferability.

Satellite-based crop identification and area estimation under the SATMIROL system

One important area where satellite-based Earth observation is applied in is agricultural statistics. The use of new digital technologies and satellite data have contributed to the modernisation of the aforementioned scientific area and is designed to overcome the problems that both agricultural statistics and the recipients of statistical data are faced with. The main aim of the article is to present an innovative system of satellite-based crop identification and crop growth monitoring – SATMIROL. The system was developed by Statistics Poland, the Institute of Geodesy and Cartography and the Space Research Centre of the Polish Academy of Sciences. SATMIROL provides agricultural statistics based on satellite data from the Copernicus programme and administrative data. The article focuses on selected modules of the system which involve the use of satellite remote sensing for crop identification. The study also presents a nationwide crop area estimation performed in three periods: 2019, 2020 and 2021. The implementation of SATMIROL allowed the presentation of statistical data on agriculture in spatial terms, which is not possible when applying traditional methods of data production. This new method of acquiring data enables their presentation at all levels of Poland’s administrative division, i.e. by voivodship, poviat (district) and gmina (commune).

Crop type classification in Southern Brazil: Integrating remote sensing, crop modeling and machine learning

Crop type mapping is essential for agricultural monitoring, but lack of terrestrial labels (herein termed as ground truth data) limit these models around the globe. In this study, we tested different methods for creating crop type maps for soybean (Glycine max L.) and corn (Zea mays L.), with aims of i) compare ground truth data related to crop modeling; ii) evaluate agricultural field masks generated by Environmental Rural Register, MapBiomas product, and random forest model; iii) evaluate models of a) unsupervised classification, b) supervised classification with ground truth data of 1 year, c) with ground truth data of 2 years, d) supervised classification with crop modeling only, e) with the combination of crop modeling and ground truth data of 1 year, and f) with ground truth data of 2 years; iv) test sample size for training the model utilizing ground truth data and crop modeling in transfer learning approach; and v) examine spatial remote sensing features to guide crop data collection. The APSIM-NG crop model was utilized for generate crop modeling simulations to compare with satellite images harmonic regression of ground truth data. We found similarity of harmonic regression coefficients derived from crop modeling and satellite imagery of Sentinel-2 of the labeled ground truth data for both soybean and corn crops. Agricultural masks showed efficiency for crop area estimation for soybean, with highest accuracy with random forest model. Crop modeling aggregated with growing season data as input for supervised learning presented the greater model performance with overall accuracy of 0.94. Crop area prediction was most accurate for soybean [R2 = 0.93, and mean absolute error (MAE) = 2,052 ha] for the model with the combination of crop modeling and ground truth data of 2 years, and least for corn, [R2 = 0.18, and MAE = 1,146 ha] when only using crop modeling. Model performance was influenced by sample size, with greater accuracy (0.93) for aggregating crop modeling (150 samples) and year data (100 samples). In addition, considering spatial field data variability as model input increased overall accuracy from 0.84 to 0.93, with higher impact when only ground truth data was utilized for input in the model. Our results suggest that these methods offer options for crop type classification when less adequate ground truth data is available and with unsupervised learning. On the other hand, supervised learning utilizing crop modeling with presence of field data did improve model performance overall.

Bootstrap Assessment of Crop Area Estimates Using Satellite Pixels Counting

Crop area estimates based on counting pixels over classified satellite images are a promising application of remote sensing to agriculture. However, such area estimates are biased, and their variance is a function of the error rates of the classification rule. To redress the bias, estimators (direct and inverse) relying on the so-called confusion matrix have been proposed, but analytic estimators for variances can be tricky to derive. This article proposes a bootstrap method for assessing statistical properties of such estimators based on information from a sample confusion matrix. The proposed method can be applied to any other type of estimator that is built upon confusion matrix information. The resampling procedure is illustrated in a small study to assess the biases and variances of estimates using purely pixel counting and estimates provided by both direct and inverse estimators. The method has the advantage of being simple to implement even when the sample confusion matrix is generated under unequal probability sample design. The results show the limitations of estimates based solely on pixel counting as well as respective advantages and drawbacks of the direct and inverse estimators with respect to their feasibility, unbiasedness, and variance.

10 m crop type mapping using Sentinel-2 reflectance and 30 m cropland data layer product

