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Smart Farming and Land Management Enabled by Remotely Sensed Big Data

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (16 July 2022) | Viewed by 28913

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Special Issue Editors

Dr. Yun Yang

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Guest Editor

ESSIC, University of Maryland, College Park, MD 20705, USA
Interests: evapotranspiration; drought mapping and monitoring; land-surface water and energy flux; vegetation stress

Dr. Zhe Zhu

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Guest Editor

Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269-4087, USA
Interests: remote sensing of forests; urban and clouds; land cover and land use change; time series analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart Farming is currently driving a revolution in agriculture, aiming at more productive and sustainable production through precise and resource-efficient decision making, with additional applications in forest and rangeland management. Remotely sensed Big Data from various satellite platforms, small unmanned aerial systems, airborne systems, and in situ and proximal sensors brings both challenges and opportunities for Smart Farming which require high spatial resolution and near real-time mapping capabilities. The main goal of this Special Issue is to report on advances in research methodologies and applications for the use of high spatial resolution or high temporal frequency remote-sensed Big Data for Smart Farming and land management application. Contributions may include (1) crop health monitoring and yield prediction, (2) vegetation stress identification, (3) soil mapping, fertilizer, and irrigation advisories, (4) the use of big data and high performance computing for agriculture, forest, and rangeland areas, and (5) the chain of data collection, storage, transfer, transformation, and analytics.

Dr. Yun Yang
Dr. Zhe Zhu
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Smart farming
Land management
Big data
Agriculture
Remote sensing
UAS
Irrigation

Published Papers (6 papers)

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Research

16 pages, 5880 KiB

Open AccessArticle

Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System

by Yun Yang, Martha Anderson, Feng Gao, Jie Xue, Kyle Knipper and Christopher Hain

Remote Sens. 2022, 14(8), 1772; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081772 - 07 Apr 2022

Cited by 14 | Viewed by 3232

Abstract

Evapotranspiration (ET) represents crop water use and is a key indicator of crop health. Accurate estimation of ET is critical for agricultural irrigation and water resource management. ET retrieval using energy balance methods with remotely sensed thermal infrared data as the key input has been widely applied for irrigation scheduling, yield prediction, drought monitoring and so on. However, limitations on the spatial and temporal resolution of available thermal satellite data combined with the effects of cloud contamination constrain the amount of detail that a single satellite can provide. Fusing satellite data from different satellites with varying spatial and temporal resolutions can provide a more continuous estimation of daily ET at field scale. In this study, we applied an ET fusion modeling system, which uses a surface energy balance model to retrieve ET using both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and then fuses the Landsat and MODIS ET retrieval timeseries using the Spatial-Temporal Adaptive Reflectance Fusion Model (STARFM). In this paper, we compared different STARFM ET fusion implementation strategies over various crop lands in the central California. In particular, the use of single versus two Landsat-MODIS pair images to constrain the fusion is explored in cases of rapidly changing crop conditions, as in frequently harvested alfalfa fields, as well as an improved dual-pair method. The daily 30 m ET retrievals are evaluated with flux tower observations and analyzed based on land cover type. This study demonstrates improvement using the new dual-pair STARFM method compared with the standard one-pair STARFM method in estimating daily field scale ET for all the major crop types in the study area. Full article

(This article belongs to the Special Issue Smart Farming and Land Management Enabled by Remotely Sensed Big Data)

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14 pages, 37012 KiB

Open AccessArticle

Monitoring Oasis Cotton Fields Expansion in Arid Zones Using the Google Earth Engine: A Case Study in the Ogan-Kucha River Oasis, Xinjiang, China

by Lijing Han, Jianli Ding, Jinjie Wang, Junyong Zhang, Boqiang Xie and Jianping Hao

Remote Sens. 2022, 14(1), 225; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010225 - 04 Jan 2022

