{"id":97982,"date":"2026-08-30T21:18:00","date_gmt":"2026-08-30T13:18:00","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/97982.html"},"modified":"2026-08-30T21:18:00","modified_gmt":"2026-08-30T13:18:00","slug":"%e5%9f%ba%e4%ba%8e-gee-%e7%9a%84-landsat-sentinel-%e5%8d%8f%e5%90%8c%e9%80%90%e5%83%8f%e5%85%83%e7%ba%bf%e6%80%a7%e5%9b%9e%e5%bd%92%e4%b8%8e%e6%ae%8b%e5%b7%ae%e6%a0%a1%e6%ad%a3%e7%9a%84%e5%9c%b0","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/97982.html","title":{"rendered":"\u57fa\u4e8e GEE \u7684 Landsat-Sentinel \u534f\u540c\u9010\u50cf\u5143\u7ebf\u6027\u56de\u5f52\u4e0e\u6b8b\u5dee\u6821\u6b63\u7684\u5730\u8868\u6e29\u5ea6\uff08LST\uff09\u964d\u5c3a\u5ea6\u7814\u7a76"},"content":{"rendered":"

\u76ee\u5f55<\/p>\n

\u4e00\u3001\u65b9\u6cd5\u539f\u7406\u6982\u8ff0<\/p>\n

（\u4e00）\u4e3a\u4ec0\u4e48\u9700\u8981\u964d\u5c3a\u5ea6<\/p>\n

（\u4e8c）\u6280\u672f\u8def\u7ebf<\/p>\n

\u4e8c\u3001\u6570\u636e\u9884\u5904\u7406\u4e0e\u8d28\u91cf\u63a7\u5236<\/p>\n

（\u4e00）Landsat\u6570\u636e\u7b5b\u9009\u4e0eLST\u8ba1\u7b97<\/p>\n

（\u4e8c）Landsat\u8d28\u91cf\u63a9\u819c（QA_PIXEL）<\/p>\n

（\u4e09）Sentinel-2\u6570\u636e\u7b5b\u9009\u4e0eSCL\u63a9\u819c<\/p>\n

（\u56db）\u6e05\u6670\u50cf\u5143\u6bd4\u4f8b（Clear Fraction）\u4e0e\u5f71\u50cf\u8bc4\u5206<\/p>\n

\u4e09\u3001\u5149\u8c31\u6307\u6570\u8ba1\u7b97<\/p>\n

（\u4e00）Landsat\u5149\u8c31\u6307\u6570<\/p>\n

（\u4e8c）Sentinel-2\u5149\u8c31\u6307\u6570<\/p>\n

\u56db\u3001\u591a\u5143\u7ebf\u6027\u56de\u5f52\u5efa\u6a21<\/p>\n

（\u4e00）\u81ea\u53d8\u91cf\u9009\u53d6\u4e0e\u5f71\u50cf\u6784\u5efa<\/p>\n

（\u4e8c）\u7ebf\u6027\u56de\u5f52\u7cfb\u6570\u6c42\u89e3<\/p>\n

（\u4e09）30\u7c73\u62df\u5408LST\u4e0e10\u7c73\u9884\u6d4bLST<\/p>\n

\u4e94\u3001\u6b8b\u5dee\u4e0a\u91c7\u6837\u4e0e\u878d\u5408<\/p>\n

（\u4e00）\u6b8b\u5dee\u8ba1\u7b97<\/p>\n

（\u4e8c）\u9ad8\u65af\u5377\u79ef\u4e0e\u4e0a\u91c7\u6837<\/p>\n

（\u4e09）\u6700\u7ec8\u964d\u5c3a\u5ea6LST<\/p>\n

\u516d\u3001\u7cbe\u5ea6\u8bc4\u4f30\u6307\u6807<\/p>\n

（\u4e00）\u56de\u5f52\u62df\u5408\u6307\u6807<\/p>\n

（\u4e8c）\u964d\u5c3a\u5ea6\u524d\u540e\u7edf\u8ba1\u5bf9\u6bd4<\/p>\n

\u4e03\u3001\u7ed3\u679c\u5bfc\u51fa\u4e0e\u53ef\u89c6\u5316<\/p>\n

（\u4e00）\u5730\u56fe\u53ef\u89c6\u5316<\/p>\n

（\u4e8c）\u6570\u636e\u5bfc\u51fa<\/p>\n

\u516b\u3001\u603b\u7ed3<\/p>\n

\u4e5d\u3001\u8fd0\u884c\u7ed3\u679c<\/p>\n

\n

\"\"<\/p>\n

\u82e5\u89c9\u5f97\u4ee3\u7801\u5bf9\u60a8\u7684\u7814\u7a76 \/ \u9879\u76ee\u6709\u5e2e\u52a9，\u6b22\u8fce\u70b9\u51fb\u6253\u8d4f\u652f\u6301！\u9700\u8981\u5b8c\u6574\u4ee3\u7801\u7684\u670b\u53cb，\u6253\u8d4f\u540e\u53ef\u5728\u540e\u53f0\u79c1\u4fe1（\u590d\u5236\u6587\u7ae0\u6807\u9898\u53d1\u7ed9\u6211），\u6211\u4f1a\u5c3d\u5feb\u53d1\u60a8\u5b8c\u6574\u53ef\u8fd0\u884c\u4ee3\u7801，\u611f\u8c22\u652f\u6301！<\/span><\/p>\n

\u9ad8\u5206\u8fa8\u7387\u70ed\u7ea2\u5916\u6570\u636e\u7a00\u7f3a\u662f\u57ce\u5e02\u70ed\u5c9b\u7cbe\u7ec6\u5206\u6790\u3001\u751f\u6001\u73af\u5883\u76d1\u6d4b\u7b49\u9886\u57df\u957f\u671f\u9762\u4e34\u7684\u75db\u70b9\u2014\u2014Landsat\u7cfb\u5217\u63d0\u4f9b30\u7c73\u5206\u8fa8\u7387\u7684\u70ed\u7ea2\u5916\u6ce2\u6bb5，\u4f46\u7a7a\u95f4\u5206\u8fa8\u7387\u4e0d\u8db3\u4ee5\u652f\u6491\u7cbe\u7ec6\u5c3a\u5ea6\u5206\u6790；Sentinel-2\u867d\u62e5\u670910\u7c73\u7a7a\u95f4\u5206\u8fa8\u7387，\u5374\u7f3a\u4e4f\u70ed\u7ea2\u5916\u6ce2\u6bb5\u3002<\/span><\/p>\n

