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Monitoring Climate Impacts on Agriculture Using Remote Sensing Techniques

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 (15 September 2022) | Viewed by 13959

Special Issue Editors

Climate Hazards Center, UC Santa Barbara, Santa Barbara, CA 93106, USA
Interests: drought monitoring and forecasting; climate trends and impacts; earth observation data; agroclimatology; hydrology; remote sensing
Department of Geographical Sciences, University of Maryland, College Park, MD, USA
Interests: machine learning; deep learning; artificial intelligence; crop type mapping; cropland mapping; remote sensing; Earth science; food security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Significant progress is needed in the detection of climate impacts on agriculture. Climate impacts on agriculture are local-to-global scale issues that influence food production, food security, economic stability, and health. Modern day issues include a globally connected agriculture market, the threat of increasing climate extremes with anthropogenic climate change, and the severe consequences that climate impacts have on the world’s poor populations. Thanks to decades of exploration into novel methods and technological advancements, remote sensing techniques continue to provide opportunities for progress. Today’s agriculture monitoring applications draw from numerous accessible and expanding remotely sensed data streams. Use of remote sensing for monitoring agriculture is at an all-time high. 

The aim of this Special Issue on “Monitoring Climate Impacts on Agriculture Using Remote Sensing Techniques” is to showcase successful recent endeavors in climate impact detection using remote sensing data and to communicate about promising new methods and datasets. 

We invite you to share your research to further our understanding as a community of observed climate impacts on agriculture, new or best practices for remote monitoring, and opportunities for early identification of seasonal crop performance. We encourage submissions that focus on remote sensing of climate impacts that can determine the success or failure of seasonal crop production. Drought, flood, temperature extremes, and climate-associated pests, e.g., locusts, are example topics of interest. We also welcome investigations to remote sensing techniques that address climate impacts on crop suitability and longer-term management decisions. In addition to the points above, topics may include but are not limited to:

  • Recent climate extremes and hazards to agriculture production;
  • Links to climate trends and regional and global climate drivers;
  • Methods of impact detection, including machine learning or data science techniques;
  • Efforts to improve field or local scale accuracy of remote monitoring;
  • Validation of remote sensing estimates with ground observations;
  • Applications of new and in practice monitoring systems;
  • New public data sets for shared benchmarks or catalyzing future method development.

Dr. Laura Harrison
Dr. Hannah Kerner
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

  • Agriculture monitoring
  • Climate extremes and climate change
  • Crop production estimates
  • Early detection and prediction
  • Water and temperature stress
  • Socioeconomic impacts
  • Machine learning
  • Public data sets

Published Papers (6 papers)

