Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

Agriculture: Management, Disturbance, and Climate around the World Using the Google Earth Engine

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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 (31 December 2021) | Viewed by 35496

Share This Special Issue

Special Issue Editors

Dr. Russell Doughty

E-Mail Website
Guest Editor

Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA
Interests: forest ecosystems; agriculture; global photosynthesis modeling; land use and land cover change; socioecological systems; policy; mapping; solar-induced chlorophyll fluorescence

Dr. Rajen Bajgain

E-Mail Website
Guest Editor

Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA
Interests: agro-ecosystem; drought; eddy covariance; climate variability; management decisions; sustainability; green house gas emissions; agroecosystem modelling
Special Issues, Collections and Topics in MDPI journals

Dr. Jie Wang

E-Mail Website
Guest Editor

Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA
Interests: agro-ecosystem; grassland degradation; crop mapping; Land use and land cover change ; Google Earth Engine; Ecosystem production; Management and sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cloud computing has become increasingly useful in the remote sensing field because the number and size of publicly available datasets is dramatically increasing, which necessitates an enormous amount of disc storage and processing power to perform analysis. Increasingly, the Google Earth Engine (GEE) is being used to develop new data analysis methods and generate striking results that may otherwise not be possible. The use of GEE in research continues to advance science and helps us to test important hypotheses and address long-standing debates/issues.

The role of human activity and management in land use, land cover change, and agriculture has also become increasingly important in understanding how our natural world responds to the interaction of climate variability and management decisions. Given the emergence of a global economy and the rapid and anthropogenic-driven change in the composition of Earth’s atmosphere, ecosystems and agriculture are increasingly viewed as systems tightly coupled with human society. Natural systems are now often strongly coupled with social dynamics rather than being extraneous processes. A new focus has emerged to better understand not only why and how agricultural productivity and systems change, but what role human activity and management play in these socioecological systems in the context of climate change.

Remote sensing technology has recently advanced very rapidly. High spatial and temporal resolution optical, microwave, and infrared data from satellite and unmanned aerial vehicles (UAVs) are becoming more common and freely accessible. In addition to the traditional vegetation indices derived from optical data, we now have additional information on vegetation cover and canopy structure from lidar measurements, information on plant function with solar-induced chlorophyll fluorescence (SIF) retrievals, and canopy temperature from thermal infrared imaging. Multisensor and multisource data analysis has become easier to perform in cloud computing platforms such as GEE.

Here, we solicit manuscripts on the use of GEE and remote sensing data to address important topics in agriculture. Preference will be given to studies that consider the role of human activity, policy, and/or economics in agriculture; utilize multiple sensors; investigate climate, drought, disturbance, or atmosphere feedbacks on agricultural productivity or the carbon, water, and nutrient cycles; or apply lidar or SIF data in novel ways.

Potential agriculture-related topics for this Special Issue include but are not limited to:

Satellite, UAV, eddy covariance, and in situ remote sensing;
Human activity and management;
Natural and human disturbances;
Mapping or modeling changes in agricultural lands over space and time;
Estimating and/or projecting productivity;
Drivers of changes in agricultural lands and/or productivity;
Feedbacks between changes in agriculture and the carbon, water, and nutrient cycles;
Drought and flash drought;
Climate change and variability;
Multisensor and multisource data analysis;
Solar-induced chlorophyll fluorescence;
Lidar;
Sustainability.

Dr. Russell Doughty
Dr. Rajen Bajgain
Dr. Jie Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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

Agricultural management and yield
Socioecological and human systems
Vegetation dynamics
Photosynthesis, respiration, and net carbon exchange
Carbon, water, and nutrient cycles
Plantations
Productivity
Livestock
Optical, lidar, and chlorophyll fluorescence remote sensing
Climate variability
Disturbance

Published Papers (8 papers)

Download All Papers

Research

25 pages, 85816 KiB

Open AccessArticle

An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China

by Yueran Hu, Hongwei Zeng, Fuyou Tian, Miao Zhang, Bingfang Wu, Sven Gilliams, Sen Li, Yuanchao Li, Yuming Lu and Honghai Yang