\u672c\u6587\u57fa\u4e8eGoogle Earth Engine（GEE）\u5e73\u53f0，\u7cfb\u7edf\u89e3\u6790\u4e00\u5957\u5c06Landsat 8\/9\u768430\u7c73\u5730\u8868\u6e29\u5ea6（LST）\u964d\u5c3a\u5ea6\u81f310\u7c73\u5206\u8fa8\u7387\u7684\u6280\u672f\u65b9\u6848\u3002\u8be5\u65b9\u6848\u901a\u8fc7\u591a\u5143\u7ebf\u6027\u56de\u5f52\u5efa\u6a21\u4e0e\u6b8b\u5dee\u878d\u5408\u6280\u672f\u4e24\u5927\u6838\u5fc3\u6b65\u9aa4，\u5229\u7528Sentinel-2\u7684\u9ad8\u5206\u8fa8\u7387\u5149\u8c31\u6307\u6570（NDVI\u3001NDBI\u3001NDWI）\u53caDEM\u3001\u5761\u5ea6\u7b49\u8f85\u52a9\u53d8\u91cf，\u5b9e\u73b0LST\u7a7a\u95f4\u5206\u8fa8\u7387\u7684\u63d0\u5347\u3002\u6587\u7ae0\u5c06\u4ece\u6570\u636e\u9884\u5904\u7406\u3001\u6307\u6570\u8ba1\u7b97\u3001\u56de\u5f52\u5efa\u6a21\u3001\u6b8b\u5dee\u4e0a\u91c7\u6837\u5230\u7cbe\u5ea6\u8bc4\u4f30\u9010\u5c42\u5c55\u5f00，\u517c\u987e\u7406\u8bba\u539f\u7406\u89e3\u6790\u4e0eGEE\u5b9e\u64cd\u907f\u5751\u8981\u70b9，\u9002\u5408\u9065\u611f\u9886\u57df\u5b66\u4e60\u8005\u548c\u79d1\u7814\u4eba\u5458\u9605\u8bfb\u53c2\u8003\u3002<\/span><\/p>\n

\u4e00\u3001\u65b9\u6cd5\u539f\u7406\u6982\u8ff0<\/span><\/h2>\n

（\u4e00）\u4e3a\u4ec0\u4e48\u9700\u8981\u964d\u5c3a\u5ea6<\/span><\/h3>\n

Landsat 8\/9\u7684Level-2\u6570\u636e\u4ea7\u54c1\u63d0\u4f9b\u4e86\u7ecf\u8fc7\u5927\u6c14\u6821\u6b63\u7684\u5730\u8868\u6e29\u5ea6（LST）\u6ce2\u6bb5（ST_B10），\u7a7a\u95f4\u5206\u8fa8\u7387\u4e3a30\u7c73\u3002\u7136\u800c，\u5728\u57ce\u5e02\u70ed\u5c9b\u6548\u5e94\u7814\u7a76\u3001\u5fae\u6c14\u5019\u5206\u6790\u7b49\u573a\u666f\u4e2d，30\u7c73\u5206\u8fa8\u7387\u5f80\u5f80\u4e0d\u8db3\u4ee5\u6355\u6349\u5efa\u7b51\u7269\u9634\u5f71\u3001\u8857\u8c37\u3001\u6c34\u4f53\u8fb9\u754c\u7b49\u7ec6\u8282\u5c3a\u5ea6\u7684\u6e29\u5ea6\u5dee\u5f02\u3002Sentinel-2\u591a\u5149\u8c31\u4eea（MSI）\u867d\u65e0\u70ed\u7ea2\u5916\u901a\u9053，\u4f46\u5176\u53ef\u89c1\u5149\/\u8fd1\u7ea2\u5916\u6ce2\u6bb5\u7a7a\u95f4\u5206\u8fa8\u7387\u8fbe10\u7c73，\u4e14\u91cd\u8bbf\u5468\u671f\u77ed（5\u5929），\u662f\u7406\u60f3\u7684\u201c\u8f85\u52a9\u6570\u636e\u6e90\u201d\u3002<\/span><\/p>\n

\u964d\u5c3a\u5ea6\u7684\u6838\u5fc3\u903b\u8f91\u662f：\u5efa\u7acbLST\u4e0e\u9ad8\u5206\u8fa8\u7387\u8f85\u52a9\u53d8\u91cf\u4e4b\u95f4\u7684\u7edf\u8ba1\u5173\u7cfb，\u7136\u540e\u5c06\u8be5\u5173\u7cfb\u5e94\u7528\u4e8e\u66f4\u9ad8\u5206\u8fa8\u7387\u7684\u8f85\u52a9\u53d8\u91cf\u4e0a，\u4ece\u800c\u201c\u63a8\u65ad\u201d\u51fa\u9ad8\u5206\u8fa8\u7387\u7684LST\u5206\u5e03\u3002<\/span><\/p>\n

（\u4e8c）\u6280\u672f\u8def\u7ebf<\/span><\/h3>\n

\u672c\u65b9\u6848\u91c7\u7528\u7684\u6280\u672f\u8def\u7ebf\u53ef\u6982\u62ec\u4e3a\u4ee5\u4e0b\u4e94\u4e2a\u6b65\u9aa4：<\/span><\/p>\n

  • \n

    Landsat LST\u63d0\u53d6\u4e0e\u8d28\u91cf\u63a7\u5236：\u7b5b\u9009\u9ad8\u8d28\u91cfLandsat\u5f71\u50cf，\u8ba1\u7b9730\u7c73LST\u53ca\u5149\u8c31\u6307\u6570（NDVI\u3001NDBI\u3001NDWI）；<\/span><\/p>\n<\/li>\n

  • \n

    Sentinel-2\u9ad8\u5206\u8fa8\u7387\u6307\u6570\u8ba1\u7b97：\u7b5b\u9009\u4e0eLandsat\u65e5\u671f\u76f8\u8fd1\u7684Sentinel-2\u5f71\u50cf，\u8ba1\u7b9710\u7c73\u5206\u8fa8\u7387\u7684\u5149\u8c31\u6307\u6570；<\/span><\/p>\n<\/li>\n

  • \n

    \u591a\u5143\u7ebf\u6027\u56de\u5f52\u5efa\u6a21：\u4ee5Landsat LST\u4e3a\u56e0\u53d8\u91cf，\u4ee5NDVI\u3001NDBI\u3001NDWI\u3001DEM\u3001\u5761\u5ea6\u4e3a\u81ea\u53d8\u91cf，\u572830\u7c73\u5c3a\u5ea6\u4e0a\u62df\u5408\u56de\u5f52\u7cfb\u6570；<\/span><\/p>\n<\/li>\n

  • \n

    \u6b8b\u5dee\u4e0a\u91c7\u6837\u4e0e\u878d\u5408：\u8ba1\u7b9730\u7c73\u5c3a\u5ea6\u7684\u56de\u5f52\u6b8b\u5dee，\u901a\u8fc7\u9ad8\u65af\u5377\u79ef\u548c\u53cc\u4e09\u6b21\u63d2\u503c\u5c06\u5176\u4e0a\u91c7\u6837\u81f310\u7c73；<\/span><\/p>\n<\/li>\n

  • \n

    \u7cbe\u5ea6\u8bc4\u4f30\u4e0e\u8f93\u51fa：\u8ba1\u7b97\u62df\u5408\u4f18\u5ea6（R\u00b2）\u3001RMSE\u3001MAE\u7b49\u6307\u6807，\u8f93\u51fa10\u7c73\u964d\u5c3a\u5ea6LST\u4ea7\u54c1\u3002<\/span><\/p>\n<\/li>\n

    \u4e8c\u3001\u6570\u636e\u9884\u5904\u7406\u4e0e\u8d28\u91cf\u63a7\u5236<\/span><\/h2>\n

