diff --git a/datasets/epa-ch4emission-v2express.data.mdx b/datasets/epa-ch4emission-v2express.data.mdx
index a612d2c17..e5b4bf6c0 100644
--- a/datasets/epa-ch4emission-v2express.data.mdx
+++ b/datasets/epa-ch4emission-v2express.data.mdx
@@ -2,34 +2,18 @@
id: epa-ch4emission-yeargrid-v2express
name: U.S. Gridded Anthropogenic Methane Emissions Inventory
description: Spatially disaggregated 0.1°x 0.1° maps of annual U.S. anthropogenic methane emissions, consistent with the U.S. Inventory of Greenhouse Gas Emissions and Sinks.
-usage:
- - url: "https://us-ghg-center.github.io/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express.html"
- label: Notebook showing data transformation to COG for ingest to the US GHG Center
- title: "Data Transformation Notebook"
- - url: "https://us-ghg-center.github.io/ghgc-docs/user_data_notebooks/epa-ch4emission-grid-v2express_User_Notebook.html"
- label: Notebook to read, visualize, and explore data statistics
- title: "Sample Data Notebook"
- - url: "https://hub.ghg.center/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FUS-GHG-Center%2Fghgc-docs&urlpath=tree%2Fghgc-docs%2Fuser_data_notebooks%2Fepa-ch4emission-grid-v2express_User_Notebook.ipynb&branch=main"
- label: Run example notebook
- title: Interactive Session in the US GHG Center JupyterHub (requires account)
- - url: https://dljsq618eotzp.cloudfront.net/browseui/index.html#epa-ch4emission-yeargrid-v2express/
- label: Browse and download the data
- title: Data Browser
media:
src: ::file ./epa-annual--cover.jpg
alt: Total Gridded Methane Emissions from the U.S. Inventory of Greenhouse Gas Emissions and Sinks
author:
name: EPA
taxonomy:
- - name: Topics
+ - name: Theme
values:
- - Anthropogenic Emissions
+ - Greenhouse Gases
- name: Source
values:
- EPA
- - name: Gas
- values:
- - CH₄
- name: Product Type
values:
- Gridded Inventory
@@ -2214,72 +2198,34 @@ layers:
- The gridded EPA U.S. anthropogenic methane greenhouse gas inventory (gridded GHGI) includes spatially disaggregated (0.1 deg x 0.1 deg or approximately 10 x 10 km resolution) maps of annual anthropogenic methane emissions for the contiguous United States (CONUS), consistent with national annual U.S. anthropogenic methane emissions reported in the U.S. EPA [Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks) (U.S. GHGI). This dataset contains methane emissions provided as fluxes, in units of molecules of methane per square cm per second, for over 25 individual emission source categories, including those from agriculture, petroleum and natural gas systems, coal mining, and waste. The data have been converted from their original NetCDF format to Cloud-Optimized GeoTIFF (COG) and scaled to Megagrams of CH4 per km2 per year (Mg/km²/yr) for use in the US GHG Center, thereby enabling user exploration of spatial anthropogenic methane emissions and their trends.
-
- **Data Version Details:**
- The gridded methane GHGI is continually updated to capture ongoing improvements and updates to the U.S. GHG Inventory. The gridded methane GHGI currently includes 2 versions, which reflect sectoral methane emissions that are consistent with different versions of the U.S. GHGI. Versions include:
-
- Current Version(s)
- - A. Gridded methane GHGI Version 2 (0.1°×0.1° annual emission maps for 2012-2018, consistent with the 2020 U.S. GHGI)
- - B. Gridded methane GHGI Version 2 - Express Extension (0.1°×0.1° annual emission maps for 2012-2020, consistent with the 2022 U.S. GHGI)
-
- Previous Versions
- - A. Gridded methane GHGI Version 1 (0.1°×0.1° annual emission maps for 2012, consistent with the 2016 U.S. GHGI)
-
- Data available on the Data Explorer page correspond to the V2 Express Extension dataset.