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17 pages, 2190 KiB  
Article
Are Climate-Dependent Impacts of Soil Constraints on Crop Growth Evident in Remote-Sensing Data?
by Fathiyya Ulfa, Thomas G. Orton, Yash P. Dang and Neal W. Menzies
Remote Sens. 2022, 14(21), 5401; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215401 - 28 Oct 2022
Cited by 4 | Viewed by 1067
Abstract
Soil constraints limit plant growth and grain yield in Australia’s grain-cropping regions, with the nature of the impact dependent on climate. In seasons with low in-crop (short for “during the crop growing season”) rainfall, soil constraints can reduce yield by limiting soil water [...] Read more.
Soil constraints limit plant growth and grain yield in Australia’s grain-cropping regions, with the nature of the impact dependent on climate. In seasons with low in-crop (short for “during the crop growing season”) rainfall, soil constraints can reduce yield by limiting soil water infiltration, storage, and crop water uptake. Conversely, soil constraints can exacerbate waterlogging in seasons with high in-crop rainfall. When average in-crop rainfall is experienced, soil constraints may only have a limited impact on yields. To investigate the relationship between climate and the impact of soil constraints on crop growth, long-term time series yield information is crucial but often not available. Vegetation indices calculated from remote-sensing imagery provide a useful proxy for yield data and offer the advantages of consistent spatial coverage and long history, which are vital for assessing patterns of spatial variation that repeat over many years. This study aimed to use an index of crop growth based on the enhanced vegetation index (EVI) to assess whether and how the within-field spatial variation of crop growth differed between years with different climates (dry, moderate, and wet years, as classified based on in-crop rainfall). Five fields from the grain-growing region of eastern Australia were selected and used to assess the consistency of the spatial variation of the index for years in the same in-crop rainfall category. For four of the five fields, no evidence of patterns of climate-dependent spatial variation was found, while for the other field, there was marginal evidence of spatial variation attributable to wet years. The correlation between measured data on soil sodicity (a soil constraint that might be expected to impact crop growth most in wetter years) and average EVI was investigated for this field. The results showed a stronger negative correlation between average EVI and sodicity in wet years than in dry years, suggesting that sodicity—through its impacts on soil structure and water movement—might be a driver of the spatial variation of crop growth in wet years for this field. Our results suggest that although there may be cases when climate-dependent within-field spatial variation of crop growth is detectable through remote-sensing data (through the multi-year consistency of the within-field variation), we should not expect this to be evident for fields as a matter of course. Full article
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18 pages, 17186 KiB  
Article
Analysis of Change in Maize Plantation Distribution and Its Driving Factors in Heilongjiang Province, China
by Rui Guo, Xiufang Zhu, Ce Zhang and Changxiu Cheng
Remote Sens. 2022, 14(15), 3590; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153590 - 27 Jul 2022
Cited by 7 | Viewed by 1715
Abstract
Accurate identification of maize plantation distribution and timely examination of key spatial-temporal drivers is a practice that can support agricultural production estimates and development decisions. Previous studies have rarely used efficient cloud processing methods to extract crop distribution, and meteorological and socioeconomic factors [...] Read more.
Accurate identification of maize plantation distribution and timely examination of key spatial-temporal drivers is a practice that can support agricultural production estimates and development decisions. Previous studies have rarely used efficient cloud processing methods to extract crop distribution, and meteorological and socioeconomic factors were often considered independently in driving force analysis. In this paper, we extract the spatial distribution of maize using classification and regression tree (CART) and random forest (RF) algorithms based on the Google Earth Engine (GEE) platform. Combining remote sensing, meteorological and statistical data, the spatio-temporal variation characteristics of maize plantation proportion (MPP) at the county scale were analyzed using trend analysis, kernel density estimation, and standard deviation ellipse analysis, and the driving forces of MPP spatio-temporal variation were explored using partial correlation analysis and geodetectors. Our empirical results in Heilongjiang province, China showed that (1) the CART algorithm achieved higher classification accuracy than the RF algorithm; (2) MPP showed an upward trend in more than 75% of counties, especially in high-latitude regions; (3) the main climatic factor affecting the inter-annual fluctuation of MPP was relative humidity; (4) the impact of socioeconomic factors on MPP spatial distribution was significantly larger than meteorological factors, the temperature was the most important meteorological factor, and the number of rural households was the most important socioeconomic factor affecting MPP spatial distribution. The interaction between different factors was greater than a single factor alone; (5) the correlation between meteorological factors and MPP differed across different latitudinal regions and landforms. This research provides a key reference for the optimal adjustment of crop cultivation distribution and agricultural development planning and policy. Full article
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28 pages, 7027 KiB  
Article
Observed Changes in Crop Yield Associated with Droughts Propagation via Natural and Human-Disturbed Agro-Ecological Zones of Pakistan
by Farhan Saleem, Arfan Arshad, Ali Mirchi, Tasneem Khaliq, Xiaodong Zeng, Md Masudur Rahman, Adil Dilawar, Quoc Bao Pham and Kashif Mahmood
Remote Sens. 2022, 14(9), 2152; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092152 - 30 Apr 2022
Cited by 10 | Viewed by 2926
Abstract
Pakistan’s agriculture and food production account for 27% of its overall gross domestic product (GDP). Despite ongoing advances in technology and crop varieties, an imbalance between water availability and demand, combined with robust shifts in drought propagation has negatively affected the agro-ecosystem and [...] Read more.
