Web-based GIS for spatiotemporal crop climate niche mapping

Web-based GIS for spatiotemporal crop climate niche mapping Interactive Google Earth Engine Application—Version 2, July 2020 https://cropniche.cartoscience.com https://cartoscience.users.earthengine.app/view/crop-niche Google Earth Engine Code /* ---------------------------------------------------------------------------------------------------------------------- # CropSuit-GEE Authors: Brad G. Peter ([e-mail]), Joseph P. Messina, and Zihan Lin Organizations: BGP, JPM - University of Alabama; ZL - Michigan State University Last Modified: 06/28/2020 To cite this code use: Peter, B. G.; Messina, J. P.; Lin, Z., 2019, "Web-based GIS for spatiotemporal crop climate niche mapping", https://doi.org/10.7910/DVN/UFC6B5, Harvard Dataverse, V1 ------------------------------------------------------------------------------------------------------------------------- This is a Google Earth Engine crop climate suitability geocommunication and map export tool designed to support agronomic development and deployment of improved crop system technologies. This content is made possible by the support of the American People provided to the Feed the Future Innovation Lab for Sustainable Intensification through the United States Agency for International Development (USAID). The contents are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. Program activities are funded by USAID under Cooperative Agreement No. AID-OAA-L-14-00006. ------------------------------------------------------------------------------------------------------------------------- Summarization of input options: There are 14 user options available. The first is a country of interest selection using a 2-digit FIPS code (link available below). This selection is used to produce a rectangular bounding box for export; however, other geometries can be selected with minimal modification to the code. Options 2 and 3 specify the complete temporal range for aggregation (averaged across seasons; single seasons may also be selected). Options 4–7 specify the growing season for calculating total seasonal rainfall and average season temperatures and NDVI (NDVI is for export only and is not used in suitability determination). Options 8–11 specify the climate parameters for the crop of interest (rainfall and temperature max/min). Option 12 enables masking to agriculture, 13 enables exporting of all data layers, and 14 is a text string for naming export files. ------------------------------------------------------------------------------------------------------------------------- ••••••••••••••••••••••••••••••••••••••••••• USER OPTIONS ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• */ // CHIRPS data availability: https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD // MOD11A2 data availability: https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A2 var country = 'MI' // [1] https://en.wikipedia.org/wiki/List_of_FIPS_country_codes var startRange = 2001 // [2] var endRange = 2017 // [3] var startSeasonMonth = 11 // [4] var startSeasonDay = 1 // [5] var endSeasonMonth = 4 // [6] var endSeasonDay = 30 // [7] var precipMin = 750 // [8] var precipMax = 1200 // [9] var tempMin = 22 // [10] var tempMax = 32 // [11] var maskToAg = 'TRUE' // [12] 'TRUE' (default) or 'FALSE' var exportLayers = 'TRUE' // [13] 'TRUE' (default) or 'FALSE' var exportNameHeader = 'crop_suit_maize' // [14] text string for naming export file // ••••••••••••••••••••••••••••••••• NO USER INPUT BEYOND THIS POINT •••••••••••••••••••••••••••••••••••••••••••••••••••• // Access precipitation and temperature ImageCollections and a global countries FeatureCollection var region = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017') .filterMetadata('country_co','equals',country) var precip = ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD').select('precipitation') var temp = ee.ImageCollection('MODIS/006/MOD11A2').select(['LST_Day_1km','LST_Night_1km']) var ndvi = ee.ImageCollection('MODIS/006/MOD13Q1').select(['NDVI']) // Create layers for masking to agriculture and masking out water bodies var waterMask = ee.Image('UMD/hansen/global_forest_change_2015').select('datamask').eq(1) var agModis = ee.ImageCollection('MODIS/006/MCD12Q1').select('LC_Type1').mode() .remap([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17], [0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0]) var agGC = ee.Image('ESA/GLOBCOVER_L4_200901_200912_V2_3').select('landcover') .remap([11,14,20,30,40,50,60,70,90,100,110,120,130,140,150,160,170,180,190,200,210,220,230], [1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) var cropland = ee.Image('USGS/GFSAD1000_V1').neq(0) var agMask = agModis.add(agGC).add(cropland).gt(0).eq(1) // Modify user input options for processing with raw data var years = ee.List.sequence(startRange,endRange) var bounds = region.geometry().bounds() var tMinMod = (tempMin+273.15)/0.02 var tMaxMod = (tempMax+273.15)/0.02 //...