    （\u4e00）Landsat\u6570\u636e\u7b5b\u9009\u4e0eLST\u8ba1\u7b97<\/span><\/h3>\n

    Landsat\u6570\u636e\u6e90\u9009\u7528Collection 2 Level-2\u4ea7\u54c1（LANDSAT\/LC08\/C02\/T1_L2\u548cLANDSAT\/LC09\/C02\/T1_L2），\u8be5\u7ea7\u522b\u4ea7\u54c1\u5df2\u5305\u542b\u7ecf\u5927\u6c14\u6821\u6b63\u7684\u5730\u8868\u53cd\u5c04\u7387\u548c\u5730\u8868\u6e29\u5ea6，\u5927\u5e45\u964d\u4f4e\u4e86\u9884\u5904\u7406\u95e8\u69db\u3002<\/span><\/p>\n

    var landsatCollection = ee.ImageCollection('LANDSAT\/LC08\/C02\/T1_L2')
    \n .merge(ee.ImageCollection('LANDSAT\/LC09\/C02\/T1_L2'))
    \n .filterBounds(region)
    \n .filterDate(startDate, endDate)
    \n .filter(ee.Filter.eq('PROCESSING_LEVEL', 'L2SP'))
    \n .filter(ee.Filter.lte('CLOUD_COVER', landsatCloud)); <\/p>\n

      \n
    • \n

      PROCESSING_LEVEL = 'L2SP'：\u4ec5\u4fdd\u7559\u7ecf\u5927\u6c14\u6821\u6b63\u7684Level-2\u79d1\u5b66\u4ea7\u54c1；<\/span><\/p>\n<\/li>\n

    • \n

      CLOUD_COVER \u2264 70%：\u4e91\u91cf\u9608\u503c\u53ef\u6839\u636e\u7814\u7a76\u533a\u5b9e\u9645\u60c5\u51b5\u8c03\u6574\u2014\u2014\u4e91\u91cf\u8fc7\u9ad8\u4f1a\u5bfc\u81f4LST\u6570\u636e\u5927\u9762\u79ef\u7f3a\u5931，\u8fc7\u4f4e\u5219\u53ef\u80fd\u65e0\u53ef\u7528\u5f71\u50cf；<\/span><\/p>\n<\/li>\n

    • \n

      \u65f6\u95f4\u8303\u56f4：startDate\u81f3endDate，\u5efa\u8bae\u9009\u62e9\u5730\u8868\u6e29\u5ea6\u5dee\u5f02\u663e\u8457\u7684\u5b63\u8282（\u5982\u5317\u534a\u7403\u590f\u5b63）\u3002<\/span><\/p>\n<\/li>\n<\/ul>\n

      Landsat C2L2\u4ea7\u54c1\u7684ST_B10\u6ce2\u6bb5\u9700\u901a\u8fc7\u5b98\u65b9\u7f29\u653e\u516c\u5f0f\u8f6c\u6362\u4e3a\u6444\u6c0f\u6e29\u5ea6：<\/span><\/p>\n

      \"\\\\text{LST}<\/span><\/p>\n

      \u5176\u4e2dDN\u4e3aST_B10\u6ce2\u6bb5\u7684\u50cf\u5143\u503c，\u8ba1\u7b97\u7ed3\u679c\u5355\u4f4d\u4e3a\u2103\u3002\u5bf9\u5e94\u4ee3\u7801\u5b9e\u73b0：<\/span><\/p>\n

      var lst = image.select('ST_B10')
      \n .multiply(0.00341802)
      \n .add(149)
      \n .subtract(273.15)
      \n .rename('LST'); <\/p>\n

      \u5149\u5b66\u6ce2\u6bb5（SR_B2~SR_B7）\u7684\u53cd\u5c04\u7387\u7f29\u653e\u516c\u5f0f\u4e3a：<\/span><\/p>\n

      \"\\\\rho<\/span><\/p>\n

      （\u4e8c）Landsat\u8d28\u91cf\u63a9\u819c（QA_PIXEL）<\/span><\/h3>\n

      Landsat Level-2\u4ea7\u54c1\u63d0\u4f9bQA_PIXEL\u6ce2\u6bb5\u7528\u4e8e\u8d28\u91cf\u8bc4\u4f30，\u9700\u5bf9\u4ee5\u4e0b\u6761\u4ef6\u8fdb\u884c\u63a9\u819c：<\/span><\/p>\n

        \n
      • \n

        \u586b\u5145\u50cf\u7d20（bit 0）\u3001 dilated cloud（bit 1）\u3001 cirrus（bit 2）\u3001 cloud（bit 3）\u3001 cloud shadow（bit 4）\u5747\u9700\u6392\u9664；<\/span><\/p>\n<\/li>\n

      • \n

        QA_RADSAT\u6ce2\u6bb5\u9700\u4e3a0（\u65e0\u8f90\u5c04\u9971\u548c）\u3002<\/span><\/p>\n<\/li>\n<\/ul>\n

        var mask = qa.bitwiseAnd(1 << 0).eq(0)
        \n .and(qa.bitwiseAnd(1 << 1).eq(0))
        \n .and(qa.bitwiseAnd(1 << 2).eq(0))
        \n .and(qa.bitwiseAnd(1 << 3).eq(0))
        \n .and(qa.bitwiseAnd(1 << 4).eq(0))
        \n .and(image.select('QA_RADSAT').eq(0)); <\/p>\n

        （\u4e09）Sentinel-2\u6570\u636e\u7b5b\u9009\u4e0eSCL\u63a9\u819c<\/span><\/h3>\n

        Sentinel-2\u6570\u636e\u6e90\u9009\u7528COPERNICUS\/S2_SR_HARMONIZED，\u8be5\u96c6\u5408\u5df2\u5bf9Sentinel-2A\u548c2B\u6570\u636e\u8fdb\u884c\u8f90\u5c04\u5f52\u4e00\u5316\u5904\u7406\u3002\u5173\u952e\u7b5b\u9009\u903b\u8f91\u662f\u4ee5Landsat\u5f71\u50cf\u65e5\u671f\u4e3a\u4e2d\u5fc3，\u5411\u524d\u540e\u5404\u6269\u5c55maxDifferenceDays\u5929（\u9ed8\u8ba430\u5929），\u5bfb\u627e\u6700\u63a5\u8fd1\u7684Sentinel-2\u5f71\u50cf：<\/span><\/p>\n

        var sentinelCollection = ee.ImageCollection('COPERNICUS\/S2_SR_HARMONIZED')
        \n .filterBounds(region)
        \n .filterDate(
        \n landsatDate.advance(-maxDifferenceDays, 'day'),
        \n landsatDate.advance(maxDifferenceDays + 1, 'day')
        \n )
        \n .filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE', sentinelCloud)); <\/p>\n

        Sentinel-2\u7684\u4e91\u63a9\u819c\u91c7\u7528SCL（Scene Classification Layer）\u00a0\u6ce2\u6bb5，\u6392\u9664\u4ee5\u4e0b\u7c7b\u522b：<\/span><\/p>\n\n Sentinel-2\u6570\u636e\u7b5b\u9009\u4e0eSCL\u63a9\u819c<\/span><\/p>\nSCL\u503c<\/span>\u542b\u4e49<\/span><\/tr>\n\n\n\n\n\n\n\n\n
        0<\/span><\/td>\n\u65e0\u6570\u636e<\/span><\/td>\n<\/tr>\n
        1<\/span><\/td>\n\u9971\u548c\/\u7f3a\u9677<\/span><\/td>\n<\/tr>\n
        3<\/span><\/td>\n\u4e91\u9634\u5f71<\/span><\/td>\n<\/tr>\n
        8<\/span><\/td>\n\u9ad8\u6982\u7387\u4e91<\/span><\/td>\n<\/tr>\n
        9<\/span><\/td>\n\u8584\u5377\u4e91<\/span><\/td>\n<\/tr>\n
        10<\/span><\/td>\n\u96ea\/\u51b0<\/span><\/td>\n<\/tr>\n
        11<\/span><\/td>\n\u96ea\/\u51b0<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        var mask = scl.neq(0).and(scl.neq(1)).and(scl.neq(3))
        \n .and(scl.neq(8)).and(scl.neq(9))
        \n .and(scl.neq(10)).and(scl.neq(11)); <\/p>\n