Remote Sens. 2022, 14(5), 1208; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051208 - 01 Mar 2022

Cited by 23 | Viewed by 4187

Abstract

Crop type classification is critical for crop production estimation and optimal water allocation. Crop type data are challenging to generate if crop reference data are lacking, especially for target years with reference data missed in collection. Is it possible to transfer a trained crop type classification model to retrace the historical spatial distribution of crop types? Taking the Hetao Irrigation District (HID) in China as the study area, this study first designed a 10 m crop type classification framework based on the Google Earth Engine (GEE) for crop type mapping in the current season. Then, its interannual transferability to accurately retrace historical crop distributions was tested. The framework used Sentinel-1/2 data as the satellite data source, combined percentile, and monthly composite approaches to generate classification metrics and employed a random forest classifier with 300 trees for crop classification. Based on the proposed framework, this study first developed a 10 m crop type map of the HID for 2020 with an overall accuracy (OA) of 0.89 and then obtained a 10 m crop type map of the HID for 2019 with an OA of 0.92 by transferring the trained model for 2020 without crop reference samples. The results indicated that the designed framework could effectively identify HID crop types and have good transferability to obtain historical crop type data with acceptable accuracy. Our results found that SWIR1, Green, and Red Edge2 were the top three reflectance bands for crop classification. The land surface water index (LSWI), normalized difference water index (NDWI), and enhanced vegetation index (EVI) were the top three vegetation indices for crop classification. April to August was the most suitable time window for crop type classification in the HID. Sentinel-1 information played a positive role in the interannual transfer of the trained model, increasing the OA from 90.73% with Sentinel 2 alone to 91.58% with Sentinel-1 and Sentinel-2 together. Full article

(This article belongs to the Special Issue Agriculture: Management, Disturbance, and Climate around the World Using the Google Earth Engine)

► Show Figures

Figure 1

17 pages, 4097 KiB

Open AccessArticle

Mapping Winter Wheat with Optical and SAR Images Based on Google Earth Engine in Henan Province, China

by Changchun Li, Weinan Chen, Yilin Wang, Yu Wang, Chunyan Ma, Yacong Li, Jingbo Li and Weiguang Zhai

Remote Sens. 2022, 14(2), 284; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020284 - 08 Jan 2022

Cited by 15 | Viewed by 3271

Abstract

The timely and accurate acquisition of winter wheat acreage is crucial for food security. This study investigated the feasibility of extracting the spatial distribution map of winter wheat in Henan Province by using synthetic aperture radar (SAR, Sentinel-1A) and optical (Sentinel-2) images. Firstly, the SAR images were aggregated based on the growth period of winter wheat, and the optical images were aggregated based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS-NDVI) curve. Then, five spectral features, two polarization features, and four texture features were selected as feature variables. Finally, the Google Earth Engine (GEE) cloud platform was employed to extract winter wheat acreage through the random forest (RF) algorithm. The results show that: (1) aggregated images based on the growth period of winter wheat and sensor characteristics can improve the mapping accuracy and efficiency; (2) the extraction accuracy of using only SAR images was improved with the accumulation of growth period. The extraction accuracy of using the SAR images in the full growth period reached 80.1%; and (3) the identification effect of integrated images was relatively good, which makes up for the shortcomings of SAR and optical images and improves the extraction accuracy of winter wheat. Full article

(This article belongs to the Special Issue Agriculture: Management, Disturbance, and Climate around the World Using the Google Earth Engine)

► Show Figures

Figure 1

27 pages, 10840 KiB

Open AccessArticle

Estimating Actual Evapotranspiration over Croplands Using Vegetation Index Methods and Dynamic Harvested Area

by Neda Abbasi, Hamideh Nouri, Kamel Didan, Armando Barreto-Muñoz, Sattar Chavoshi Borujeni, Hamidreza Salemi, Christian Opp, Stefan Siebert and Pamela Nagler

Remote Sens. 2021, 13(24), 5167; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245167 - 20 Dec 2021