-
- For more information on the current data set versions, see the associated publication: [Massakkers et al., 2023.](https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138) or visit the [EPA gridded inventory webpage](https://www.epa.gov/ghgemissions/us-gridded-methane-emissions). For more information on the previous version, see the associated publication: [Massakkers et al., 2016.](https://pubs.acs.org/doi/10.1021/acs.est.6b02878)
-
- - **Temporal Extent:** 2012 - 2020
- - **Temporal Resolution:** Annual
- - **Spatial Extent:** Contiguous United States
- - **Spatial Resolution:** 0.1° x 0.1°
- - **Data Units:** Megagrams of methane per square kilometer per year (Mg CH₄/km²/yr)
- - **Data type:** Research (v2 express extension)
- - **Data Latency:** N/A
-
- **Scientific Details:** The gridded methane GHGI is developed by spatially allocating national annual methane emissions from individual source categories from the Inventory of U.S. Greenhouse Gas Emissions and Sinks (U.S. GHGI) to a 0.1 deg x 0.1 deg (~10 x 10 km) grid using a series of spatial and temporal proxy datasets at the state, county, and grid levels. Where possible, the proxy data are the same as those used to develop the GHGI so that the gridded emissions can be, as closely as possible, a spatial and temporal representation of those in the national-level U.S. GHGI Report.
-
- The development of the gridded GHGI enables more direct comparisons between the methane emissions reported in the annual U.S. GHGI and those derived from atmospheric methane observations, such as through inverse analyses, with the aim of improving national inventory estimates and better understanding uncertain sources of methane emissions.
-
- Details of the methodological development of this dataset are described in the paper Maasakkers et al., 2023: [https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138](https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138)
+ The gridded EPA U.S. anthropogenic methane greenhouse gas inventory (gridded GHGI) includes spatially disaggregated (0.1 deg x 0.1 deg or approximately 10 x 10 km resolution) maps of annual anthropogenic methane emissions for the contiguous United States (CONUS), consistent with national annual U.S. anthropogenic methane emissions reported in the U.S. EPA [Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks) (U.S. GHGI). The gridded GHGI dataset contains methane emissions provided as fluxes, in units of molecules of methane per square centimeter per second, for over 25 individual emission source categories, including those from agriculture, petroleum and natural gas systems, coal mining, and waste. The data have been scaled to Megagrams of methane per square kilometer per year (Mg/km²/yr) for use in the Earth.gov, thereby enabling user exploration of spatial anthropogenic methane emissions and their trends.
+ ## Data Summary
+
+ - **Temporal Extent:** 2012 - 2020
+ - **Temporal Resolution:** Annual
+ - **Spatial Extent:** Contiguous United States
+ - **Spatial Resolution:** 0.1 degrees x 0.1 degrees
+ - **Data Units:** Megagrams of methane per square kilometer per year (Mg CH₄/km²/yr)
+ - **Data Type:** Research (v2 express extension)
- ## Source Data Product Citation
- Gridded GHGI Version 2 & Express Extension (this dataset in US GHG Center):
- McDuffie, E. E., Maasakkers, J. D., Sulprizio, M. P., Chen, C., Schultz, M., Brunelle, L., Thrush, R., Steller, J., Sherry, C., Jacob, Daniel, J., Jeong, S., Irving, B., & Weitz, M. (2023). Gridded EPA U.S. Anthropogenic Methane Greenhouse Gas Inventory (gridded GHGI) (v1.0) [Data set]. Zenodo. [https://doi.org/10.5281/zenodo.8367082](https://doi.org/10.5281/zenodo.8367082)
-
- Gridded GHGI Version 1:
-
- Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Turner, A. J., Weitz, M., Wirth, T., Hight, C., DeFigueiredo, M., Desai, M., Schmeltz, R., Hockstad, L., Bloom, A. A., Bowman, K. W., Jeong, S., Fischer, M. L. (2016) A Gridded National Inventory of U.S. Methane Emissions [Data set]. Available at: [https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions#data](https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions#data)
-
- ## Dataset Accuracy
- Uncertainties underlying the development of national methane emission estimates are discussed in each annual U.S. GHGI Report. Additional characterization of resolution-dependent uncertainties are discussed in [Maasakkers et al. (2023)](https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138).