        （\u56db）\u6e05\u6670\u50cf\u5143\u6bd4\u4f8b（Clear Fraction）\u4e0e\u5f71\u50cf\u8bc4\u5206<\/span><\/h3>\n

        \u4e3a\u4ece\u5f71\u50cf\u96c6\u5408\u4e2d\u81ea\u52a8\u9009\u51fa\u6700\u4f18\u5f71\u50cf，\u4ee3\u7801\u5f15\u5165\u6e05\u6670\u50cf\u5143\u6bd4\u4f8b\u548c\u5f71\u50cf\u8bc4\u5206\u4e24\u4e2a\u6307\u6807：<\/span><\/p>\n

          \n
        • \n

          \u6e05\u6670\u50cf\u5143\u6bd4\u4f8b：\u6709\u6548\u50cf\u5143（\u975e\u63a9\u819c\u50cf\u5143）\u5360\u7814\u7a76\u533a\u603b\u9762\u79ef\u7684\u6bd4\u4f8b；<\/span><\/p>\n<\/li>\n

        • \n

          \u5f71\u50cf\u8bc4\u5206：\u7efc\u5408\u4e91\u91cf\u3001\u6e05\u6670\u50cf\u5143\u6bd4\u4f8b\u3001\u65e5\u671f\u5dee\u5f02\u7b49\u56e0\u7d20\u7684\u52a0\u6743\u5206\u6570，\u5206\u6570\u8d8a\u4f4e\u8868\u793a\u5f71\u50cf\u8d28\u91cf\u8d8a\u597d\u3002<\/span><\/p>\n<\/li>\n<\/ul>\n

          var score = ee.Number(1)
          \n .subtract(clearFraction)
          \n .multiply(100)
          \n .add(cloud.multiply(0.1)); <\/p>\n

          \u6700\u7ec8\u901a\u8fc7.sort('imageScore').first()\u9009\u51fa\u8bc4\u5206\u6700\u4f18\u7684Landsat\u548cSentinel-2\u5f71\u50cf\u5bf9\u3002<\/span><\/p>\n

          \u4e09\u3001\u5149\u8c31\u6307\u6570\u8ba1\u7b97<\/span><\/h2>\n

          （\u4e00）Landsat\u5149\u8c31\u6307\u6570<\/span><\/h3>\n

          \u57fa\u4e8eLandsat\u5730\u8868\u53cd\u5c04\u7387\u6ce2\u6bb5，\u8ba1\u7b97\u4ee5\u4e0b\u4e09\u79cd\u5e38\u7528\u5149\u8c31\u6307\u6570：<\/span><\/p>\n

          \u5f52\u4e00\u5316\u690d\u88ab\u6307\u6570（NDVI）\u00a0：<\/span><\/p>\n

          \"\\\\text{NDVI}<\/span><\/p>\n

          \u5f52\u4e00\u5316\u6c34\u4f53\u6307\u6570（NDWI）\u00a0：<\/span><\/p>\n

          \"\\\\text{NDWI}<\/span><\/p>\n

          \u5f52\u4e00\u5316\u5efa\u7b51\u6307\u6570（NDBI）\u00a0：<\/span><\/p>\n

          \"\\\\text{NDBI}<\/span><\/p>\n

          \u4ee3\u7801\u4e2d\u901a\u8fc7\u6cdb\u5316\u51fd\u6570\u5b9e\u73b0\u6307\u6570\u8ba1\u7b97，\u907f\u514d\u5206\u6bcd\u4e3a\u96f6：<\/span><\/p>\n

          function getIndex(band1, band2, name) {
          \n var sum = band1.add(band2);
          \n return band1.subtract(band2)
          \n .divide(sum)
          \n .updateMask(sum.neq(0))
          \n .rename(name);
          \n} <\/p>\n

          Landsat\u6ce2\u6bb5\u5bf9\u5e94\u5173\u7cfb：SR_B4（Red）\u3001SR_B5（NIR）\u3001SR_B3（Green）\u3001SR_B6（SWIR1）\u3002<\/span><\/p>\n

          （\u4e8c）Sentinel-2\u5149\u8c31\u6307\u6570<\/span><\/h3>\n

          Sentinel-2\u6ce2\u6bb5\u5bf9\u5e94\u5173\u7cfb：B4（Red，10m）\u3001B8（NIR，10m）\u3001B3（Green，10m）\u3001B11（SWIR，20m\u91cd\u91c7\u6837\u81f310m）\u3002<\/span><\/p>\n

          var sentinelGreen = sentinel.select('B3')
          \n .resample('bilinear')
          \n .reproject({ crs: sentinelProjection, scale: 10 }); <\/p>\n

          \u6ce8\u610f\u4e8b\u9879：Sentinel-2\u7684B11（SWIR）\u539f\u751f\u5206\u8fa8\u7387\u4e3a20\u7c73，\u9700\u901a\u8fc7resample()\u548creproject()\u91cd\u91c7\u6837\u81f310\u7c73，\u91cd\u91c7\u6837\u65b9\u6cd5\u9009\u62e9bilinear（\u53cc\u7ebf\u6027\u63d2\u503c）\u53ef\u5728\u4fdd\u6301\u7a7a\u95f4\u8fde\u7eed\u6027\u7684\u540c\u65f6\u907f\u514d\u8fc7\u5ea6\u5e73\u6ed1\u3002<\/span><\/p>\n

          \u56db\u3001\u591a\u5143\u7ebf\u6027\u56de\u5f52\u5efa\u6a21<\/span><\/h2>\n

          （\u4e00）\u81ea\u53d8\u91cf\u9009\u53d6\u4e0e\u5f71\u50cf\u6784\u5efa<\/span><\/h3>\n

          \u56de\u5f52\u6a21\u578b\u7684\u81ea\u53d8\u91cf\u5305\u62ec：<\/span><\/p>\n

            \n
          • \n

            \u5149\u8c31\u6307\u6570：NDVI\u3001NDBI\u3001NDWI（\u53cd\u6620\u690d\u88ab\u3001\u5efa\u7b51\u3001\u6c34\u4f53\u7b49\u5730\u8868\u8986\u76d6\u7c7b\u578b）；<\/span><\/p>\n<\/li>\n

          • \n

            \u5730\u5f62\u56e0\u5b50：DEM（\u6570\u5b57\u9ad8\u7a0b\u6a21\u578b）\u3001Slope（\u5761\u5ea6）；<\/span><\/p>\n<\/li>\n