Cited by 10 | Viewed by 2789

Abstract

Rapid and accurate mapping of the spatial distribution of cotton fields is helpful to ensure safe production of cotton fields and the rationalization of land-resource planning. As cotton is an important economic pillar in Xinjiang, accurate and efficient mapping of cotton fields helps the implementation of rural revitalization strategy in Xinjiang region. In this paper, based on the Google Earth Engine cloud computing platform, we use a random forest machine-learning algorithm to classify Landsat 5 and 8 and Sentinel 2 satellite images to obtain the spatial distribution characteristics of cotton fields in 2011, 2015 and 2020 in the Ogan-Kucha River oasis, Xinjiang. Unlike previous studies, the mulching process was considered when using cotton field phenology information as a classification feature. The results show that both Landsat 5, Landsat 8 and Sentinel 2 satellites can successfully classify cotton field information when the mulching process is considered, but Sentinel 2 satellite classification results have the best user accuracy of 0.947. Sentinel 2 images can distinguish some cotton fields from roads well because they have higher spatial resolution than Landsat 8. After the cotton fields were mulched, there was a significant increase in spectral reflectance in the visible, red-edge and near-infrared bands, and a decrease in the short-wave infrared band. The increase in the area of oasis cotton fields and the extensive use of mulched drip-irrigation water saving facilities may lead to a decrease in the groundwater level. Overall, the use of mulch as a phenological feature for classification mapping is a good indicator in cotton-growing areas covered by mulch, and mulch drip irrigation may lead to a decrease in groundwater levels in oases in arid areas. Full article

(This article belongs to the Special Issue Smart Farming and Land Management Enabled by Remotely Sensed Big Data)

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22 pages, 9458 KiB

Open AccessArticle

A Rapid Model (COV_PSDI) for Winter Wheat Mapping in Fallow Rotation Area Using MODIS NDVI Time-Series Satellite Observations: The Case of the Heilonggang Region

by Xiaoyuan Zhang, Kai Liu, Shudong Wang, Xin Long and Xueke Li

Remote Sens. 2021, 13(23), 4870; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234870 - 30 Nov 2021

Cited by 5 | Viewed by 2301

Abstract

Rapid and accurate monitoring of spatial distribution patterns of winter wheat over a long period is of great significance for crop yield prediction and farmland water consumption estimation. However, weather conditions and relatively long revisit cycles often result in an insufficient number of continuous medium-high resolution images over large areas for many years. In addition, the cropland pattern changes frequently in the fallow rotation area. A novel rapid mapping model for winter wheat based on the normalized difference vegetation index (NDVI) time-series coefficient of variation (NDVI_COV_fp) and peak-slope difference index (PSDI) is proposed in this study. NDVI_COV_fp uses the time-series index volatility to distinguish cultivated land from background land-cover types. PSDI combines the key growth stages of winter wheat phenology and special bimodal characteristics, substantially reducing the impact of abandoned land and other crops. Taking the Heilonggang as an example, this study carried out a rapid mapping of winter wheat for four consecutive years (2014–2017), and compared the proposed COV_PSDI with two state-of-the-art methods and traditional methods (the Spectral Angle Mapping (SAM) and the Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA)). The verification results revealed that the COV_PSDI model improved the overall accuracy (94.10%) by 4% compared with the two state-of-art methods (90.80%, 89.00%) and two traditional methods (90.70%, 87.70%). User accuracy was the highest, which was 93.74%. Compared with the other four methods, the percentage error (PE) of COV_PSDI for four years was the lowest in the same year, with the minimum variation range of PE being 1.6–3.6%. The other methods resulted in serious overestimation. This demonstrated the effectiveness and stability of the method proposed in the rapid and accurate extraction of winter wheat in a large area of fallow crop rotation region. Our study provides insight for remote sensing monitoring of spatiotemporal patterns of winter wheat and evaluation of “fallow rotation” policy implementation. Full article

(This article belongs to the Special Issue Smart Farming and Land Management Enabled by Remotely Sensed Big Data)

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23 pages, 3388 KiB

Open AccessArticle

Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning

by Luwei Feng, Zhou Zhang, Yuchi Ma, Qingyun Du, Parker Williams, Jessica Drewry and Brian Luck