-
- ## Disclaimer
- All data provided in the US GHG Center has been transformed from the original format (NetCDF) into Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org/)). Careful quality checks are used to ensure data transformation has been performed correctly. The manuscript describing the gridded methane GHGI has been peer-reviewed, but is not part of the same annual expert and public review processes as the U.S. EPA National and State-level Inventory.
+ ## Source Data Access
- Users of these datasets are asked to cite the original references [Maasakkers et al. (2023)](https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138) or [Maasakkers, et al., (2016)](https://pubs.acs.org/doi/10.1021/acs.est.6b02878) in their publications and are encouraged to reach out to the development team with further questions.
+ The dataset presented here in Earth.gov is the Gridded Methane GHGI Version 2 - Express Extension:
- ## Key Publications
- Maasakkers, J. D., McDuffie, E. E.,, Sulprizio, M. P., Chen, C., Schultz, M., Brunelle, L., Thrush, R., Steller, J., Sherry, C., Jacob, D. J., Jeong, S., Irving, B., & Weitz, M. (2023). A gridded inventory of annual 2012-2018 U.S. anthropogenic methane emissions. Environmental Science & Technology, 57(43), 16276-16288. https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138
-
- ## Other Relevant Publications
- Maasakkers, J., Jacob, D., Sulprizio, M., Turner, A., Weitz, M., Wirth, T., Hight, C., DeFigueiredo, M., Desai, M., Schmeltz, R., Hockstad, L., Bloom, A., Bowman, K., Jeong, S., Fischer, M. (2016). Gridded National Inventory of U.S. Methane Emissions. *Environmental Science & Technology*, 50(23), 13123-13133. https://doi.org/10.1021/acs.est.6b02878
+ McDuffie, E. E., Maasakkers, J. D., Sulprizio, M. P., Chen, C., Schultz, M., Brunelle, L., Thrush, R., Steller, J., Sherry, C., Jacob, Daniel, J., Jeong, S., Irving, B., & Weitz, M. (2023). Gridded EPA U.S. Anthropogenic Methane Greenhouse Gas Inventory (gridded GHGI) (v1.0) [Data set]. Zenodo. [https://doi.org/10.5281/zenodo.8367082](https://doi.org/10.5281/zenodo.8367082)
+ Users of this dataset are asked to cite the original reference [Maasakkers et al. (2023)](https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138) in their publications and are encouraged to reach out to the development team with further questions. More information is available on the [EPA Gridded Methane Emissions landing page](https://www.epa.gov/ghgemissions/us-gridded-methane-emissions).
+
## Acknowledgment
- This dataset was developed in collaboration between researchers at the U.S. EPA, Netherlands Institute for Space Research (SRON), Harvard University, and Lawrence Berkeley National Laboratory.
-
- ## License
- [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) (CC BY 4.0)
+ This dataset was developed in collaboration between researchers at the U.S. EPA, SRON Netherlands Institute for Space Research, Harvard University, and Lawrence Berkeley National Laboratory.