          • \n

            \u622a\u8ddd\u9879：\u5e38\u65701\u3002<\/span><\/p>\n<\/li>\n<\/ul>\n

            \u56e0\u53d8\u91cf\u4e3aLandsat LST（30\u7c73\u5206\u8fa8\u7387）\u3002<\/span><\/p>\n

            \u56de\u5f52\u65b9\u7a0b\u53ef\u8868\u793a\u4e3a：<\/span><\/p>\n

            \"\\\\text{LST}_{30}<\/span><\/p>\n

            \u5176\u4e2d\"\\\\varepsilon\"\u4e3a\u56de\u5f52\u6b8b\u5dee\u3002<\/span><\/p>\n

            \u4ee3\u7801\u901a\u8fc7\u6784\u5efa\u591a\u6ce2\u6bb5\u5f71\u50cf，\u5c066\u4e2a\u81ea\u53d8\u91cf\u5806\u53e0\u4e3a\u4e00\u4e2a\u591a\u6ce2\u6bb5\u5f71\u50cf：<\/span><\/p>\n

            var regressionImage = constant
            \n .addBands(landsatNDVI)
            \n .addBands(landsatNDBI)
            \n .addBands(landsatNDWI)
            \n .addBands(dem30)
            \n .addBands(slope30)
            \n .addBands(lst30); <\/p>\n

            （\u4e8c）\u7ebf\u6027\u56de\u5f52\u7cfb\u6570\u6c42\u89e3<\/span><\/h3>\n

            GEE\u7684ee.Reducer.linearRegression()\u5b9e\u73b0\u4e86\u666e\u901a\u6700\u5c0f\u4e8c\u4e58\u6cd5（OLS）\u00a0，\u53ef\u540c\u65f6\u5904\u7406\u591a\u4e2a\u81ea\u53d8\u91cf\u548c\u4e00\u4e2a\u56e0\u53d8\u91cf：<\/span><\/p>\n

            var regression = regressionImage.reduceRegion({
            \n reducer: ee.Reducer.linearRegression({ numX: 6, numY: 1 }),
            \n geometry: region,
            \n scale: 30,
            \n crs: landsatProjection,
            \n maxPixels: 1e13,
            \n tileScale: 4
            \n}); <\/p>\n

            \u56de\u5f52\u7ed3\u679c\u4e3a\u4e00\u4e2a\u7cfb\u6570\u6570\u7ec4，\u4f9d\u6b21\u5bf9\u5e94：\u622a\u8ddd（b0）\u3001NDVI\u3001NDBI\u3001NDWI\u3001DEM\u3001Slope\u3002\u901a\u8fc7\u6570\u7ec4\u7d22\u5f15\u63d0\u53d6\u5404\u7cfb\u6570：<\/span><\/p>\n

            var coefficientArray = ee.Array(regression.get('coefficients'));
            \nvar b0 = ee.Number(coefficientList.get(0));
            \nvar bNDVI = ee.Number(coefficientList.get(1));
            \n\/\/ … \u4f9d\u6b21\u63d0\u53d6 <\/p>\n

            （\u4e09）30\u7c73\u62df\u5408LST\u4e0e10\u7c73\u9884\u6d4bLST<\/span><\/h3>\n

            \u5229\u7528\u56de\u5f52\u7cfb\u6570，\u53ef\u8ba1\u7b9730\u7c73\u5c3a\u5ea6\u7684\u62df\u5408LST（\u7528\u4e8e\u6b8b\u5dee\u8ba1\u7b97）\u548c10\u7c73\u5c3a\u5ea6\u7684\u9884\u6d4bLST（\u7528\u4e8e\u6700\u7ec8\u964d\u5c3a\u5ea6）：<\/span><\/p>\n

            \/\/ 30\u7c73\u62df\u5408\u503c
            \nvar fittedLST30 = landsatNDVI.multiply(bNDVI)
            \n .add(landsatNDBI.multiply(bNDBI))
            \n .add(landsatNDWI.multiply(bNDWI))
            \n .add(dem30.multiply(bDEM))
            \n .add(slope30.multiply(bSlope))
            \n .add(b0);<\/p>\n

            \/\/ 10\u7c73\u9884\u6d4b\u503c（\u57fa\u4e8eSentinel-2\u6307\u6570）
            \nvar predictedLST10 = sentinelNDVI.multiply(bNDVI)
            \n .add(sentinelNDBI.multiply(bNDBI))
            \n .add(sentinelNDWI.multiply(bNDWI))
            \n .add(dem10.multiply(bDEM))
            \n .add(slope10.multiply(bSlope))
            \n .add(b0); <\/p>\n

            \u7406\u8bba\u8981\u70b9：\u56de\u5f52\u7cfb\u6570\u572830\u7c73\u5c3a\u5ea6\u4e0a\u62df\u5408，\u53cd\u6620\u4e86LST\u4e0e\u5404\u8f85\u52a9\u53d8\u91cf\u4e4b\u95f4\u7684\u7edf\u8ba1\u5173\u7cfb\u3002\u5c06\u540c\u4e00\u7ec4\u7cfb\u6570\u5e94\u7528\u4e8e10\u7c73\u5c3a\u5ea6\u7684\u8f85\u52a9\u53d8\u91cf，\u672c\u8d28\u4e0a\u662f\u5047\u8bbe\u8fd9\u79cd\u7edf\u8ba1\u5173\u7cfb\u5728\u4e0d\u540c\u7a7a\u95f4\u5c3a\u5ea6\u4e0a\u5177\u6709\u5c3a\u5ea6\u4e0d\u53d8\u6027\u2014\u2014\u8fd9\u662f\u7edf\u8ba1\u964d\u5c3a\u5ea6\u65b9\u6cd5\u7684\u6838\u5fc3\u5047\u8bbe\u3002<\/span><\/p>\n

            \u4e94\u3001\u6b8b\u5dee\u4e0a\u91c7\u6837\u4e0e\u878d\u5408<\/span><\/h2>\n

            （\u4e00）\u6b8b\u5dee\u8ba1\u7b97<\/span><\/h3>\n

            30\u7c73\u5c3a\u5ea6\u7684\u56de\u5f52\u6b8b\u5dee\u5b9a\u4e49\u4e3a\u89c2\u6d4bLST\u4e0e\u62df\u5408LST\u4e4b\u5dee：<\/span><\/p>\n

            \"\\\\text{Residual}_{30}<\/span><\/p>\n

            \u6b8b\u5dee\u5305\u542b\u4e86\u56de\u5f52\u6a21\u578b\u672a\u80fd\u89e3\u91ca\u7684LST\u7a7a\u95f4\u53d8\u5f02（\u5982\u5c40\u5730\u5fae\u6c14\u5019\u6548\u5e94\u3001\u571f\u58e4\u6e7f\u5ea6\u5dee\u5f02\u7b49）\u3002<\/span><\/p>\n

            var residual30 = lst30.subtract(fittedLST30).rename('Residual_30m'); <\/p>\n

            （\u4e8c）\u9ad8\u65af\u5377\u79ef\u4e0e\u4e0a\u91c7\u6837<\/span><\/h3>\n

            \u6b8b\u5dee\u4ece30\u7c73\u4e0a\u91c7\u6837\u81f310\u7c73\u7684\u8fc7\u7a0b\u4e2d，\u76f4\u63a5\u4f7f\u7528\u63d2\u503c\u65b9\u6cd5（\u5982\u53cc\u4e09\u6b21\u63d2\u503c）\u53ef\u80fd\u5f15\u5165\u566a\u58f0\u3002\u4ee3\u7801\u91c7\u7528\u5148\u5377\u79ef\u540e\u63d2\u503c\u7684\u7b56\u7565：<\/span><\/p>\n