Remote Sens. 2020, 12(12), 2028; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12122028 - 24 Jun 2020

Cited by 144 | Viewed by 8530

Abstract

Alfalfa is a valuable and intensively produced forage crop in the United States, and the timely estimation of its yield can inform precision management decisions. However, traditional yield assessment approaches are laborious and time-consuming, and thus hinder the acquisition of timely information at the field scale. Recently, unmanned aerial vehicles (UAVs) have gained significant attention in precision agriculture due to their efficiency in data acquisition. In addition, compared with other imaging modalities, hyperspectral data can offer higher spectral fidelity for constructing narrow-band vegetation indices which are of great importance in yield modeling. In this study, we performed an in-season alfalfa yield prediction using UAV-based hyperspectral images. Specifically, we firstly extracted a large number of hyperspectral indices from the original data and performed a feature selection to reduce the data dimensionality. Then, an ensemble machine learning model was developed by combining three widely used base learners including random forest (RF), support vector regression (SVR) and K-nearest neighbors (KNN). The model performance was evaluated on experimental fields in Wisconsin. Our results showed that the ensemble model outperformed all the base learners and a coefficient of determination (R²) of 0.874 was achieved when using the selected features. In addition, we also evaluated the model adaptability on different machinery compaction treatments, and the results further demonstrate the efficacy of the proposed ensemble model. Full article

(This article belongs to the Special Issue Smart Farming and Land Management Enabled by Remotely Sensed Big Data)

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21 pages, 10536 KiB

Open AccessArticle

Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States

by Yumiao Wang, Zhou Zhang, Luwei Feng, Qingyun Du and Troy Runge

Remote Sens. 2020, 12(8), 1232; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081232 - 12 Apr 2020

Cited by 94 | Viewed by 6703

Abstract

Winter wheat (Triticum aestivum L.) is one of the most important cereal crops, supplying essential food for the world population. Because the United States is a major producer and exporter of wheat to the world market, accurate and timely forecasting of wheat yield in the United States (U.S.) is fundamental to national crop management as well as global food security. Previous studies mainly have focused on developing empirical models using only satellite remote sensing images, while other yield determinants have not yet been adequately explored. In addition, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study used advanced machine learning algorithms to establish within-season yield prediction models for winter wheat using multi-source data to address these issues. Specifically, yield driving factors were extracted from four different data sources, including satellite images, climate data, soil maps, and historical yield records. Subsequently, two linear regression methods, including ordinary least square (OLS) and least absolute shrinkage and selection operator (LASSO), and four well-known machine learning methods, including support vector machine (SVM), random forest (RF), Adaptive Boosting (AdaBoost), and deep neural network (DNN), were applied and compared for estimating the county-level winter wheat yield in the Conterminous United States (CONUS) within the growing season. Our models were trained on data from 2008 to 2016 and evaluated on data from 2017 and 2018, with the results demonstrating that the machine learning approaches performed better than the linear regression models, with the best performance being achieved using the AdaBoost model (R² = 0.86, RMSE = 0.51 t/ha, MAE = 0.39 t/ha). Additionally, the results showed that combining data from multiple sources outperformed single source satellite data, with the highest accuracy being obtained when the four data sources were all considered in the model development. Finally, the prediction accuracy was also evaluated against timeliness within the growing season, with reliable predictions (R² > 0.84) being able to be achieved 2.5 months before the harvest when the multi-source data were combined. Full article

(This article belongs to the Special Issue Smart Farming and Land Management Enabled by Remotely Sensed Big Data)

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18 pages, 10775 KiB

Open AccessArticle

Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

by Yukun Lin, Zhe Zhu, Wenxuan Guo, Yazhou Sun, Xiaoyuan Yang and Valeriy Kovalskyy

Remote Sens. 2020, 12(7), 1176; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071176 - 06 Apr 2020

Cited by 10 | Viewed by 4272

Abstract

Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R² = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R² = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred. Full article

(This article belongs to the Special Issue Smart Farming and Land Management Enabled by Remotely Sensed Big Data)

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