Cited by 16 | Viewed by 4086

Abstract

Advances in estimating actual evapotranspiration (ETa) with remote sensing (RS) have contributed to improving hydrological, agricultural, and climatological studies. In this study, we evaluated the applicability of Vegetation-Index (VI) -based ETa (ET-VI) for mapping and monitoring drought in arid agricultural systems in a region where a lack of ground data hampers ETa work. To map ETa (2000–2019), ET-VIs were translated and localized using Landsat-derived 3- and 2-band Enhanced Vegetation Indices (EVI and EVI2) over croplands in the Zayandehrud River Basin (ZRB) in Iran. Since EVI and EVI2 were optimized for the MODerate Imaging Spectroradiometer (MODIS), using these VIs with Landsat sensors required a cross-sensor transformation to allow for their use in the ET-VI algorithm. The before- and after- impact of applying these empirical translation methods on the ETa estimations was examined. We also compared the effect of cropping patterns’ interannual change on the annual ETa rate using the maximum Normalized Difference Vegetation Index (NDVI) time series. The performance of the different ET-VIs products was then evaluated. Our results show that ETa estimates agreed well with each other and are all suitable to monitor ETa in the ZRB. Compared to ETc values, ETa estimations from MODIS-based continuity corrected Landsat-EVI (EVI2) (EVI_MccL and EVI2_MccL) performed slightly better across croplands than those of Landsat-EVI (EVI2) without transformation. The analysis of harvested areas and ET-VIs anomalies revealed a decline in the extent of cultivated areas and a loss of corresponding water resources downstream. The findings show the importance of continuity correction across sensors when using empirical algorithms designed and optimized for specific sensors. Our comprehensive ETa estimation of agricultural water use at 30 m spatial resolution provides an inexpensive monitoring tool for cropping areas and their water consumption. Full article

(This article belongs to the Special Issue Agriculture: Management, Disturbance, and Climate around the World Using the Google Earth Engine)

► Show Figures

Figure 1

26 pages, 27345 KiB

Open AccessArticle

National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine

by Abdelaziz Htitiou, Abdelghani Boudhar, Abdelghani Chehbouni and Tarik Benabdelouahab

Remote Sens. 2021, 13(21), 4378; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214378 - 30 Oct 2021

Cited by 22 | Viewed by 4321

Abstract

Many challenges prevail in cropland mapping over large areas, including dealing with massive volumes of datasets and computing capabilities. Accordingly, new opportunities have been opened at a breakneck pace with the launch of new satellites, the continuous improvements in data retrieval technology, and the upsurge of cloud computing solutions such as Google Earth Engine (GEE). Therefore, the present work is an attempt to automate the extraction of multi-year (2016–2020) cropland phenological metrics on GEE and use them as inputs with environmental covariates in a trained machine-learning model to generate high-resolution cropland and crop field-probabilities maps in Morocco. The comparison of our phenological retrievals against the MODIS phenology product shows very close agreement, implying that the suggested approach accurately captures crop phenology dynamics, which allows better cropland classification. The entire country is mapped using a large volume of reference samples collected and labelled with a visual interpretation of high-resolution imagery on Collect-Earth-Online, an online platform for systematically collecting geospatial data. The cropland classification product for the nominal year 2019–2020 showed an overall accuracy of 97.86% with a Kappa of 0.95. When compared to Morocco’s utilized agricultural land (SAU) areas, the cropland probabilities maps demonstrated the ability to accurately estimate sub-national SAU areas with an R-value of 0.9. Furthermore, analyzing cropland dynamics reveals a dramatic decrease in the 2019–2020 season by 2% since the 2018–2019 season and by 5% between 2016 and 2020, which is partly driven by climate conditions, but even more so by the novel coronavirus disease 2019 (COVID-19) that impacted the planting and managing of crops due to government measures taken at the national level, like complete lockdown. Such a result proves how much these methods and associated maps are critical for scientific studies and decision-making related to food security and agriculture. Full article

(This article belongs to the Special Issue Agriculture: Management, Disturbance, and Climate around the World Using the Google Earth Engine)