- ## Data Stewardship
- - [Data Workflow](https://github.com/US-GHG-Center/ghgc-docs/blob/main/data_workflow/media/epa-ch4emission-grid-v2express_Data_Flow.png)
- - [Data Transformation Code](https://us-ghg-center.github.io/ghgc-docs/cog_transformation/epa-ch4emission-grid-v2express.html)
- - [US GHG Center Data Intake Processing and Verification Report](https://us-ghg-center.github.io/ghgc-docs/processing_and_verification_reports/epa-ch4emission-grid-v2express_Processing%20and%20Verification%20Report.html)
+ ## Dataset Preparation & Disclaimer
+ All data provided in Earth.gov has been transformed from the original format (NetCDF) into Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org/)). Careful quality checks are used to ensure data transformation has been performed correctly. The manuscript describing the gridded methane GHGI has been peer-reviewed, but is not part of the same annual expert and public review processes as the U.S. EPA National and State-level Inventory. Please see the following publications for details on how the data were created:
+ Maasakkers, J. D., McDuffie, E. E., Sulprizio, M. P., Chen, C., Schultz, M., Brunelle, L., Thrush, R., Steller, J., Sherry, C., Jacob, D. J., Jeong, S., Irving, B., & Weitz, M. (2023). A gridded inventory of annual 2012-2018 U.S. anthropogenic methane emissions. Environmental Science & Technology, 57(43), 16276-16288. https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138
diff --git a/datasets/geoglam.data.mdx b/datasets/geoglam.data.mdx
index cb98e9f82..9749b6056 100644
--- a/datasets/geoglam.data.mdx
+++ b/datasets/geoglam.data.mdx
@@ -9,18 +9,22 @@ media:
name: Jean Wimmerlin
url: https://unsplash.com/photos/RUj5b4YXaHE
taxonomy:
- - name: Topics
+ - name: Theme
values:
- Agriculture
- name: Source
values:
- - GEOGLAM
+ - USDA
+ - name: Product Type
+ values:
+ - Satellite Observations
+ - Model Output
layers:
- id: geoglam
stacCol: geoglam
name: GEOGLAM Crop Conditions
type: raster
- description: Combined crop conditions across both the Crop Monitor for AMIS and Crop Monitor for Early Warning
+ description: Combined crop conditions across both the Crop Monitor for the Agricultural Market Information System (AMIS) and Crop Monitor for Early Warning
zoomExtent:
- 0
- 16
@@ -37,7 +41,7 @@ layers:
- color: "#3A8DC6"
label: "Exceptional"
- color: "#62D246"
- label: "Favourable"
+ label: "Favorable"
- color: "#FFFF00"
label: "Watch"
- color: "#EC5830"
@@ -45,78 +49,58 @@ layers:
- color: "#891911"
label: "Failure"
- color: "#787878"
- label: "Out of season"
+ label: "Out of Season"
- color: "#804115"
- label: "No data"
+ label: "No Data"
---
-
+
- ## Examples of COVID-19 Impact on Global Food Supplies
+ The Group on Earth Observations, a partnership of governments and international organizations, developed the Global Agricultural Monitoring (GEOGLAM) initiative in response to growing calls for improved agricultural information. The goal of GEOGLAM is to strengthen the international community’s capacity to produce and disseminate relevant, timely and accurate forecasts of agricultural production at national, regional and global scales through the use of Earth Observations (EO), which include satellite and ground-based observations. The GEOGLAM initiative is designed to build on existing agricultural monitoring programs and initiatives at national, regional and global levels and to enhance and strengthen them through international networking, operationally focused research, and data/method sharing.
- Measures to slow the spread of COVID-19 affected the food supply chain in many ways, including the availability of inputs, labor, transport, and cross-border trade. The Group on Earth Observation's Global Agricultural Monitoring Initiative (GEOGLAM) Global Crop Monitor uses remote sensing data like global precipitation and soil moisture measurements to help reduce uncertainty, promote market transparency, and provide early warning for crop failures through multi-agency collaboration. During the pandemic, this tool - developed in conjunction with NASA's food and agriculture program (NASA Harvest), ESA (European Space Agency) and JAXA, Japan Aerospace Exploration Agency - is increasingly used in lieu of on-the-ground validation of crop conditions.
+ Presented here are GEOGLAM Crop Monitor data, which provides an international and transparent multi-source, consensus assessment of crop growing conditions, status, and agro-climatic conditions, likely to impact global production. It covers the four primary crop types (wheat, maize, rice, and soy) within the main agricultural producing regions of countries participating in the [Agricultural Market Information System (AMIS)](https://www.amis-outlook.org/amis-about/en/). These assessments have been produced operationally since September 2013 and are published in the [AMIS Market Monitor Bulletin](https://www.amis-outlook.org/index.php?id=48514). The Crop Monitor reports provide cartographic and textual summaries of crop conditions as of the 28th of each month, according to crop type. Assessments from January 2020 and onward are available to view on Earth.gov.