          • \n

            \u9ad8\u65af\u5377\u79ef：\u4f7f\u7528\u9ad8\u65af\u6838\u5bf9\u6b8b\u5dee\u8fdb\u884c\u5e73\u6ed1\u5904\u7406，\u6d88\u9664\u9ad8\u9891\u566a\u58f0；<\/span><\/p>\n<\/li>\n

          • \n

            \u53cc\u4e09\u6b21\u63d2\u503c：\u5c06\u5e73\u6ed1\u540e\u7684\u6b8b\u5dee\u91cd\u91c7\u6837\u81f310\u7c73\u3002<\/span><\/p>\n<\/li>\n

            \u9ad8\u65af\u6838\u7684\u5b9a\u4e49\u5982\u4e0b：<\/span><\/p>\n

            var gaussianKernel = ee.Kernel.gaussian({
            \n radius: 1.5,
            \n sigma: 1,
            \n units: 'pixels',
            \n normalize: true
            \n}); <\/p>\n

            \u5176\u4e2dsigma=1\u63a7\u5236\u5e73\u6ed1\u7a0b\u5ea6，radius=1.5\u4e3a\u6838\u534a\u5f84（\u7ea6\u8986\u76d63\u00d73\u50cf\u5143\u7a97\u53e3）\u3002<\/span><\/p>\n

            var residual10 = residual30
            \n .unmask(0)
            \n .convolve(gaussianKernel)
            \n .resample('bicubic')
            \n .reproject({ crs: sentinelProjection, scale: 10 })
            \n .rename('Residual_10m'); <\/p>\n

            （\u4e09）\u6700\u7ec8\u964d\u5c3a\u5ea6LST<\/span><\/h3>\n

            \u5c0610\u7c73\u9884\u6d4bLST\u4e0e10\u7c73\u6b8b\u5dee\u76f8\u52a0，\u5f97\u5230\u6700\u7ec8\u768410\u7c73\u964d\u5c3a\u5ea6LST：<\/span><\/p>\n

            \"\\\\text{LST}_{10}<\/span><\/p>\n

            var downscaledLST10 = predictedLST10
            \n .add(residual10.unmask(0))
            \n .updateMask(predictedLST10.mask())
            \n .rename('LST_10m')
            \n .clip(region); <\/p>\n

            \u6b8b\u5dee\u878d\u5408\u6280\u672f\u7684\u6838\u5fc3\u4ef7\u503c\u5728\u4e8e\u4fdd\u8bc1\u4e86\u964d\u5c3a\u5ea6\u7ed3\u679c\u5728\u7c97\u5c3a\u5ea6\u4e0a\u7684\u603b\u91cf\u4e00\u81f4\u6027\u2014\u2014\u537310\u7c73\u964d\u5c3a\u5ea6\u7ed3\u679c\u572830\u7c73\u805a\u5408\u540e\u7684\u5e73\u5747\u503c\u4e0e\u539f\u59cb30\u7c73LST\u4e00\u81f4（\u6216\u9ad8\u5ea6\u63a5\u8fd1）\u3002\u8fd9\u907f\u514d\u4e86\u5355\u7eaf\u4f9d\u8d56\u56de\u5f52\u6a21\u578b\u53ef\u80fd\u5bfc\u81f4\u7684\u7cfb\u7edf\u6027\u504f\u5dee\u3002<\/span><\/p>\n

            \u516d\u3001\u7cbe\u5ea6\u8bc4\u4f30\u6307\u6807<\/span><\/h2>\n

            \u4e3a\u91cf\u5316\u56de\u5f52\u62df\u5408\u6548\u679c\u548c\u964d\u5c3a\u5ea6\u7ed3\u679c\u8d28\u91cf，\u4ee3\u7801\u5b9e\u73b0\u4e86\u4e00\u5957\u5b8c\u6574\u7684\u7cbe\u5ea6\u8bc4\u4f30\u6307\u6807\u4f53\u7cfb\u3002<\/span><\/p>\n

            （\u4e00）\u56de\u5f52\u62df\u5408\u6307\u6807<\/span><\/h3>\n

            \u57fa\u4e8e30\u7c73\u5c3a\u5ea6\u7684\u89c2\u6d4bLST（lst30）\u548c\u62df\u5408LST（fittedLST30），\u8ba1\u7b97\u4ee5\u4e0b\u6307\u6807：<\/span><\/p>\n

            \u51b3\u5b9a\u7cfb\u6570（R\u00b2）\u00a0：<\/span><\/p>\n

            \"R^2<\/span><\/p>\n

            \u5747\u65b9\u6839\u8bef\u5dee（RMSE）\u00a0：<\/span><\/p>\n

            \"\\\\text{RMSE}<\/span><\/p>\n

            \u5e73\u5747\u7edd\u5bf9\u8bef\u5dee（MAE）\u00a0：<\/span><\/p>\n

            \"\\\\text{MAE}<\/span><\/p>\n

            \u504f\u5dee（Bias）\u00a0：<\/span><\/p>\n

            \"\\\\text{Bias}<\/span><\/p>\n

            function getMetrics(observed, predicted) {
            \n \/\/ \u8ba1\u7b97SSE\u3001SST\u3001MSE\u3001MAE\u3001Bias\u7b49
            \n \/\/ \u8fd4\u56de\u5305\u542bR2\u3001RMSE\u3001MAE\u3001Bias\u3001N\u7684\u5b57\u5178
            \n} <\/p>\n

            （\u4e8c）\u964d\u5c3a\u5ea6\u524d\u540e\u7edf\u8ba1\u5bf9\u6bd4<\/span><\/h3>\n

            \u901a\u8fc7\u6bd4\u8f83\u964d\u5c3a\u5ea6\u524d\u540eLST\u7684\u7edf\u8ba1\u7279\u5f81（\u6700\u5c0f\u503c\u3001\u6700\u5927\u503c\u3001\u5747\u503c），\u53ef\u521d\u6b65\u8bc4\u4f30\u964d\u5c3a\u5ea6\u7ed3\u679c\u7684\u5408\u7406\u6027：<\/span><\/p>\n

            var beforeStatistics = lst30.reduceRegion({
            \n reducer: ee.Reducer.minMax().combine(ee.Reducer.mean(), …),
            \n geometry: region,
            \n scale: 30
            \n});<\/p>\n

            var afterStatistics = downscaledLST10.reduceRegion({
            \n reducer: ee.Reducer.minMax().combine(ee.Reducer.mean(), …),
            \n geometry: region,
            \n scale: 10
            \n}); <\/p>\n

            \u4e03\u3001\u7ed3\u679c\u5bfc\u51fa\u4e0e\u53ef\u89c6\u5316<\/span><\/h2>\n

            （\u4e00）\u5730\u56fe\u53ef\u89c6\u5316<\/span><\/h3>\n

            \u4ee3\u7801\u63d0\u4f9b\u964d\u5c3a\u5ea6\u524d\u540e\u7684LST\u53ef\u89c6\u5316\u5bf9\u6bd4：<\/span><\/p>\n

            var tempVis = { min: 20, max: 45, palette: ['blue', 'cyan', 'green', 'yellow', 'red'] };
            \nMap.addLayer(lst30, tempVis, 'LST_30m (Before)');
            \nMap.addLayer(downscaledLST10, tempVis, 'LST_10m (After)'); <\/p>\n