Pakistan’s agriculture and food production account for 27% of its overall gross domestic product (GDP). Despite ongoing advances in technology and crop varieties, an imbalance between water availability and demand, combined with robust shifts in drought propagation has negatively affected the agro-ecosystem and environmental conditions. In this study, we examined hydro-meteorological drought propagation and its associated impacts on crop yield across natural and human-disturbed agro-ecological zones (AEZs) in Pakistan. Multisource datasets (i.e., ground observations, reanalysis, and satellites) were used to characterize the most extensive, intense drought episodes from 1981 to 2018 based on the standardized precipitation evaporation index (SPEI), standardized streamflow index (SSFI), standardized surface water storage index (SSWSI), and standardized groundwater storage index (SGWI). The most common and intense drought episodes characterized by SPEI, SSFI, SSWSI, and SGWI were observed in years 1981–1983, 2000–2003, 2005, and 2018. SPEI yielded the maximum number of drought months (90) followed by SSFI (85), SSWSI (75), and SGWI (35). Droughts were frequently longer and had a slower termination rate in the human-disturbed AEZs (e.g., North Irrigated Plain and South Irrigated Plain) compared to natural zones (e.g., Wet Mountains and Northern Dry Mountains). The historical droughts are likely caused by the anomalous large-scale patterns of geopotential height, near-surface air temperature, total precipitation, and prevailing soil moisture conditions. The negative values (<−2) of standardized drought severity index (DSI) observed during the drought episodes (1988, 2000, and 2002) indicated a decline in vegetation growth and yield of major crops such as sugarcane, maize, wheat, cotton, and rice. A large number of low-yield years (SYRI ≤ −1.5) were recorded for sugarcane and maize (10 years), followed by rice (9 years), wheat (8 years), and cotton (6 years). Maximum crop yield reductions relative to the historic mean (1981–2017) were recorded in 1983 (38% for cotton), 1985 (51% for maize), 1999 (15% for wheat), 2000 (29% for cotton), 2001 (37% for rice), 2002 (21% for rice), and 2004 (32% for maize). The percentage yield losses associated with shifts in SSFI and SSWSI were greater than those in SPEI, likely due to longer drought termination duration and a slower termination rate in the human-disturbed AEZs. The study’s findings will assist policymakers to adopt sustainable agricultural and water management practices, and make climate change adaptation plans to mitigate drought impacts in the study region. Full article
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20 pages, 3703 KiB  
Article
Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine
by Xiaoyang Zhao, Haoming Xia, Baoying Liu and Wenzhe Jiao
Remote Sens. 2022, 14(7), 1570; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071570 - 24 Mar 2022
Cited by 21 | Viewed by 3156
Abstract
As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues [...] Read more.
As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R2 = 0.9606, p < 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R2 = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (p > 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and p values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and p values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future. Full article
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18 pages, 22296 KiB  
Article
Mapping the Location and Extent of 2019 Prevent Planting Acres in South Dakota Using Remote Sensing Techniques
by Afolarin Lawal, Hannah Kerner, Inbal Becker-Reshef and Seth Meyer
Remote Sens. 2021, 13(13), 2430; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132430 - 22 Jun 2021
Cited by 7 | Viewed by 2329
Abstract
The inability of a farmer to plant an insured crop by the policy’s final planting date can pose financial challenges for the grower and cause reduced production for a widely impacted region. Prevented planting is primarily caused by excess moisture or rainfall such [...] Read more.
The inability of a farmer to plant an insured crop by the policy’s final planting date can pose financial challenges for the grower and cause reduced production for a widely impacted region. Prevented planting is primarily caused by excess moisture or rainfall such as the catastrophic flooding and widespread conditions that prevented active field work in the midwestern region of United States in 2019. While the Farm Service Agency reports the number of such “prevent plant” acres each year at the county scale, field-scale maps of prevent plant fields—which would enable analyses related to assessing and mitigating the impact of climate on agriculture—are not currently available. The aim of this study is to demonstrate a method for mapping likely prevent plant fields based on flood mapping and historical cropland maps. We focused on a study region in eastern South Dakota and created flood maps using Landsat 8 and Sentinel 1 images from 2018 and 2019. We used automatic threshold-based change detection using NDVI and NDWI to accentuate changes likely caused by flooding. The NDVI change detection map showed vegetation loss in the eastern parts of the study area while NDWI values showed increased water content, both indicating possible flooding events. The VH polarization of Sentinel 1 was also particularly useful in identifying potential flooded areas as the VH values for 2019 were substantially lower than those of 2018, especially in the northern part of the study area, likely indicating standing water or reduced biomass. We combined the flood maps from Landsat 8 and Sentinel 1 to form a complete flood likelihood map over the entire study area. We intersected this flood map with a map of fallow pixels extracted from the Cropland Data Layer to produce a map of predicted prevent plant acres across several counties in South Dakota. The predicted figures were within 10% error of Farm Service Agency reports, with low errors in the most affected counties in the state such as Beadle, Hanson, and Hand. Full article
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12 pages, 2859 KiB  
Technical Note
First Form, Then Function: 3D Reconstruction of Cucumber Plants (Cucumis sativus L.) Allows Early Detection of Stress Effects through Leaf Dimensions
by Dany Moualeu-Ngangué, Maria Bötzl and Hartmut Stützel
Remote Sens. 2022, 14(5), 1094; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051094 - 23 Feb 2022
Viewed by 1458
Abstract
Detection of morphological stress symptoms through 3D examination of plants might be a cost-efficient way to avoid yield losses and ensure product quality in agricultural and horticultural production. Although the 3D reconstruction of plants was intensively performed, the relationships between morphological and physiological [...] Read more.
Detection of morphological stress symptoms through 3D examination of plants might be a cost-efficient way to avoid yield losses and ensure product quality in agricultural and horticultural production. Although the 3D reconstruction of plants was intensively performed, the relationships between morphological and physiological plant responses to salinity stress need to be established. Therefore, cucumber plants were grown in a greenhouse in nutrient solutions under three salinity treatments: 0, 25, and 50 mM NaCl. To detect stress-induced changes in leaf transversal and longitudinal angles and dimensions, photographs were taken from plants for 3D reconstruction through photogrammetry. For assessment of physiological stress responses, invasive leaf measurements, including the determination of leaf osmotic potential, leaf relative water content, and the leaf dry to fresh weight ratio, were performed. The transversal and longitudinal leaf dimensions revealed statistically significant differences between stressed and control plants after 60 °Cd (day 3) for the leaves which appeared before stress imposition. Strong correlations were found between the transversal width and some investigated physiological traits. Morphological changes were shown as indicators of physiological responses of leaves under salinity stress. Full article
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