► Show Figures

Graphical abstract

19 pages, 14137 KiB

Open AccessArticle

Mapping Croplands in the Granary of the Tibetan Plateau Using All Available Landsat Imagery, A Phenology-Based Approach, and Google Earth Engine

by Yuanyuan Di, Geli Zhang, Nanshan You, Tong Yang, Qiang Zhang, Ruoqi Liu, Russell B. Doughty and Yangjian Zhang

Remote Sens. 2021, 13(12), 2289; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122289 - 11 Jun 2021

Cited by 10 | Viewed by 2918

Abstract

The Tibetan Plateau (TP), known as “The Roof of World”, has expansive alpine grasslands and is a hotspot for climate change studies. However, cropland expansion and increasing anthropogenic activities have been poorly documented, let alone the effects of agricultural activities on food security and environmental change in the TP. The existing cropland mapping products do not depict the spatiotemporal characteristics of the TP due to low accuracies and inconsistent cropland distribution, which is affected by complicated topography and impedes our understanding of cropland expansion and its associated environmental impacts. One of the biggest challenges of cropland mapping in the TP is the diverse crop phenology across a wide range of elevations. To decrease the classification errors due to elevational differences in crop phenology, we developed two pixel- and phenology-based algorithms to map croplands using Landsat imagery and the Google Earth Engine platform along the Brahmaputra River and its two tributaries (BRTT) in the Tibet Autonomous Region, also known as the granary of TP, in 2015–2019. Our first phenology-based cropland mapping algorithm (PCM1) used different thresholds of land surface water index (LSWI) by considering varied crop phenology along different elevations. The second algorithm (PCM2) further offsets the phenological discrepancy along elevational gradients by considering the length and peak of the growing season. We found that PCM2 had a higher accuracy with fewer images compared with PCM1. The number of images for PCM2 was 279 less than PCM1, and the Matthews correlation coefficient for PCM2 was 0.036 higher than PCM1. We also found that the cropland area in BRTT was estimated to be 1979 ± 52 km² in the late 2010s. Croplands were mainly distributed in the BRTT basins with elevations of 3800–4000 m asl. Our phenology-based methods were effective for mapping croplands in mountainous areas. The spatially explicit information on cropland area and distribution in the TP aid future research into the effects of cropland expansion on food security and environmental change in the TP. Full article

(This article belongs to the Special Issue Agriculture: Management, Disturbance, and Climate around the World Using the Google Earth Engine)

► Show Figures

Graphical abstract

24 pages, 3857 KiB

Open AccessArticle

An Analysis of Bare Soil Occurrence in Arable Croplands for Remote Sensing Topsoil Applications

by Nada Mzid, Stefano Pignatti, Wenjiang Huang and Raffaele Casa

Remote Sens. 2021, 13(3), 474; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030474 - 29 Jan 2021

Cited by 23 | Viewed by 4202

Abstract

A better comprehension of soil properties and processes permits a progress in agricultural management effectiveness, together with a diminution of environmental damage and more beneficial use of resources. This research investigated the usage of multispectral (Sentinel-2 MSI) satellite data at the farm/regional level, for the identification of agronomic bare soil presence, utilizing bands of the spectral range from visible to shortwave infrared. The research purpose was to assess the frequency of cloud-free bare soil time-series images available during the year in typical agricultural areas, needed for the development of digital soil mapping (DSM) approaches for agricultural applications, using hyperspectral satellite missions such as current PRISMA and the planned EnMAP or CHIME. The research exploited the Google Earth Engine platform, by processing all available cloud-free Sentinel-2 images throughout a time span of four years. Two main results were obtained: (i) bare soil frequency, indicating where and when a pixel (or an agricultural field) was detected as bare surface in three representative agricultural areas of Italy, and (ii) a temporal sensitivity analysis, providing the acquisition frequency of useful bare soil images applicable for the retrieval of soil variables of interest. It was shown that, in order to provide for an effective agricultural soil monitoring capability, a revisit frequency in the range of five to seven days is required, which is less than the planned specifications e.g., of PRISMA or CHIME hyperspectral missions, but could be addressed by combining data from the two sensors. Full article

(This article belongs to the Special Issue Agriculture: Management, Disturbance, and Climate around the World Using the Google Earth Engine)