            \u6e29\u5ea6\u8272\u9636\u7684min\/max\u9700\u6839\u636e\u7814\u7a76\u533a\u5b9e\u9645LST\u8303\u56f4\u8c03\u6574\u2014\u2014\u53ef\u901a\u8fc7print(lst30.reduceRegion({reducer: ee.Reducer.minMax()…}))\u83b7\u53d6\u3002<\/span><\/p>\n

            （\u4e8c）\u6570\u636e\u5bfc\u51fa<\/span><\/h3>\n

            \u6700\u7ec8\u7ed3\u679c\u901a\u8fc7Export.image.toDrive()\u5bfc\u51fa\u4e3aCloud Optimized GeoTIFF：<\/span><\/p>\n

            Export.image.toDrive({
            \n image: lst30.toFloat(),
            \n description: 'LST_before_downscaling_30m',
            \n folder: 'LST_downscaling',
            \n scale: 30,
            \n fileFormat: 'GeoTIFF',
            \n formatOptions: { cloudOptimized: true }
            \n}); <\/p>\n

            \u5efa\u8bae\u540c\u65f6\u5bfc\u51fa\u964d\u5c3a\u5ea6\u524d\u540e\u7684LST，\u4fbf\u4e8e\u540e\u7eed\u5728GIS\u6216Python\u73af\u5883\u4e2d\u8fdb\u884c\u5bf9\u6bd4\u5206\u6790\u3002<\/span><\/p>\n

            \u516b\u3001\u603b\u7ed3<\/span><\/h2>\n

            \u672c\u6587\u7cfb\u7edf\u89e3\u6790\u4e86\u4e00\u5957\u57fa\u4e8eGEE\u5e73\u53f0\u7684Landsat-Sentinel-2 LST\u964d\u5c3a\u5ea6\u6280\u672f\u65b9\u6848，\u6838\u5fc3\u8981\u70b9\u53ef\u5f52\u7eb3\u5982\u4e0b：<\/span><\/p>\n

            \u65b9\u6cd5\u6846\u67b6：\u901a\u8fc7\u591a\u5143\u7ebf\u6027\u56de\u5f52\u5efa\u7acbLST\u4e0e\u9ad8\u5206\u8fa8\u7387\u5149\u8c31\u6307\u6570（NDVI\u3001NDBI\u3001NDWI）\u53ca\u5730\u5f62\u56e0\u5b50（DEM\u3001\u5761\u5ea6）\u4e4b\u95f4\u7684\u7edf\u8ba1\u5173\u7cfb，\u7ed3\u5408\u6b8b\u5dee\u878d\u5408\u6280\u672f\u5b9e\u73b0\u4ece30\u7c73\u523010\u7c73\u7684\u7a7a\u95f4\u5206\u8fa8\u7387\u63d0\u5347\u3002<\/span><\/p>\n

            \u6280\u672f\u4eae\u70b9：<\/span><\/p>\n

              \n
            • \n

              \u5229\u7528Landsat C2L2\u4ea7\u54c1\u7684\u201c\u5f00\u7bb1\u5373\u7528\u201dLST\u6ce2\u6bb5，\u5927\u5e45\u964d\u4f4e\u9884\u5904\u7406\u95e8\u69db；<\/span><\/p>\n<\/li>\n

            • \n

              \u5f15\u5165\u6e05\u6670\u50cf\u5143\u6bd4\u4f8b\u548c\u5f71\u50cf\u8bc4\u5206\u673a\u5236，\u5b9e\u73b0\u5f71\u50cf\u7684\u81ea\u52a8\u4f18\u9009；<\/span><\/p>\n<\/li>\n

            • \n

              \u9ad8\u65af\u5377\u79ef+\u53cc\u4e09\u6b21\u63d2\u503c\u7684\u6b8b\u5dee\u4e0a\u91c7\u6837\u7b56\u7565，\u5728\u4fdd\u6301\u7a7a\u95f4\u7ec6\u8282\u7684\u540c\u65f6\u6291\u5236\u566a\u58f0；<\/span><\/p>\n<\/li>\n

            • \n

              \u5b8c\u6574\u7684\u7cbe\u5ea6\u8bc4\u4f30\u4f53\u7cfb（R\u00b2\u3001RMSE\u3001MAE\u3001Bias），\u91cf\u5316\u56de\u5f52\u62df\u5408\u6548\u679c\u3002<\/span><\/p>\n<\/li>\n<\/ul>\n

              \u9002\u7528\u573a\u666f：<\/span><\/p>\n

                \n
              • \n

                \u57ce\u5e02\u70ed\u5c9b\u6548\u5e94\u7684\u7cbe\u7ec6\u5c3a\u5ea6\u5206\u6790（\u5982\u8857\u533a\u7ea7\u70ed\u73af\u5883\u8bc4\u4f30）；<\/span><\/p>\n<\/li>\n

              • \n

                \u751f\u6001\u73af\u5883\u76d1\u6d4b（\u5982\u81ea\u7136\u4fdd\u62a4\u533a\u3001\u6e7f\u5730\u6e29\u5ea6\u52a8\u6001\u76d1\u6d4b）；<\/span><\/p>\n<\/li>\n

              • \n

                \u519c\u4e1a\u5e72\u65f1\u76d1\u6d4b\u4e0e\u4f5c\u7269\u70ed\u80c1\u8feb\u8bc4\u4f30；<\/span><\/p>\n<\/li>\n

              • \n

                \u4efb\u4f55\u9700\u8981\u9ad8\u5206\u8fa8\u7387LST\u6570\u636e\u4f46\u53d7\u9650\u4e8e\u70ed\u7ea2\u5916\u4f20\u611f\u5668\u7a7a\u95f4\u5206\u8fa8\u7387\u7684\u5e94\u7528\u573a\u666f\u3002<\/span><\/p>\n<\/li>\n<\/ul>\n

                \u53ef\u590d\u7528\u7ecf\u9a8c：<\/span><\/p>\n

                  \n
                • \n

                  \u591a\u6e90\u9065\u611f\u6570\u636e\u878d\u5408\u7684\u201c\u7c97\u5c3a\u5ea6\u5efa\u6a21 + \u7ec6\u5c3a\u5ea6\u9884\u6d4b + \u6b8b\u5dee\u6821\u6b63\u201d\u8303\u5f0f，\u53ef\u63a8\u5e7f\u81f3\u5176\u4ed6\u5730\u8868\u53c2\u6570（\u5982\u571f\u58e4\u6c34\u5206\u3001\u84b8\u6563\u53d1）\u7684\u964d\u5c3a\u5ea6；<\/span><\/p>\n<\/li>\n

                • \n

                  GEE\u5e73\u53f0\u4e0bee.Reducer.linearRegression()\u914d\u5408reduceRegion()\u7684\u56de\u5f52\u5efa\u6a21\u6d41\u7a0b，\u9002\u7528\u4e8e\u5404\u7c7b\u50cf\u5143\u7ea7\u7edf\u8ba1\u5efa\u6a21\u4efb\u52a1；<\/span><\/p>\n<\/li>\n