- Data from the GEOGLAM Crop Monitor inform two different agricultural tools that have helped lessen global concerns over food security during the novel coronavirus pandemic: the Agricultural Market Information System (AMIS) and the Crop Monitor for Early Warning (CM4EW). AMIS provides agricultural information based on remote sensing observations for the major producing nations of four primary crops - wheat, maize, rice, and soybeans. CM4EW provides agricultural data for countries at higher risk of food insecurity.
-
-
-
-
- Global crop conditions as of July 28, 2020. Blue and green colors indicate exceptional and favorable crop conditions, while red and burgundy indicate poor crop conditions and crop failure. Yellow areas are currently under watch for potential negative impacts on crops.
-
-
-
+
+ ## Data Summary
-
-
- ### Major Producing and Exporting Countries
+ - **Temporal Extent:** January 2020 - Ongoing
+ - **Temporal Resolution:** Monthly
+ - **Spatial Extent:** Global
+ - **Spatial Resolution:** 5 km x 5 km
+ - **Data Units:** Crop condition classification: Exceptional, Favorable, Watch, Poor, Failure, Out of Season, No Data
+ - **Data Type:** Operational
- Current estimates from GEOGLAM Crop Monitor data indicate the global food supply is adequate. While many countries experienced lockdowns and travel bans as coronavirus spread, most farmers were able to continue operations due to the rural nature of most farm communities and the relatively less labor-intensive cultivation techniques associated with key crops.
+ *Crop Condition Class Definitions:*
+ - **Exceptional**: Conditions are much better than average at time of reporting, where the average is the mean conditions over the most recent 5 years. This label is used only during the grain-filling through harvest stages.
+ - **Favorable**: Conditions range from slightly below to slightly above average at reporting time.
+ - **Watch**: Conditions are not far from average but there is a potential risk to final yields. There is still time and possibility for the crop to recover to average conditions if the ground situation improves. This label is only used during the planting-early vegetative and the vegetative-reproductive stages.
+ - **Poor**: Crop conditions are well below average. Crop yields are likely to be 5% below average. This is only used when conditions are not likely to be able to recover, and impact on yields is likely.
+ - **Out of Season**: Crops are not currently planted or in development during this time.
+ - **No Data**: No reliable source of data is available at this time.
- However, the spread of the coronavirus did have an impact on the ability of governments and agricultural organizations to perform in-person field surveys of sowing, crop progress, and harvesting. This reinforced the need for strong remote sensing capabilities. Satellite-based information from AMIS helped confirm that global food production during the early parts of the pandemic was secure, leading to the resumption of normal trade flows after some large producer and export countries issued temporary trade restrictions.
-
- "Assessing the global supply situation and being able to predict unexpected shortfalls is the single most important task to guarantee global food security,” explained Abdolreza Abbassian, Secretary of AMIS and a U.N. Food and Agriculture Organization senior economist. “However, such assessments must be evidence-based and credible, and this is where reliance on timely information from remote sensing plays a fundamental role.”
-
-
-
- Maize 1 conditions across East Africa as of July 28, 2020. Data inputs from a wide variety of Earth observation satellites combined with field statistics are used to generate meaningful crop condition reports.
-
-
- ## COVID-19 Impacts in East Africa
+ ## Source Data Access
+ Becker-Reshef, Inbal (2015). GEOGLAM (GEO Global Agricultural Monitoring) Crop Assessment Tool. Ag Data Commons. [https://doi.org/10.15482/USDA.ADC/1234202](https://doi.org/10.15482/USDA.ADC/1234202)
+
+ ## Acknowledgment
+ The Crop Monitor assessment is conducted by GEOGLAM with coordination from the University of Maryland. Inputs are from the following partners (in alphabetical order): Argentina (Buenos Aires Grains Exchange, INTA), Asia Rice Countries (AFSIS, ASEAN+3 & Asia RiCE), Australia (ABARES & CSIRO), Brazil (CONAB & INPE), Canada (AAFC), China (CAS), EU (EC JRC MARS), Indonesia (LAPAN & MOA), International (CIMMYT, FAO, IFPRI & IRRI), Japan (JAXA ), Mexico (SIAP), Russian Federation (IKI), South Africa (ARC & GeoTerraImage & SANSA), Thailand (GISTDA & OAE), Ukraine (NASU-NSAU & UHMC), USA (NASA, UMD, USGS – FEWS NET, USDA (FAS, NASS)), Viet nam (VAST & VIMHE-MARD).