The 30 m resolution U.S. Department of Agriculture (USDA) crop data layer (CDL) is a widely used crop type map for agricultural management and assessment, environmental impact assessment, and food security. A finer resolution crop type map can potentially reduce errors related to crop area estimation, field size characterization, and precision agriculture activities that requires crop growth information at scales finer than crop field. This study is to develop a method for crop type mapping using Sentinel-2 10 m bands (i.e., red, green, blue, and near-infrared) and to examine the benefit of the derived 10 m crop type map. The crop type mapping was conducted for two study areas with significantly different field sizes and crop types in South Dakota and California, respectively. The Sentinel-2 10 m surface reflectance and the derived normalized difference vegetation index (NDVI) acquired in the 2019 growing season were used to generate monthly median composites as classification input. The training and evaluation samples were derived from CDL by (i) finding good quality 30 m CDL pixels and (ii) identifying a single representative Sentinel-2 10 m pixel time series for each 30 m good quality CDL pixel. The random forest algorithm was trained using 80% of the samples and evaluated using the 20% remaining samples, and the results showed high overall accuracies of 94% and 83% for South Dakota and California study areas, respectively. The major crops in both study areas obtained high user’s and producer’s accuracies (>87%). There is a good agreement between the class proportions in the 10 m crop type map and 30 m CDL for both study areas with R2 ≥ 0.94 and root mean square error (RMSE) ≤ 3%. More importantly, compared to the 30 m CDL, the 10 m crop type map has much less salt-pepper and crop boundary-aliasing effects and defines better the small surface features (e.g., small fields, roads, and rivers). The potential of the method for large area 10 m crop type mapping is discussed.

Fodder crop estimation using Sentinel-2A/B satellite data for West Bengal, India

Mapping, monitoring and estimation of fodder crop area can support farmers, policy makers to make right decisions in various conditions to improve the livestock productivity. The freely available high-resolution Sentinel-2 MSI data has increased the application of precision agriculture for a broader range. This study investigated the capabilities of Sentinel-2A/B MSI satellite data to identify and discriminate the fodder crops from other crops and estimate the area utilized for fodder cultivation in five districts of West Bengal during 2019–20 in rabi season (October to March). Multi-date NDVI based spectral profiles were generated from satellite imagery to ascertain the time of sowing which plays an important role to differentiate fodder crops from other crops. Image classification has been done through the ISODATA clustering technique. A field survey has been carried out in the study area and a total of 610 GT points were collected from different locations of fodder crops using handheld GPS and smartphone-based applications to validate the classification of crops. Accuracy assessment has also been performed between four classes (Fodder crops, Other crops, Forest and Waterbody) which is 81.61% for fodder crops, and 88.97% overall accuracy was observed in classification. The area under fodder cultivation was 0.68% of the total agricultural area, which has been estimated to the tune of 6.78 per thousand hectares. This study can be implemented for fodder crop identification and cultivation management plan to improve the livestock productivity by supply of fodder throughout the year.

Mapping Maize Area in Heterogeneous Agricultural Landscape with Multi-Temporal Sentinel-1 and Sentinel-2 Images Based on Random Forest

Accurate estimation of crop area is essential to adjusting the regional crop planting structure and the rational planning of water resources. However, it is quite challenging to map crops accurately by high-resolution remote sensing images because of the ecological gradient and ecological convergence between crops and non-crops. The purpose of this study is to explore the combining application of high-resolution multi-temporal Sentinel-1 (S1) radar backscatter and Sentinel-2 (S2) optical reflectance images for maize mapping in highly complex and heterogeneous landscapes in the middle reaches of Heihe River, northwest China. We proposed a new two-step method of vegetation extraction and followed by maize extraction, that is, extract the vegetation-covered areas first to reduce the inter-class variance by using a Random Forest (RF) classifier based on S2 data, and then extract the maize distribution in the vegetation area by using another RF classifier based on S1 and/or S2 data. The results demonstrate that the vegetation extraction classifier successfully identified vegetation-covered regions with an overall accuracy above 96% in the study area, and the accuracy of the maize extraction classifier constructed by the combined multi-temporal S1 and S2 images is significantly improved compared with that S1 (alone) or S2 (alone), with an overall accuracy of 87.63%, F1_Score of 0.86, and Kappa coefficient of 0.75. In addition, with the introduction of multi-temporal S1 and/or S2 images in crop growing season, the constructed RF model is more beneficial to maize mapping.

The estimation of non-irrigated crop area and production using the regression analysis approach: A case study of Bursa Region (Turkey) in the mid-nineteenth century.

Agricultural land cover and its changing extent are directly related to human activities, which have an adverse impact on the environment and ecosystems. The historical knowledge of crop production and its cultivation area is a key element. Such data provide a base for monitoring and mapping spatio-temporal changes in agricultural land cover/use, which is of great significance to examine its impacts on environmental systems. Historical maps and related data obtained from historical archives can be effectively used for reconstruction purposes through using sample data from ground observations, government inventories, or other historical sources. This study considered historical population and cropland survey data obtained from Ottoman Archives and cropland suitability map, accessibility, and geophysical attributes as ancillary data to estimate non-irrigated crop production and its corresponding cultivation area in the 1840s Bursa Region, Turkey. We used the regression analysis approach to estimate agricultural land area and grain production for the unknown data points in the study region. We provide the spatial distribution of production and its cultivation area based on the estimates of regression models. The reconstruction can be used in line with future historical research aiming to model landscape, climate, and ecosystems to assess the impact of human activities on the environmental systems in preindustrial times in the Bursa Region context.