                • \n

                  \u57fa\u4e8eSCL\u6ce2\u6bb5\u7684Sentinel-2\u50cf\u7d20\u7ea7\u4e91\u63a9\u819c\u7b56\u7565，\u53ef\u590d\u7528\u4e8e\u5176\u4ed6Sentinel-2\u5206\u6790\u6d41\u7a0b\u3002<\/span><\/p>\n<\/li>\n<\/ul>\n

                  \u672c\u6587\u5c55\u793a\u7684\u662f\u57fa\u4e8e\u591a\u5143\u7ebf\u6027\u56de\u5f52\u7684\u964d\u5c3a\u5ea6\u65b9\u6cd5，\u5176\u4f18\u52bf\u5728\u4e8e\u8ba1\u7b97\u6548\u7387\u9ad8\u3001\u53ef\u89e3\u91ca\u6027\u5f3a\u3002\u82e5\u7814\u7a76\u533a\u5730\u8868\u8986\u76d6\u9ad8\u5ea6\u5f02\u8d28\u3001LST\u4e0e\u8f85\u52a9\u53d8\u91cf\u4e4b\u95f4\u5b58\u5728\u975e\u7ebf\u6027\u5173\u7cfb，\u53ef\u8003\u8651\u5c06\u56de\u5f52\u6a21\u578b\u66ff\u6362\u4e3a\u968f\u673a\u68ee\u6797\u6216XGBoost\u7b49\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u2014\u2014GEE\u5e73\u53f0\u5df2\u539f\u751f\u652f\u6301\u8fd9\u4e9b\u7b97\u6cd5，\u53ef\u5b9e\u73b0\u7c7b\u4f3c\u7684\u964d\u5c3a\u5ea6\u6d41\u7a0b\u3002<\/span><\/p>\n

                  \u4e5d\u3001\u8fd0\u884c\u7ed3\u679c<\/span><\/h2>\n

                  \"\" <\/p>\n

                  \u539f\u59cb\u768430m\u7a7a\u95f4\u5206\u8fa8\u7387\u5730\u8868\u6e29\u5ea6（LST）\u53ef\u89c6\u5316\u7ed3\u679c<\/p>\n

                  \"\" <\/p>\n

                  \u964d\u5c3a\u5ea6\u540e\u768410m\u7a7a\u95f4\u5206\u8fa8\u7387\u5730\u8868\u6e29\u5ea6（LST）\u53ef\u89c6\u5316\u7ed3\u679c<\/p>\n

                  \"\" <\/p>\n

                  \u539f\u59cb\u768430m\u7a7a\u95f4\u5206\u8fa8\u7387\u5730\u8868\u6e29\u5ea6（LST）\u5c40\u90e8\u7ec6\u8282\u5c55\u793a（1）<\/p>\n

                  \"\" <\/p>\n

                  \u964d\u5c3a\u5ea6\u540e\u768410m\u7a7a\u95f4\u5206\u8fa8\u7387\u5730\u8868\u6e29\u5ea6（LST）\u5c40\u90e8\u7ec6\u8282\u5c55\u793a（1）<\/p>\n

                  \"\" <\/p>\n

                  \u539f\u59cb\u768430m\u7a7a\u95f4\u5206\u8fa8\u7387\u5730\u8868\u6e29\u5ea6（LST）\u5c40\u90e8\u7ec6\u8282\u5c55\u793a（2）<\/p>\n

                  \"\" <\/p>\n

                  \u964d\u5c3a\u5ea6\u540e\u768410m\u7a7a\u95f4\u5206\u8fa8\u7387\u5730\u8868\u6e29\u5ea6（LST）\u5c40\u90e8\u7ec6\u8282\u5c55\u793a（2）<\/p>\n

                  \"\" <\/p>\n

                  \u63a7\u5236\u53f0\u8f93\u51fa\u7684\u6570\u636e\u7edf\u8ba1\u4fe1\u606f<\/p>\n

                  \"\" <\/p>\n

                  \u70b9\u51fbRUN\u5373\u53ef\u4e0b\u8f7d\u539f\u59cb\u768430m\u7a7a\u95f4\u5206\u8fa8\u7387\u548c\u964d\u5c3a\u5ea6\u540e\u768410m\u7a7a\u95f4\u5206\u8fa8\u7387\u5730\u8868\u6e29\u5ea6（LST）\u6570\u636e<\/p>\n

                  \u82e5\u89c9\u5f97\u4ee3\u7801\u5bf9\u60a8\u7684\u7814\u7a76 \/ \u9879\u76ee\u6709\u5e2e\u52a9，\u6b22\u8fce\u70b9\u51fb\u6253\u8d4f\u652f\u6301！\u9700\u8981\u5b8c\u6574\u4ee3\u7801\u7684\u670b\u53cb，\u6253\u8d4f\u540e\u53ef\u5728\u540e\u53f0\u79c1\u4fe1（\u590d\u5236\u6587\u7ae0\u6807\u9898\u53d1\u7ed9\u6211），\u6211\u4f1a\u5c3d\u5feb\u53d1\u60a8\u5b8c\u6574\u53ef\u8fd0\u884c\u4ee3\u7801，\u611f\u8c22\u652f\u6301！<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

                  \u76ee\u5f55
                  \n\u4e00\u3001\u65b9\u6cd5\u539f\u7406\u6982\u8ff0
                  \n（\u4e00）\u4e3a\u4ec0\u4e48\u9700\u8981\u964d\u5c3a\u5ea6
                  \n（\u4e8c）\u6280\u672f\u8def\u7ebf
                  \n\u4e8c\u3001\u6570\u636e\u9884\u5904\u7406\u4e0e\u8d28\u91cf\u63a7\u5236
                  \n（\u4e00）Landsat\u6570\u636e\u7b5b\u9009\u4e0eLST\u8ba1\u7b97
                  \n（\u4e8c）Landsat\u8d28\u91cf\u63a9\u819c（QA_PIXEL）
                  \n（\u4e09）Sentinel-2\u6570\u636e\u7b5b\u9009\u4e0eSCL\u63a9\u819c
                  \n（\u56db）\u6e05\u6670\u50cf\u5143\u6bd4\u4f8b（Cl<\/p>\n","protected":false},"author":2,"featured_media":97962,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[11227,2418,829,97,5134],"topic":[],"class_list":["post-97982","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-11227","tag-gee","tag-829","tag-97","tag-5134"],"yoast_head":"\n\u57fa\u4e8e GEE \u7684 Landsat-Sentinel \u534f\u540c\u9010\u50cf\u5143\u7ebf\u6027\u56de\u5f52\u4e0e\u6b8b\u5dee\u6821\u6b63\u7684\u5730\u8868\u6e29\u5ea6\uff08LST\uff09\u964d\u5c3a\u5ea6\u7814\u7a76 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/97982.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u57fa\u4e8e GEE \u7684 Landsat-Sentinel \u534f\u540c\u9010\u50cf\u5143\u7ebf\u6027\u56de\u5f52\u4e0e\u6b8b\u5dee\u6821\u6b63\u7684\u5730\u8868\u6e29\u5ea6\uff08LST\uff09\u964d\u5c3a\u5ea6\u7814\u7a76 - 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