- During the 2020 growing season in East Africa, agricultural production faced the triple threat of desert locusts, deadly flooding and COVID-19 impacts.
+ ## Dataset Preparation & Disclaimer
+ Learn more at the GEOGLAM website and in the below featured articles:
+ [https://cropmonitor.org/](https://cropmonitor.org/)
- The overall impact of the pandemic on agricultural production of major grains within the region was generally limited, and supplies of staple foods were reported to be sufficient. However, production was disrupted in some areas through COVID-19 restrictions, causing agricultural labor supply shortages and disrupting supply chains, limiting farmers' access to seeds, fertilizers, and other inputs. This resulted in reported declines in planted area and yields in Ethiopia, Somalia and elsewhere across the region, and it will be critical to continue to monitor the situation and to provide timely and evidence driven crop assessments.
-
-
+ Justice, C; Becker-Reshef, I; McGaughey, K; Hansen, M; Whitcraft, A; Barker, B.; Humber, M.; Deshayes, M., “Enhancing Agricultural Monitoring with EO-based Information” [http://www.apogeospatial.com/issues/AO_wi2015.pdf](http://www.apogeospatial.com/issues/AO_wi2015.pdf)
-
-
- ## COVID-19 Impacts in Southeast Asia
+ Whitcraft AK, Becker-Reshef I, Justice CO. A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM). Remote Sensing. 2015; 7(2):1461-1481. [https://doi.org/10.3390/rs70201461](https://doi.org/10.3390/rs70201461)
- In Southern Asia, the GEOGLAM crop condition assessments are coordinated by the Asian Rice Crop Estimation & Monitoring (Asia-RiCE) initiative led by the Japan Aerospace Exploration Agency (JAXA) with inputs from the region's national ministries of agriculture. COVID-19 impacted the region by restricting the ability of governments to do field surveys, particularly during the height of the outbreak.
+ The findings and conclusions in the GEOGLAM reports are consensual statements from the GEOGLAM experts, and do not necessarily reflect those of the individual agencies represented by these experts.
- Currently, on the northern side of Southeast Asia, the dry-season rice has come to a close and the wet-season rice (main producing season) is underway. The dry season, which ended in May-June, was affected by persistent dry conditions that drove down yields and planted area in Myanmar, Thailand, and Laos. The wet-season rice began under generally favorable conditions, with ample rainfall in most areas except for southern Vietnam. Additionally, there has been some flooding in Bangladesh.
+ Map data sources: Major crop type areas based on the IFPRI/IIASA SPAM 2005 beta release (2013), USDA/NASS 2013 CDL, 2013 AAFC Annual Crop Inventory Map, GLAM/UMD, GLAD/UMD, Australian Land Use and Management Classification (Version 7), SIAP, ARC, and JRC. The GEOGLAM crop calendars are compiled with information from AAFC, ABARES, ARC, Asia RiCE, Bolsa de cereales, CONAB, INPE, JRC, FAO, FEWS NET, IKI, INTA, SIAP, UHMC, USDA FAS, and USDA NASS.
- In the southern side (Indonesia), during the wet-season, reduced rainfall delayed the sowing of the rice and eventually resulted in less total sown area and a reduction in yields. As a consequence of the delay in the wet-season, the sowing of dry-season rice was delayed. Despite the delay, good rainfall continued into the traditional dry season.
+ All data displayed in Earth.gov has been transformed from the original format into Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org/)). Careful quality checks are used to ensure data transformation has been performed correctly.
-
-
-
- Rice conditions across Southeast Asia as of July 28, 2020. Remotely sensed data is useful to visualize crop conditions and regions susceptible to potential crop failure
-