► Show Figures

Graphical abstract

19 pages, 3610 KiB

Open AccessArticle

An Approach to High-Resolution Rice Paddy Mapping Using Time-Series Sentinel-1 SAR Data in the Mun River Basin, Thailand

by He Li, Dongjie Fu, Chong Huang, Fenzhen Su, Qingsheng Liu, Gaohuan Liu and Shangrong Wu

Remote Sens. 2020, 12(23), 3959; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233959 - 03 Dec 2020

Cited by 20 | Viewed by 3355

Abstract

Timely and accurate regional rice paddy monitoring plays a significant role in maintaining the sustainable rice production, food security, and agricultural development. This study proposes an operational automatic approach to mapping rice paddies using time-series SAR data. The proposed method integrates time-series Sentinel-1 data, auxiliary data of global surface water, and rice phenological characteristics with Google Earth Engine cloud computing platform. A total of 402 Sentinel-1 scenes from 2017 were used for mapping rice paddies extent in the Mun River basin. First, the calculated minimum and maximum values of the backscattering coefficient of permanent water (a classification type within global surface water data) in a year was used as the threshold range for extracting the potential extent. Then, three rice phenological characteristics were extracted based on the time-series curve of each pixel, namely the date of the beginning of the season (DBS), date of maximum backscatter during the peak growing season (DMP), and length of the vegetative stage (LVS). After setting a threshold for each phenological parameter, the final rice paddy extent was identified. Rice paddy map produced in this study was highly accurate and agreed well with field plot data and rice map products from the International Rice Research Institute (IRRI). The results had a total accuracy of 89.52% and an F1 score of 0.91, showing that the spatiotemporal pattern of extracted rice cover was consistent with ground truth samples in the Mun River basin. This approach could be expanded to other rice-growing regions at the national scale, or even the entire Indochina Peninsula and Southeast Asia. Full article

(This article belongs to the Special Issue Agriculture: Management, Disturbance, and Climate around the World Using the Google Earth Engine)

► Show Figures

Graphical abstract

13 pages, 2295 KiB

Open AccessArticle

Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data

by Jing-Xian Xu, Jun Ma, Ya-Nan Tang, Wei-Xiong Wu, Jin-Hua Shao, Wan-Ben Wu, Shu-Yun Wei, Yi-Fei Liu, Yuan-Chen Wang and Hai-Qiang Guo

Remote Sens. 2020, 12(17), 2823; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172823 - 31 Aug 2020

Cited by 50 | Viewed by 7117

Abstract

Sugarcane is a multifunctional crop mainly used for sugar and renewable bioenergy production. Accurate and timely estimation of the sugarcane yield before harvest plays a particularly important role in the management of agroecosystems. The rapid development of remote sensing technologies, especially Light Detecting and Ranging (LiDAR), significantly enhances aboveground fresh weight (AFW) estimations. In our study, we evaluated the capability of LiDAR mounted on an Unmanned Aerial Vehicle (UAV) in estimating the sugarcane AFW in Fusui county, Chongzuo city of Guangxi province, China. We measured the height and the fresh weight of sugarcane plants in 105 sampling plots, and eight variables were extracted from the field-based measurements. Six regression algorithms were used to build the sugarcane AFW model: multiple linear regression (MLR), stepwise multiple regression (SMR), generalized linear model (GLM), generalized boosted model (GBM), kernel-based regularized least squares (KRLS), and random forest regression (RFR). The results demonstrate that RFR (R² = 0.96, RMSE = 1.27 kg m⁻²) performs better than other models in terms of prediction accuracy. The final fitted sugarcane AFW distribution maps exhibited good agreement with the observed values (R² = 0.97, RMSE = 1.33 kg m⁻²). Canopy cover, the distance to the road, and tillage methods all have an impact on sugarcane AFW. Our study provides guidance for calculating the optimum planting density, reducing the negative impact of human activities, and selecting suitable tillage methods in actual cultivation and production. Full article

(This article belongs to the Special Issue Agriculture: Management, Disturbance, and Climate around the World Using the Google Earth Engine)

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

Agriculture: Management, Disturbance, and Climate around the World Using the Google Earth Engine

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI